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TOC Alert for Publication# 6221036 2018February 15<![CDATA[Table of contents]]>483C1825170<![CDATA[IEEE Transactions on Cybernetics]]>483C2C273<![CDATA[Nonrigid Point Set Registration by Preserving Local Connectivity]]>${k}$ -connected neighbors and connectivity matrix are given and the local connectivity constraint is constructed as a weighted least square error item. The point set registration problem is formulated in the expectation-maximization algorithm scheme and the optimal spatial transformation and correspondence matrix are estimated simultaneously. The effectiveness of the proposed method is verified by applying the method to synthetic point sets and real scenarios of hand shapes and surface-mount technology components.]]>4838268351276<![CDATA[Nyström Approximated Temporally Constrained Multisimilarity Spectral Clustering Approach for Movie Scene Detection]]>4838368471328<![CDATA[Degeneration Recognizing Clonal Selection Algorithm for Multimodal Optimization]]>4838488612052<![CDATA[Object Discovery via Cohesion Measurement]]>4838628752012<![CDATA[Joint Feature Selection and Classification for Multilabel Learning]]>4838768892613<![CDATA[Fast Variable Structure Stochastic Automaton for Discovering and Tracking Spatiotemporal Event Patterns]]>4838909031531<![CDATA[Compositional Model-Based Sketch Generator in Facial Entertainment]]>4839049152422<![CDATA[View-Based 3-D Model Retrieval: A Benchmark]]>4839169281844<![CDATA[A Generic Deep-Learning-Based Approach for Automated Surface Inspection]]>4839299401717<![CDATA[Distributed Coordination for Optimal Energy Generation and Distribution in Cyber-Physical Energy Networks]]>483941954896<![CDATA[Greedy Criterion in Orthogonal Greedy Learning]]>$ {delta }$ -greedy threshold” for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL. Numerical experiments are also provided to support that this new scheme can achieve almost optimal generalization performance while requiring less computation than OGL.]]>4839559661381<![CDATA[A Regularization Approach for Instance-Based Superset Label Learning]]>4839679781456<![CDATA[A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks]]>4839799921063<![CDATA[Optimizing Evaluation Metrics for Multitask Learning via the Alternating Direction Method of Multipliers]]>$F$ -score, area under the ROC curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared errors for regression. In this paper, we propose a method to directly optimize the evaluation metrics for a large family of MTL problems. The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: 1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks and 2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task. This formulation is challenging in optimization because both of its parts are nonsmooth. To tackle this issue, we propose a novel optimization procedure based on the alternating direction scheme of multipliers, where we decompose the whole optimization problem into a subproblem corresponding to the regularizer and another subproblem corresponding to the structured hinge losses. For a large family of MTL problems, the first subproblem has closed-form solutions. To solve the second subproblem, we propose an efficient primal-dual algorithm via coordinate ascent. Extensive evaluation results demonstrate that, in a large family of MTL problems, the proposed MTL method of directly optimization evaluation metrics has superior performance gains against the corresponding baseline methods.]]>48399310063530<![CDATA[Event-Based Variance-Constrained ${mathcal {H}}_{infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements]]>${mathcal {H}}_{infty }$ filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the ${mathcal {H}}_{infty }$ requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.]]>48310071017789<![CDATA[Design and Validation of a Virtual Player for Studying Interpersonal Coordination in the Mirror Game]]>483101810291571<![CDATA[Constrained Superpixel Tracking]]>483103010412324<![CDATA[The Overcomplete Dictionary-Based Directional Estimation Model and Nonconvex Reconstruction Methods]]>483104210534158<![CDATA[Denoising Hyperspectral Image With Non-i.i.d. Noise Structure]]>483105410663348<![CDATA[Active Learning of Regular Expressions for Entity Extraction]]>regular expression, from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. In this paper, we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline.]]>483106710801842<![CDATA[Semantically Enhanced Online Configuration of Feedback Control Schemes]]>483108110941715<![CDATA[Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition]]>483109511081731<![CDATA[IEEE Transactions on Cybernetics]]>483C3C3188<![CDATA[IEEE Transactions on Cybernetics]]>483C4C478