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TOC Alert for Publication# 6221036 2017December 11<![CDATA[Table of contents]]>4712C14013172<![CDATA[IEEE Transactions on Cybernetics]]>4712C2C292<![CDATA[Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking]]>4712401440241171<![CDATA[Limited Rationality and Its Quantification Through the Interval Number Judgments With Permutations]]>471240254037843<![CDATA[Cooperative Exploration and Networking While Preserving Collision Avoidance]]>4712403840482470<![CDATA[Multirelational Social Recommendations via Multigraph Ranking]]>4712404940611746<![CDATA[Coordinated Dynamic Behaviors for Multirobot Systems With Collision Avoidance]]>4712406240732468<![CDATA[Estimation and LQG Control Over Unreliable Network With Acknowledgment Randomly Lost]]>471240744085855<![CDATA[Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems]]>471240864095828<![CDATA[Granular Flow Graph, Adaptive Rule Generation and Tracking]]>4712409641071430<![CDATA[Test Problems for Large-Scale Multiobjective and Many-Objective Optimization]]>4712410841211577<![CDATA[On Group Synchronization for Interacting Clusters of Heterogeneous Systems]]>4712412241331270<![CDATA[Cooperative Control of Mobile Sensor Networks for Environmental Monitoring: An Event-Triggered Finite-Time Control Scheme]]>4712413441476439<![CDATA[Cascade Learning by Optimally Partitioning]]>$boldsymbol {r_{i}}$ of each stage by directly minimizing the computation cost of the cascade. Theorems are provided to guarantee the existence of the unique optimal solution. Theorems are also given for the proposed efficient algorithm of searching optimal parameters $boldsymbol {r_{i}}$ . Once a new stage is added, the parameter $boldsymbol {r_{i}}$ for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. Moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage. In addition, we prove in theory why more new weak classifiers in the current stage are required compared to that of the previous stage. Experimental results on face detection and pedestrian detection demonstrate the effectiveness and efficiency of the proposed algorithm.]]>4712414841611649<![CDATA[Edge Event-Triggered Synchronization in Networks of Coupled Harmonic Oscillators]]>471241624168942<![CDATA[Admittance-Adaptive Model-Based Approach to Mitigate Biodynamic Feedthrough]]>4712416941812239<![CDATA[Geometric Hypergraph Learning for Visual Tracking]]>4712418241952960<![CDATA[Boundary Constraints for Minimum Cost Control of Directed Networks]]>4712419642071089<![CDATA[Toward Efficient Team Formation for Crowdsourcing in Noncooperative Social Networks]]>4712420842221917<![CDATA[Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach]]>4712422342341212<![CDATA[An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames]]>4712423542492307<![CDATA[Constrained Low-Rank Learning Using Least Squares-Based Regularization]]>4712425042621790<![CDATA[A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification]]>${F}$ -measure, and ${G}$ -mean.]]>4712426342743120<![CDATA[Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval]]>4712427542883089<![CDATA[Adaptive Compressive Tracking via Online Vector Boosting Feature Selection]]>4712428943012955<![CDATA[A Heuristic Initialized Stochastic Memetic Algorithm for MDPVRP With Interdependent Depot Operations]]>4712430243152390<![CDATA[The Costs of Indeterminacy: How to Determine Them?]]>4712431643271187<![CDATA[Community Detection Using Dual-Representation Chemical Reaction Optimization]]>4712432843412234<![CDATA[Cross-View Retrieval via Probability-Based Semantics-Preserving Hashing]]>4712434243551951<![CDATA[Feature Selection Through Message Passing]]>4712435643661378<![CDATA[Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage]]>4712436743791622<![CDATA[Spectral Contextual Classification of Hyperspectral Imagery With Probabilistic Relaxation Labeling]]>4712438043912706<![CDATA[Excavation Equipment Recognition Based on Novel Acoustic Statistical Features]]>4712439244043057<![CDATA[Robust Learning Control Design for Quantum Unitary Transformations]]>4712440544171755<![CDATA[A New Kind of Nonparametric Test for Statistical Comparison of Multiple Classifiers Over Multiple Datasets]]>4712441844311024<![CDATA[Collaborative Tracking Control of Dual Linear Switched Reluctance Machines Over Communication Network With Time Delays]]>4712443244421645<![CDATA[Partial Label Learning via Gaussian Processes]]>$boldsymbol {max (cdot )}$ function involved in likelihood function, not only is a likelihood function equivalent to the max-loss function defined, which has been proved to be tighter than other loss functions, but also a differentiable convex objective function is presented. The experimental results on six UCI data sets and three real-world PL problems show that the proposed algorithm can get higher accuracy than the state-of-the-art PL algorithms.]]>471244434450760<![CDATA[Single-Image Distance Measurement by a Smart Mobile Device]]>4712445144622242<![CDATA[Game Theoretic Analysis of Cooperative Message Forwarding in Opportunistic Mobile Networks]]>cohesive, in which disjoint coalitions always combine to form grand coalition. In OCF, a node reaches a stable grand coalition when all the nodes of the OMN are members of overlapping coalition of the node. No node gains by deviating from the grand coalition in SCF and OCF. Simulation results based on synthetic mobility model and real-life traces show that the message delivery ratio of OMNs increase by up to 67%, as compared to the noncooperative scenario. Moreover, the message overhead ratio using the proposed coalition-based schemes reduces by up to about (1/3)rd of that of the noncooperative communication scheme.]]>4712446344741060<![CDATA[Granular Data Description: Designing Ellipsoidal Information Granules]]>4712447544841056<![CDATA[Class-Specific Kernel Discriminant Analysis Revisited: Further Analysis and Extensions]]>4712448544961423<![CDATA[Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method]]>intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.]]>4712449745081786<![CDATA[Extreme Kernel Sparse Learning for Tactile Object Recognition]]>representer theorem, we develop a reduced kernel dictionary learning method by introducing row-sparsity constraint. A globally convergent algorithm is developed to solve the optimization problem and the theoretical proof is provided. Finally, we perform extensive experimental validations on some public available tactile sequence datasets and show the advantages of the proposed method.]]>4712450945201180<![CDATA[Toward Simultaneous Visual Comfort and Depth Sensation Optimization for Stereoscopic 3-D Experience]]>4712452145331817<![CDATA[Constrained Low-Rank Representation for Robust Subspace Clustering]]>4712453445461796<![CDATA[Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes]]>4712454745581070<![CDATA[No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization]]>471245594565990<![CDATA[Introducing IEEE Collabratec]]>4712456645662198<![CDATA[Member Get-A-Member (MGM) Program]]>4712456745673500<![CDATA[Together, we are advancing technology]]>471245684568537<![CDATA[2017 IndexIEEE Transactions on CyberneticsVol. 47]]>471245694620420<![CDATA[IEEE Transactions on Cybernetics]]>4712C3C3234<![CDATA[IEEE Transactions on Cybernetics]]>4712C4C4109