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TOC Alert for Publication# 6221036 2019February 18<![CDATA[Table of contents]]>493C1733172<![CDATA[IEEE Transactions on Cybernetics]]>493C2C2110<![CDATA[Remote Nonlinear State Estimation With Stochastic Event-Triggered Sensor Schedule]]>493734745882<![CDATA[Multiplicative Update Methods for Incremental Quantile Estimation]]>4937467561476<![CDATA[Distributed Adaptive Event-Triggered Fault-Tolerant Consensus of Multiagent Systems With General Linear Dynamics]]>4937577671360<![CDATA[Effects of Target Signal Shape and System Dynamics on Feedforward in Manual Control]]>negative feedforward time delay estimates.]]>4937687802812<![CDATA[Error Correcting Input and Output Hashing]]>sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing methods.]]>4937817911611<![CDATA[Consensus of Leader-Following Multiagent Systems: A Distributed Event-Triggered Impulsive Control Strategy]]>4937928011954<![CDATA[Multiscale Amplitude Feature and Significance of Enhanced Vocal Tract Information for Emotion Classification]]>4938028152278<![CDATA[Improved Space Forest: A Meta Ensemble Method]]>493816826906<![CDATA[Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks]]>4938278381169<![CDATA[Spatial–Temporal Recurrent Neural Network for Emotion Recognition]]>4938398471565<![CDATA[Data-Driven Distributed Output Consensus Control for Partially Observable Multiagent Systems]]>493848858930<![CDATA[An Efficient Nondominated Sorting Algorithm for Large Number of Fronts]]>${O(MN^{2})}$ , where ${N}$ is the number of solutions and ${M}$ is the number of objectives. For stochastic algorithms like MOEAs, it is important to devise an algorithm which has better average case performance. In this paper, we propose a new algorithm that makes use of faster scalar sorting algorithm to perform nondominated sorting. It finds partial orders of each solution from all objectives and use these orders to skip unnecessary solution comparisons. We also propose a specific order of objectives that reduces objective comparisons. The proposed method introduces a weighted binary search over the fronts when the rank of a solution is determined. It further reduces total computational effort by a large factor when there is large number of fronts. We prove that the worst case complexity can be reduced to ${Theta }({MNC}_{{max}}mathrm {log}_{{2}} {(F+1)})$ , where the number of fronts is ${F}$ and the maximum number of solutions per front is ${C}_{mathrm {max}}$ ; however, in general cases, our worst case complexity is still ${O(MN^{2})}$ . Our best case time complexity is ${O}({MN}mathrm {log} {N})$ . We also achieve the best case complexity ${O}({MN}mathrm {log} {N+N^{2}})$ , when all solutions are in a single front. This method is compared with other state-of-the-art algorithms—efficient nondomination level update, deductive sort, corner sort, efficient nondominated sort and divide-and-conquer sort—in four different datasets. Experimental results show that our method, namely, bounded best order sort, is computationally more efficient than all other competing algorithms.]]>4938598691336<![CDATA[Distributed <inline-formula> <tex-math notation="LaTeX">$H_infty$ </tex-math></inline-formula> State Estimation Over a Filtering Network With Time-Varying and Switching Topology and Partial Information Exchange]]>${H_infty }$ state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain. The probability transition matrix of the nonhomogeneous Markov chain is time-varying. In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal ${H_{infty }}$ disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed ${H_infty }$ state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.]]>4938708821132<![CDATA[Bifurcation and Oscillatory Dynamics of Delayed Cyclic Gene Networks Including Small RNAs]]>4938838963012<![CDATA[Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning]]>493897906732<![CDATA[Large-Scale Robust Semisupervised Classification]]>$ell _{boldsymbol {2,p}}$ -norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped $ell _{boldsymbol {2,p}}$ -norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method.]]>4939079171451<![CDATA[Multiobjective Learning in the Model Space for Time Series Classification]]>4939189322074<![CDATA[Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis]]>4939339461817<![CDATA[Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection]]>http://www.leizhang.tk/ tempcode.html.]]>4939479604564<![CDATA[Adaptive Fuzzy Containment Control of Nonlinear Systems With Unmeasurable States]]>4939619731732<![CDATA[Game-Based Memetic Algorithm to the Vertex Cover of Networks]]>k,l)-exchanges for various numbers of ${k}$ and ${l}$ to remove ${k}$ vertices from and add ${l}$ vertices into the solution set, thus is much better than the previous (1,0)-exchange. Beyond that, the proposed local search is able to deal with the constraint, such that the crossover operator can be very simple and efficient. Degree-based initialization method is also provided which is much better than the previous uniform random initialization. Each individual of the GMA-MVC is designed as a snowdrift game state of the network. Each vertex is treated as an intelligent agent playing the snowdrift game with its neighbors, which is the local refinement process. The game is designed such that its strict Nash equilibrium (SNE) is always a vertex cover of the network. Most of the SNEs are only local optima of the problem. Then an EA is employed to guide the game to escape from those local optimal Nash equilibriums to reach a better Nash equilibrium. From comparison with the state of the art algorithms in experiments on various networks, the proposed algorithm always obtains the best solutions.]]>4939749882147<![CDATA[An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network]]>4939899991216<![CDATA[Event-Triggered Coordination for Formation Tracking Control in Constrained Space With Limited Communication]]>493100010111878<![CDATA[Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems]]>493101210251631<![CDATA[Multiagent Rendezvous With Shortest Distance to Convex Regions With Empty Intersection: Algorithms and Experiments]]>a priori unknown to each agent and has the shortest total squared distance to these regions. First, a distributed time-varying algorithm is introduced, where a corresponding condition is given to guarantee that all agents rendezvous at the optimal location asymptotically for bounded convex regions. Then a distributed tracking algorithm combined with a distributed estimation algorithm is proposed. It is first shown that for general possibly unbounded convex regions, all agents rendezvous in finite time and then collectively slide to the optimal location asymptotically. Then it is shown that for convex regions with certain projection compressibility, all agents collectively rendezvous at the optimal location in finite time, even when the regions are time varying. The algorithms are experimentally implemented on multiple ground robots to illustrate the obtained theoretical results.]]>493102610341041<![CDATA[Reach-Avoid Games With Two Defenders and One Attacker: An Analytical Approach]]>493103510461485<![CDATA[Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults]]>493104710571016<![CDATA[Fast Large-Scale Spectral Clustering via Explicit Feature Mapping]]>${m}$ training points from a total of ${n}$ data points, Nyström method requires ${O(nmd+m^{3}+nm^{2})}$ operations, where ${d}$ is the input dimension. In contrast, our proposed method requires ${O(nDd+D^{3}+n'D^{2})}$ , where ${n}'$ is the number of data points needed until convergence and ${D}$ is the kernel mapped dimension. In large-scale datasets where ${n' ll n}$ hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60 000 data points), the proposed method is similar in clustering accuracy to Nyström methods while its speed is twice as fast as Nyström.]]>493105810711919<![CDATA[Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution]]>${k}$ diverse yet representative prototype webpages are identified from a submodularity function. Experimental results not only show the improved accuracies for the Web topic detection task, but also increase the interpretation of a topic by its prototypes on two public datasets.]]>493107210832452<![CDATA[A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System]]>493108410962310<![CDATA[A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold]]>$Omega $ to improve solution quality. An energy consumption threshold is obtained by optimization. Extensive experiments are conducted to test the performance of TPM finally. The computational results show that TPM is a very competitive algorithm for the considered FJSP.]]>493109711091687<![CDATA[EmotionMeter: A Multimodal Framework for Recognizing Human Emotions]]>EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.]]>493111011221966<![CDATA[3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation]]>493112311362948<![CDATA[IEEE Transactions on Cybernetics]]>493C3C3165<![CDATA[IEEE Transactions on Cybernetics]]>493C4C471