<![CDATA[ IEEE Transactions on Systems, Man, and Cybernetics: Systems - new TOC ]]>
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TOC Alert for Publication# 6221021 2021June 10<![CDATA[Table of contents]]>516C13663446<![CDATA[IEEE Transactions on Systems, Man, and Cybernetics publication information]]>516C2C2145<![CDATA[Efficient Batch-Mode Reinforcement Learning Using Extreme Learning Machines]]>516366436771873<![CDATA[Adaptive Neural Network Control Design for Uncertain Nonstrict Feedback Nonlinear System With State Constraints]]>516367836861027<![CDATA[Neural Network-Based Adaptive Fault-Tolerant Control for Markovian Jump Systems With Nonlinearity and Actuator Faults]]>516368736981125<![CDATA[E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction]]>516369937122321<![CDATA[Exponential Chaotic Model for Generating Robust Chaos]]>516371337243563<![CDATA[Dynamic Control of Fraud Information Spreading in Mobile Social Networks]]>how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals’ losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.]]>516372537381417<![CDATA[Finite-Time Synchronization of Memristive Neural Networks With Fractional-Order]]>516373937501149<![CDATA[Experimental Output Regulation of Linear Motor Driven Inverted Pendulum With Friction Compensation]]>516375137582618<![CDATA[Vision-Assisted Autonomous Lower-Limb Exoskeleton Robot]]>516375937703955<![CDATA[Proximal Parameter Distribution Optimization]]>516377137803245<![CDATA[Multilevel Image-Enhanced Sentence Representation Net for Natural Language Inference]]>${p}$ ) and a hypothesis sentence (${h}$ ), which demands sufficient understanding about sentences semantic. Due to the issues, such as polysemy, ambiguity, as well as fuzziness of sentences, intense sentence understanding is very challenging. To this end, in this article, we introduce the corresponding image of sentences as reference information for enhancing sentence semantic understanding and representing. Specifically, we first propose an image-enhanced multilevel sentence representation net (IEMLRN), that utilizes the image features from pretrained models for enhancing the sentence semantic understanding at different scales, i.e., lexical-level, phrase-level, and sentence-level. The proposed model advances the performance on NLI tasks by leveraging the pretrained global features of images. However, as these pretrained image features are optimized on specific image classification datasets, they may not show the best performance on NLI tasks. Therefore, we further propose to design an adaptive image feature generator that extracts fine-grained image labels from the corresponding sentences. After that, we extend the IEMLRN to multilevel image-enhanced sentence representation net (MIESR) by utilizing not only the coarse-grained pretrained features of images, but also the fine-grained adaptive features of images. Therefore, sentence semantic can be evaluated and enhanced more comprehensively and precisely. Extensive experiments on two benchmark datasets (SNLI, SICK) clearly show our proposed IEMLRN significantly outperform the state-of-the-art baselines, and our proposed MIESR model achieves the best performance by considering not only the text but also images in an adaptive multigranularities way.]]>516378137952823<![CDATA[Location-Aware Deep Collaborative Filtering for Service Recommendation]]>516379638071595<![CDATA[Extended Dissipativity Performance of High-Speed Train Including Actuator Faults and Probabilistic Time-Delays Under Resilient Reliable Control]]>$(mathbb {Q},mathbb {S},mathbb {R})$ -dissipativity index. Specifically, a sequence of random variables responding the Bernoulli distribution is exploited to govern the probabilistic time-delays. By utilizing the tighter integral inequality (TII) and reciprocally convex inequality (RCI) technique, some criteria are launched in terms of the linear matrix inequalities (LMIs). Finally, simulations show the effectiveness and applicability of the offered control law by inspecting the Japan Shinkansen HST with its experimental values, along with a comparison study also been exploited to showing the merits and generalization of the proposed control design technique.]]>516380838191521<![CDATA[Quantized Static Output Feedback Fuzzy Tracking Control for Discrete-Time Nonlinear Networked Systems With Asynchronous Event-Triggered Constraints]]>$mathcal {H}_{infty }$ static output feedback tracking control problem is studied for discrete-time nonlinear networked systems subject to quantization effects and asynchronous event-triggered constraints. The Takagi–Sugeno (T–S) fuzzy model is utilized to represent the investigated nonlinear networked systems. A novel asynchronous event-triggered strategy is given to reduce the network communication burdens in both communication channels from the plant to the controller and from the reference model to the controller. The objective of this article is to design a quantized event-triggered tracking controller such that the resulting system is asymptotically stable and the given $mathcal {H}_{infty }$ tracking performance is guaranteed. The sufficient design conditions for the tracking controller are formulated in the form of the linear matrix inequalities (LMIs). Furthermore, a simulation example will be utilized to show the effectiveness of the developed design strategy.]]>51638203831994<![CDATA[Unknown Dynamics Estimator-Based Output-Feedback Control for Nonlinear Pure-Feedback Systems]]>516383238432397<![CDATA[Quick Convex Hull-Based Rendezvous Planning for Delay-Harsh Mobile Data Gathering in Disjoint Sensor Networks]]>516384438541592<![CDATA[Event-Triggered Control for Multiagent Systems With Sensor Faults and Input Saturation]]>516385538664073<![CDATA[Finite-Time Bipartite Consensus For Multiagent Systems Under Detail-Balanced Antagonistic Interactions]]>516386738751523<![CDATA[On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes]]>51638763888996<![CDATA[Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies]]>516388938991911<![CDATA[A Personalized Feedback Mechanism Based on Bounded Confidence Learning to Support Consensus Reaching in Group Decision Making]]>516390039101861<![CDATA[Mixed Coalitional Stabilities With Full Participation of Sanctioning Opponents Within the Graph Model for Conflict Resolution]]>516391139251455<![CDATA[Observer-Based PID Security Control for Discrete Time-Delay Systems Under Cyber-Attacks]]>51639263938805<![CDATA[KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data]]>$rho $ -approximate DBSCAN and AnyDBC.]]>516393939534118<![CDATA[Chaotic Local Search-Based Differential Evolution Algorithms for Optimization]]>516395439672517<![CDATA[A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint]]>516396839791149<![CDATA[A Distance Measure for Intuitionistic Fuzzy Sets and Its Application to Pattern Classification Problems]]>516398039921432<![CDATA[Muscle-Synergies-Based Neuromuscular Control for Motion Learning and Generalization of a Musculoskeletal System]]>516399340063556<![CDATA[Effects of Outliers on the Maximum Correntropy Estimation: A Robustness Analysis]]>51640074012948<![CDATA[TechRxiv: Share Your Preprint Research with the World!]]>51640134013370<![CDATA[Introducing IEEE Collabratec]]>516401440142168<![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]>516C3C3137<![CDATA[Information for authors]]>516C4C4141