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TOC Alert for Publication# 6221036 2022May 19<![CDATA[Table of Contents]]>525C13358179<![CDATA[IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY]]>525C2C2113<![CDATA[Event-Triggered Fault Detection Filter Design for Discrete-Time Memristive Neural Networks With Time Delays]]>$H_{infty }$ performance level of the residual system is designed. Finally, numerical simulations are provided to illustrate the effectiveness of the presented results.]]>52533593369857<![CDATA[Passivity-Based Output Synchronization With Switching Graphs and Transmission Delays]]>52533703379662<![CDATA[Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite]]>525338033934916<![CDATA[Cross-Domain Missingness-Aware Time-Series Adaptation With Similarity Distillation in Medical Applications]]>525339434073415<![CDATA[Adaptive Neural Dynamic Surface Control With Prespecified Tracking Accuracy of Uncertain Stochastic Nonstrict-Feedback Systems]]>a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.]]>525340834211485<![CDATA[ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition]]>525342234332511<![CDATA[Reliable Control for Flexible Spacecraft Systems With Aperiodic Sampling and Stochastic Actuator Failures]]>52534343445796<![CDATA[Automated Segmentation of the Clinical Target Volume in the Planning CT for Breast Cancer Using Deep Neural Networks]]>525344634562662<![CDATA[Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application]]>525345734683767<![CDATA[Multiview Consensus Structure Discovery]]>525346934822231<![CDATA[On Exponential Stability of Delayed Discrete-Time Complex-Valued Inertial Neural Networks]]>52534833494567<![CDATA[A Fuzzy Decomposition-Based Multi/Many-Objective Evolutionary Algorithm]]>${N}$ least similar solutions are extracted as weight vectors to obtain ${N}$ constrained fuzzy subproblems (${N}$ is the population size), and accordingly, a shared weight vector is calculated for all subproblems to provide a stable search direction. Finally, the corner solution for each of ${m}$ least similar subproblems (${m}$ is the objective number) is preserved to maintain diversity, while one solution having the best aggregated value on the shared weight vector is selected for each of the remaining subproblems to speed up convergence. When compared to several competitive MOEAs in solving a variety of test MOPs, the proposed algorithm shows some advantages at fitting their different PF shapes.]]>525349535093179<![CDATA[Zonotopic Fault Detection for 2-D Systems Under Event-Triggered Mechanism]]>$ell _{infty }/h_{infty }$ index is derived to ensure the residual signal is sensitive to a fault signal while robust to disturbance and noise, based on which an optimal mixed $ell _{infty }/h_{infty }$ FD filter design criterion is provided. Instead of constant thresholds, novel zonotope-based dynamic thresholds are utilized for residual evaluation. Finally, simulation results are presented to illustrate the effectiveness of the developed mechanism.]]>525351035181822<![CDATA[Progressive Multistage Learning for Discriminative Tracking]]>525351935302757<![CDATA[Event-Triggered Synchronization of Switched Nonlinear System Based on Sampled Measurements]]>525353135381019<![CDATA[Discrete-Time Advanced Zeroing Neurodynamic Algorithm Applied to Future Equality-Constrained Nonlinear Optimization With Various Noises]]>et al. discretization (ZeaD) formulas to provide an effective general framework for finding various ZeaD formulas by the idea of high-order derivative simultaneous elimination. Then, to solve the problem of future equality-constrained nonlinear optimization (ECNO) with various noises, a specific ZeaD formula originating from the general ZeaD formula is further studied for the discretization of a noise-perturbed continuous-time advanced zeroing neurodynamic model. Subsequently, the resulting noise-perturbed discrete-time advanced zeroing neurodynamic (NP-DTAZN) algorithm is proposed for the real-time solution to the future ECNO problem with various noises suppressed simultaneously. Moreover, theoretical and numerical results are presented to show the convergence and precision of the proposed NP-DTAZN algorithm in the perturbation of various noises. Finally, comparative numerical and physical experiments based on a Kinova JACO^{2} robot manipulator are conducted to further substantiate the efficacy, superiority, and practicability of the proposed NP-DTAZN algorithm for solving the future ECNO problem with various noises.]]>525353935523097<![CDATA[Trans-Causalizing NAT-Modeled Bayesian Networks]]>trans-causalization of NAT-modeled BNs, by which causal independence embedded in NAT models is exploited for more efficient inference. We show that trans-causalization is exact and yields polynomial space complexity. We demonstrate significant efficiency gain on inference based on lazy propagation and sum–product networks.]]>525355335662332<![CDATA[Hybrid Model-Based Emotion Contextual Recognition for Cognitive Assistance Services]]>525356735762262<![CDATA[Inter-Algorithm Multiobjective Cooperation for Phylogenetic Reconstruction on Amino Acid Data]]>525357735912235<![CDATA[Continuous Support Vector Regression for Nonstationary Streaming Data]]>525359236051792<![CDATA[Navigation of Three Cooperative Object-Transportation Robots Using a Multistage Evolutionary Fuzzy Control Approach]]>525360636194323<![CDATA[Event-Triggered Distributed State Estimation for Cyber-Physical Systems Under DoS Attacks]]>525362036312046<![CDATA[Optimal Adaptive Robust Control Based on Cooperative Game Theory for a Class of Fuzzy Underactuated Mechanical Systems]]>525363236441944<![CDATA[A Controlled Strengthened Dominance Relation for Evolutionary Many-Objective Optimization]]>525364536575072<![CDATA[Semisupervised Multiple Choice Learning for Ensemble Classification]]>$ell _{1}$ -norm regularization when minimizing the conditional entropy with respect to the posterior probability distribution. Extensive experiments on multiple real-world datasets are conducted to verify the effectiveness and superiority of the proposed SemiMCL model.]]>525365836681472<![CDATA[Unsupervised Visual–Textual Correlation Learning With Fine-Grained Semantic Alignment]]>525366936835261<![CDATA[Spectral–Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification]]>525368436952523<![CDATA[A Reference Vector-Based Simplified Covariance Matrix Adaptation Evolution Strategy for Constrained Global Optimization]]>525369637091424<![CDATA[Regularized Matrix Factorization for Multilabel Learning With Missing Labels]]>525371037212763<![CDATA[Fixed-Time Prescribed Tracking Control for Stochastic Nonlinear Systems With Unknown Measurement Sensitivity]]>525372237321261<![CDATA[Distributed Maximum Correntropy Filtering for Stochastic Nonlinear Systems Under Deception Attacks]]>52537333744869<![CDATA[V-Fuzz: Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs]]>vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.]]>525374537562383<![CDATA[Line Integral Approach to Extended Dissipative Filtering for Interval Type-2 Fuzzy Systems]]>52537573768945<![CDATA[Learning Latent Representation for IoT Anomaly Detection]]>525376937821558<![CDATA[Event-Triggered Finite-Time Consensus of Second-Order Leader–Follower Multiagent Systems With Uncertain Disturbances]]>525378337931022<![CDATA[Data-Driven Discovery of Block-Oriented Nonlinear Models Using Sparse Null-Subspace Methods]]>525379438041251<![CDATA[A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data]]>$K$ -nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding $K$ majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental-
results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC’s superiority in class imbalanced learning.]]>525380538182254<![CDATA[Advanced Energy Kernel-Based Feature Extraction Scheme for Improved EMG-PR-Based Prosthesis Control Against Force Variation]]>$approx ~92$ %. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.]]>525381938283198<![CDATA[SE(<italic>n</italic>)++: An Efficient Solution to Multiple Pose Estimation Problems]]>${mathrm{ SE}}(n)$ . However, due to the nonconvexity of ${mathrm{ SE}}(n)$ , many of these solvers treat rotation and translation separately, and the computational efficiency is still unsatisfactory. A new technique called the ${mathrm{ SE}}(n)++$ is proposed in this article that exploits a novel mapping from ${mathrm{ SE}}(n)$ to ${mathrm{ SO}}(n + 1)$ . The mapping transforms the coupling between rotation and translation into a unified formulation on the Lie group and gives better analytical results and computational performances. Specifically, three major pose problems are considered in this article, that is, the point-cloud registration, the hand–eye calibration, and the ${mathrm{ SE}}(n)$ synchronization. Experimental validations have confirmed the effectiveness of the proposed ${mathrm{ SE}}(n)++$ method in open datasets.]]>525382938402925<![CDATA[Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model]]>525384138542135<![CDATA[Quasisynchronization of Heterogeneous Neural Networks With Time-Varying Delays via Event-Triggered Impulsive Controls]]>525385538661278<![CDATA[Two-Level LSTM for Sentiment Analysis With Lexicon Embedding and Polar Flipping]]>$rho $ -hot encoding, is proposed to alleviate the drawbacks of one-hot encoding and, thus, effectively incorporate useful lexical cues. Moreover, the sentimental polarity of a word may change in different sentences due to label noise or context. A flipping model is proposed to model the polar flipping of words in a sentence. We compile three Chinese datasets on the basis of our label strategy and proposed methodology. Experiments demonstrate that the proposed method outperforms state-of-the-art algorithms on both benchmark English data and our compiled Chinese data.]]>525386738792662<![CDATA[Fixed-Time Fuzzy Control for a Class of Nonlinear Systems]]>52538803887901<![CDATA[A Multiobjective Evolutionary Algorithm Based on Objective-Space Localization Selection]]>525388839011606<![CDATA[Model-Based Event-Triggered Sliding-Mode Control for Multi-Input Systems: Performance Analysis and Optimization]]>525390239131421<![CDATA[Early Screening of Autism in Toddlers via Response-To-Instructions Protocol]]>525391439242174<![CDATA[Event-Triggered Quantized Communication-Based Consensus in Multiagent Systems via Sliding Mode]]>525392539351386<![CDATA[Infinite Bayesian Max-Margin Discriminant Projection]]>525393639461324<![CDATA[Fuzzy Control Design of Nonlinear Time-Delay Parabolic PDE Systems Under Mobile Collocated Actuators and Sensors]]>52539473956741<![CDATA[Detection of Small Aerial Object Using Random Projection Feature With Region Clustering]]>525395739702681<![CDATA[Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area]]>525397139833090<![CDATA[Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization]]>525398439973034<![CDATA[Guarding a Subspace in High-Dimensional Space With Two Defenders and One Attacker]]>priori information about the game result, a critical payoff function is designed when the defenders can win the game. Then, the optimal strategy for each player is explicitly reformulated as a saddle-point equilibrium. Finally, we apply these theoretical results to two half-space and half-plane guarding games in 3-D space and 2-D plane, respectively. Since the entire achieved developments are analytical, they require a little memory without the computational burden and allow for real-time updates, beyond the capacity of the traditional Hamilton–Jacobi–Isaacs method. It is worth noting that this is the first time in the current work to consider the target guarding games for arbitrary high-dimensional space and in a fully analytical form.]]>52539984011677<![CDATA[RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems]]>$K$ -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.]]>52540124026990<![CDATA[A New Evidential Reasoning Rule-Based Safety Assessment Method With Sensor Reliability for Complex Systems]]>525402740383320<![CDATA[Stabilization of Perturbed Continuous-Time Systems Using Event-Triggered Model Predictive Control]]>525403940511724<![CDATA[Predicting Network Controllability Robustness: A Convolutional Neural Network Approach]]>525405240636712<![CDATA[Weighted Generalized Cross-Validation-Based Regularization for Broad Learning System]]>525406440721717<![CDATA[Asymptotic Tracking Control of State-Constrained Nonlinear Systems With Time-Varying Powers]]>52540734078434<![CDATA[Introducing IEEE Collabratec]]>525407940792095<![CDATA[TechRxiv: Share Your Preprint Research with the World!]]>52540804080373<![CDATA[IEEE Transactions on Cybernetics]]>525C3C3107<![CDATA[IEEE Transactions on Cybernetics]]>525C4C4215