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CYBR 780 - Design and Operations for Cyber Human Systems

This is a research guide for masters level students enrolled in CYBR 780 - Design and Operations for Cyber Human Systems on the Salina Campus

Library Research Guide

Stay Informed!

As you pursue your master's degree, it is essential that you stay informed on the latest research and developments in your field.  Remember that you are not a mere consumer of information, but rather a contributor of knowledge in your field of study.  To remain an active participant in the conversation, you must remain engaged in the latest news and research.

This page provides the latest articles from some of the top publications and conferences in Cyber Human Systems. Some articles are distributed through RSS, while others are accessed through K-State Databases.  This page will update as new articles are added to the respective journals.

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Humans-in-the-Loop Articles from ACM

Here you will find some of the latest articles from ACM on humans-in-the-loop design, procedures, events, and research.

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ACM Transactions on Autonomous and Adaptive Systems

TAAS aims to publish papers that provably advance the state of the art, or that provide new insights and knowledge into specific issues related to autonomous and adaptive systems. Here you will find some of the latest and best articles on Machine Learning and Autonomous Systems research.

IEEE Transactions on Systems, Man, and Cybernetics: Systems

IEEE Transactions on Systems, Man, and Cybernetics: Systems covers the fields of systems engineering. It addresses issue formulation, analysis, modeling, decision making, and interpretation for any of the systems engineering lifecycle phases associated with the definition, development, and deployment of large systems.

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  • IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication InformationThis link opens in a new windowApr 16, 2025
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  • Hyperbolic Sine Function-Based Full-State Feedback Attitude Tracking Control for Rigid SpacecraftThis link opens in a new windowFeb 28, 2025
    The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted closed-loop system can achieve two stable equilibria. By Lyapunov stability theory and Barbalat’s Lemma, it is proven that the obtained closed-loop system is almost globally asymptotically stable, and achieves unwinding-free performance. Further, by constructing a strict Lyapunov function, it is demonstrated that the two stable equilibria are exponentially stable. Moreover, subsets of attraction regions corresponding to each stable equilibrium are characterized. The simulation results illustrate that the proposed attitude control scheme can effectively avoid the unwinding problem during attitude tracking.
  • Spatial Coordination of Multiple Nonholonomic Agents With Sensory Connectivity MaintenanceThis link opens in a new windowMar 5, 2025
    This article aims to propose a general control strategy for coordination of multiple nonholonomic agents in three dimensional space. For real-world applications, since the field sensor equipped on the mobile agents for local information detection and estimation has some limited detecting range, it is necessary to guarantee that the nearby agents must stay within this range of the onboard sensor. This is called sensory connectivity maintenance. A function termed as coordination function with sensory connectivity maintenance (CFSCM) is defined to describe the performances of the coordination as well as the sensory connectivity status between the agents. Then, a general control strategy is designed based on the proposed CFSCM for spatial coordination of multiple nonholonomic agents with sensory connectivity maintenance. Moreover, the applications of the proposed control strategy for formation with omnidirectional sensors and flocking with directional sensors are shown, respectively. Finally, some numerical examples are conducted to validate the theoretical analysis.
  • Fixed-Time Distributed Optimization via Edge-Based Adaptive AlgorithmsThis link opens in a new windowFeb 11, 2025
    This article presents two fixed-time (FXT) distributed adaptive algorithms to solve a class of convex optimization problems for multiagent systems. First, a distributed adaptive protocol based on edge weights is developed to achieve global FXT optimization, in which the initial states are the local optimal points. Subsequently, an adaptive power-law algorithm is designed to realize local FXT optimization for each agent with arbitrary initial state. In the convergence analysis, unlike previous analysis method based on Lyapunov FXT stability criteria, this study employs the definition of FXT stability with Laplace transformation and a method of contradiction, several sufficient conditions are obtained to ensure that the states of all agents converge to the global optimal value within a fixed time, and the upper bound of convergence time is estimated. Furthermore, these adaptive algorithms on undirected graphs are extended to weight-balanced digraphs. Finally, the validity of the proposed edge-based adaptive distributed algorithms is demonstrated through numerical simulations of two packet-level charge-state balance problems.
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IEEE Transactions on Cybernetics

 IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. This journal publishes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.

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  • Z-Number Generation Model and Its Application in a Rule-Based Classification SystemThis link opens in a new windowMar 25, 2025
    Due to their unique structure and powerful capability to handle uncertainty and partial reliability of information, Z-numbers have achieved significant success in various fields. Zadeh previously asserted that a Z-number can be regarded as a summary of probability distributions. Researchers have proposed various methods for determining the underlying probability distributions from a given Z-number. Conversely, can a Z-number be used to summarize a set of probability distributions? This problem remains unexplored. In this article, we propose a nonlinear model, termed Maximum Expected Minimum Entropy (MEME), for generating a Z-number from a set of probability distributions. Through this model, Z-numbers can be generated directly from data without requiring expert knowledge. Additionally, we applied the MEME model to classification problems, introducing a novel if-then rule form, termed Z-valuation if-then rules. These rules replace the deterministic consequent part of a fuzzy rule with an uncertain Z-valuation, thereby further summarizing the uncertain information in the rule’s consequent. Based on the Z-valuation rules, we propose a Z-valuation rule-based (ZVRB) classification system, which aims to enhance decision-making processes in scenarios where uncertainty plays a key role. To validate the effectiveness of the ZVRB classification system, we conducted two experiments comparing it with both classic and advanced nonfuzzy classifiers as well as fuzzy classification systems. The results show that the ZVRB model is superior to the other comparative classifiers in terms of classification performance.
  • Data-Driven Inverse Reinforcement Learning for Heterogeneous Optimal Robust Formation ControlThis link opens in a new windowMar 14, 2025
    This article presents novel data-driven inverse reinforcement learning (IRL) algorithms to optimally address heterogeneous formation control problems in the presence of disturbances. We propose expert-estimator-learner multiagent systems (MASs) as independent systems with similar interaction graphs. First, a model-based IRL algorithm is introduced for the estimator MAS to determine its optimal control and reward functions. Using the estimator IRL algorithm results, a robust algorithm for model-free IRL is presented to reconstruct the learner MAS’s optimal control and reward functions without knowing the learners’ dynamics. Therefore, estimator MAS aims to estimate experts’ desired formation and learner MAS wants to track the estimators’ trajectories optimally. As a final step, data-driven implementations of these proposed IRL algorithms are presented. Consequently, this research contributes to identifying unknown reward functions and optimal controls by conducting demonstrations. Our analysis shows that the stability and convergence of MASs are thoroughly ensured. The effectiveness of the given algorithms is demonstrated via simulation results.
  • AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion RecognitionThis link opens in a new windowMar 27, 2025
    The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of EEG representations, overlooking the topology variability in capturing the dependencies between channels, which may limit the performance of EEG emotion recognition. To tackle this issue, this article proposes an adaptive attention-modulated graph network (AdamGraph) to enhance the subject adaptability of EEG emotion recognition against connection variability and representation variability. Specifically, an attention-modulated graph connection module is proposed to explicitly capture the individual important relationships among channels adaptively. Through modulating the attention matrix of individual functional connections using spatial connections based on prior knowledge, the attention-modulated weights can be learned to construct individual connections adaptively, thereby mitigating individual differences. Besides, a deep node-graph representation learning module is designed to extract long-range interaction characteristics among channels and alleviate the over-smoothing problem of representations. Furthermore, a graph domain co-regularized learning module is imposed to tackle the individual distribution discrepancies in connection and representations across different domains. Extensive experiments on three public EEG emotion datasets, i.e., SEED, DREAMER, and MPED, validate the superior performance of AdamGraph compared with state-of-the-art methods.
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