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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China University of Chinese Academy of Science Academy of Mathematics and System Science Chinese Academy of Science Beijing China Kth Royal Institute of Technology and the Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. © 2024 IEEE.

关键词： Hierarchical systems

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Designing low-diameter interconnection networks with multi-ported host-switch graphs

Designing low-diameter interconnection networks with multi-p...

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作者： Yasudo, Ryota Nakano, Koji Koibuchi, Michihiro Matsutani, Hiroki Amano, Hideharu Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

A host-switch graph was originally proposed as a graph that represents a network topology of a computer systems with 1-port host computers and (Formula presented.) -port switches. It has been studied from both theoretical and practical aspects in terms of the diameter, the average shortest path length, and the performance of real applications. In recent high-performance computing systems, however, a host computer is connected to multiple switches by using InfiniBand, NVSwitch, or Omni-Path, and consequently they provide high bandwidths. Since a host-switch graph cannot represent such systems, this article extends a host-switch graph so that it can represent such systems. As a result, a host-switch graph can include multi-ported hosts. Furthermore, we propose to use multi-port hosts for reducing the diameter. We show that the diameter minimization is equivalent to solving the degree diameter problem for bipartite graphs of diameter three. Our experimental results show that we can drastically reduce the diameter as well as increasing the bandwidth and improves performance of MPI applications by up to 162% as compared with networks with single-ported hosts. © 2020 John Wiley & Sons, Ltd.

关键词： Graph theory

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Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 717-732页

作者： Marwani, Maher Kaddoum, Georges École de Technologie Supérieure Electrical Engineering Department MontrealQCH3C 1K3 Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of Computer Science and Mathematics Beirut797751 Lebanon

The proliferation of wireless technologies and the escalating performance requirements of wireless applications have led to diverse and dynamic wireless environments, presenting formidable challenges to existing radio resource management (RRM) frameworks. researchers have proposed utilizing deep learning (DL) models to address these challenges to learn patterns from wireless data and leverage the extracted information to resolve multiple RRM tasks, such as channel allocation and power control. However, it is noteworthy that the majority of existing DL architectures are designed to operate on Euclidean data, thereby disregarding a substantial amount of information about the topological structure of wireless networks. As a result, the performance of DL models may be suboptimal when applied to wireless environments due to the failure to capture the network's non-Euclidean geometry. This study presents a novel approach to address the challenge of power control and spectrum allocation in an N-link interference environment with shared channels, utilizing a graph neural network (GNN) based framework. In this type of wireless environment, the available bandwidth can be divided into blocks, offering greater flexibility in allocating bandwidth to communication links, but also requiring effective management of interference. One potential solution to mitigate the impact of interference is to control the transmission power of each link while ensuring the network's data rate performance. Therefore, the power control and spectrum allocation problems are inherently coupled and should be solved jointly. The proposed GNN-based framework presents a promising avenue for tackling this complex challenge. Our experimental results demonstrate that our proposed approach yields significant improvements compared to other existing methods in terms of convergence, generalization, performance, and robustness, particularly in the context of an imperfect channel. © 2023 CCBY.

关键词： Wireless networks

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Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

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science China(Technological sciences) 2023年第11期66卷 3129-3138页

作者： FENG ZhangCheng XU WenYing CAO JinDe YANG ShaoFu RUTKOWSKI Leszek Jiangsu Provincial Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 211189China Frontiers Science Center for Mobile Information Communication and Security Nanjing 210096China School of Computer Science and Engineering Southeast UniversityNanjing 211189China The Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447Poland AGH University of Science and Technology Krakow 30-059Poland

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their *** primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information *** utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent ***,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit ***,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： online game bandit feedback partial-decision two-point gradient estimator

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An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

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IEEE systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Experimental observation of coherent-perfect-absorber and laser points in anti-PT symmetry

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Physical Review A 2024年第3期110卷 033504页

作者： Minye Yang Qi Zhong Zhilu Ye Şahin K. Özdemir Mohamed Farhat Ramy El-Ganainy Pai-Yen Chen State Key Laboratory for Manufacturing Systems Engineering School of Electronic Science and Engineering Department of Electrical and Computer Engineering Department of Engineering Science and Mechanics The Materials Research Institute Computer Electrical and Mathematical Science and Engineering Division Department of Physics Henes Center for Quantum Phenomena

The coexistence of coherent perfect absorber and laser or amplifier (CPAL) point is a peculiar spectral singularity associated with the scattering matrices of non-Hermitian systems. While the potential of CPAL systems for sensing application has been highlighted recently, the extreme sensitivity of parity-time (PT)-symmetric CPAL devices to the input signal deviations has so far impeded their practical utilization. Here we explore a strategy for implementing CPAL circuits by exploiting another type of non-Hermitian symmetry, namely anti-PT (APT) symmetry. We demonstrate that the condition for building CPAL in our proposed APT-symmetric electronic circuits additionally requires parity symmetry, which simplifies the circuit design and implementation. Additionally, we show that this newly proposed structure is 1.85 times more robust compared to previous CPAL devices studied in the literature.

关键词： Noise Quantum sensing Non-Hermitian systems PT-symmetry

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Medical image registration and its application in retinal images:a review

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

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npj Flexible Electronics 2024年第1期8卷 192-203页

作者： Hee Kyu Lee Sang Uk Park Sunga Kong Heyin Ryu Hyun Bin Kim SangHoon Lee Danbee Kang Sun Hye Shin Ki Jun Yu Juhee Cho Joohoon Kang Il Yong Chun Hye Yun Park Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Department of Clinical Research Design and Evaluation SAIHSTSungkyunkwan UniversitySeoulRepublic of Korea Patient-Centered Outcomes Research Institute Samsung Medical CenterSeoulRepublic of Korea Center for Clinical Epidemiology Samsung Medical CenterSeoulRepublic of Korea Division of Pulmonary and Critical Care Medicine Department of MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea School of Electrical and Electronic Engineering Yonsei UniversitySeoulRepublic of Korea School of Advanced Materials Science and Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Center for Neuroscience Imaging Research Institute for Basic ScienceSuwonRepublic of Korea

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning(ML)algorithm *** findings from these previous studies have established a strong foundation for the analysis of highresolution,intricate signals,typically through frequency domain *** this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 *** interface provides real-time data visualization and communication with the ML server,creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

关键词： system diagnosis mounted

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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