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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Visually enhanced multi-secret sharing scheme with arithmetic modulo, DWT, and particle swarm optimization for Meaningful Shares

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Cluster Computing 2025年第5期28卷 1-28页

作者： Rawat, Arjun Singh Deshmukh, Maroti Singh, Maheep Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar Garhwal India Department of Computer Science Doon University Dehradun India Department of Computer Science and Engineering National Institute of Technology Delhi India

Traditional multi-secret sharing (MSS) schemes generate random shares to secure secrets, but their noisy appearance can raise suspicion. To address this, we present an advanced (n+1,n+1) MSS scheme that generates meaningful, high-quality visual shares, reducing the risk of detection by attackers. Our scheme combines arithmetic Modulo, discrete wavelet transform (DWT), and particle swarm optimization (PSO), balancing security and visual appeal. The scheme operates through two main processes: the first involves the generation of randomized shares and the embedding of watermarks in the meaningful share generation process, while the second focuses on the extraction of randomized shares or watermark images from meaningful shares and the reconstruction of the secret during the secret reconstruction process. PSO optimizes embedding factors, achieving an ideal balance between imperceptibility and visual quality, often exceeding that of the meaningful cover images. Quantitative assessments with metrics like correlation, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) show marked improvements in both robustness and visual quality of the shared images. This solution addresses the limitations of traditional schemes, providing a secure and visually appealing approach to multi-secret image sharing. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Discrete wavelet transforms

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A Survey for Blockchain-Enabled Resource Management in Edge Networks

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IEEE Access 2025年 13卷 77252-77277页

作者： Boamah, Sharon A. Louis, Jean D. McNair, Janise Gilbert, Juan E. University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Florida Department of Computer and Information Science and Engineering GainesvilleFL32611 United States

As more people and devices come online with the rapid growth of the internet, the need for secure and efficient network resource management becomes increasingly important. The boom of these connections on Next Generation Wireless Networks through the advent of technologies like the Internet of Things has changed the landscape of traditional edge networks. This necessitates a drastic change in the existing edge network infrastructure. As these devices come online, make requests, and go offline, the allocation and management of edge network resources become paramount. Blockchain, with its growing popularity, presents itself as a potential solution. The inherent features of blockchain, such as decentralization, transparency, security, and immutability, make it possible for network providers to manage network resources efficiently. This survey paper delves into the diverse applications of blockchain in edge network resource management, offering a comprehensive discussion of its potential to revolutionize network infrastructure. Unlike other surveys, we focus this work on three specific contexts of blockchain integration for network resource management: Cloud and Edge Computing, Radio Access Networks in Next Generation Wireless Networks, Reinforcement Learning, and Federated Learning. We provide an extensive analysis of recent research advances in these areas, and the main findings are highlighted. Understanding the current use of blockchain and network resource management opens the door to new research and more efficient and secure networks. The open research issues that arise from the combination of blockchain with the management of edge network resources are identified. Finally, the future directions to fuel innovative research are presented in this survey. © 2013 IEEE.

关键词： Blockchains Surveys Cloud computing Network resource management Internet of Things Edge computing Federated learning Peer-to-peer computing Network slicing Wireless networks

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Intelligent integrated sensing and communication:a survey

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science China(Information sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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A Magnetic Signal Based Device Fingerprinting Scheme in Wireless Charging

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2943-2958页

作者： Li, Jiachun Meng, Yan Zhang, Le Chen, Guoxing Tian, Yuan Zhu, Haojin Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Electrical and Computer Engineering Department Institute for Technology Los AngelesCA90095 United States

Wireless charging is widely used to charge smart devices with limited battery capacity. However, it is susceptible to the identity spoofing attack, where adversaries can impersonate malicious devices as legitimate ones to gain unauthorized access and potentially disrupt the wireless charging system (e.g., resulting in incorrect billing, overheating, or even explosions). Device fingerprinting is a classical method for defending against identity spoofing attacks. However, applying existing schemes in wireless charging scenarios has drawbacks such as inconvenience (e.g., requiring specialized devices or user participation) and ineffectiveness (e.g., vulnerability to spoofing). Thus, we design a novel passive, effective, and robust device fingerprinting scheme called MagID for wireless charging systems. The insight of MagID lies in the fact that during wireless charging, the magnetic signal around a device can reflect inherent hardware differences. These differences can be extracted as unique fingerprints for authentication purposes. MagID leverages a novel scheme, SUPER-ARRAY, to precisely measure magnetic data and generate effective fingerprints for authenticating a device's identity before starting charging progress. Experimental results demonstrate that MagID achieves an accuracy rate of 98.14% across various charging devices. We have also tested its performance under different impact factors and verified its compatibility with various wireless charging pads. © 2004-2012 IEEE.

关键词： Charging stations

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Drug–target interactions prediction based on similarity graph features extraction and deep learning

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Neural Computing and Applications 2025年第6期37卷 4303-4322页

作者： Torkey, Hanaa El-Behery, Heba Attia, Abdel-Fattah El-Fishawy, Nawal Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create efficient computer algorithms to forecast possible DTIs. However, current computational approaches that predict potential drug–target interactions (DTIs) suffer from some limitations, like finding the best similarity measures or negative samples, and thus require substantial performance improvement. This study proposes an integrated approach based on feature representation and deep learning to predict DTIs. We extract the relevant features of drugs and proteins from heterogeneous networks using graph mining techniques. The proposed approach constructs a heterogeneous graph from the known drug–protein interactions, protein–protein, and drug–drug similarities. Then applying two feature extraction techniques to extract the features, then utilizing these features in training a deep learning model to predict the potential DTIs. Also, a novel algorithm is proposed to find the negative samples based on the drug and protein similarity matrices. Four Benchmark datasets are used to evaluate the proposed approach. Our approach achieves the highest AUC (area under the ROC curve) across all datasets (0.98) with around 2% increases over the existing methods. Experimental results demonstrate that our proposed approach outperforms the baseline methods in predicting DTI, and our negative sample-identifying algorithm could be established as a competitive solution. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Peripheral Teleportation: A Rest Frame Design to Mitigate Cybersickness during Virtual Locomotion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2891-2900页

作者： Nie, Tongyu Pospick, Courtney Hutton Cantory, Ville Zhang, Danhua Deguzman, Jasmine Joyce Interrante, Victoria Adhanom, Isayas Berhe Rosenberg, Evan Suma University of Minnesota Department of Computer Science & Engineering United States University of Central Florida Department of Computer Science United States Texas State University Department of Computer Science United States

Mitigating cybersickness can improve the usability of virtual reality (VR) and increase its adoption. The most widely used technique, dynamic field-of-view (FOV) restriction, mitigates cybersickness by blacking out the peripheral region of the user's FOV. However, this approach reduces the visibility of the virtual environment. We propose peripheral teleportation, a novel technique that creates a rest frame (RF) in the user's peripheral vision using content rendered from the current virtual environment. Specifically, the peripheral region is rendered by a pair of RF cameras whose transforms are updated by the user's physical motion. We apply alternating teleportations during translations, or snap turns during rotations, to the RF cameras to keep them close to the current viewpoint transformation. Consequently, the optical flow generated by RF cameras matches the user's physical motion, creating a stable peripheral view. In a between-subjects study (N=90), we compared peripheral teleportation with a traditional black FOV restrictor and an unrestricted control condition. The results showed that peripheral teleportation significantly reduced discomfort and enabled participants to stay immersed in the virtual environment for a longer duration of time. Overall, these findings suggest that peripheral teleportation is a promising technique that VR practitioners may consider adding to their cybersickness mitigation toolset. © 1995-2012 IEEE.

关键词： Virtual environments

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Feature Reconstruction Attacks and Countermeasures of DNN Training in Vertical Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2659-2669页

作者： Ye, Peng Jiang, Zhifeng Wang, Wei Li, Bo Li, Baochun Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Toronto Department of Electrical and Computer Engineering TorontoM5S 1A1 Canada

Federated learning (FL) has increasingly been deployed, in its vertical form, among organizations to facilitate secure collaborative training. In vertical FL (VFL), participants hold disjoint features of the same set of sample instances. The one with labels - the active party, initiates training and interacts with other participants - the passive parties. It remains largely unknown whether and how an active party can extract private feature data owned by passive parties, especially when training deep neural network (DNN) models. This work examines the feature security problem of DNN training in VFL. We consider a DNN model partitioned between active and passive parties, where the passive party holds a subset of the input layer with some features of binary values. Though proved to be NP-hard. we demonstrate that, unless the feature dimension is exceedingly large, it remains feasible, both theoretically and practically, to launch a reconstruction attack with an efficient search-based algorithm that prevails over current feature protection. We propose a novel feature protection scheme by perturbing intermediate results and fabricated input features, which effectively misleads reconstruction attacks towards pre-specified random values. The evaluation shows it sustains feature reconstruction attack in various VFL applications with negligible impact on model performance. © 2004-2012 IEEE.

关键词： Federated learning

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CPT:A Configurable Predictability Testbed for DNN Inference in AVs

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Tsinghua science and Technology 2025年第1期30卷 87-99页

作者： Liangkai Liu Yanzhi Wang Weisong Shi Department of Computer and Information Science University of DelawareNewarkDE 19713USA Department of Electrical&Computer Engineering Northeastern UniversityBostonMA 02115USA

Predictability is an essential challenge for autonomous vehicles(AVs)’*** neural networks have been widely deployed in the AV’s perception ***,it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks(DNNs)model combinations and system configurations when deploying DNNs in *** paper proposes configurable predictability testbed(CPT),a configurable testbed for quantifying the predictability in AV’s perception *** provides flexible configurations of the perception pipeline on data,DNN models,fusion policy,scheduling policies,and predictability *** top of CPT,the researchers can profile and optimize the predictability issue caused by different application and system *** has been open-sourced at:https://***/Torreskai0722/CPT.

关键词： predictability deep neural network autonomous driving

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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