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arXiv 2025年

作者： Ning, Zhiyuan Wang, Zaitian Zhang, Ran Xu, Ping Liu, Kunpeng Wang, Pengyang Chen, Chong Wang, Pengfei Zhou, Yuanchun Cambria, Erik The Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences China The Department of Computer Science Portland State University United States Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau China The Future City Lab Terminus Group China The School of Computer Science and Engineering Nanyang Technological University Singapore

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there is a common issue of representation collapse in existing GNN-based deep graph clustering algorithms. We attribute two main reasons for such issue: (i) the inductive bias of GNN models: GNNs tend to generate similar representations for proximal nodes. Since graphs often contain a non-negligible amount of inter-cluster links, the bias results in error message passing and leads to biased clustering;(ii) the clustering guided loss function: most traditional approaches strive to make all samples closer to pre-learned cluster centers, which cause a degenerate solution assigning all data points to a single label thus make all samples and less discriminative. To address these challenges, we investigate graph clustering from a graph cut perspective and propose an innovative and non-GNN-based Deep Cut-informed Graph embedding and Clustering framework, namely DCGC. This framework includes two modules: (i) cut-informed graph encoding;(ii) self-supervised graph clustering via optimal transport. For the encoding module, we derive a cut-informed graph embedding objective to fuse graph structure and attributes by minimizing their joint normalized cut. For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments, which can balance the guidance of "proximity to the pre-learned cluster center". With the above two tailored designs, DCGC is more suitable for the graph clustering task, which can effectively alleviate the problem of representation collapse and achieve better performance. We conduct extensive experiments to demonstrate that our method is simple but effective compared with benchmarks. © 2025, CC BY.

关键词： Graph embeddings

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Wavelet Convolutional Neural network for Low-Resolution Brain MRI Images

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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IEEE International Symposium on Biomedical Imaging

作者： Soo Min Oh Yifan Li Jo Woon Chong Wireless Information and Network Sciences Laboratory Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Statistics Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training.

关键词： Wavelet transforms Training Accuracy Magnetic resonance imaging Computational modeling Brain modeling Feature extraction Data models Convolutional neural networks Tumors

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC

引用

IEEE Internet of Things Journal 2025年

作者： Zhao, Liang Li, Longjia Tan, Zhiyuan Hawbani, Ammar He, Qiang Liu, Zhi Shenyang Aerospace University School of Computer Science Shenyang110136 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Northeastern University School of Medicine and Biological Information Engineering Shenyang110169 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs' perception capacity of the region of interest (RoI) while alleviating the pressure of intensive computation on onboard resources. However, existing CP and computation schemes are based on inefficient broadcast communications and still face challenges such as highly dynamic communication link channel conditions caused by vehicle mobility, and limited computing resources in VEC environments. Considering the delay sensitivity of CAVs' perception tasks and the need for enhanced perception, we propose a unicast-based cooperative perception and computation scheme to achieve more efficient resource utilization and perception task execution in VEC scenarios. Our goal is to maximize CP gain and minimize task execution delay by optimizing the decision of each ego CAVs. To solve the sequential decision-making problem of multi-objective optimization, we propose a solution based on improved multi-agent proximal policy optimization deep reinforcement learning, where CAVs agents make adaptive decisions distributed based on partial observations. Simulation results show that compared with the baseline algorithm, our proposed scheme effectively reduces the execution delay of ego CAVs perception tasks and ensures a high perception gain. © 2025 IEEE.

关键词： Deep reinforcement learning

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

引用

arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

引用

IEEE Transactions on Affective Computing 2025年

作者： Zhang, Xiang Lu, Yan Yan, Huan Huang, Jinyang Gu, Yu Ji, Yusheng Liu, Zhi Liu, Bin University of Science and Technology of China School of Cyber Science and Technology Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China AI Lab Shanghai200030 China Guizhou Normal University School of Computer and Information Guiyang230601 China National Institute of Informatics Tokyo101-8430 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

Because of the ambiguous and subjective property of the facial expression, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current methods often directly predict whether the label is noised or not, aiming to reduce the contribution of the noised data. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning. In this paper, we propose a more reliable noise-label suppression method called ReSup. First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on popular datasets shows the effectiveness of ReSup. © 2010-2012 IEEE.

关键词： Affective computing facial expression label noise label noise modeling label noise suppression

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Object-Attribute-Relation Representation Based Video Semantic Communication

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IEEE Journal on Selected Areas in Communications 2025年

作者： Du, Qiyuan Duan, Yiping Yang, Qianqian Tao, Xiaoming Debbah, Merouane Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Xinjiang University School of Computer Science and Technology Xinjiang China Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission. © 1983-2012 IEEE.

关键词： Video streaming

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Trading Computing Power For Reducing Communication Loads: A Semantic Communication Perspective

引用

IEEE Transactions on Communications 2025年

作者： Zheng, Guangyuan Wen, Miaowen Xu, Lexi Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates China United Network Communications Corporation Research Institute Beijing100048 China Quzhou324003 China

As a new paradigm focusing on transmitting the meaning of information, semantic communications (SCs) have been revealed significant potential in alleviating network congestion and improving energy efficiency. By extracting a small-size semantic feature from the large-size raw-data, the communication loads can be reduced at a price of more computing power, i.e., the computational resource used in semantic extraction. In this paper, we explore the computation dimension to improve the communication performance in an uplink SC system. The SC-oriented user first compresses its original data via local computing during other users’ transmission time and then transmits it to the base station within the assigned time. To achieve a balanced tradeoff between communications and computing, we formulate an optimization problem to minimize the energy consumption of all users by jointly considering the compression ratio and time allocation. We first propose an efficient general algorithm that can be applied to different SC models. To gain more insights, we then derive the closed-form solutions for two special cases: equal-time allocation and two-user transmission. The obtained analytical results reveal a pronounced energy saving of adopting SC, especially for large transmitted data size, high transmission energy coefficient, scarce time resources, and poor channel conditions. Simulation results reveal that our proposed SC-based scheme achieves excellent performance in terms of saving energy consumption compared to the conventional communication. © 2025 IEEE. All rights reserved.

关键词： Energy utilization

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Towards High-Performance network Coding: FPGA Acceleration With Bounded-value Generators

arXiv

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arXiv 2025年

作者： Qing, Jiaxin Leong, Philip H.W. Lee, Kin-Hong Yeung, Raymond W. The Chinese University of Hong Kong Hong Kong The School of Electrical and Information Engineering The University of Sydney Australia The Department of Computer Science Engineering The Chinese University of Hong Kong Hong Kong The Department of Information Engineering The Chinese University of Hong Kong Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Limited Hong Kong

network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, and power demands of finite field operations. Most literature focuses on algorithmic-level techniques to improve coding efficiency. Optimization with an algorithm/hardware co-designing approach has long been neglected. Leveraging the unique structure of BATS codes, we first present CS-BATS, a hardware-friendly variant. Next we propose a simple but effective bounded-value generator, to reduce the size of a finite field multiplier by up to 70%. Finally, we report on a scalable and resource-efficient FPGA-based network coding accelerator that achieves a throughput of 27 Gbps, a speedup of more than 300 over software. © 2025, CC BY-NC-ND.

关键词： Integrated circuit design

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