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arXiv 2023年

作者： Wang, Jie Yan, Zheng Lan, Jiahe Bertino, Elisa Pedrycz, Witold State Key Laboratory on Integrated Services Networks School of Cyber Engineering Xidian University Xi’an710071 China Department of Computer Science Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Engineering Istinye University Sariyer Istanbul34010 Turkey

Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Joint Admission and Power Control for Big Data Access Management Using GAT

Joint Admission and Power Control for Big Data Access Manage...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mengke Yang Daosen Zhai Ruonan Zhang Haotong Cao Lin Cai F. Richard Yu School of Electronics and Information Northwestern Polytechnical University Xi'an Shaanxi China National Mobile Communications Research Laboratory Southeast University Nanjing Jiangsu China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Department of Systems and Computer Engineering Carleton University Ottawa ON

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

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CauSkelNet: Causal Representation Learning for Human Behaviour Analysis

arXiv

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arXiv 2024年

作者： Gu, Xingrui Jiang, Chuyi Wang, Erte Wu, Zekun Cui, Qiang Tian, Leimin Wu, Lianlong Song, Siyang Yu, Chuang EECS University of California Berkeley BerkeleyCA United States Department of Electrical Engineering Columbia University New YorkNY United States Department of Computer Science University College London London United Kingdom Holistic AI University College London London United Kingdom The Future Laboratory Tsinghua University Beijing China School of Electrical and Computer Systems Engineering Monash University Melbourne Australia Department of Computer Science Oxford University Oxford United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom UCL Interaction Centre University College London London United Kingdom

Constrained by the lack of model interpretability and a deep understanding of human movement in traditional movement recognition machine learning methods, this study introduces a novel representation learning method based on causal inference to better understand human joint dynamics and complex behaviors. We propose a two-stage framework that combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between joints. Our method effectively captures interactions and produces interpretable, robust representations. Experiments on the EmoPain dataset show that our causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, especially in detecting protective behaviors. The model is also highly invariant to data scale changes, enhancing its reliability in practical applications. Our approach advances human motion analysis and paves the way for more adaptive intelligent healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory 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 IEEE. All rights reserved.

关键词： Markov processes

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Measurement of paracetamol concentration using a fiber laser system

Measurement of paracetamol concentration using a fiber laser...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Liliana Soares Rosa Ana Perez-Herrera Susana Novais António Ferreira Orlando Frazão Susana Silva Faculty of Engineering INESC TEC - Institute for Systems and Computer Engineering Technology and Science University of Porto Porto Portugal Department of Electrical Electronic and Communication Engineering Public Univ. of Navarra ISC - Institute of Smart Cities Public Univ. of Navarra Pamplona Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal LEPABE-Laboratory for Process Engineering Environment Biotechnology and Energy University of Porto Porto Portugal ALiCE - Associate Laboratory in Chemical Engineering University of Porto Porto Portugal

A linear fiber laser system for measurements of paracetamol concentration is experimentally demonstrated. The cavity is based on a fiber loop mirror and an FBG centered at 1567.8 nm. The sensing head corresponds to a refractometric sensor, whose which principle of operation is based on Fresnel reflection in the fiber tip (FBG side). The system works at detected variations of paracetamol concentrations with a sensitivity of $[(8.74\pm 0.34)\times 10^{-5}]\ \ \mu \mathrm{W}/(\mathrm{g}/\text{kg})$ and a resolution of 2.77 g/kg. The results prove that the fiber laser system could be an asset for processing industries, specifically for non-invasive and real-time measurements of concentration.

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OpenVNA: A Framework for Analyzing the Behavior of Multimodal Language Understanding System under Noisy Scenarios

arXiv

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arXiv 2024年

作者： Yuan, Ziqi Zhang, Baozheng Xu, Hua Liang, Zhiyun Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China China School of Information Science and Engineering Hebei University of Science and Technology China College of Information and Electrical Engineering China Agricultural University China Technology Development Co. Ltd. China

We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at https://***/thuiar/OpenVNA, with a demonstration video available at https://***/0Z9cW7RGct4. © 2024, CC BY-NC-SA.

关键词： Graphical user interfaces

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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UAV-Assisted Downlink-and-Uplink Communication in the Presence of Multiple Malicious Jammers

UAV-Assisted Downlink-and-Uplink Communication in the Presen...

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IEEE Conference on Wireless Communications and networking

作者： Zhiyu Huang Zhichao Sheng Ali A. Nasir Cheng Yin Antonino Masaracchia Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Department of Electrical Engineering and Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Institute for Communication Systems University of Surrey U.K. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast U.K.

This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.

关键词： Three-dimensional displays Processor scheduling Benchmark testing Throughput Autonomous aerial vehicles Downlink Scheduling

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