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Task Offloading in Internet of Vehicles: A DRL-based Approach with Representation Learning for DAG Scheduling

IEEE Transactions on Mobile Computing 2025年第6期24卷 5045-5060页

作者： Deng, Xiaoheng Yang, Haoyu Zhang, Jingjing Gui, Jinsong Lin, Siyu Wang, Xin Min, Geyong Central South University School of Electronic Information Changsha410075 China Central South University Shenzhen Research Institute China Beijing Jiaotong University School of Electronic and Information Engineering The State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models 31

Chain-of-Specificity: Enhancing Task-Specific Constraint Adh...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Kaiwen Zhong, Jiang Zhang, Hongzhi Zhang, Di Jin, Li Yu, Yue Zhang, Jingyuan College of Computer Science Chongqing University China Kuaishou Inc. China Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing, rewriting input instructions, or reflecting on prior failings, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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An optical fiber sensor based on FeOOH nanorod arrays for humidity and temperature measurement

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IEEE Sensors Journal 2025年第10期25卷 17110-17116页

作者： Cao, Pengju Wu, Tingting Wen, Jian Tan, Haidong Sheng, Su Jiang, Chao Hubei Normal University Hubei Key Laboratory of Photoelectric Conversion Materials and Devices Hubei Engineering Research Center of Micro and Nano optoelectronic devices and integration School of Physics and Electronic Science Huangshi435002 China Hubei Normal University School of Computer and Information Engineering Huangshi435002 China

It remains a major challenge to prepare a temperature or humidity optical fiber sensor with a simple structure, low cost and high sensitivity. In this work, a Mach-Zehnder interferometer with "SMF-MMF-TCF-MMF-SMF" structure is proposed to measure ambient temperature and humidity. The FeOOH nanorod arrays were grown on the surface of TCF to improve the sensitivity of this sensor. This sensor exhibits a high temperature sensitivity of 859.01 pm/°C in the range of 26 °C to 52 °C and a humidity sensitivity of 335.98 pm/% RH in the range of 40% RH to 90% RH. The sensor also exhibits excellent stability and repeatability, with the maximum wavelength deviation caused by temperature change and humidity change being only 0.544 nm and 0.557 nm respectively. This sensor is expected to be applied in some fields such as agriculture, chemical industry, semiconductor manufacturing. © 2001-2012 IEEE.

关键词： Mach-Zehnder interferometers

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A community detection model based on hierarchical graph clustering network with probability distribution

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Kongzhi yu Juece/Control and Decision 2025年第6期40卷 1969-1974页

作者： Xu, Sen Liu, Xuan-Qi Chen, Chao-Feng Guo, Nai-Xuan Bian, Xue-Sheng Ma, Fu-Rong Hua, Xiao-Peng Zhou, Tian School of Information Engineering Yancheng Institute of Technology Yancheng224051 China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing210000 China School of Underwater Sound Engineering Harbin Engineering University Harbin150001 China

In order to capture the hidden structure of networks and reduce the dependence of community detection models on the choice of initial parameters, this paper proposes a community detection model based on hierarchical graph clustering networks with probability distribution (HGCPD). Firstly, graph convolutional networks are utilized to learn and cache feature representations of nodes in the graph. Then, a hierarchical clustering method based on node pair similarity probability is introduced to recursively construct community structures at different levels. Finally, the problem of model hyperparameter optimization is explored, and the Bayesian optimization methods are designed to automatically adjust parameters, thereby improving the efficiency of the model. Experiments on multiple network datasets of different scales show that the HGCPD model is superior to mainstream methods in terms of the accuracy and effectiveness of community detection, and the model's interpretability is verified through visualization. © 2025 Northeast University. All rights reserved.

关键词： Normal distribution

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv

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arXiv 2025年

作者： Wang, Zhenglin Wu, Jialong Li, Pengfei Jiang, Yong Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Tongyi Lab Alibaba Group China

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Feature Augmented Meta-Learning on Domain Generalization for Evolving Malware Classification 24th

Feature Augmented Meta-Learning on Domain Generalization fo...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Fangwei Chen, Yinhe Song, Ruixin Li, Qingru Wang, Changguang Key Laboratory of Network and Information Security of Hebei Province College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819615278

The proliferation of malware has resulted in substantial harm to various sectors and economies. Various deep learning-based malware classification methods have been suggested as a means of mitigating malware threats. These methods typically operate under the assumption of independent and identically distributed training and test data. However, this assumption becomes invalid with the evolving malware family. While domain adaptation models offer a potential solution to this issue, their implementation is hindered by the difficulty of collecting new malware variants. In order to address the previously mentioned problem, we suggest an image-based technique for categorizing malware families utilizing domain generalization. Initially, malware is transformed into gray-scale images that depict byte patterns of the malware. Subsequently, these gray-scale images are fed into a model incorporating convolutional block attention to extract features. Furthermore, data augmentation is implemented at the feature level to broaden the distribution of the source domain and enhance the model’s generalization capabilities. Finally, meta-learning is utilized as a training approach to effectively extract domain-invariant representations. A series of experiments are conducted on the BIG2015 and BenchMFC-G1P1P2. The proposed method demonstrates a higher accuracy rate of 88.66% on the BIG2015 and 80.25% on the BenchMFC-G1P1P2, which is better than the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain Generalization Domain Shift Feature Argumentation Malware Classification

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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SteDM: Efficient Image Steganography with Diffusion Models 24th

SteDM: Efficient Image Steganography with Diffusion Models

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Changguang Shi, Haoyi Li, Qingru Zhao, Dongmei Wang, Fangwei Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789819615506

Existing deep learning-based image steganography schemes mostly neglect the latent space of images. These schemes merely adopt simple concatenation of image feature vectors, resulting in a low utilization rate of features, low steganographic image quality, and poor image robustness. This paper introduces the latent diffusion model into an image steganography scheme, namely SteDM. The SteDM firstly uses an encoder to transform the cover image and the secret image into the latent space, then employing a cross-attention mechanism to fuse them during the inverse diffusion process. Then we use a decoder to obtain a steganographic image containing secret image features. During the extraction process, the latent space-based diffusion model is similarly employed. Training loss is defined as a joint optimization of the autoencoder and diffusion model during the training process. Experimental results demonstrate that the SteDM outperforms existing steganography schemes in some aspects such as visual effects, security, and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Steganography

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Accurate 3D Wireless Charging

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4733-4746页

作者： Yang, Wei Lin, Chi Sun, Yu Dai, Haipeng Ren, Jiankang Wang, Lei Wu, Guowei Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology DUT-RU International School of Information Science and Engineering Dalian China Nanjing University State Key Laboratory for Novel Software Technology Jiangsu Nanjing210023 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

Wireless Rechargeable Sensor networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors. © 2002-2012 IEEE.

关键词： Charging stations

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