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arXiv 2025年

作者： Liu, Cheng Liu, Yu-Hong Kuang, Le-Man Nori, Franco Liao, Jie-Qiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Hunan Research Center of the Basic Discipline for Quantum Effects and Quantum Technologies Hunan Normal University Changsha410081 China Quantum Computing Center RIKEN Saitama Wakoshi351-0198 Japan Department of Physics The University of Michigan Ann ArborMI48109-1040 United States Institute of Interdisciplinary Studies Hunan Normal University Changsha410081 China

Highly efficient transfer of quantum resources including quantum excitations, states, and information on quantum networks is an important task in quantum information science. Here, we propose a bipartite-graph framework for studying quantum excitation transfer in bosonic networks by diagonalizing the intermediate sub-network between the sender and the receiver to construct a bipartite-graph configuration. We examine the statistical properties of the bosonic networks in both the original and bipartite-graph representations. In particular, we investigate quantum excitation transfer in both the finite and infinite intermediate-normal-mode cases, and show the dependence of the transfer efficiency on the network configurations and system parameters. We find the bound of maximally transferred excitations for various network configurations and reveal the underlying physical mechanisms. We also find that the dark-mode effect will degrade the excitation transfer efficiency. Our findings provide a new insight for the design and optimization of quantum networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Graphic methods

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Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-Based Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4874-4889页

作者： Cao, Hangcheng Xu, Guowen He, Ziyang Shi, Shaoqing Xu, Shengmin Wu, Cong Ning, Jianting Hunan University College of Computer Science and Electronic Engineering Changsha410012 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450001 China Zhengzhou University International College Zhengzhou450001 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350007 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science Macau China

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures;the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively. © 2005-2012 IEEE.

关键词： Unsupervised learning

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PFV2: Packet fragmentation with variable size and vigorous mapping in time-sensitive networking

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Journal of Systems Architecture 2025年 166卷

作者： Wenyan Yan Bin Fu Dongsheng Wei Renfa Li Yixue Lei Yuhang Jia Guoqi Xie Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha 410082 Hunan China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing 401120 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems School of Computer and Artificial Intelligence Xiangnan University Chenzhou 423000 Hunan China Future Network Laboratory Tencent Beijing 100080 China

With the rapid advancement of intelligent automobiles, the ever-growing communication data put forward high bandwidth and low latency requirements. To meet these requirements, automotive Original Equipment Manufacturers (OEMs) widely adopt domain-centralized Electrical/Electronic (E/E) architecture. In this architecture, Time-Sensitive networking (TSN) is expected to serve as the backbone network because of its high bandwidth and deterministic communication. TSN uses the Gate Control List (GCL) to divide its time length into multiple time slots, and the time intervals of these time slots are non-uniform (equal or unequal). Different flows have varying requirements for time slot sizes. To enhance the acceptance ratio of Time-Triggered (TT) flows through GCL time slot allocation, packet fragmentation (i.e., flow fragmentation) is introduced into TSN in the recent study. The state-of-the-art packet fragmentation solution divides one un-schedulable TT flow into multiple equal-sized packets (i.e., equal-sized time slots). In other words, these fixed-size packets are difficult to be mapped into the time slots with different sizes. This study develops a Packet Fragmentation with Variable-size and Vigorous-mapping (PFV2) technique based on the following three innovations: (1) we implement variable-size packet fragmentation, which iteratively divides the un-schedulable TT flow into smaller packets and then dynamically reschedules these packets; (2) we implement the vigorous mapping solution from packets to time slots by deeply searching for available time slots within the flow’s deadline; and (3) we verify PFV2 based on the LS1028A with Cortex-A72 (i.e., the NXP automotive-grade development board). PFV2 improves the acceptance ratio by up to 20.18% and bandwidth utilization by up to 7.024% compared with the state-of-the-art solution. The theoretical and practical co-verification experiments demonstrate that the PFV2 can effectively improve the flow acceptance ratio and outpe

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Partial Offloading Strategy Based on Deep Reinforcement Learning in the Internet of Vehicles

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IEEE Transactions on Mobile computing 2025年

作者： Tian, Shujuan Zhu, Xinjie Feng, Bochao Zheng, Zhirun Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Xiangtan University School of Mathematics and Compulational Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou510632 China

Driven by the increasing demands of vehicular tasks, edge offloading has emerged as a promising paradigm to enhance quality of experience (QoE) in Internet of Vehicles (IoV) networks. This approach enables vehicles to offload computation-intensive tasks to edge servers, resulting in reduced computation delays and lower energy consumption. However, traditional binary offloading limits the efficiency of edge offloading. To address this gap, we propose a partial offloading strategy that jointly optimizes the offloading ratio, computation, and communication resources in IoV. Recognizing the varying priorities of vehicular tasks regarding task delay and energy consumption, we formulate two distinct scenarios: one focused on minimizing delay and the other on minimizing energy consumption. Furthermore, we employ a reinforcement learning approach to establish a multi-dimensional joint optimization function by setting different objectives for each scenario. Based on this framework, we introduce a multi-state iteration deep deterministic policy gradient algorithm (SIDDPG), which effectively determines task partitioning and resource allocation. Simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of task delay and energy consumption. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Delay-derivative-dependent Stability Conditions for Uncertain T-S Fuzzy Systems with Interval Time-varying Delay

Delay-derivative-dependent Stability Conditions for Uncertai...

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第三十二届中国控制会议

作者： AN Ji-yao XIA Xian-zhong LI Ren-fa College of Information Science and Engineering Hunan University Key Laboratory of Embedded and Network Computing of Hunan Province Hunan University

ISBN: (纸本)9781479900305

This paper considers the problem of stability analysis of uncertain T-S fuzzy systems with interval time-varying delay. The system uncertainties are assumed to be time-varying and *** new delay-derivative-dependent stability conditions are derived by constructing a novel delay-dependent Lyapunov-Krasovskii functional and estimating a tighter upper bound of its time derivative without ignoring some useful terms and involving any direct *** proposed approach leads to some less conservative LMI conditions as shown through some numerical examples.

关键词： Uncertain systems T-S fuzzy systems Stability Lyapunov-Krasovskii Functional(LKF) LMI

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A long lifetime,low error rate RRAM design with self-repair module

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Journal of Semiconductors 2016年第11期37卷 94-98页

作者：尤志强胡飞黄黎明刘鹏邝继顺李实英 Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic EngineeringHunan University

Resistive random access memory （RRAM） is one of the promising candidates for future universal memory. However, it suffers from serious error rate and endurance problems. Therefore, exploring a technical solution is greatly demanded to enhance endurance and reduce error rate. In this paper, we propose a reliable RRAM architecture that includes two reliability modules： error correction code （ECC） and self-repair modules. The ECC module is used to detect errors and decrease error rate. The self-repair module, which is proposed for the first time for RRAM, can get the information of error bits and repair wear-out cells by a repair voltage. Simulation results show that the proposed architecture can achieve lowest error rate and longest lifetime compared to previous reliable designs.

关键词： self-repair ECC RRAM memristor

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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular networking

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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Reliability-driven real-time task replication for fault tolerance in hierarchical scheduling multiprocessor systems

Reliability-driven real-time task replication for fault tole...

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IET International Conference on Information and Communications Technologies, IETICT 2013

作者： Liu, Lin Li, Renfa Li, Rui Xie, Yong Zeng, Qingguang Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

ISBN: (纸本)9781849196536

The increasing complexity of systems poses a big challenge to design systems with fault-tolerant capability. The aim of this study was to establish fault-tolerant system for multiprocessor platform with the help of hierarchical scheduling framework. A mathematical fault and reliability model for hierarchical scheduling framework was proposed firstly, and then a heuristic algorithm by exploiting the difference of task resource overhead and the probability of task failures was presented and a task replication scheme was given lastly. The experimental results demonstrated the proposed scheme could effectively guarantee reliability goal and reduce resource consumption simultaneously.

关键词： Fault tolerance

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ASDIA: An Adversarial Sample to Preserve Privacy Program in Federated Learning

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Tian, Shujuan Tan, Yi Wang, Han Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Department of Electrical Engineering and Computer Science University of Kansas 1520 W 15th St LawrenceKS United States Jinan University College of Information Science and Technology Guangzhou510632 China

Federated learning enables training across multiple entities while ensuring data security and the effectiveness of knowledge dissemination. Despite its benefits, it remains susceptible to privacy breaches by both external and internal adversaries, who may exploit data or model parameters to glean sensitive participant information or disrupt the training process, thus compromising participant privacy and security. This paper proposes a novel methodology, Adversarial Samples for Defense Inference Attack (ASDIA), aimed at dual protection of data privacy and model robustness within federated learning through adversarial samples and gradient reconstruction. ASDIA includes gradient processing approach before uploading: initially identifying privacy-sensitive gradient, followed by the injection of well-calibrated noise to these gradients. This method not only obfuscates the adversary's classification demarcations but also aids in model performance recovery, all the while maintaining computational efficiency. ASDIA reduces the efficacy of attacks to near-random guessing levels and shows better balance between the model utility and privacy protection compared to the most advanced defense strategies. Additionally, regarding model performance, ASDIA proves its merit across diverse datasets under overfitting and non-overfitting scenarios. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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Personalized News Recommendation Based on the Text and Image Integration

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Computers, Materials & Continua 2020年第7期64卷 557-570页

作者： Kehua Yang Shaosong Long Wei Zhang Jiqing Yao Jing Liu College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company New York10007USA

The personalized news recommendation has been very popular in the news recommendation *** most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual ***,in this paper,a model that combines images and texts in the news is *** this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is *** AT algorithm selects the tags the user is interested in based on the user *** particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user *** proposed AT algorithm is verified by *** experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.

关键词： News recommendation F1 AUC MRR feedback correlation graph

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