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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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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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PSSNet: An Optimized High-Accuracy Method for Forest Fire Smoke Detection

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IEEE Internet of Things Journal 2025年

作者： Lin, Shuqi Li, Ziming Xu, Zhuonong Sun, Lixiang Zhou, Guoxiong Han, Guangjie Central South University of Forestry and Technology Institute of Applied Artificial Intelligence Hunan Changsha410004 China Department of Forest and Grassland Fire Prevention Monitoring and Dispatching Evaluation Center of Hunan Province Changsha410004 China Hohai University Department of Internet of Things Engineering Changzhou213000 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In the field of early automatic detection of smoke from forest fires, there is the issue of the small size and interference of smoke detection by clouds. The conventional NMS (Non-Maximum Suppression) requires manual adjustment of the threshold, which may result in missed or erroneous detection. This paper proposes a high-accuracy anti-interference forest fire smoke detection network for small objects. Firstly, a window feature extractor based on singular value decomposition (SVD-STR) is designed. This extractor is capable of extracting more representative features, of capturing small and inconspicuous features in the image, and of reducing the complexity and computation of the model. Secondly, a SinThreshold Screening Attention Mechanism (SinAttention) is proposed, which can filter interference information and enhance the discriminative power of the features, thereby facilitating the accurate recognition and distinction of smoke and clouds. Subsequently, a variational particle swarm soft suppression optimization (PGS) is proposed as a means of further enhancing the optimization effect. This is achieved by adjusting the suppression strategy and incorporating a Gaussian variational particle swarm algorithm. In conclusion, an IoT forest fire detection system based on PSSNet has been constructed. The experimental results demonstrate that the mAP50 value of the method is 98.2%, the value of mAP50-95 is 80.4%, and the FPS value is 35.7. These values are superior to those of current forest fire smoke detection methods and can be utilized for the precise detection of forest fire smoke, thereby providing technical support for forest ecological protection. © 2014 IEEE.

关键词： Smoke detectors

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A novel graph kernel algorithm for improving the effect of text classification

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Computer Speech & Language 2026年 95卷

作者： Fan Yang Tan Zhu Jing Huang Zhilin Huang Guoqi Xie College of Computer and Information Engineering Central South University of Forestry and Technology Changsha Hunan 41004 PR China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan 411201 PR China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan 410082 PR China

Text classification is an important topic in natural language processing. In recent years, both graph kernel methods and deep learning methods have been widely employed in text classification tasks. However, previous graph kernel algorithms focused too much on the graph structure itself, such as the shortest path subgraph,while focusing limited attention to the information of the text itself. Previous deep learning methods have often resulted in substantial utilization of computational resources. Therefore,we propose a new graph kernel algorithm to address the disadvantages. First,we extract the textual information of the document using the term weighting scheme. Second,we collect the structural information on the document graph. Third, graph kernel is used for similarity measurement for text classification. We compared eight baseline methods on three experimental datasets, including traditional deep learning methods and graph-based classification methods, and tested our algorithm on multiple indicators. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of accuracy. Furthermore, it achieves a minimum reduction of 69% in memory consumption and a minimum decrease of 23% in runtime. Furthermore, as we decrease the percentage of training data, our algorithm continues to achieve superior results compared to other deep learning methods. The excellent experimental results show that our algorithm can improve the efficiency of text classification tasks and reduce the occupation of computer resources under the premise of ensuring high accuracy.

关键词： Document similarity measure Graph kernel Machine learning Term weighting Text classification

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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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An Efficient Three-Factor Privacy-Preserving Authentication and key Agreement Protocol for Vehicular Ad-Hoc network

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China Communications 2021年第12期18卷 315-331页

作者： Tao Xu Cheng Xu Zisang Xu School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha 410082China

As an indispensable part of the Intelli-gent Transportation System(ITS),the vehicular ad-hoc network(VANET)has attracted widespread atten-tion from academia and *** order to ensure the security of VANET,vehicles need to be authen-ticated before accessing the *** existing authentication protocols in VANET adopt the Trusted Authority(TA)with centralized structure which is re-sponsible for the authentication tasks of all ***,the large-scale network consume a lot of computing resources,which leads to unacceptable de-lay in message transmission in *** reducing the computational cost of TA,an efficient three-factor privacy-preserving authentication and key agreement protocol was proposed in our *** from before,the RoadSide Unit(RSU)no longer acts as an intermediate node but is responsible for assisting user authentication,which lead to the computational cost of TA is very *** formal and informal analysis,our protocol demonstrates excellent ***-pared with previous studies,our work emerges advan-tages and superiorities in the following aspects:com-putational cost,communication cost,security proper-ties and functions,message loss ratio,and message *** data and evidence indicate that our protocol is an ideal choice for large-scale VANET.

关键词： authentication vehicular ad-hoc network security three-factor

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Optimized roles set algorithm in distributed parallel computing system

Optimized roles set algorithm in distributed parallel comput...

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26th International Conference on Parallel Computational Fluid Dynamics, ParCFD 2013

作者： Wu, Wenkang Tang, Zhuo Key Laboratory for Embedded and Network Computing of Hunan Province Changsha Hunan 410082 China

ISBN: (纸本)9783642539619

With the rapid development of computer technology, distributed computing and parallel computing have made great progress. Distributed parallel computing system is a combination of distributed computing and parallel computing, which can provide fast solutions to information sharing and subtasks in high-speed network environment. In this paper, we propose a new access control model-Distributed Parallel computing Role Based Access Control (DPCRBAC) Model on the basis of the traditional RBAC model. We propose a new roles set assignment algorithm-Optimized Roles Set (ORS) algorithm. According to the results of simulation, the proposed algorithm ORS can effectively improve the efficiency roles and is effective in reducing role conflicts in the process of role assignment, and ensure the safety and reliability of the distributed parallel computing system. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Distributed computer systems

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Multi-Label Learning Based on Transfer Learning and Label Correlation

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Computers, Materials & Continua 2019年第7期61卷 155-169页

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

In recent years,multi-label learning has received a lot of ***,most of the existing methods only consider global label correlation or local label *** fact,on the one hand,both global and local label correlations can appear in real-world situation at same *** the other hand,we should not be limited to pairwise labels while ignoring the high-order label *** this paper,we propose a novel and effective method called GLLCBN for multi-label ***,we obtain the global label correlation by exploiting label semantic ***,we analyze the pairwise labels in the label space of the data set to acquire the local ***,we build the original version of the label dependency model by global and local label *** that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent ***,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label *** experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

关键词： Bayesian networks multi-label learning global and local label correlations transfer learning

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