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arXiv 2021年

作者： Wu, Di Xie, Xiaofeng Nie, Xiang Fu, Bin Deng, Hanhui Zeng, Haibo Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Department of Electrical and Computer Engineering Virginia Tech United States EECS Queen Mary University of London United Kingdom

—The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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RTISM: Real-Time Inter-VM Communication Based on Shared Memory for Mixed-Criticality Flows

RTISM: Real-Time Inter-VM Communication Based on Shared Memo...

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Real-Time Systems Symposium (RTSS)

作者： Zonghong Li Guoqi Xie Wenhong Ma Xiongren Xiao Yong Xie Wei Ren Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University China Research Institute of Hunan University in Chongqing China Nanjing University of Posts and Telecommunications China Huawei Technologies China

Virtual machines (VMs) have been increasingly deployed in real-time systems to enhance heterogeneous resource sharing. Despite being isolated for security and prevention of failure propagation, VMs need to communicate with each other to complete certain tasks jointly. A real-time inter-VM communication framework has been proposed based on VirtIO, which is in essence a mechanism of message passing with high latency and low scalability on data amount. In contrary, IVSHMEM implements inter-VM communication with shared memory, which is generally fast and large in throughput. Unfortunately, IVSHMEM does not resolve resource contention between flows and hence cannot be applied in real-time scenarios. In this work, we propose a real-time inter-VM communication framework RTISM built upon shared memory and IVSHMEM. RTISM provides priority-based scheduling and supports mixed-criticality flows. Worst-Case Response Time (WCRT) analysis is reported to bound the end-to-end communication delay and a Limited Priority Assignment (LPA) algorithm is developed to enhance schedulability. Experimental evaluation shows that (i) RTISM has about 4 times higher throughput than the VirtIO-based inter-VM communication framework; (ii) LPA improves schedulability by over 25% compared to the state-of-the-art; (iii) WCRT produces a tight bound.

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Secret sharing based countermeasure for AES S-box

Secret sharing based countermeasure for AES S-box

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International Symposium on Integrated Circuits (ISIC)

作者： Yi Wang Zheng Yuan Zhican Li Renfa Li Embedded Systems & Networking Laboratory Hunan Provincial Key Laboratory of Network and Information Security Hunan University Changsha China

Cryptographic devices are vulnerable to Differential Power Attack (DPA) in embedded systems. Masking methods are popularly used to defend against DPA by masking all intermediate data with random values. However, masking schemes on algorithm level are vulnerable to Higher-Order DPA (HODPA), while on gate level glitch attack is the biggest threaten attack. In this paper, we proposed a secret sharing based countermeasure for AES S-box, which can defend against both HODPA and glitch attack. The experimental results show that our proposed design takes up less hardware resources and achieves faster speed compared with the existing methods.

关键词： Logic gates Hardware Resistance Encryption Resists Algorithm design and analysis

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A deep hashing method based on attention module for image retrieval 13

A deep hashing method based on attention module for image re...

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13th International Conference on Intelligent Computation Technology and Automation, ICICTA 2020

作者： Long, Jun Wei, Xiangxiang Qi, Qianqian Wang, Ye School of Computer Science and Engineering Central South University Changsha410083 China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha410083 China

ISBN: (纸本)9781728186665

Hashing retrieval technique has received widespread attention since its low storage consumption and fast retrieval speed. Image retrieval based on deep hashing method uses image features with high-level semantic information and discriminative expression capabilities. However, there are still some limitations: (1) Compared with the traditional feature vector, the length of the learned hash codes is short, and each bit of the code is -1 or 1, the discrimination capability of feature representation is limited;(2) the existing deep hash algorithms cannot directly learn the discrete hash codes using sign activation function, therefore, they use relaxation-based scheme to learn the hash codes, resulting in a large quantization error. In order to solve the above problems, a deep hashing retrieval algorithm incorporate with attention model is proposed. This algorithm embeds spatial and channel attention models in the feature extraction network, and uses a novel activation function in the hash layer. In addition, a novel triple loss function is further proposed to improve the learning capability of the network. Finally, experimental results on the two benchmark databases, i.e., CIFAR-10 and MINIST datasets, show the effectiveness and superiority compared with the baselines. © 2020 IEEE.

关键词： Image retrieval

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Dynamic negotiation strategy based parallel-offer in cloud computing

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Journal of Computational Information Systems 2015年第10期11卷 3691-3699页

作者： Hu, Jun Xu, Ru Xiao, Deyu College of Computer Science and Electronics Engineering Hunan University Changsha China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China

Cloud computing can dynamically provide service based on service level agreements that between cloud services and consumers. Establish a viable and efficient dynamic negotiation strategy is one of hot research in cloud negotiation. This paper proposes a dynamic negotiation strategy based on parallel-offer under cloud computing environment, which combines dynamic concession strategy with parallel-offer tradeoff mechanism. The strategy can adjust negotiation strategy according to the supply demand relationship and tradeoff strategy of multi-issue, to achieve flexible and efficient cloud computing negotiation. This work firstly, establishes predicting market supply-demand relationship model, to build the dynamic concession strategy. Then on the base of above, introduces a parallel offer multi-issue tradeoff strategy into negotiation strategy, to achieve the dynamic concession tradeoff strategy (DCT). Finally, DCT mechanism is implemented in CloudSim test bed, and empirical results show DCT negotiation mechanism reach faster agreements and achieve higher utilities than other related approaches in cloud computing environment. ©, 2015, Binary Information Press. All right reserved.

关键词： Cloud computing

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Non-cooperative game approach for task offloading in edge clouds

arXiv

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arXiv 2018年

作者： Yang, Bo. Li, Zhiyong Liu, Wenbin College of Information and Management Hunan University of Finance and Economics Changsha410205 China College of Computer Science and Electronic Engineering of Hunan University National Supercomputing Center in Changsha Key Laboratory for Embedded and Network Computing of Hunan Province Changsha410082 China

—Task offloading provides a promising way to enhance the capability of the mobile terminal (also called terminal user) that is distributed on network edge and communicates edge clouds with wireless. Generally, there are multiple edge cloud nodes with distinct processing capability in a geographic area, which can offer computing service for various terminal users. Furthermore, the terminal users are competitive and selfish, i.e., each user takes into account only maximizing her own profit, while conducting task offloading strategies. In this paper, we focus on the resource management optimization for edge clouds, and formulate the problem of resource competition among terminal users as a non-cooperative game, in which the terminal user who acts as the player always pursues the minimization of the expected response time for her tasks by optimizing allocation strategies. We present the utility function of the user with queuing theory, and then prove the existence of Nash equilibrium for the formulated game. Using the concept of Nash bargaining solution to calculate the optimal task offloading scheme for the user, we propose a distributed task offloading algorithm with low computation complexity. The results of simulated experiments demonstrate that our method can quickly reach the Nash equilibrium point, and deliver satisfying performance at the expected response time of the user’s tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory

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EBCRO: Hybrid chemical reaction with employed bee operator

EBCRO: Hybrid chemical reaction with employed bee operator

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhiyong Li Tingkun Yuan Bo Yang Shilong Jiang Yanmei Xie College of Computer Science and Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China PKU-HKUST Shenzhen-Hongkong Institution Shenzhen China Hunan Vocational College of Modern Logistics Changsha China

His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.

关键词： Optimization Chemicals Convergence Particle swarm optimization Linear programming Kinetic energy

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A Real-Time CAN-CAN Gateway with Tight Latency Analysis and Targeted Priority Assignment

A Real-Time CAN-CAN Gateway with Tight Latency Analysis and ...

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Real-Time Systems Symposium (RTSS)

作者： Guoqi Xie Haijie Gong Yunbo Han Samarjit Chakraborty Wanli Chang College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Future Network Lab CSIG Tencent China University of North Carolina at Chapel Hill USA University of York UK

ISBN: (数字)9781728183244

ISBN: (纸本)9781728183251

There is a demand in the automotive industry to connect two CAN-based subsystems. The commercial CAN-CAN gateway supports basic message forwarding with no real-time behavior. To address this issue, a new gateway architecture is described, on which we present a novel worst-case latency analysis. Specifically, we bound the arrival of the messages at the gateway, which is then used by the Pointer Reachability Exploration (PRE) to derive the interfering message jobs. Our analysis computes a safe gateway latency tighter than the conventional one applied in CAN. Furthermore, we propose a Targeted Priority Assignment (TPA) algorithm that targets at the priorities assigned at the CAN bus and runs a reordering at the gateway to enhance the schedulability. TPA performs better than DMPO (Deadline Monotonic Priority Ordering), while OPA (Audsley's Optimal Priority Assignment) cannot be applied in this context. Evaluation over real-life and scalable CAN message sets is conducted. The reported analysis and priority assignment algorithm are developed for dynamic use to improve the acceptance ratio and can also be deployed statically to provide timing guarantees. This work can be easily extended to support multiple CAN subsystems.

关键词： Heuristic algorithms Prototypes Computer architecture Logic gates Real-time systems Timing Vehicle dynamics

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WkNER: Enhancing Named Entity Recognition with Word Segmentation Constraints and kNN Retrieval 30

WkNER: Enhancing Named Entity Recognition with Word Segmenta...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Yanchun Deng, Senlin Shen, Dongsu Tian, Shujuan Long, Saiqin School of Computer Science School of Cyberspace Science Xiangtan University China Hunan International Scientific and Technological Cooperation Base of Intelligent Network China Key Laboratory of Hunan Province for Internet of Things and Information Security China College of Information Science and Technology Jinan University China

ISBN: (纸本)9782493814104

Fine-tuning Pre-trained Language Models (PLMs) is a popular Natural Language Processing (NLP) paradigm for addressing Named Entity Recognition (NER) tasks. However, neural network models often demonstrate poor generalization capabilities due to significant disparities between the knowledge learned by PLMs and the distribution of the target dataset, as well as data scarcity issues. In addition, token omission in predictions due to insufficient learning remains a challenge in NER. In this paper, we propose a kNN retrieval enhancement algorithm (WkNER) that incorporates word segmentation information to enhance the model's generalization ability and alleviate the problem of missing entity tokens in prediction. The introduction of word segmentation information is used to preliminarily determine the boundaries of entities and alleviate the common prediction errors of missing tokens within entities made by the fine-tuned model. Secondly, we find that non-entities in the retrieval table contain a large amount of redundant information, and explore the effects of introducing non-entity information of different scales on the model. Experimental results show that our proposed method significantly improves the performance of baseline models, and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets. Especially in low-resource scenarios, our method achieves higher accuracy on 20% of the dataset than the original method on the full dataset. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

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System-Level Energy-Aware Design Methodology Towards End-To-End Response Time Optimization

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2019年

作者： Xie, Guoqi Huang, Jing Li, Yan Liu Renfa Li, Keqin Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan 410082 China. College of Computer Science and Electronic Engineering Hunan University China 410082 China and also is with the Department of Computer Science State University of New York New Paltz New York 12561 USA.

Energy provision is known and limited for battery-constrained/powered systems (e.g., mobile phones, electric cars, robots, and wireless sensor networks). System-level energy-aware design methodologies have been proposed to control energy usage for energy provision guarantee, and to optimize the end-to-end response time for high-performance in heterogeneous energy-aware systems, where energy pre-assignment strategies are considered as their main contributions. However, when pre-assigning energy to tasks, these strategies either are overly pessimistic or adopt an average manner without a customizable approach to specific tasks. In this paper, we propose an effective algorithm called ratio-based energy pre-assignment (REP) to be customizable to split the available energy according to the energy ratios of individual tasks. Experiments confirm that REP outperforms the state-of-the-art three algorithms of the same type. After REP is used, we further propose a new innovation algorithm called energy surplus reduction (ESR) to reduce the execution time of small partial tasks, thereby optimizing the end-to-end response time within its energy provision. Experiments confirm ESR can reduce the energy usages of small partial tasks to achieve sufficient response time optimization. IEEE

关键词： Wireless sensor networks

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