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

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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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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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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keyphrase Generation Based on Self-Attention Mechanism

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Computers, Materials & Continua 2019年第8期61卷 569-581页

作者： Kehua Yang Yaodong Wang Wei Zhang Jiqing Yao Yuquan Le 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

keyphrase greatly provides summarized and valuable *** information can help us not only understand text semantics,but also organize and retrieve text content *** task of automatically generating it has received considerable attention in recent *** the previous studies,we can see many workable solutions for obtaining *** method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important *** disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the *** approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context *** works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be *** by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention *** is an encoder-decoder model that can make up the above disadvantage *** addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the *** proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.

关键词： keyphrase generation self-attention mechanism encoder-decoder framework

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FPGA based unified architecture for public key and private key cryptosystems

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Frontiers of Computer Science 2013年第3期7卷 307-316页

作者： Yi WANG Renfa LI Embedded Systems and Networking Laboratory Hunan Province Key Laboratory of Network and Information Security Hunan University Changsha 410082 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

Recently, security in embedded system arises attentions because of modern electronic devices need cau- tiously either exchange or communicate with the sensitive data. Although security is classical research topic in world- wide communication, the researchers still face the problems of how to deal with these resource constraint devices and en- hance the features of assurance and certification. Therefore, some computations of cryptographic algorithms are built on hardware platforms, such as field program gate arrays （FPGAs）. The commonly used cryptographic algorithms for digital signature algorithm （DSA） are rivest-shamir-adleman （RSA） and elliptic curve cryptosystems （ECC） which based on the presumed difficulty of factoring large integers and the algebraic structure of elliptic curves over finite fields. Usu- ally, RSA is computed over GF（p）, and ECC is computed over GF（p） or GF（2P）. Moreover, embedded applications need advance encryption standard （AES） algorithms to pro- cess encryption and decryption procedures. In order to reuse the hardware resources and meet the trade-off between area and performance, we proposed a new triple functional arith- metic unit for computing high radix RSA and ECC operations over GF（p） and GF（2P）, which also can be extended to support AES operations. A new high radix signed digital （SD） adder has been proposed to eliminate the carry propagations over GF（p）. The proposed unified design took up 28.7% less hardware resources than implementing RSA, ECC, and AES individually, and the experimental results show that our proposed architecture can achieve 141.8 MHz using approxi- mately 5.5k CLBs on Virtex-5 FPGA.

关键词： AES RSA ECC signed-digit number FPGA cryptographic algorithms high radix arithmetic unit

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An Evidence Combination Method based on DBSCAN Clustering

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Computers, Materials & Continua 2018年第11期57卷 269-281页

作者： Kehua Yang Tian Tan Wei Zhang College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan ProvinceHunan UniversityChangshaHunan410082China Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityVirginiaBlacksburg24060USA

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple ***,the combination rules can be paradoxical when the evidence seriously conflict with each *** the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density *** the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict ***,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density ***,we conduct the information fusion for the revised evidence sets by D-S combination *** results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

关键词： D-S evidence theory information fusion DBSCAN combination rules

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An offline scheduling algorithm for certifiable mixed-critical embedded system

An offline scheduling algorithm for certifiable mixed-critic...

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

作者： Wu, Chengtao Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province Changsha 410086 China

ISBN: (纸本)9783642539619

In modern embedded platforms, safety-critical functionalities that must be certified correctly to high level of assurance co-exist with less critical software that are not subjected to certification requirements. These caused many scheduling problems. Nowadays, many researches focused on promising techniques for meeting the following two goals: (1) being able to certify the safety-critical functionalities under very conservative assumptions, and (2) ensuring high utilization of platform resources under less pessimistic assumptions. In this paper we propose an offline scheduling algorithm called OSS for certifiable mixed-criticality systems on a uniprocessor platform. Compared to previous proposed algorithm CBEDF, OSS can supply better schedulability. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Scheduling algorithms

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The implementation of MapReduce scheduling algorithm based on priority

The implementation of MapReduce scheduling algorithm based o...

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

作者： Gu, Lianjun Tang, Zhuo Xie, Guoqi Key Laboratory for Embedded and Network Computing of Hunan Province Changsha 410082 China

ISBN: (纸本)9783642539619

Nowadays cloud computing has become a popular platform for scientific applications. Cloud computing intends to share a large scale resources and equipments of computation, storage, information and knowledge for scientific researches. Job Scheduling problem is a core and challenging research issue in the current cloud computation area, and the aim is to the reasonable control of the job execution sequence as well as the allocation of computing resources, making the job total completion time of the shortest and resources are fully utilized. Data locality is one of the main factors to influence scheduling algorithm. The paper proposed an improved scheduling algorithm based on priority, after taking full account of data locality (IPDSA), which can distinguish the user's job levels, so as to reduce the job execution time and avoid losing into locally optimal solution. The experimental results on the Hadoop platform show that the new scheduling algorithm can reduce the job average execution time, and raises the rate availability of resources. © Springer-Verlag Berlin Heidelberg 2014.

关键词： MapReduce

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Dynamic slack reclamation with EDL scheduling for periodic multimode real-time task

Dynamic slack reclamation with EDL scheduling for periodic m...

引用

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

作者： Hu, Huan Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province Changsha 410086 China

ISBN: (纸本)9783642539619

The multiframe task has been considered in dynamic real-time scheduling, its different execution mode optional feature can handle the resource waste caused by pessimistic estimation of the execution requirement. But in runtime, the selection of an instance execution mode with more execution requirement may cause a deadline miss. In this paper, we propose a slack reclamation mechanism of configuring an appropriate execution mode under deadline guarantee. This mechanism is composed of offline phase and online phase. In offline, the initial execution mode of task instances is selected and the static slack is calculated. In online, on the basis of EDL scheduling list, mode upgrade list and actual execution requirement of the instance, the slack is assigned to the instance with the highest value of the r/R in Slack available zone. Our simulation shows that this scheme can obtain a better result of the accured reward when the task's BCET is greater than 40% of its WCET. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Scheduling

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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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