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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Tian, Xiaomei Zhao, Huihuang College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Multiple-set membership query problem plays a very important role in many network systems, including packet forwarding architectures in big data centers, routing switching devices and internet firewall systems. Many solutions to multiple-set membership query problem are based on Bloom filters. And each of these solutions has its own merits. Some have high query speed and some have good accuracy. But generally, they cannot have both at the same time. In this paper, we propose a probabilistic data structure named constant-weight group coded Bloom Filter (CWGCBF) for fast and accurate multi-set membership query. The key technique in CWGCBF is encoding each elements set ID to constant weight code, and directly recording this constant-weight code in the bloom filter vector. Experimental results show that in terms of false positive ratio, CWGCBF, Shifting Bloom Filter and Combinatorial Bloom Filter have great advantages to the other compared Bloom filters, and in terms of time efficiency, CWGCBF and ID Bloom Filter are several times faster than the others. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data structures

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Hyperchaotic Image Encryption Algorithm Based on BD-Zigzag Transformation and DNA Coding 12th

Hyperchaotic Image Encryption Algorithm Based on BD-Zigzag T...

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12th International Conference on Computer Engineering and Networks, CENet 2022

作者： Wang, Lujie Chen, Zhong College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

For the features of Zigzag transformation, a Bidirectional Deformed Zigzag (BD-Zigzag) transformation is proposed in this paper, which starts from both sides of the diagonal and crosses in both directions. This method is more complex than the traditional Zigzag transformation method and has better dislocation effect. Then, a Hyperchaotic image encryption algorithm based on BD-Zigzag transformation and DNA coding is proposed. First, the images are processed with multiple rounds of permutations using the BD-Zigzag transformation. Second, a 4D-hyperchaotic Lorenz system is used to produce random numbers to permute the images at the bit level. Finally, DNA encoding is used to diffuse the pixels in the image. The experimental simulations and performance analysis data can prove that the encryption algorithm has high-level security capability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： DNA

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Research on Task Scheduling in Distributed Vulnerability Scanning System 10th

Research on Task Scheduling in Distributed Vulnerability Sca...

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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Jiang, Jie Tang, Sixin College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Network threats caused by system vulnerabilities is increasing gradually, the distributed vulnerability scanning system can scan large-scale and complex networks and report the vulnerability information. Task scheduling is one of the core components in a distributed system. In this paper, we use dynamic optimization algorithms to improve the task scheduling efficiency of distributed vulnerability scanning system, we propose a PSO-based task scheduling scheme and improves the search ability of particles by adjusting algorithm parameters. We compared the time consume when using existing ‘Resource Aware Scheduling algorithm (RASA) with the basic particle swarm optimization (PSO) algorithm and the improved particle swarm optimization (IPSO) algorithm. Our results show that IPSO has better performance than other scheduling methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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A Image Adaptive Steganography Algorithm Combining Chaotic Encryption and Minimum Distortion Function 10th

A Image Adaptive Steganography Algorithm Combining Chaotic E...

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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Jiao, Ge Liu, Jiahao Zhou, Sheng Luo, Ning College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Digital image has the characteristics of high redundancy and convenient processing, which is the ideal carrier of information hiding application. Therefore, digital image steganography has become a hot research direction in the field of information security. In order to ensure the concealment, reliability and security of image steganographic information, an image adaptive steganographic algorithm combining chaotic encryption and minimum distortion function is designed. The algorithm generates random keys, encrypt the secret information using Logistic and ChebyShev mapping, and then embeds the key and the encrypted secret information into the carrier image using the HILL steganographic algorithm. The algorithm stores the embedded key and the decryption key separately, so the attacker cannot get the correct secret information even if he gets the embedded key, which improves the security of the key. Experiments show that the pixel change rate of the algorithm is 6.76%, the peak signal-to-noise ratio is 59.51 db, and it has a very good anti-steganographic analysis ability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Design of preventing electricity-stolen intelligent video surveillance terminal based on 4G network

Design of preventing electricity-stolen intelligent video su...

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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Xiong, Dezhi Chen, Yilei Yang, Jie Chen, Xiangqun Hunan Province Key Laboratory of Intelligent Electrical Measurement and Application Technology State Grid Hunan Electric Power Company Power Supply Service Center Changsha China Key Laboratory of Fiber Optic Sensing Technology and Information Processing Wuhan University of Technology Wuhan China

ISBN: (纸本)9781538614273

The current common preventing electricity-stolen technology and existing defects is analyzed, the key technologies such as 4G communication module, intelligent video forensics are proposed, a intelligent video monitoring terminal basing on 4G network and integrating electricity larceny prevention and live video monitoring, is developed, it provides the important means for combating power theft in accordance with the law. The overall design scheme of intelligent video monitoring terminal basing on 4G networks electricity-stolen prevention is proposed, the terminal system hardware, MPU, each function module and the peripheral interface are designed, terminal image acquisition and image compression technology, up image data transmission and 4G communication technology is studied and implemented. The design methods of terminal system software architecture, software function, and the related system interface are proposed, the image acquisition process of electricity larceny prevention device is emphatically designed, the design schemes of gate node switch trigger and infrared pyroelectric detect trigger camera are proposed. The device can timely find the scene of the various power theft, and to obtain evidence, can effectively solve the difficulty of electricity larceny evidence collection, has a strong practical value and broad application prospects. © 2017 IEEE.

关键词： Image compression

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Federated Multitask Learning for HyperFace

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 788-797页

作者： Zhang, Chen Bai, Hang Zhang, Yonggang Niu, Xinyi Yu, Bin Gao, Yuan Xie, Yu Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Province Science and Technology Resources Coordination Center Xi'an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xi'an710071 China Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan030006 China

Multitask learning (MTL) is a promising field in machine learning owing to its capability to improve the generalization performance of all tasks by sharing knowledge among the related tasks. MTL has attracted a large amount of attention in the multitask-related community. In recent years, with the rapid development of distributed machine learning, MTL in a distributed environment becomes a hot research topic. Although MTL can reap great fruit in a distributed environment, the sensitive information, such as photos and voice recordings of the owner, in distributed data may be leaked during training. Consequently, in the distributed settings, one prominent challenge of MTL is to prevent the disclosure of sensitive private data. In this article, we propose a novel approach, federated multitask learning (FMTL), which applies federated learning to MTL in a distributed environment in order to protect the MTL model from being leaked and to better optimize the utility of the MTL model. In FMTL, all the participants independently train their local models on their own dataset in parallel and only transmit their model updates to the central server for aggregation at every epoch. As a result, their learning accuracy is enhanced beyond what is achieved only on their own training data. In this manner, it can achieve the best tradeoff between the utility and privacy: participants not only preserve the privacy of their own data, but also benefit from the models of other participants. The experimental results on annotated facial landmarks in the wild (AFLW) and annotated faces in the wild (AFW) benchmark datasets verify the effectiveness of our framework. © 2020 IEEE.

关键词： Linearization

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Dynamic maintenance of decision rules for decision attribute values’ changing 25th

Dynamic maintenance of decision rules for decision attribute...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Wang, Yingyao Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency. © Springer Nature Switzerland AG 2018.

关键词： Rough set theory

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Label distribution learning based on ensemble neural networks 25th

Label distribution learning based on ensemble neural network...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Zhai, Yansheng Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Label distribution learning (LDL), as an extension of multi-label learning, is a new arising machine learning technique to deal with label ambiguity problems. The maximum entropy model is commonly used in label distribution learning. However, it does not consider the correlation between the labels and is not suitable for nonlinear relationships, and the prediction performance is also limited. In this paper, we propose a label distribution learning algorithm based on ensemble neural networks. The algorithm trains neural networks with preferences using training sets with different label sets to construct base learners, and combines the base learners with the weights, which is learned by the combined learner to obtain the final learning results. Experimental results show that the proposed algorithm is effective for label distribution data. © Springer Nature Switzerland AG 2018.

关键词： Learning algorithms

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HSCS:a hybrid shared cache scheduling scheme for multiprogrammed workloads

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Frontiers of Computer Science 2018年第6期12卷 1090-1104页

作者： Jingyu ZHANG Chentao WU Dingyu YANG Yuanyi CHEN Xiaodong MENG Liting XU Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004China School of Electronics and Information Shanghai Dianji UniversityShanghai 200240China

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

关键词： multicore system shared cache workload performance

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