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An Online/Offline HIBS Scheme for Privacy Protection of People-centric Sensing

An Online/Offline HIBS Scheme for Privacy Protection of Peop...

International Conference on Industrial Informatics, Computing technology, Intelligent technology, Industrial Information Integration (ICIICII)

作者： Peixin Chen Xiaofeng Wang Jinshu Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467383134

People-Centric Sensing (PCS), which collects information closely related to human activity and interactions in societies, is stepping into a flourishing time. Along with its great benefits, PCS poses new security challenges such as data integrity, participant privacy. Hierarchical Identity-Based Signature (HIBS) scheme can efficiently provide high integrity messaging, secure communication and privacy protection to PCS. However, the low computation efficiency primarily hinders the PCS adoption of HIBS scheme. In this paper, we propose an online/offline HIBS (HIBOOS) scheme for securing PCS. By splitting the signing phase into online and offline procedures, our scheme achieves high signing efficiency.

关键词： Games Sensors Privacy Public key Generators Wireless sensor networks

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Research on Neutral Mutation Algorithm in DNA Polymorphism Testing based on OpenMP

Research on Neutral Mutation Algorithm in DNA Polymorphism T...

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2015 IEEE Advanced Information technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Jun Li Jun Lu Ruiqing Jing Qiong Wang Cheng Chang College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

ISBN: (纸本)9781479919819

Soybean genotypes are adopted as the data source and the Tajima's D statistical method is parallel implemented by OpenMP *** method is used for testing neutral mutation hypothesis of DNA *** setting the sliding window size,all chromosomes containing the massive windows with multiple SNP sites are parallel processed,then the number of individual in sample,the number of segregating sites(S) and average number of nucleotide differences(π) are *** parameters filtering,the Tajima's D values for the effective SNP sites in each sliding window are parallel *** to the experimental data,the serial/parallel algorithms are compared and the conclusion shows that the efficiency of testing neutral mutation can be improved by using the OpenMP *** is a realistic significance for processing massive biological information data.

关键词： genotype Tajima's D OpenMP SNP

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Research on Jukes-Cantor Model parallel Algorithm Based on OpenMP

Research on Jukes-Cantor Model Parallel Algorithm Based on O...

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2015大数据技术及应用论坛

作者： Kang Wan Jun Lu Jiaju Zheng Zihan Ren College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

The parallel algorithm of the evolutionary distance between different species is implemented by using Open MP parallel technique in this paper. In order to get the best degree of algorithm parallelism, the method of making loop variable corresponding to the rower and column labels of distance matrix is adopted. It is to say that the double loop can be converted into single loop to improve the parallel efficiency. The serial algorithm and parallel algorithm are compared in this paper. The experiment result shows that the highest speedup is14.1. It improves the running efficiency of the program. It is a great significance to dealing with massive bioinformatics data.

关键词： OpenMP parallel Evolutionary distance Distance matrix

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Research on parallel F-statistics estimation algorithm based on OpenMP

Research on Parallel F-statistics estimation algorithm based...

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2015 IEEE Advanced Information technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Ruiqing Jing Jun Lu Jun Li Kang Wan Haoyu Zhang College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

ISBN: (纸本)9781479919819

The genetic variation information file of glycine max is adopted as experimental data in this *** genetic variation information of soybean is processed and the corresponding algorithm is paralleled by the OpenMP parallel *** experiment extracted the corresponding biologic information of population individuals from genetic variation information,implemented corresponding process and *** relevant serial algorithm and parallel algorithm are compared and *** experiment results show that the efficiency of the data processing and analysis of F statistic are effectively improved by OpenMP parallel technology[l].It has actual significance on processing massive bioinformatic data.

关键词： F-statistics OpenMP population differentiation variation site

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Improving vertex-frontier based GPU breadth-first search

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Journal of Central South University 2014年第10期21卷 3828-3836页

作者：杨博卢凯高颖慧徐凯王小平程志权 Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology Department of Electronic Science and Engineering National University of Defense Technology Avatar Science Company

Breadth-first search(BFS) is an important kernel for graph traversal and has been used by many graph processing applications. Extensive studies have been devoted in boosting the performance of BFS. As the most effective solution, GPU-acceleration achieves the state-of-the-art result of 3.3×109 traversed edges per second on a NVIDIA Tesla C2050 GPU. A novel vertex frontier based GPU BFS algorithm is proposed, and its main features are three-fold. Firstly, to obtain a better workload balance for irregular graphs, a virtual-queue task decomposition and mapping strategy is introduced for vertex frontier expanding. Secondly, a global deduplicate detection scheme is proposed to remove reduplicative vertices from vertex frontier effectively. Finally, a GPU-based bottom-up BFS approach is employed to process large frontier. The experimental results demonstrate that the algorithm can achieve 10% improvement over the state-of-the-art method on diverse graphs. Especially, it exhibits 2-3 times speedup on low-diameter and scale-free graphs over the state-of-the-art on a NVIDIA Tesla K20 c GPU, reaching a peak traversal rate of 11.2×109 edges/s.

关键词： breadth-first search GPU graph traversal vertex frontier

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DREAMS: Dynamic resource allocation for MapReduce with data skew

DREAMS: Dynamic resource allocation for MapReduce with data ...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Zhihong Liu Qi Zhang Mohamed Faten Zhani Raouf Boutaba Yaping Liu Zhenghu Gong College of Computer National University of Defense Technology Changsha China David R. Cheriton School of Computer Science University of Waterloo Waterloo ON Canada Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781479982424

MapReduce has become a popular model for large-scale data processing in recent years. However, existing MapRe-duce schedulers still suffer from an issue known as partitioning skew, where the output of map tasks is unevenly distributed among reduce tasks. In this paper, we present DREAMS, a framework that provides run-time partitioning skew mitigation. Unlike previous approaches that try to balance the workload of reducers by repartitioning the intermediate data assigned to each reduce task, in DREAMS we cope with partitioning skew by adjusting task run-time resource allocation. We show that our approach allows DREAMS to eliminate the overhead of data repartitioning. Through experiments using both real and synthetic workloads running on a 11-node virtual virtualised Hadoop cluster, we show that DREAMS can effectively mitigate negative impact of partitioning skew, thereby improving job performance by up to 20.3%.

关键词： Resource management Containers Predictive models Mathematical model Monitoring Biomedical monitoring Yarn

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Transductive Convolutive Nonnegative Matrix Factorization for Speech Separation

Transductive Convolutive Nonnegative Matrix Factorization fo...

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International Conference on computer science and Network technology

作者： Yaodan Mai Long Lan Naiyang Guan Xiang Zhang Zhigang Luo College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781467381741

Nonnegative matrix factorization (NMF) is an effective speech separation approach of extracting discriminative components of different speaker. However, traditional NMF focuses only on the additive combination of the components and ignores the dependencies of speeches. Convolutive NMF (CNMF) captures the dependencies of speeches by overlapping components and achieves better separation performance. NMF and CNMF learn dictionaries for speakers in the absence of mixture, and thus they are unable to get enough information to learn dictionaries accurately when testing speeches are available. To handle this problem, transductive NMF (TNMF) is proposed which simultaneously utilizes speech of each speaker and mixture to learn more meaningful features of speakers, and significantly boost speech separation. CNMF addresses the dependencies of speech signals while it ignores the positive effect of mixtures in learning dictionaries. TNMF emphasizes the transductive learning of dictionaries while it fails to consider dependencies of speeches. This paper proposes transductive convolutive NMF (TCNMF) to overcome the deficiencies of both CNMF and TNMF. Experimental results show that our method makes significant improvement compared to aforementioned NMFbased methods.

关键词： Nonnegative matrix factorization single-channel speech separation transductive convolutive dictionary learning speech separation Speech matrix decomposition Loudspeakers Dictionaries as Topic NMF protocol Nonnegative mixtures DEPENDENCE

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Symmetric Non-negative Matrix Factorization Based Link Partition Method for Overlapping Community Detection

Symmetric Non-negative Matrix Factorization Based Link Parti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiang Zhang Naiyang Guan Wenju Zhang Xuhui Huang Shuyi Wu Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan P.R. China Institute of Software College of Computer National University of Defense Technology Hunan P.R. China Department of Computer Science and Technology College of Computer National University of Defense Technology Hunan P.R. China

ISBN: (纸本)9781479986989

Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency. In particular, SNMF-Link represents data in a lower-dimensional space spanned by the node-link incidence matrix. By solving a lighter SNMF problem, SNMF-Link learns the clustering indicators of each links. Since traditional multiplicative update rule (MUR) based optimization algorithm for SNMF suffers from slow convergence, we applied the augmented Lagrangian method (ALM) to efficiently optimize SNMF. Experimental results show that SNMF-Link is much more efficient than the representative clustering algorithms without reducing the OCD performance.

关键词： Symmetric matrices Convergence Optimization Partitioning algorithms Chlorine Complex networks Image edge detection

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A GPU-based Fast Solution for Riesz Space Fractional Reaction-Diffusion Equation

A GPU-based Fast Solution for Riesz Space Fractional Reactio...

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International Conference on Network-Based Information Systems (NBIS)

作者： Qinglin Wang Jie Liu Chunye Gong Yang Zhang Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China College of Aerospace Science and Engineering National University of Defense Technology Changsha China Science and Technology on Space Physics Laboratory Beijing China

The fast numerical solutions of Riesz fractional equation have computational cost of O(NMlogM), where M, N are the number of grid points and time steps. In this paper, we present a GPU-based fast solution for Riesz space fractional equation. The GPU-based fast solution, which is based on the fast method using FFT and implemented with CUDA programming model, consists of parallel FFT, vector-vector addition and vector-vector multiplication on GPU. The experimental results show that the GPU-based fast solution compares well with the exact solution. Compared to the known parallel fast solution on 8-core Intel E5-2670 CPU, the overall performance speedup on NVIDIA GTX650 GPU reaches 2.12 times and that on NVIDIA K20C GPU achieves 10.93 times.

关键词： Graphics processing units Instruction sets Mathematical model Arrays Yttrium Approximation methods parallel processing

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Iaso： an autonomous fault-tolerant management system for supercomputers

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Frontiers of computer science 2014年第3期8卷 378-390页

作者： Kai LU Xiaoping WANG Gen LI Ruibo WANG Wanqing CHI Yongpeng LIU Hongwei TANG Hua FENG Yinghui GAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China ATR Laboratory National University of Defense Technology Changsha 410073 China

With the increase of system scale, the inherent reliability of supercomputers becomes lower and lower. The cost of fault handling and task recovery increases so rapidly that the reliability issue will soon harm the usability of supercomputers. This issue is referred to as the ＂reliability wall＂, which is regarded as a critical problem for current and future supercomputers. To address this problem, we propose an autonomous fault-tolerant system, named Iaso, in MilkyWay- 2 system. Iaso introduces the concept of autonomous management in supercomputers. By autonomous management, the computer itself, rather than manpower, takes charge of the fault management work. Iaso automatically manage the whole lifecycle of faults, including fault detection, fault diagnosis, fault isolation, and task recovery. Iaso endows the autonomous features with MilkyWay-2 system, such as self-awareness, self-diagnosis, self-healing, and self-protection. With the help of Iaso, the cost of fault handling in supercomputers reduces from several hours to a few seconds. Iaso greatly improves the usability and reliability of MilkyWay-2 system.

关键词： supercomputer autonomous management fault tolerant fault management MilkyWay-2 system

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