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IEEE International Conference on Cluster Computing

作者： Jia Li Dongsheng Li Yiming Zhang National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9781467365994

Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation. The proposed method is based on the bootstrap trials. We implemented this method as an Intelligent Bootstrap Library (IBL) on Spark to support efficient data clustering. Intensive evaluations show that IBL can provide a 2x speed-up over the state of art solution with the same error bound.

关键词： Sparks Accuracy Data mining Estimation error distributed databases Approximation methods

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Feverfew: a scalable coverage-based hybrid overlay for Internet-scale pub/sub networks

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Science China(Information Sciences) 2014年第5期57卷 51-64页

作者： MA XingKong WANG YiJie SUN WeiDong National Key Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

The publish/subscribe(pub/sub)paradigm is a popular communication model for data dissemination in large-scale distributed ***,scalability comes with a contradiction between the delivery latency and the memory *** one hand,constructing a separate overly per topic guarantees real-time dissemination,while the number of node degrees rapidly increases with the number of *** the other hand,maintaining a bounded number of connections per node guarantees small memory cost,while each message has to traverse a large number of uninterested nodes before reaching the *** this paper,we propose Feverfew,a coverage-based hybrid overlay that disseminates messages to all subscribers without uninterested nodes involved in,and increases the average number of node connections slowly with an increase in the number of subscribers and *** major novelty of Feverfew lies in its heuristic coverage mechanism implemented by combining a gossip-based sampling protocol with a probabilistic searching *** on the practical workload,our experimental results show that Feverfew significantly outperforms existing coverage-based overlay and DHT-based overlay in various dynamic network environments.

关键词： publish/subscribe coverage-based data dissemination topic-based gossip

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Virtual Frame Aggregation: Clustered Channel Access in Wireless Networks

Virtual Frame Aggregation: Clustered Channel Access in Wirel...

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IEEE International Conference on Communications

作者： Xuan Dong Shaohe Lv Chunsheng Zhu Rukhsana Ruby Xiaodong Wang Xingming Zhou Victor C. M. Leung National Laboratory of Parallel and Distributed Processing National University of Defense Technology Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (纸本)9781467364300

Coordination among users is inevitable in wireless communication for efficient medium access. Even though the data rate of individual user increases significantly, the performance of wireless network does not grow up accordingly due to the high MAC coordination overhead. In this paper, we present VFA, namely virtual frame aggregation, to achieve high coordination efficiency by amortizing the overhead over multiple transmissions. VFA provides a novel way to construct a winner cluster and allow the winners to transmit without interruption. Specifically, in a multicarrier network, every contending node chooses a subcarrier and the nodes are ordered by the index of the chosen subcarrier. When there are some subcarriers chosen by two or more nodes, an additional slot is exploited to reorder the collided nodes. Finally, all ordered nodes form a cluster and the transmissions are issued sequentially and uninterruptedly. Simulation results show that usually two slots are enough to construct a sufficiently large winner cluster. Moreover, VFA achieves a notable throughput gain over IEEE 802.11 as high as 120% with better fairness under various scenarios.

关键词： Wireless communication Antennas Wires

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AutoMal: Automatic clustering and signature generation for malwares based on the network flow

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Security and Communication Networks 2015年第10期8卷 1845-1854页

作者： Hao, Sun Wang, Wen Lu, Huabiao Ren, Peige School of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

The volume of malwares is growing at an exponential speed nowadays. This huge growth makes it extremely hard to analyse malware manually. Most existing signatures extracting methods are based on string signatures, and string matching is not accurate and time consuming. Therefore, this paper presents AutoMal, a system for automatically extracting signatures from large-scale malwares. Firstly, the system proposes to represent the network flows by using feature hashing, which can dramatically reduce the high-dimensional feature spaces that are general in malware analysis. Then, we design a clustering and median filtering method to classify the malware vectors into different types. Finally, it introduces the signature generation algorithm based on Bayesian method. The system can extract both the byte signature and the hash signature of malwares from its network flow with low false positive and zero false negative. Our evaluation shows that AutoMal can generate strongly noise-resisted signatures that exactly depict the characteristics of malware. © 2014 John Wiley & Sons, Ltd.

关键词： Malware

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Design, Implementation and Evaluation of an Application-Layer Virtualized Network

Design, Implementation and Evaluation of an Application-Laye...

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2015 IEEE Symposium on Service-Oriented System engineering

作者： Yiming Zhang Dongsheng Li Yijie Wang Zhigang Sun Feng Zhao Jinshu Su National University of Defense Technology Changsha Hunan CN School of Computer National Laboratory for Parallel and Distributed Processing (PDL) Changsha China

The performance of virtualized networks is critical to cloud applications. The "distributed line graphs" (DLG) are a universal technique for designing network topologies based on arbitrary regular graphs. In this paper we implement a prototype (C library) for a DLG-enabled network (called DLG-Kautz), as an application-layer virtualized network service. The effectiveness of our design and implementation is demonstrated through prototype evaluations.

关键词： Routing Message systems Ports (computers) Servers Payloads Prototypes Libraries

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High-energy-density electron beam from interaction of two successive laser pulses with subcritical-density plasma

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Physical Review Accelerators and Beams 2016年第2期19卷 021301-021301页

作者： J. W. Wang W. Yu M. Y. Yu H. Xu J. J. Ju S. X. Luan M. Murakami M. Zepf S. Rykovanov Helmholtz Institute Jena Jena 07743 Germany State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Institute for Fusion Theory and Simulation and the Department of Physics Zhejiang University Hangzhou 310027 China Institute for Theoretical Physics I Ruhr University Bochum D-44780 Germany National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China Institute of Laser Engineering Osaka University Osaka 565-0871 Japan Centre for Plasma Physics School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN United Kingdom

It is shown by particle-in-cell simulations that a narrow electron beam with high energy and charge density can be generated in a subcritical-density plasma by two consecutive laser pulses. Although the first laser pulse dissipates rapidly, the second pulse can propagate for a long distance in the thin wake channel created by the first pulse and can further accelerate the preaccelerated electrons therein. Given that the second pulse also self-focuses, the resulting electron beam has a narrow waist and high charge and energy densities. Such beams are useful for enhancing the target-back space-charge field in target normal sheath acceleration of ions and bremsstrahlung sources, among others.

关键词： Plasma acceleration & new acceleration techniques

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Comparison of heavy-ion induced SEU for D- and TMR-flip-flop designs in 65-nm bulk CMOS technology

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Science China(Information Sciences) 2014年第10期57卷 223-229页

作者： HE YiBai CHEN ShuMing School of Computer Science National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Heavy ion experiments were performed on D flip-flop(DFF) and TMR flip-flop(TMRFF) fabricated in a 65-nm bulk CMOS process. The experiment results show that TMRFF has about 92% decrease in SEU crosssection compared to the standard DFF design in static test mode. In dynamic test mode, TMRFF shows much stronger frequency dependency than the DFF design, which reduces its advantage over DFF at higher operation frequency. At 160 MHz, the TMRFF is only 3.2× harder than the standard DFF. Such small improvement in the SEU performance of the TMR design may warrant reconsideration for its use in hardening design.

关键词： SEU flip-flop TMR heavy-ion frequency

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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing Sparse coding Sparse coding Classification Methods remote sensing hyperspectral remote sensing hyperspectral imagery Image classification

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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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Non-negative low-rank and group-sparse matrix factorization 21

Non-negative low-rank and group-sparse matrix factorization

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wu, Shuyi Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** paper proposes a non-negative low-rank and group-sparse matrix factorization (NLRGS) method to overcome this deficiency. Particularly, NLRGS captures the relationships among examples by constraining rank of the coefficients meanwhile identifies the grouping structure via group sparsity regularization. By both constraints, NLRGS boosts NMF in both classification and clustering. However, NLRGS is difficult to be optimized because it needs to deal with the low-rank constraint. To relax such hard constraint, we approximate the low-rank constraint with the nuclear norm and then develop an optimization algorithm for NLRGS in the frame of augmented Lagrangian method(ALM). Experimental results of both face recognition and clustering on four popular face datasets demonstrate the effectiveness of NLRGS in quantities. © Springer International Publishing Switzerland 2015.

关键词： Non-negative matrix factorization

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