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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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Maximizing the information diffusion opportunity in the cyber-physical network

Maximizing the information diffusion opportunity in the cybe...

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International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QSHINE)

作者： Hongliang Lu Xuan Dong Wenxiang Li Saohe Lv Xiaodong Wang Wei Chen Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Hunan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781479982172

Our daily life is changing by the smart objects, such as smart watches, smart phones etc. They make the cyber world and the physical world integrated by their abundant abilities of sensing, communication and computation etc. Focusing on a wide range of the integrated network, a statistical based strategy was introduced to get a special kind of link between objects, the statistical probability communication link. To get a maximized information spread probability for grouped people, this paper introduced a distributed, yet efficient algorithm naming DMPID algorithm, for finding a sub-network to spread people oriented inforamtion. The DMPID algorithm take the size of the selection and the information spread probability into account, and made a balance between the two parameters. Extended simulation showed that the DMPID algorithm performs well in different distributed networks.

关键词： distributed algorithms probability statistical analysis telecommunication networks DMPID algorithm cyber world cyber-physical network distributed maximizing probability of information diffusion information diffusion opportunity information spread probability statistical probability communication link

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Performance analysis of end-to-end services in virtualized computing environments

Performance analysis of end-to-end services in virtualized c...

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11th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2014

作者： Yan, Guofeng Peng, Yuxing School of Computer and Communication Hunan Institute of Engineering China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Science and Technology Changsha 410073 China

ISBN: (纸本)9783662449165

In this paper, we present a novel stochastic analyzing model for e2e virtualized cloud services using hierarchical Quasi-Birth Death structures (QBDs). We divide the overall virtualized cloud services into three sub-hierarchies, and then, analyze each individual sub-hierarchy using QBDs. Our approach reduces the complexity of performance analysis. Our results are useful to prevent the cloud center from entering unsafe operation, and also reveal practical insights into load balancing and capacity planning for virtualized computing environments. © 2014 IFIP International Federation for Information processing.

关键词： Stochastic systems

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Merge-Weighted Dynamic Time Warping for Speech Recognition

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Journal of Computer Science & Technology 2014年第6期29卷 1072-1082页

作者：张湘莉兰骆志刚李明 State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and Epidemiology Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology David R.Cheriton School of Computer Science University of WaterlooWaterlooOntario N2L 3G1Canada ACM IEEE

Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.

关键词： merge-weighted dynamic time warping natural language processing speech recognition and synthesis tem-plate confidence index

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A blind motion deblurring for daytime and nighttime images

A blind motion deblurring for daytime and nighttime images

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International Conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Fei Zhang Yong Dou Xin Niu National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781479972098

This paper presents an automatic deblurring approach for motion blur images. The approach explores the prior of the intensity and gradient to estimate the motion blur kernel from single blurred image. In this way, motion blur kernel could be well estimated not only on daytime images, but also on nighttime images. Efficient optimization method was given for the prior-based approach. Besides, a cost-effective method was also proposed to remove the noise of the kernel in the iterative kernel estimation. The proposed kernel enhancement approach could also improve the performance of other blind deblurring algorithm. Experimental results showed that the performance of the proposed algorithm was superior to many state-of-the-art blind deblurring algorithms, especially for nighttime images with saturated regions.

关键词： Kernel Estimation Deconvolution Noise Image edge detection Image restoration Algorithm design and analysis

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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CLASSIFICATION OF LAND COVER BASED ON DEEP BELIEF NETWORKS USING POLARIMETRIC RADARSAT-2 DATA

CLASSIFICATION OF LAND COVER BASED ON DEEP BELIEF NETWORKS U...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Baoliang Li National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781479953141

Urban land use and land cover (LULC) classification is one of the core applications in Geographic Information System (GIS). In this paper, a novel classification approach based on Deep Belief Network (DBN) for detailed urban mapping is proposed. Deep Belief Network (DBN) is a widely investigated and deployed deep learning model. By applying the DBN model, effective spatio-temporal mapping features can be automatically extracted to improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) data over the Great Toronto Area were used for evaluation. Experimental results showed that the proposed method can outperform SVM and contextual approaches using adaptive MRF.

关键词： Land cover classification Deep learning Restricted Boltzmann Machines (RBMs) Deep Belief Network (DBN) PolSAR

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION METHODS Dataset computer vision

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A Exploratory Study of @-Mention in GitHub's Pull-Requests

A Exploratory Study of @-Mention in GitHub's Pull-Requests

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Asia-Pacific Conference on Software Engineering

作者： Yang Zhang Gang Yin Yue Yu Huaimin Wang National laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781479974276

Pull-request mechanism is an outstanding social development method in Git Hub. @-mention is a social media tool that deeply integrated with pull-request mechanism. Recently, many research results show that social media tools can promote the collaborative software development, but few work focuses on the impacts of @-mention. In this paper, we conduct an exploratory study of @-mention in pull-request based software development, including its current situation and benefits. We obtain some interesting findings which indicate that @-mention is beneficial to the processing of pull-request. Our work also proposes some possible research directions and problems of the @-mention. It helps the developers and researchers notice the significance of @-mention in the pull-request based software development.

关键词： Media Software Collaboration Software engineering Correlation Information services Electronic publishing

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Efficient deterministic multithreading without global barriers 14

Efficient deterministic multithreading without global barrie...

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Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming

作者： Lu, Kai Zhou, Xu Bergan, Tom Wang, Xiaoping Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China University of Washington Computer Science and Engineering United States

ISBN: (纸本)9781450326568

Multithreaded programs execute nondeterministically on conventional architectures and operating systems. This complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 17 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with DThreads-a start-of-the-art DMT system. Copyright © 2014 ACM.

关键词： C++ (programming language)

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