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International Joint Conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint Conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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Array receiving scheme for ultra-wideband WSN OFDM signals

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Ruan Jian Xue Bao/Journal of Software 2014年 25卷 30-38页

作者： Du, Xiao-Li Lü, Shao-He Wang, Xiao-Dong Zhou, Xing-Ming Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

This paper proposes an array receiving scheme for ultra-wideband (UWB) OFDM signals in WSN networks. The major feature of the proposed scheme is recovering the UWB OFDM signal by frequency stitching. Firstly, the UWB OFDM signal is divided into multiple sub-bands. Every two adjacent sub-bands share at least one overlapped subcarrier(s), which is used for time alignment later. A sub-signal of the UWB OFDM signal is received on each sub-band by a narrow-band receiver. The received data sets of narrow-band receivers are time aligned by performing peak value alignment retrieval (PVAR) on the shared subcarriers. Then the UWB OFDM signal is recovered by fusing the received data sets according to the result of PVAR. As the implementation of narrow-band receivers only needs low-speed ADCs, the challenge of high-speed ADC in traditional UWB OFDM receiver is addressed. Extensive simulations are performed to demonstrate the validity of the proposed scheme and further look into three performance metrics: (1) synchronization error-tolerance, (2) extensibility, (3) performance under different SNR values. ©2014 ISCAS.

关键词： Wireless sensor networks

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distributed Relation Discovery in Internet of Things

Distributed Relation Discovery in Internet of Things

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Weiping Zhu Hongliang Lu Xiaohui Cui International School of Software Wuhan University China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781479966226

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： Internet of things Synchronization Buildings Servers Algorithm design and analysis Delays

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Continuous probabilistic reverse skyline monitoring over uncertain data streams

Continuous probabilistic reverse skyline monitoring over unc...

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2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013

作者： Yang, Yong Tao Wang, Yi Jie Guo, Min Li, Xiao Yong National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9783037858202

Reverse skyline is useful for supporting many applications, such as marketing decision, environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a need for processing probabilistic reverse skyline queries. In this paper, we study the problem of efficiently processing these queries on uncertain data streams. We first show the formal definitions of reverse skyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRS to maintain the most recent N uncertain data elements and to process continuous queries on them. CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structure named Characteristic Rectangle, are incorporated into it to handling the extra computing complexity arising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniques are very efficient in handling uncertain data streams. © (2013) Trans Tech Publications, Switzerland.

关键词： Decision trees

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A Runtime Verification Based Trace-Oriented Monitoring Framework for Cloud Systems

A Runtime Verification Based Trace-Oriented Monitoring Frame...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Jingwen Zhou Zhenbang Chen Ji Wang Zibin Zheng Wei Dong College 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 Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Cloud computing provides a new paradigm for resource utilization and sharing. However, the reliability problems, like system failures, often happen in cloud systems and bring enormous loss. Trace-oriented monitoring is an important runtime method to improve the reliability of cloud systems. In this paper, we propose to bring runtime verification into trace-oriented monitoring, to facilitate the specification of monitoring requirements and to improve the efficiency of monitoring cloud systems. Based on a data set collected from a cloud storage system in a real environment, we validate our approach by monitoring the critical properties of the storage system. The preliminary experimental results indicate the promise of our approach.

关键词： Monitoring Runtime Cloud computing Educational institutions Reliability Google Computers

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HESA: The construction and evaluation of hierarchical software feature repository 25

HESA: The construction and evaluation of hierarchical softwa...

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25th International Conference on Software Engineering and Knowledge Engineering, SEKE 2013

作者： Yu, Yue Wang, Huaimin Yin, Gang Li, Xiang Yang, Cheng National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)1891706330

Nowadays, the demand for software resources on different granularity is becoming prominent in software engineering field. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software features, a kind of important knowledge for software reuse, are ideal materials to characterize software resources. Our preliminary study shows that the effectiveness of feature-related tasks, such as software resource retrieval and feature location, will be greatly improved, if a multi-grained feature repository is available. In this paper, we construct a Hierarchical rEpository of Software feAture (HESA), in which a novel hierarchical clustering approach is proposed. For a given domain, we first aggregate a large number of feature descriptions from multiple online software repositories. Then we cluster these descriptions into a flexible hierarchy by mining their hidden semantic structures. Finally, we implement an online search engine on HESA and conduct a user study to evaluate our approach quantitatively. The results show that HESA can organize software features in a more reasonable way compared to the classic and the state-of-the-art approaches. Copyright © 2013 by Knowledge Systems Institute Graduate School.

关键词： Computer software reusability

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An integration framework of cloud computing with wireless sensor networks

An integration framework of cloud computing with wireless se...

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7th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2012

作者： You, Pengfei Li, Huiba Peng, Yuxing Li, Ziyang National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology 410073 Changsha China

ISBN: (纸本)9789400758568

Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emanating from WSN is not efficiently stored and used. Besides, the data from multiple different types and locations of WSN needs to be well analyzed, fused and supplied to various types of clients, such as PC, workstation and smart phone. The emerging cloud computing technology provides scalable data process and storage power and some types of connectable services, which can helpfully utilize sensor data from WSN. In this paper, we propose an integration framework of cloud computing with WSN, in which sensor data is transmitted from WSN to cloud, and processed and stored in cloud, then mined and analyzed so as to be supplied to various clients. By applying virtualization and cloud storage technology, and Infrastructure as a Service (IaaS) and Software as a Service (SaaS) of cloud service model, the framework can fully process and store mass sensor data from multiple types of WSN. Besides, it efficiently mines and analyzes sensor data, based on which the data applications are well supplied to various types of clients in form of services. © 2013 Springer Science+Business Media.

关键词： Virtualization

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Tree-Structured parallel Regeneration for Multiple Data Losses in distributed Storage Systems Based on Erasure Codes

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China Communications 2013年第4期10卷 113-125页

作者：孙伟东王意洁裴晓强 National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the scheme, two algorithms are proposed for the construction of multiple regeneration trees, namely the edge-disjoint algorithm and edge-sharing algorithm. The edge-disjoint algorithm constructs multiple independent trees, and is simple and appropriate for environments where newcomers and their providers are distributed over a large area and have few intersections. The edge-sharing algorithm constructs multiple trees that compete to utilize the bandwidth, and make a better utilization of the bandwidth, although it needs to measure the available band-width and deal with the bandwidth changes; it is therefore difficult to implement in practical systems. The parallel regeneration for multiple data losses of TPR primarily includes two optimizations: firstly, transferring the data through the bandwidth optimized-paths in a pipe-line manner; secondly, executing data regeneration over multiple trees in parallel. To evaluate the proposal, we implement an event-based simulator and make a detailed comparison with some popular regeneration methods. The quantitative comparison results show that the use of TPR employing either the edge-disjoint algorithm or edge-sharing algorithm reduces the regeneration time significantly.

关键词： distributed storage system erasure code replication regeneration tree

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Translation Non-negative Matrix Factorization with Fast Optimization

Translation Non-negative Matrix Factorization with Fast Opti...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yuanyuan Wang Naiyang Guan Bin Mao Xuhui Huang Zhigang Luo Department of Basic Courses Army Officer Academy Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science National University of Defense Technology College of Science National University of Defense Technology College of Computer Science National University of Defense Technology

ISBN: (纸本)9781479938414

Non-negative matrix factorization (NMF) reconstructs the original samples in a lower dimensional space and has been widely used in pattern recognition and data mining because it usually yields sparse representation. Since NMF leads to unsatisfactory reconstruction for the datasets that contain translations of large magnitude, it is required to develop translation NMF (TNMF) to first remove the translation and then conduct a decomposition. However, existing multiplicative update rule based algorithm for TNMF is not efficient enough. In this paper, we reformulate TNMF and show that it can be efficiently solved by using the state-of-the-art solvers such as NeNMF. Experimental results on face image datasets confirm both efficiency and effectiveness of the reformulated TNMF.

关键词： Non-negative matrix factorization (NMF) Translation transformation NeNMF

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