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A survey on dependability improvement techniques for pervasive computing systems

science China(Information sciences) 2015年第5期58卷 19-32页

作者： YANG WenHua LIU YePang XU Chang CHEUNG S.C. State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

The goal of this survey is to summarize the state-of-the-art research results and identify research challenges of developing and deploying dependable pervasive computing systems. We discuss the factors that affect the system dependability and the studies conducted to improve it with respect to these factors. These studies were categorized according to their similarities and differences in hope of shedding some insight into future research. There are three categories: context management, fault detection, and uncertainty *** three categories of work address the three most difficult problems of pervasive computing systems. First,pervasive computing systems’ perceived environments, which are also called their contexts, can vary intensively,and thus have a great impact on the systems’ dependability. Second, it is challenging to guarantee the correctness of the systems’ internal computations integrated with interactions with external environments for *** detection is then an important issue for improving dependability for these systems. Last but not least importantly, pervasive computing systems interact with their environments frequently. These interactions can be affected by many uncertainties, which can jeopardize the systems’ dependability. After a discussion of these pieces of work, we present an outlook for its future research directions.

关键词： pervasive computing system dependability context fault detection uncertainty

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Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

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Journal of computer science & technology 2016年第6期31卷 1110-1123页

作者： Wei Shen Wan-Chun Dou Fan Wu Shaojie Tang Qiang Ni State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Information Systems University of Texas at Dallas Texas TX 75080 U.S.A. School of Computing and Communications Lancaster University Lancaster LA1 4WA U.K.

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

关键词： voluntary cloud system architecture resource reservation

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Generalized Regular Quadrilateral Mesh Generation based on Surface Foliation

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Procedia Engineering 2017年 203卷 336-348页

作者： Na Lei Xiaopeng Zheng Hang Si Zhongxuan Luo Xianfeng Gu DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian 116620 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian 116620 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing 100048 China Weierstrass Institute for Applied Analysis and Stochastics Berlin 10117 Germany Computer Science Department Stony Brook University Stony Brook 11794 NY USA

This work introduces a novel algorithm for quad-mesh generation based on surface foliation theory. The algorithm is based on the equivalence among colorable quad-meshes, measure foliations and holomorphic differentials. The holomorphic differentials can be obtained by graph-valued harmonic maps. The algorithm has several merits: it can be applied for surfaces with general topologies; the resulting quad-meshes have global tensor product structure and the least number of singularities; the algorithmic pipeline is fully automatic. The experimental results demonstrate the efficiency and efficacy of the proposed method.

关键词： quadrilateral quad-mesh structured foliation harmonic map holomorphic differential

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Improved dynamic regret for non-degenerate functions 17

Improved dynamic regret for non-degenerate functions

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Lijun Zhang Tianbao Yangt Jinfeng Yi Rong Jin Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science The University of Iowa Iowa City AI Foundations Lab IBM Thomas J. Watson Research Center Yorktown Heights NY Alibaba Group Seattle

ISBN: (纸本)9781510860964

Recently, there has been a growing research interest in the analysis of dynamic regret, which measures the performance of an online learner against a sequence of local minimizers. By exploiting the strong convexity, previous studies have shown that the dynamic regret can be upper bounded by the path-length of the comparator sequence. In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to query the gradient of the function multiple times, and meanwhile the strong convexity can be weakened to other non-degenerate conditions. Specifically, we introduce the squared path-length, which could be much smaller than the path-length, as a new regularity of the comparator sequence. When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length. We then extend our theoretical guarantee to functions that are semi-strongly convex or self-concordant. To the best of our knowledge, this is the first time that semi-strong convexity and self-concordance are utilized to tighten the dynamic regret.

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Hidden space reconstruction inspires link prediction in complex networks

arXiv

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arXiv 2017年

作者： Liao, Hao Zhou, Mingyang Wei, Zong-Wen Mao, Rui Vidmer, Alexandre Zhang, Yi-Cheng Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 Department of Physics University of Fribourg Chemin du Musée 3 FribourgCH-1700 Department of Modern Physics University of Science and Technology of China Hefei230027 China

As a fundamental challenge in vast disciplines, link prediction aims to identify potential links in a network based on the incomplete observed information, which has broad applications ranging from uncovering missing protein-protein interaction to predicting the evolution of networks. One of the most influential methods rely on similarity indices characterized by the common neighbors or its variations. We construct a hidden space mapping a network into Euclidean space based solely on the connection structures of a network. Compared with real geographical locations of nodes, our reconstructed locations are in conformity with those real ones. The distances between nodes in our hidden space could serve as a novel similarity metric in link prediction. In addition, we hybrid our hidden space method with other state-of-the-art similarity methods which substantially outperforms the existing methods on the prediction accuracy. Hence, our hidden space reconstruction model provides a fresh perspective to understand the network structure, which in particular casts a new light on link prediction. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Density Based Self Organizing Incremental Neural Network For Data Stream Clustering

Density Based Self Organizing Incremental Neural Network For...

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International Joint Conference on Neural Networks

作者： Baile Xu Furao Shen Jinxi Zhao National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509006212

Clustering is an important technique widely used in many areas such as machine learning, pattern recognition, data analysis etc. Data stream clustering is a branch of clustering that draws much attention in recent years, where data objects are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network (DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining on-line unsupervised learning and topology learning by means of competitive Hebbian learning rule [19]. By adopting a density-based clustering mechanism, DenSOINN can discover arbitrarily shaped clusters and diminish the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance equally on both raw data and normalized data.

关键词： Cluster Analysis data flow Noise effects data objects organizing Self Neural network Unsupervised learning branches Incremental

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Exploiting Correlation Subspace to Predict Heterogeneous Cross-Project Defects

Exploiting Correlation Subspace to Predict Heterogeneous Cro...

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作者： Cheng, Ming Wu, Guoqing Wan, Hongyan You, Guoan Yuan, Mengting Jiang, Min State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan Hubei China Department of Cognitive Science and Technology School of Information Science and Engineering Xiamen University Xiamen Fujian China Fujian Key Laboratory of Brain-Like Intelligent Systems School of Information Science and Engineering Xiamen University Xiamen Fujian China

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods. © 2016 World Scientific Publishing Company.

关键词： Support vector machines

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MEDAPs: secure multi-entities delegated authentication protocols for mobile cloud computing

MEDAPs: secure multi-entities delegated authentication proto...

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作者： Zhang, Lei Wei, Lifei Huang, Dongmei Zhang, Kai Dong, Mianxiong Ota, Kaoru College of Information Technology Shanghai Ocean University Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Software Engineering East China Normal University Shanghai China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran Japan

Because the technology of mobile cloud computing has brought several benefits to information world, many applications in mobile devices based on cloud have emerged and boomed in the last years. According to the storage limitation, data owners would like to upload and further share the data through the cloud. Because of the safety requirements, mobile data owners are requested to provide credentials such as authentication tags along with the data. However, it is impossible to require mobile data owners to provide every authenticated computational results. The solution that signers' privilege is outsourced to the cloud would be a promising way. To solve this problem, we propose three secure multi-entities delegated authentication protocols (MEDAPs) in mobile cloud computing, which enables the multiple mobile data owners to authorize a group-designated cloud servers with the signing rights. The security of MEDAPs is constructed on three cryptographic primitive identity-based multi-proxy signature, identity-based proxy multi-signature, and identity-based multi-proxy multi-signature, relied on the cubic residues, equaling to the integer factorization assumption. We also give the formal security proof under adaptively chosen message attacks and chosen identity/warrant attacks. Furthermore, compared with the pairing-based protocol, MEDAPs are quite efficient, and the communication overhead is nearly not a linear growth with the number of cloud servers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Authentication

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Biterm Pseudo Document Topic Model for Short Text

Biterm Pseudo Document Topic Model for Short Text

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International Conference on Tools with Artificial Intelligence

作者： Lan Jiang Hengyang Lu Ming Xu Chongjun Wang National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509044603

In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant space to improve the effectiveness of these short text specific methods. In this paper, we proposed a novel word co-occurrence network based method, referred to as biterm pseudo document topic model (BPDTM), which extended the previous biterm topic model (BTM) for short text. We utilized the word co-occurrence network to construct biterm pseudo documents. The proposed model is promising since it represents words with their semantic adjacent biterms and is able to model the corpus-level semantic relation between two words. Besides, BPDTM naturally lengthens the documents, which alleviate the influence for performance exerted by data sparsity. Experiments demonstrated that our model outperformed two baselines, i.e. LDA and BTM, which proved its effectiveness on short text topic modeling task.

关键词： texts laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Pseudo Word

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