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The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

作者： Zhijie Wei Zuohua Ding Jueliang Hu Lab of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou Zhejiang 310018China

ISBN: (纸本)9781479951499

Self-tuning performance of the database management system is a challenging and time-consuming work since multiple performance parameters need to be automatically *** this paper,we present a new self-tuning method based on fuzzy *** first automatically extract Automatic Workload Repository(AWR)report from a database system,and then construct fuzzy rules by identifying the relationship among the performance parameters and performance ***,we develop an adaption algorithm to reconfigure the performance parameters with the fuzzy *** on TPC-C workload show that the proposed method is feasible and effective.

关键词： Database Management System(DBMS) Performance Self-Tuning Fuzzy Rules

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Test case regeneration for changed requirements

Test case regeneration for changed requirements

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2014 International Conference on Computational Intelligence and Industrial engineering(CIIE 2014)

作者： Lei Zhu Zuohua Ding Wei Han Shouguang Wang Lab of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University School of Information & Electronic Engineering Zhejiang Gongshang University

Model-based testing(MBT) is software testing in which test cases are generated in whole or in part from a model that describes specifications of the system under test(SUT). However,specifications evolution can't be avoided in MBT. To handle specifications evolution efficiently,this paper presents two algorithms. The first algorithm captures the evolution of specifications,automatically modifies the original Petri net model correspondingly and marks the changed part in the new Petri net model. Based on the new Petri net model,the second algorithm recognizes the test cases which can be reused,and achieves regenerated test cases to cover the marked part. The reused and the regenerated test cases compose the new test case suits.

关键词： model-based testing requirement evolution petri net

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Joint Multiple Dictionary Learning for Tensor Sparse Coding

Joint Multiple Dictionary Learning for Tensor Sparse Coding

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International Joint Conference on Neural Networks

作者： Yifan Fu Junbin Gao Yanfeng Sun Xia Hong School of Computing and Mathematics Charles Sturt University Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing University of Technology School of Systems Engineering University of Reading

ISBN: (纸本)9781479914821

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form - a tensor - by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

关键词： Tensile stress Dictionaries Joints Vectors Correlation Databases Encoding

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Social sensing enhanced time estimation for bus service

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Concurrency and Computation: Practice and Experience 2015年第15期27卷

作者： Jin Liu Juan Li Xiaohui Cui Xiaoguang Niu Xiaoping Sun Jing Zhou State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEA s and road condition attributions PRCA s. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEA s of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： social sensing in-time bus service bus route arrival time estimation

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Dimensionality Reduction Assisted Tensor Clustering

Dimensionality Reduction Assisted Tensor Clustering

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International Joint Conference on Neural Networks

作者： Yanfeng Sun Junbin Gao Xia Hong Yi Guo Chris J. Harris Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing University of Technology School of Computing and Mathematics Charles Sturt University School of Systems Engineering University of Reading CSIRO Mathematics Informatics and Statistics Electronics and Computer Science University of Southampton

ISBN: (纸本)9781479914821

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

关键词： Tensor Tucker Decomposition Tensor Clustering Matrix Factorization Tensor PCA

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A petri net based public-key cryptosystem

A petri net based public-key cryptosystem

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

作者： Zhou, Hui Ding, Zuo Hua Huang, Hong Yun Lab of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou Zhejiang 310018 China Library Zhejiang Sci-Tech University Hangzhou Zhejiang 310018 China

ISBN: (纸本)9783037858202

In this paper, we present a new method to build public-key Cryptosystem. The method is based on the state explosion problem occurred in the computing of average number of tokens in the places of Stochastic Petri Net (SPN). The reachable markings in the coverability tree of SPN are used as the encryption keys. Accordingly, multiple encryption keys can be generated, thus we can perform multiple encryption to get as strong security as we expect. The decryption is realized through solving a group of ordinary differential equations from Continuous Petri Net (CPN), which has the same underlying Petri net as that of SPN. The decipherment difficulty for attackers is in exponential order. The contribution of this paper is that we can use continuous mathematics to design cryptosystems instead of discrete mathematics. © (2013) Trans Tech Publications, Switzerland.

关键词： Petri nets

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Flexible navigation in smartphones and tablets using scalable storyboards 13

Flexible navigation in smartphones and tablets using scalabl...

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3rd ACM International Conference on Multimedia Retrieval, ICMR 2013

作者： Zheng, Shuai Herranz, Luis Jiang, Shuqiang Key Lab. of Intelligent Information Processing Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Software Engineering Beijing Jiaotong University Beijing 100044 China

ISBN: (纸本)9781450320337

In this demo paper we present a multiscale browsing interface for handheld devices, in which the user can interactively change the scale of the storyboard to easily adjust the amount of information desired. Conventional and hierarchical storyboards provide one or very few possible lengths. In contrast, scalable storyboards allow the number of images and the storyboard itself to be adapted to the device constraints (e.g. aspect ratio, resolution) and navigation state with much finer granularity. Several levels and modes, including segment of interest, are provided for more intuitive and convenient navigation. © 2013 Authors.

关键词： Aspect ratio

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Semi-supervised learning with local and global consistency by geodesic distance and sparse representation 1

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3rd Sino-Foreign-Interchange Workshop on intelligent Science and intelligent Data engineering, IScIDE 2012

作者： Gui, Jie Zhao, Zhongqiu Hu, Rongxiang Jia, Wei Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui 230031 China State Key Laboratory for Novel Software Technology Nanjing University China College of Computer Science and Information Engineering Hefei University of Technology China Institue of Energy Safety Chinese Academy of Sciences Hefei Anhui 230031 China

ISBN: (数字)9783642366697

ISBN: (纸本)9783642366680

In many practical data mining applications, such as web categorization, key gene selection, etc., generally, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, in recent years, semi-supervised learning algorithms such as graph-based methods have attracted much attention. However, most of these traditional methods adopted a Gaussian function to calculate the edge weights of the graph. In this paper, a novel weight for semi-supervised graph-based methods is proposed. The new method adds the label information from problem into the target function, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting the calculation. In addition, we also add class prior knowledge from problem into semi-supervised learning algorithm. Here we address the problem of learning with local and global consistency (LGC). It was found that the effect of class prior knowledge was probably different between under high label rate and low label rate. Furthermore, we integrate sparse representation (SR) in LGC algorithm. Experiments on a UCI data set show that our proposed method outperforms the original algorithms. © Springer-Verlag 2013.

关键词： Learning algorithms

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Behavior analysis of software systems based on Petri net slicing

Behavior analysis of software systems based on Petri net sli...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Ma, Jiaying Han, Wei Ding, Zuohua Lab. of Intelligent Computing and Software Engineering Zhejiang Sci.-Tech. University Hangzhou Zhejiang 310018 China Institute of Logic and Cognition Sun Yat-sen University Guangzhou Guangdong 510275 China

ISBN: (纸本)9783642315879

This paper presents a new method to analyze system behavior. A software system is modeled by a Petri net, and then the Petri net is sliced into several parts based on T-invariant. It has been shown that the behavior of the slices is equivalent to the behavior of the original Petri net. Thus we can analyze the system behaviors by checking each slice. With this method, state space explosion problem in the static analysis can be solved to some extent. The Dining Philosophy problem has been used to demonstrate the benefit of our method. © 2012 Springer-Verlag.

关键词： Petri nets

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Irregular community discovery for cloud service improvement

Irregular community discovery for cloud service improvement

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作者： Liu, Jin Zhou, Jing Wang, Junfeng Zhang, Feng Liu, Fei State Key Lab. of Software Engineering Wuhan University Wuhan 430072 China Lab. of Complex Systems and Intelligence Science Institute of Automation CAS Beijing 100190 China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Computer Science Communication University of China Beijing 100024 China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China College of Computer Science Sichuan University Chengdu 610064 China Software School Sun Yat-sen University Guangzhou 510006 China

Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quality of utility services in cloud computing. Communities with overlapping features or prominent peripheral vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from CR networks, whereas these irregular shapes typically play an important role in finding prominent customers which are ignored in social CRM otherwise. We present a novel method of discovering irregular communities. It firstly finds and merges primitive maximal cliques and the irregular features of overlapping and prominent sparse vertices are further considered. An empirical case and a methodology comparison confirm the feasibility and efficiency of our approach. © 2012 Springer Science+Business Media, LLC.

关键词： Cloud computing

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