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2016 International Conference on Communications, information Management and Network Security (CIMNS2016)

作者： Nengneng Li Yongsheng Zhang Yueqin Fan Liang Chang School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology

ISBN: (纸本)9781510830981

With the further development of the cloud computing, the security of the cloud computing is becoming increasingly prominent. Based on analyzing the development status of cloud computing, this article will focus on studying the security of cloud computing in data, and discuss the data security problems in the process of data life cycle in cloud environment. Then the paper will put forward the corresponding measures and data encryption technology, in order to protect the data security of cloud computing.

关键词： cloud computing cloud security data security encryption erase technology

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A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

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Chinese Journal of Electronics 2015年第4期24卷 694-701页

作者： TRAN Dang Cong WU Zhijian WANG Zelin DENG Changshou State Key Laboratory of Software Engineering School of Computer Wuhan University Vietnam Academy of Science and Technology School of Information Science and Technology Jiujiang University

To improve the performance of K-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and Kmeans(EABCK). In EABCK, the original artificial bee colony algorithm(called ABC) is enhanced by a new mutation operation and guided by the global best solution(called EABC). Then, the best solution is updated by Kmeans in each iteration for data clustering. In the experiments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK outperform other comparative ABC variants and data clustering algorithms, respectively.

关键词： Data clustering K-means Artificial bee colony Mutation operation

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Petri nets-based method to model and analyse the self-healing web service composition

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International Journal of High Performance Computing and Networking 2016年第1-2期9卷 8-18页

作者： Chen, Liqiong Xiao, Lizhong Fan, Guisheng Zhang, Huanhuan Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

Service composition is an important means for integrating the individual web services to create new value added systems that can satisfy complex requirements. Such applications are subject to unexpected failure owing to the distributive and heterogeneous environment of web service. In this paper, we propose a Petri nets-based method to model and analyse the self-healing service composition. Aspect oriented programming is used to extract composition processes and self-healing strategy as the core and crosscutting concerns. A self-healing strategy of component and service composition is proposed, and the related operations of self-healing are abstracted as meta-objects by using reflection mechanism. Petri nets are used to construct the base layer model, meta layer model, meta-object protocol and other components, thus forming the self-healing model. The operational semantics and related theories of Petri nets help prove its effectiveness and correctness. A simulation example shows that our approach correctly describes the self-healing process of service composition, and contributes to improving the quality. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Petri nets

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Sparse Tensor Discriminative Locality Alignment for Gait Recognition

Sparse Tensor Discriminative Locality Alignment for Gait Rec...

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International Joint Conference on Neural Networks

作者： Nengwen Zhao Lefei Zhang Bo Du Liangpei Zhang Dacheng Tao Jane You State Key Laboratory of Software Engineering School of Computer Wuhan University Department of Computing The Hong Kong Polytechnic University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781509006212

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and sub-sequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.

关键词： Gait recognition dimensionality reduction images computer vision Tensor Locomotion Gait principal components analysis vector representation Alignment

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A new private-preserving algorithm based on SMART

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Journal of computers (Taiwan) 2016年第4期27卷 171-185页

作者： Han, Bowen Zhang, Zhenjiang Uden, Lorna School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Jiaotong University Beijing100044 China School of Software Engineering Key Laboratory of Communication and Information Systems Beijing Jiaotong University Beijing100044 China School of Computing FACS Staffordshire University The Octagon Beaconside StaffordSt180AD United Kingdom

Privacy is a critical issue in many WSN applications because wireless sensor networks (WSNs) are vulnerable to malicious attacks due to their characteristics. Existing hop by hop and shuffling based privacy preserving protocols does not provide an energy efficient, accurate and secure data aggregation because of the energy consuming decryption at the aggregator node. How to provide effective privacy-preserving algorithm during data aggregation is important. Our main aim is to provide an energy efficient and secure data aggregation scheme, which guarantees the privacy, authenticity and freshness of individual sensed data as well as the confidentiality, accuracy and integrity of aggregated data. This paper improved on the limitations of the SMART (Slice-Mix-AggRegaTe) algorithm, and proposes a new privacy protection algorithm for wireless sensor network (WSN). We have different operations between different nodes, bring in probabilistic segmentation, and also add the data coefficient as well as the positive and negative factors. In addition we also consider making exchange time random in the algorithm. Simulation results demonstrate that the proposed algorithm reduces the communication consumption and the data traffic, improves the precision of data aggregation and the degree of privacy-preserving to a certain extent. © 2016, computer Society of the Republic of China. All rights reserved.

关键词： Aggregates

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A MapReduce-based parallel K-means clustering for large-scale CIM data verification

A MapReduce-based parallel K-means clustering for large-scal...

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作者： Deng, Chuang Liu, Yang Xu, Lixiong Yang, Jie Liu, Junyong Li, Siguang Li, Maozhen School of Electrical Engineering and Information Sichuan University Chengdu610065 China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai200092 China

The Common information Model (CIM) has been heavily used in electric power grids for data exchange among a number of auxiliary systems such as communication systems, monitoring systems, and marketing systems. With a rapid deployment of digitalized devices in electric power networks, the volume of data continuously grows, which makes verification of CIM data a challenging issue. This paper presents a parallel K-means clustering algorithm for large-scale CIM data verification. The parallel K-means builds on the MapReduce computing model which has been widely taken up by the community in dealing with data-intensive applications. A genetic algorithm-based load-balancing scheme is designed to balance the workloads among the heterogeneous computing nodes for a further improvement in computation efficiency. The performance of the parallel K-means is initially evaluated in a small-scale in-house MapReduce cluster and subsequently evaluated in a commercial cloud computing platform. Finally, the parallel K-means is evaluated in large-scale simulated MapReduce environments. Both the experimental and simulation results show that the parallel K-means reduces the CIM data-verification time significantly compared with the sequential K-means clustering, while generating a high level of precision in data verification. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： MapReduce

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Online Machine Learning for Big Data Analytics by Cognitive Robots

Online Machine Learning for Big Data Analytics by Cognitive ...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Yingxu Wang FICIC FWIF Information Systems Lab Stanford University Laboratory for Computational Intelligence Denotational Mathematicsand Software Science Dept.of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of Calgary

It is recognized that both sources of and solutions to the big data challenges are human collective *** are an abstract representation of the quantity of realistic entities and perceived *** data play an indispensable role not only in a wide range of engineering applications,but also in the cognitive mechanisms of both humans and cognitive robots such as sensation,quantification,qualification,estimation,memory,and *** keynote lecture presents online big data analytic theories by machine learning as well as knowledge extraction by cognitive ***,information,knowledge,and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom *** is discovered by the author that,although the cognitive unit of data is bit,that of knowledge is bir,i.e.,a binary relation.A resent finding towards big data science is that big data systems in nature are a recursive ndimensional typed hyperstructure(RNDTHS).This topological property of big data reveals that the mathematical foundation of big data science is underpinned by big data algebra(BDA),which is a denotational mathematical structure for efficiently dealing with the inherited complexities and unprecedented challenges in big data *** leads to a coherent theory for big data modeling,analyses,mining,information elicitation,knowledge extraction,and intelligent generation for cognitive *** experiments demonstrate that cognitive robots may autonomously transform big data in vast linguistic databases(corpuses) into sophistic knowledge bases by cognitive machine learning.

关键词： Big data big data science denotational mathematics mathematical models recursively hierarchical hyperstructures big data algebra cogniitve machine learning formal concepts knowledge extraction knowledge generation cognitive informatics cognitive computing computational intelligence applications

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Event identification in social networks

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Encyclopedia with Semantic Computing and Robotic Intelligence 2017年第1期1卷

作者： Fattane Zarrinkalam Ebrahim Bagheri Laboratory for Systems Software and Semantics (LS3) Ryerson University Toronto Canada Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran

Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. The ability to model emerging topics is a substantial step to monitor and summarize the information originating from social sources. Applying traditional methods for event detection which are often proposed for processing large, formal and structured documents, are less effective, due to the short length, noisiness and informality of the social posts. Recent event detection techniques address these challenges by exploiting the opportunities behind abundant information available in social networks. This article provides an overview of the state of the art in event detection from social networks.

关键词： Event detection social network analysis topic detection and tracking

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Data collection in open ended learning environment for learning analytics

Data collection in open ended learning environment for learn...

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24th International Conference on computers in Education - Workshop, ICCE 2016

作者： Tscholl, Michael Rajendran, Ramkumar Biswas, Gautam Goldberg, Benjamin S. Sottilare, Robert A. Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 United States U.S. Army Research Laboratory - Human Research and Engineering Simulation and Training Technology Center OrlandoFL32826 United States

ISBN: (纸本)9789869401210

As the development and use of Open-Ended Learning Environments (OELEs) continues to increase, supporting students' learning in these environments with Intelligent Tutoring is rapidly becoming an important area of research. Many existing learning environments guide students in step-by-step processes to reach their learning goal;consequently, data preprocessing is well defined. In OELEs, in contrast, students may achieve task goals through multiple pathways, and there exist multiple ways to assess performance. We present a simulation OELE designed to teach students decision-making in a complex problem solving task. To provide Intelligent Tutoring Support, we are required to track performance along several dimensions. We present our approach to extract data for performance assessments that can be leveraged to provide Intelligent Tutoring Support. We generalize our approach and present guidelines applicable for similar OELEs. Copyright 2016 Asia-Pacific Society for computers in Education. All rights reserved.

关键词： Students

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