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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

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Walkie-Talkie: Motion-Assisted Automatic Key Generation for Secure On-Body Device Communication

Walkie-Talkie: Motion-Assisted Automatic Key Generation for ...

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International Symposium on Information Processing in Sensor Networks (IPSN)

作者： Weitao Xu Girish Revadigar Chengwen Luo Neil Bergmann Wen Hu School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia CSIRO Data61 Australia School of Computer Science and Engineering University of New South Wales Sydney Australia College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Ubiquity of wearable and implantable devices sparks a new set of mobile computing applications that leverage the prolific information of sensors. For many of these applications, to ensure the security of communication between legitimate devices is a crucial problem. In this paper, we design Walkie-Talkie, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users' walking characteristics (gait). The intuition is that the sensors on different locations of the same body experience similar accelerometer signal when the user is walking. However, the accelerometer also captures motion signal produced by other body parts (e.g., swinging arms). We address this issue by employing Blind Source Separation (BSS) technique to extract the informative signal produced by the unique gait pattern. Our experimental results show that the keys generated by two independent devices on the same body are able to achieve up to 100% bit agreement rate. To demonstrate the feasibility, we implement the proposed key generation scheme on modern smartphones. The evaluation results show that the proposed scheme can run in real-time on modern mobile devices and incurs low system overhead.

关键词： Acceleration Legged locomotion Sensors Authentication Smart phones Source separation

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Distance learning by treating negative samples differently and exploiting impostors with symmetric triplet constraint for person re-identification

Distance learning by treating negative samples differently a...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Xiao-Yuan Jing Fei Wu Weishi Zheng Ruimin Hu Chunxia Xiao Chao Liang School of Computer and Information Engineering Henan University State Key Laboratory of Software Engineering Wuhan University College of Automation Nanjing University of Posts and Telecommunications School of Data and Computer Science Sun Yat-sen University National Engineering Research Center for Multimedia Software Computer School of Wuhan Univ. Collaborative Innovation Center of Geospatial Technology China

ISBN: (纸本)9781467372596

Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other well separable negative samples (WSN-samples). Therefore, how to make full use of the different discriminative information conveyed by all negative samples in the DL process is a critical issue to be investigated. In this paper, we propose a novel DL approach for PR-ID. Specifically, for each target sample, we divide its negative samples into impostors and WSN-samples. Then we learn the distance metric by utilizing impostors and WSN-samples differently. For impostors, we design a symmetric triplet constraint, which requires the impostor to be far away from both samples of its corresponding positive sample pair simultaneously; for WSN-samples, we require them to keep their favorable separability. Experimental results on three benchmark datasets demonstrate the effectiveness and efficiency of our approach.

关键词： Wireless sensor networks Measurement Thyristors Computer aided instruction Cameras Linear programming Computers

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Modeling parameter interactions in ranking SVM 15

Modeling parameter interactions in ranking SVM

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhang, Yaogong Xu, Jun Lan, Yanyan Guo, Jiafeng Xie, Maoqiang Huang, Yalou Cheng, Xueqi College of Computer and Control Engineering Nankai University China CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China College of Software Nankai University China

ISBN: (纸本)9781450337946

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Σ(i,j)αij(xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = (υi,υj), where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM. © 2015 ACM.

关键词： Information retrieval

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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A mixture language model for the classification of Chinese online reviews

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International Journal of Information and Communication Technology 2015年第1期7卷 109-122页

作者： Jiang, Ming Wang, Jian Wang, Xingqi Tang, Jingfan Wu, Chunming Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

In this essay, we propose an unsupervised topic and sentiment mixture model (LSS model) mainly on the basis of LDA model and combining with the sentiment factor, as an approach to sentiment classification of online reviews. There is an extra constraint that all words in a sentence are generated from one topic and one sentiment. Which means the hypothesis of the model is a word in a sentence is of one meaning only. LSS model is totally unsupervised and it needs no labelled corpora or any other sentiment seed words. Experiments show that LSS model performs better than JST model and ASUM model. The F-1 value of sentiment classification is 8% higher than ASUM model and 12.5% than JST model.

关键词： Mixtures

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Rail Inspection Meets Big data: Methods and Trends

Rail Inspection Meets Big Data: Methods and Trends

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International Conference on Network-Based Information Systems (NBIS)

作者： Qingyong Li Zhangdui Zhong Zhengping Liang Yong Liang Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China College of Computer Science and Software Engineering Shenzhen University Communication & Power Utilization Technology Subcompany State Grid Electric Power Research Institute State Grid Beijing China

Rail inspection is one of the most important tasks for rail industry, in order to guarantee the safety of railway systems and control their cost. Rail are systematically inspected for defects using various non-destructive evaluation (NDE) techniques, which include ultrasonic inspection, visual detection, magnetic flux leakage method, acoustic emission inspection etc. The data obtained by these NDE devices are going to increase in both quality and quantity, therefore big data has emerged as a potential challenge for rail inspection. This paper reviews the advanced NDE techniques for rail inspection, and brings forward a new framework of rail inspection based on big data, according to the characteristics of inspection data.

关键词： Rails Inspection Acoustics Surface cracks Visualization Surface treatment Big data

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Composable and efficient functional big data processing framework

Composable and efficient functional big data processing fram...

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IEEE International Conference on Big data

作者： Dongyao Wu Sherif Sakr Liming Zhu Qinghua Lu Data61 Eveleigh NSW AU King Saud bin Abdulaziz University for Health Sciences Saudi Arabia Software Systems Research Group NICTA Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia College of Computer and Communication Engineering University of Petroleum Qingdao China

Over the past years, frameworks such as MapReduce and Spark have been introduced to ease the task of developing big data programs and applications. However, the jobs in these frameworks are roughly defined and packaged as executable jars without any functionality being exposed or described. This means that deployed jobs are not natively composable and reusable for subsequent development. Besides, it also hampers the ability for applying optimizations on the data flow of job sequences and pipelines. In this paper, we present the Hierarchically Distributed data Matrix (HDM) which is a functional, strongly-typed data representation for writing composable big data applications. Along with HDM, a runtime framework is provided to support the execution of HDM applications on distributed infrastructures. Based on the functional data dependency graph of HDM, multiple optimizations are applied to improve the performance of executing HDM jobs. The experimental results show that our optimizations can achieve improvements of between 10% to 60% of the Job-Completion-Time for different types of operation sequences when compared with the current state of art, Apache Spark.

关键词： Semantics Optimization Big data Distributed databases Sparks Programming Writing

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Extensions to quantile regression forests for very high-dimensional data

Extensions to quantile regression forests for very high-dime...

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18th Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2014

作者： Tung, Nguyen Thanh Huang, Joshua Zhexue Khan, Imran Li, Mark Junjie Williams, Graham Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9783319066042

This paper describes new extensions to the state-of-the-art regression random forests Quantile Regression Forests (QRF) for applications to high-dimensional data with thousands of features. We propose a new subspace sampling method that randomly samples a subset of features from two separate feature sets, one containing important features and the other one containing less important features. The two feature sets partition the input data based on the importance measures of features. The partition is generated by using feature permutation to produce raw importance feature scores first and then applying p-value assessment to separate important features from the less important ones. The new subspace sampling method enables to generate trees from bagged sample data with smaller regression errors. For point regression, we choose the prediction value of Y from the range between two quantiles Q0.05 and Q0.95 instead of the conditional mean used in regression random forests. Our experiment results have shown that random forests with these extensions outperformed regression random forests and quantile regression forests in reduction of root mean square residuals. © 2014 Springer International Publishing.

关键词： data mining

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