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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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Foreword

Proceedings of the 2016 International Conference on Image Pr...

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Proceedings of the 2016 International Conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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From categorical to numerical: Multiple transitive distance learning and embedding

From categorical to numerical: Multiple transitive distance ...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Zhang, Kai Wang, Qiaojun Chen, Zhengzhang Marsic, Ivan Kumar, Vipin Jiang, Guofei Zhang, Jie School of Computer Science Southwest Petroleum University Chengdu Sichuan Province610500 China Institute of Data Science and Technology Alibaba Group SeattleWA98101 United States Department of Electrical and Computer Engineering Rutger-s the State University of New Jersey NJ08854 United States NEC Laboratories America Inc. PrincetonNJ08540 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Center for Computational Systems Biology Fudan University Shanghai200433 China

ISBN: (纸本)9781510811522

Categorical data are ubiquitous in real-world databases. However, due to the lack of an intrinsic proximity measure, many powerful algorithms for numerical data analysis may not work well on their categorical counterparts, making it a bottleneck in practical applications. In this paper, we propose a novel method to transform categorical data to numerical representations, so that abundant numerical learning methods can be exploited in categorical data mining. Our key idea is to learn a pairwise dissimilarity among categorical symbol-s, henceforth a continuous embedding, which can then be used for subsequent numerical treatment. There are two important criteria for learning the dissimilarities. First, it should capture the important "transitivity" which has shown to be particularly useful in measuring the proximity relation in categorical data. Second, the pairwise sample geometry arising from the learned symbol distances should be maximally consistent with prior knowledge (e.g., class labels) to obtain a good generalization performance. We achieve them through multiple transitive distance learning and embedding. Encouraging results are observed on a number of benchmark classification tasks against state-of-the-art. Copyright © SIAM.

关键词： Distance education

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VH-DSI: Speeding up data Visualization via a Heterogeneous Distributed Storage Infrastructure

VH-DSI: Speeding up Data Visualization via a Heterogeneous D...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yongqing Zhu Samsudin Juniarto Haixiang Shi Jun Wang Data Storage Institute A STAR (Agency for Science Technology and Research Singapore Department of Electrical Engineering and Computer Science University of Central Florida USA

Visualizing and analyzing large-scale datasets are both critical and challenging, as they require substantial resources for data processing and storage. While the speed of supercomputers continues to set higher standard, the I/O systems have not kept in pace, resulting in a significant performance bottleneck. To alleviate the I/O bottleneck for scientific visualization applications, we propose a Visualization via a Heterogeneous Distributed Storage Infrastructure (VH-DSI) solution to improve I/O speed and accelerate overall visualization performance. VH-DSI replaces the traditional parallel file system with a distributed file system to support visualization applications. A new scheduling algorithm HeterSche is proposed in VH-DSI to assign computing tasks to data nodes with the consideration of cluster heterogeneity and data locality. VH-DSI also includes a design to support POSIX-IO for distributed file system. The performance evaluation has shown that the proposed VH-DSI solution can achieve significant performance improvement for visualization applications. Compared to the traditional visualization, the VH-DSI solution reduces the response time by at least 5 times. The HeterSche scheduling algorithm is capable to speed up visualization compared to other scheduling algorithms especially for large scale datasets.

关键词： data visualization File systems Distributed databases Scheduling algorithms Pipelines computer architecture

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A Note on the Evaluation of Mutation Prioritization Algorithms

A Note on the Evaluation of Mutation Prioritization Algorith...

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IEEE Symposium Series on Computational Intelligence

作者： Dusan Popovic Jesse Davis Alejandro Sifrim Bart De Moor STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Department of Electrical Engineering (ESAT) KU Leuven Department of Computer Science KU Leuven Wellcome Trust Sanger Institute Wellcome Trust Genome Campus

ISBN: (纸本)9781479975617

Recent developments in the field of gene sequencing technology greatly accelerated discovery of mutations that cause various genetic disorders. At the same time, a typical sequencing experiment generates a large number of candidate mutations, hence detecting single or few causative variants is still a formidable problem. Many computational methods have been proposed to assist this process, from which a large portion employ statistical learning in some form. Consequently, each newly designed algorithm is routinely compared to other competing systems in hope to demonstrate advantageous performance. In this work we review and discuss several issues related to the current practice of evaluation of mutation prioritization algorithms and suggest possible directions for improvements.

关键词： Statistical learning Hereditary Diseases Mutation Hopefulness algorithms Base Sequence Design Algorithm Note Taking computational methods

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A High-Performance Energy-Efficient Hybrid Redundant MAC for Error-Resilient Applications

A High-Performance Energy-Efficient Hybrid Redundant MAC for...

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International Conference on VLSI Design

作者： Sunil Dutt Anshu Chauhan Rahul Bhadoriya Sukumar Nandi Gaurav Trivedi Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati India Department of Electronics and Signal Processing INP-ENSEEIHT Toulouse France Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

In the majority of Digital Signal processing (DSP) applications, such as image, audio and video processing, the final result is interpreted by human senses, and, the fact of confined perception of human senses declines the strict restriction on accuracy. Thus, by adopting the emerging concept of approximate computing, we propose an approximate radix-2 hybrid redundant Multiply-and-Accumulate (Approx MAC) unit which stems a novel Speed-Power-Accuracy-Area (SPAA) metrics. The Approx MAC unit attains tremendous improvements in computational performance, energy efficiency and silicon area with a trivial degradation in the output quality. To inspect the effectiveness of the proposed approach in real-time DSP applications, we demonstrate an Approx MAC unit embedded JPEG-E-X IP core architecture. The Approx MAC unit with 40 approximate LSBs ensures 7.177x and 1.526x speedup, 1.594x and 4.163x energy efficiency, and 1.131x and 1.277x silicon area improvements over binary and hybrid redundant MAC units, respectively. Moreover, the Approx MAC unit with 40 approximate LSBs decorates power precision and delay-precision metrics by 14.71% and 32.95%, respectively.

关键词： Adders Approximation methods Digital signal processing computer architecture Silicon Measurement Approximation algorithms

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Sum-Rate and Power Scaling of Massive MIMO Systems with Channel Aging

Sum-Rate and Power Scaling of Massive MIMO Systems with Chan...

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作者： Kong, Chuili Zhong, Caijun Papazafeiropoulos, Anastasios K. Matthaiou, Michail Zhang, Zhaoyang Institute of Information and Communication Engineering Zhejiang University Hangzhou310027 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Communications and Signal Processing Group Imperial College London London United Kingdom School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law;instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively. © 2015 IEEE.

关键词： Channel state information

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Risk Management Using Big Real Time data

Risk Management Using Big Real Time Data

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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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 new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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