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International Conference on data science and Advanced Analytics (DSAA)

作者： Ren-Hao Pan Lin-Yu Tseng I-En Liao Chien-Lung Chan K. Robert Lai Kai-Biao Lin Innovation Center for Big data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Computer Science and Communication Engineering Providence University Taichung Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan School of Computer & Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781467382748

MicroRNAs (miRNAs) are a group of small noncoding RNA (ncRNA) molecules that play an important role in biological functions. This paper proposes a microRNA prediction application, which is based on the multi-layer hierarchical MapReduce framework and provides four prediction workflows for four different datasets: miRNA-like sequences, miRNA cluster sequences, unknown miRNA sequences and the next generation sequencing (NGS) sequences. These workflows include four core procedures for finding the genome location, applying the biological filtering criteria and a genetic algorithm based pre-miRNA classifier. Each procedure works as a MapReduce task and uses JSON format to translate the MapReduce output to the next MapReduce procedure. Experimental results show that the proposed miRNA predictor not only achieves high sensitivity and accuracy, but also has ability to process more than million sequences in acceptable time by relying on the multi-layer hierarchical MapReduce framework.

关键词： RNA Bioinformatics Genetic algorithms Genomics Sequential analysis Proteins

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence Applied to Assistive Technologies and Smart Environments;Cognitive Computing for Augmented Human Intelligence;computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open data to Models, Standards, and Reasoning;Sequential Decision Making with big data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Brain Tumor Segmentation in Multi-modality MRIs Using Multiple Classifier System and Spatial Constraint

Brain Tumor Segmentation in Multi-modality MRIs Using Multip...

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International Conference on computer, Information and Application (CIA)

作者： Tianming Zhan Yongzhao Zhan Yao Ji Shenghua Gu Jin Wang Lei Jiang School of Computer Science & communications Engineering Jiangsu University Zhenjiang P. R. China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing P. R. China Petrochina Southwest Oil &Gasfield Company Luzhou P. R. China

ISBN: (纸本)9781467375726

Delineating brain tumor boundaries from multi-modality magnetic resonance images (MRIs) is a crucial step in brain cancer surgical and treatment planning. In this paper, we propose a fully automatic technique for brain tumor segmentation from multi-modality human brain MRIs. We first use the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues. Then, the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image. At last, the spatial-contextual information is proposed by constraining the classified neighbors to improve the classification accuracy. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.

关键词： Tumors Image segmentation Magnetic resonance imaging Niobium Yttrium Probability Brain

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Current-aware scheduling for flash storage devices 20

Current-aware scheduling for flash storage devices

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20th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2014

作者： Huang, Tzu-Jung Ho, Chien-Chung Huang, Po-Chun Chang, Yuan-Hao Chang, Che-Wei Kuo, Tei-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science Engineering and Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering School of Electrical and Computer Engineering Chang Gung University Taoyuan320 Taiwan

ISBN: (纸本)9781479939534

As NAND flash memory has become a major choice of storage media in diversified computing environments, the performance issue of flash memory has been extensively addressed in many excellent designs. Among them, an effective strategy is to adopt multiple channels and flash-memory chips to improve the performance on data accesses. However, the degree of data access parallelism cannot be increased by simply increasing the number of channels and chips in the storage device, because it is seriously limited by the maximum current constraint of the bus interface and affected by the access patterns of user data. As a consequence, to maximize the degree of access parallelism, it is of paramount significance to have a proper scheduling strategy to determine the order that read/write requests are served. In this paper, a current-aware scheduling strategy for read/write requests is proposed to maximize the read performance without violating the bus current constraint and without missing (the deadline of) written data. The proposed strategy is then evaluated through a series of experiments, in which the results are quite encouraging. © 2014 IEEE.

关键词： Flash memory

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Using Feature Selection to Improve the Utility of Differentially Private data Publishing

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Procedia computer science 2014年 37卷 511-516页

作者： Yasser Jafer Stan Matwin Marina Sokolova School of Electrical Engineering and Computer Science University of Ottawa Canada Institute for Big Data Analytics Dalhousie University Canada Institute for Computer Science Polish Academy of Sciences Faculty of Medicine University of Ottawa Canada

Protection of patient's privacy is an obligation enforced by laws and regulations in the US, Canada, and other jurisdictions. With exponential growth of exchange of personal health information (PHI) brought about by e-health, there is a need for smart algorithms that help the data publisher to protect PHI. Within exiting privacy models, differential privacy is considered one of the strongest privacy protection techniques that does not make any assumption about the attacker's background knowledge. One way to achieve differential privacy in the non-interactive mode is to derive a contingency table of the raw data over the database domain, to add noise to each count, and to publish the resulting noisy table of counts. This approach, however, is not suitable for high-dimensional data with large domains as the added noise substantially destroys the utility of the data. In this work, we show that when the K-anonymity is preceded by feature selection, it is possible to obtain a contingency table with higher counts. As a result, when noise is added to satisfy differential privacy, its distorting effect is minimized and high utility of the data is preserved. We propose the TOP_Diff algorithm which offers a trade-off between anonymization level K and the privacy budget ɛ, and enables us to publish privacy preserving datasets with high utility. Our approach is capable of handling both numerical and categorical features.

关键词： Privacy Feature Selection K-anonymity Differential Privacy Classification

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A Hierarchy Method Based on LDA and SVM for News Classification

A Hierarchy Method Based on LDA and SVM for News Classificat...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Limeng Cui Fan Meng Yong Shi Minqiang Li An Liu Research Center on Fictitious Economy & Data Science Key Research Lab on Big Data Mining and Knowledge Management Beijing CAS China School of Managemt Tianjin University Tianjin China School of Computer Science & Technology Soochow University Suzhou China

ISBN: (纸本)9781479942732

He growth of the online data provides the user a access to information on the Internet but also creates the challenges to obtain the valuable knowledge. In this paper we focus on news text classification, which is meaningful for information provider to organize and display the news but also for the users to reach the valuable information easily. A hierarchy method based on LDA and SVM is proposed to accomplish this task and several experiments are conducted to evaluate our method. The results show that our method is promising in text classification problems.

关键词： Support vector machines Classification algorithms Text categorization Runtime Kernel Computational modeling

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Privacy-aware filter-based feature selection

Privacy-aware filter-based feature selection

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IEEE International Conference on big data

作者： Yasser Jafer Stan Matwin Marina Sokolova School of Electrical Engineering and Computer Science University of Ottawa Canada Institute for Big Data Analytics Dalhousie University Canada Polish Academy of Sciences Institute for Computer Science Poland Faculty of Medicine University of Ottawa Canada

ISBN: (纸本)9781479956678

A large amount of digital information collected and stored in databases creates new opportunities for knowledge discovery and data mining. The datasets, however, may contain personally identifiable information that needs to be protected. With high dimensionality of many large datasets, dimensionality reduction such as feature selection becomes indispensible. In this work, we aim at incorporating privacy into the very process of feature selection and as such, propose a privacy-aware filter-based feature selection method (PF-IFR). Our method enables data custodians to define a trade-off measure for controlling the amount of privacy and efficacy using filter-based feature selection techniques.

关键词： Correlation Privacy Accuracy data privacy Filtering algorithms Educational institutions Publishing

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Misclassification Minimization Based on Multiple Criteria Linear Programming

Misclassification Minimization Based on Multiple Criteria Li...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Bo Wang Yong Shi Wayne Wei Huang Guanfeng Liu The Key Research Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Management Xi'an Jiaotong University Xi'an China School of Computer Science and Technology Soochow University Suzhou China

Misclassification minimization is an important and interesting topic in classification problem. Obviously, exploring the solution for this topic will benefit to many real life problems, such as credit card clients classification. This paper focuses on misclassification minimization based on multiple criteria linear programming (MCLP), proposing two different schemes to minimize the number of misclassified points in original MCLP. Especially, the complementarity is used to construct the first scheme and linear approximation technique is applied to solve it. Furthermore, successive linearization algorithm (SLA) is employed to achieve minimization the second scheme. Finally, numerical experiment tests the effect of this idea.

关键词： Minimization Linear programming Vectors Optimization Programming Credit cards Educational institutions

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The royal birth of 2013: Analysing and visualising public sentiment in the UK using Twitter

The royal birth of 2013: Analysing and visualising public se...

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2013 IEEE International Conference on big data, big data 2013

作者： Nguyen, Vu Dung Varghese, Blesson Barker, Adam Big Data Laboratory School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781479912926

Analysis of information retrieved from microblog-ging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods. © 2013 IEEE.

关键词： Sentiment analysis

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Active Learning with Nonparallel Support Vector Machine for Binary Classification

Active Learning with Nonparallel Support Vector Machine for ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Xi Zhao Zhensong Chen Yong Shi School of Computer Science and Technology Beijing Institute of Technology Beijing China University of Chinese Academy of Sciences Beijing China Key Res. Lab. on Big Data Min. & Knowledge Manage. Beijing China

ISBN: (纸本)9781479942732

Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance of the proposed method compared with classical active learning based on support vector machine.

关键词： Support vector machines Mathematical model Accuracy Kernel Equations Educational institutions Optimization

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