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IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom-up and top-down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

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Translating maps and coordinates from the Great War

Translating maps and coordinates from the Great War

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Terra Cognita - Semantic Sensor Networks, TC-SSN 2014 - 6th International Workshop on the Foundations, Technologies and Applications of the Geospatial Web and 7th International Workshop on Semantic Sensor Networks, co-located with 13th International Semantic Web Conference, ISWC 2014

作者： Warren, Rob Evans, David Big Data Institute Dalhousie University Halifax Canada Department of Computing and Mathematics University of Derby Derby United Kingdom

Many obsolete coordinate systems used in the past have fallen into disuse. However, the contents of historical documents still refer to these obsolete coordinates and thus translation systems are important in locating historical events. We present a specialized Linked Open data API constructed to translate obsolete British Trench Map coordinates from the Great War into modern WGS84 coordinates. We report on the design of the API, the construction of the triple structures used to answer queries and the methods used to enrich query results while ensuring network performance. Several use cases for the API are presented and we close with a discussion of our experiences in linking to external data providers.

关键词： Query processing

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Personalized Financial News Recommendation Algorithm Based on Ontology

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Procedia Computer Science 2015年 55卷 843-851页

作者： Rui Ren Lingling Zhang Limeng Cui Bo Deng Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

To deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender.

关键词： news recommendation algorithm ontology relevence feedback OF-IDF

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big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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Pipelining acoustic model training for speech recognition using storm

Pipelining acoustic model training for speech recognition us...

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2013 5th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2013

作者： Sitaram, Dinkar Srinivasaraghavan, Haripriya Agarwal, Kapish Agrawal, Amritanshu Joshi, Neha Ray, Debraj Centre for Cloud Computing and Big Data Computer Science Department PES Institute of Technology Bangalore India

ISBN: (纸本)9780769551555

Speech recognition has been increasingly used on mobile devices, which has in turn increased the need for creation of new acoustic models for various languages, dialects, accents, speakers and environmental conditions. This involves training and adapting a huge number of acoustic models, some of them in real-time. Training acoustic models is thus essential for speech recognition because these models determine the accuracy and quality of the recognition process. This paper, discusses the use of Storm, a distributed real time computational system, to pipeline the creation of acoustic models by CMU Sphinx, an open-source software project for speech recognition and training. Software pipelining reduces the time required for training and optimizes system resource utilization, thus enabling huge amounts of data to be trained in considerably less amount of time than taken by the conventional sequential process. Pipelining is achieved by grouping the stages of the training process into a set of five stages, and running each stage on individual nodes in a Storm cluster. Thus acoustic models are created by training multiple streams of speech samples using the same SphinxTrain setup, also resulting in improvement of training time and throughput. © 2013 IEEE.

关键词： Speech recognition

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Keystone federated security

Keystone federated security

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2013 8th International Conference for Internet Technology and Secured Transactions, ICITST 2013

作者： Sitaram, Dinkar Phalachandra, H.L. Vishwanath, Anush Ramesh, Pramod Prashanth, Meghana Joshi, Akshay G. Desai, Anoop R. Harikrishna Prabhu, C.R. Prafulla, P. Shwetha, R. Yashaswini, A. Center for Cloud Computing and Big Data Department of Computer Science PES Institute of Technology Bangalore India

ISBN: (纸本)9781908320209

Hybrid clouds are increasingly becoming important in cloud computing. We see a rapid raise in the demand for a secure infrastructure that would enable sharing of computing resources between multiple hybrid cloud deployments to facilitate accommodation of situations where the demand outstrips supply, load balancing, and other such infrastructure constraints. From the end user perspective, this would also mean that the end users can host applications with their choice of federated cloud provider, as opposed to choosing from a host of global cloud providers on the market. The following paper describes a federated infrastructure for hybrid clouds, in particular, Openstack. © 2013 Infonomics Society.

关键词： cloud computing federation hybrid clouds security

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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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Cheetah: Accelerating Dynamic Graph Mining with Grouping Updates

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Yi Zhang Xiaomeng Yi Yu Huang Jingrui Yuan Chuangyi Gui Dan Chen Long Zheng Jianhui Yue Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Software Engineering Huazhong University of Science and Technology Wuhan China Eastern Institute of Technology Ningbo China National University of Singapore Singapore Singapore Michigan Technological University Houghton United States School of Computer Science and Engieering UNSW Sydney Kensington Australia

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.

关键词： Graph mining system dynamic graph incremental computing

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