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27th AAAI Conference on Artificial Intelligence, AAAI 2013

作者： Chen, Yan Zhao, Jichang Hu, Xia Zhang, Xiaoming Li, Zhoujun Chua, Tat-Seng State Key Laboratory of Software Development Environment Beihang University China School of Computing National University of Singapore Singapore Singapore Department of Physics Bar-Ilan University Israel Department of Computer Science and Engineering Arizona State University United States

ISBN: (纸本)9781577356158

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The high-dimension content generated by these users provides an unique opportunity to understand their behavior deeply. As one of the most fundamental topics, location estimation attracts more and more research efforts. Different from the previous literature, we find that user's location is strongly related to user interest. Based on this, we first build a detection model to mine user interest from short text. We then establish the mapping between location function and user interest before presenting an efficient framework to predict the user's location with convincing fidelity. Thorough evaluations and comparisons on an authentic data set show that our proposed model significantly outperforms the state-of-the-arts approaches. Moreover, the high efficiency of our model also guarantees its applicability in real-world scenarios. Copyright © 2013, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Social networking (online)

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Recovering software architectural knowledge from documentation using conceptual model 25

Recovering software architectural knowledge from documentati...

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25th International Conference on software engineering and Knowledge engineering, SEKE 2013

作者： Shahin, Mojtaba Liang, Peng Li, Zengyang State Key Lab of Software Engineering Computer School Wuhan University China Department of Computer Engineering Islamic Azad University Neyriz Branch Iran Department of Computing Science University of Groningen Netherlands

ISBN: (纸本)1891706330

software architectural knowledge (AK) is the integrated representation of the software architecture (SA) of a software-intensive system, the architectural design decisions, and the external context/environment. AK annotation using AK conceptual model is used to recover formal AK from SA documentation, including architecture design as well as the design decisions, rationale, context, and other factors that together determine architecture solutions. But there is no evidence on how architects, especially junior architects, understand and annotate SA documents and recover formal AK from the documents using an AK model, which is right the case when a new architect jumps into a project, trying to understand the SA documents created by previous architects. This paper first presents AKRCM (AK Recovery using Conceptual Model) approach for recovering AK from SA documents. Second, we conduct a descriptive study using experiment to investigate how junior architects annotate SA documents and recover AK using AKRCM approach. We found that an AK conceptual model is beneficial for junior architects to get a fair understanding of SA documents, and to recover better-quality AK from SA documents. Copyright © 2013 by Knowledge Systems Institute Graduate School.

关键词： software architecture

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Reciprocal hash tables for nearest neighbor search

Reciprocal hash tables for nearest neighbor search

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27th AAAI Conference on Artificial Intelligence, AAAI 2013

作者： Liu, Xianglong He, Junfeng Lang, Bo State Key Lab of Software Development Environment Beihang University Beijing China Department of Electrical Engineering Columbia University New York NY United States Facebook 1601 Willow Rd Menlo Park CA United States

ISBN: (纸本)9781577356158

Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way. In this paper, we regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. The construction method is general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. Extensive experiments on two large-scale benchmarks demonstrate that the proposed method outperforms both naive construction method and state-of-the-art hashing algorithms, with up to 65.93% accuracy gains. Copyright © 2013, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Hash functions

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Adaptive Fault Detection for Testing Tenant Applications in Multi-tenancy SaaS Systems

Adaptive Fault Detection for Testing Tenant Applications in ...

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IEEE International Conference on Cloud engineering (IC2E)

作者： W.T. Tsai Qingyang Li Charles J. Colbourn Xiaoying Bai School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA State Key Laboratory of Software Development Environment Beihang University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

SaaS (software-as-a-Service) often uses multi-tenancy architecture (MTA) where tenant developers compose their applications online using the components stored in the SaaS database. Tenant applications need to be tested, and combinatorial testing can be used. While numerous combinatorial testing techniques are available, most of them produce static sequences of test configurations and their goal is often to provide sufficient coverage such as 2-way interaction coverage. But the goal of SaaS testing is to identify those compositions that are faulty for tenant applications. This paper proposes an adaptive test configuration generation algorithm AR (Adaptive Reasoning) that can rapidly identify those faulty combinations so that those faulty combinations cannot be selected by tenant developers for composition. The AR algorithm has been evaluated by both simulation and real experimentation using a MTA SaaS sample running on GAE (Google App Engine). Both the simulation and experiment showed show that the AR algorithm can identify those faulty combinations rapidly. Whenever a new component is submitted to the SaaS database, the AR algorithm can be applied so that any faulty interactions with new components can be identified to continue to support future tenant applications.

关键词： Testing software as a service Color Databases Image color analysis Fault location

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Test algebra for combinatorial testing

Test algebra for combinatorial testing

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International Workshop on Automation of software Test (AST)

作者： Wei-Tek Tsai Charles J. Colbourn Jie Luo Guanqiu Qi Qingyang Li Xiaoying Bai School of Computing Informatics and Decision Systems Arizona State University Tempe AZ USA Arizona State University Tempe AZ US State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science and Technology INLIST Tsinghua University Beijing China

ISBN: (纸本)9781467361620

This paper proposes a new algebraic system, Test Algebra (TA), for identifying faults in combinatorial testing for SaaS (software-as-a-Service) applications. SaaS as a part of cloud computing is a new software delivery model, and mission-critical applications are composed, deployed, and executed in cloud platforms. Testing SaaS applications is a challenging task because new applications need to be tested when they are composed before they can be deployed for execution. Combinatorial testing algorithms can be used to identify faulty configurations and interactions from 2-way all the way to k-way where k is the number of components in the application. The TA defines rules to identify faulty configurations and interactions. Using the rules defined in the TA, a collection of configurations can be tested concurrently in different servers and in any order and the results obtained will be still same due to the algebraic constraints.

关键词： Testing software as a service Servers Fault diagnosis Algebra Scalability Program processors

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USO- a new slope one algorithm based on modified user similarity

USO- a new slope one algorithm based on modified user simila...

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2012 International Conference on Information Management, Innovation Management and Industrial engineering, ICIII 2012

作者： Sun, Mingtao Zhang, Hui Song, Shiyu Wu, Kejia State Key Software Development Environment Lab. Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467319324

Recommendation algorithm based on collaborative filtering strategy has achieved great success in the field of personalized recommendation, However, based on collaborative filtering recommendation algorithm complexity is too high, over-fitting problem, the recommended accuracy requirements lead to the algorithm difficult to achieve in practical applications. Slope One algorithm greatly simplifies the complex process of collaborative filtering algorithm, dramatically reducing the recommended system and maintenance difficulty, but at the same time recommended the accuracy will decline. Recommended in order to take into account the recommendation algorithm of low complexity and high accuracy, the authors propose the improved algorithm based on the correct user similarity Slope One - USO algorithm The inspection results show that the algorithm can effectively improve the accuracy of the recommendation of the slope-one algorithm. ©2012 IEEE.

关键词： Collaborative filtering

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Personalized video recommendation based on viewing history with the study on YouTube 12

Personalized video recommendation based on viewing history w...

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4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012

作者： Zhao, Xiaojian Luan, Huanbo Cai, Junjie Yuan, Jin Chen, Xiaoming Li, Zhoujun State Key Laboratory of Software Development Environment Beihang University China School of Computing National University of Singapore Singapore Department of Computer Science University of Texas at San Antonio United States School of Computer Science and Engineering Beihang University China

ISBN: (纸本)9781450316002

With internet delivery of video content surging to an un-precedented level, video recommendation has become an important approach for helping people access interesting videos. In this paper, we propose a novel approach to integrate viewing history for personalized video recommendation. For a given user, our approach calculates a recommendation score for each video candidate, that composes of two parts: the interest degree of this video by the user's friends, and the taste similarities between the user and his friends. We measure the interest degree of each video by considering its textual, visual and popularity information. Meanwhile, we construct tag set for each user based on his/her viewing history to estimate the taste similarities between different users. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conduct experiments with 45 users and more than 11, 000 videos, and the results demonstrate the feasibility and effectiveness of our approach. Copyright © 2012 ACM.

关键词： viewing history multimodal similarity tag set personalized video recommendation

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Independent domination on tree convex bipartite graphs

Independent domination on tree convex bipartite graphs

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6th International Frontiers of Algorithmics Workshop, FAW 2012 and 8th International Conference on Algorithmic Aspects of Information and Management, AAIM 2012

作者： Song, Yu Liu, Tian Xu, Ke Key Laboratory of High Confidence Software Technologies School of Electronic Engineering and Computer Science Peking University Beijing 100871 China National Lab. of Software Development Environment Beihang University Beijing 100191 China

ISBN: (纸本)9783642296994

An independent dominating set in a graph is a subset of vertices, such that every vertex outside this subset has a neighbor in this subset (dominating), and the induced subgraph of this subset contains no edge (independent). It was known that finding the minimum independent dominating set (Independent Domination) is -complete on bipartite graphs, but tractable on convex bipartite graphs. A bipartite graph is called tree convex, if there is a tree defined on one part of the vertices, such that for every vertex in another part, the neighborhood of this vertex is a connected subtree. A convex bipartite graph is just a tree convex one where the tree is a path. We find that the sum of larger-than-two degrees of the tree is a key quantity to classify the computational complexity of independent domination on tree convex bipartite graphs. That is, when the sum is bounded by a constant, the problem is tractable, but when the sum is unbounded, and even when the maximum degree of the tree is bounded, the problem is -complete. © 2012 Springer-Verlag.

关键词： Forestry

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A satellite data portal developed for crowdsourcing data analysis and interpretation

A satellite data portal developed for crowdsourcing data ana...

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IEEE International Conference on e-science and Grid Computing

作者： Zhenghui Hu Wenjun Wu State Key Lab of Software Development Environment Department of Computer Science and Engineering Beihang University Beijing China

Satellite data products derived from the remote sensing observations describe features of the land, ocean and atmosphere. And by data processing, they can be used to study processes and trends on local/global scale for real-time environmental research and applications. However, the advances of cutting-edge remote sensing technology bring the challenge of data deluge for satellite data analysis and interpretation. With combinations of human intelligence and machine intelligence, we develop a satellite data portal for crowdsourcing data analysis and interpretation through teaching and learning to cope with the overwhelming data deluge. Compared with all the existing data portals and crowdsourcing systems, it is the first attempt to embed crowdsourcing into a data portal to provide integrated services of satellite data access and analysis.

关键词： Satellites Portals Data analysis Remote sensing Education Publishing Communities

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Poster abstracts

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Sleep and Biological Rhythms 2016年第1期5卷 A1-A189页

作者： J. Ferrie M. Shipley F. Cappuccio E. Brunner M. Miller M. Kumari M. Marmot A. Coenen J. L. Castillo F. Araya G. Bustamante L. Montecino C. Torres S. Oporto J. Gronli E. Fiske R. Murison B. Bjorvatn E. Sorensen R. Ursin C. M. Portas S. Rajaraman A. Gribok N. Wesensten T. Balkin J. Reifman N. Dursunoglu S. Ozkurt S. Baser O. Delen S. Sarikaya P. Sadler P. Mitchell D. Françon M. Decobert B. Herve A. Richard G. Griebel P. Avenet B. Scatton G. L. Fur D. Eckert A. Jordan A. Wellman S. Smith A. Malhotra D. White D. Bruck I. Thomas A. Kritikos W. Oertel K. Stiasny-Kolster D. Garcia-Borreguero W. Poewe B. Hoegl R. Kohnen E. Schollmayer J. Keffel C. Trenkwalder A. Valle S. Roizenblatt F. Fregni P. Boggio S. Tufik K. Ward L. Robertson L. Palmer P. Eastwood D. Hillman J. Lee S. Mukherjee V. de Padova G. Barbato G. Ficca I. Zilli P. Salzarulo M. Veldi T. Hion V. Vasar M. Kull L. Nowak J. Davis Y. Latzer O. Tzischinsky S. Crowley M. Carskadon M. Anca-Herschkovitsch D. Frey J. Ortega C. Wiseman C. Farley K. Wright A. Campbell A. Neill K. Spiegel R. Leproult E. Tasali N. Scherberg E. van Cauter A. T. Noradina N. A. Karim I. Norlinah A. A. Raymond R. Sahathevan B. Hamidon E. Werth R. Poryazova R. Khatami C. Bassetti R. G. Beran L. Ainley G. Holand J. Duncan H. Kinney B. Davis B. Hood S. Frey C. Schmidt M. Hofstetter P. Peigneux C. Cajochen W.-P. Hu J.-D. Li C. Zhang L. Boehmer J. Siegel Q.-Y. Zhou Y. Sagawa H. Kondo T. Takemura H. Kanayama Y. Kaneko M. Sato T. Kanbayashi Y. Hishikawa T. Shimizu A. Viola L. James L. Schlangen D.-J. Dijk R. Andretic Y.-C. Kim K.-A. Han F. Jones R. Greenspan L. Sanford L. Yang X. Tang K. Dieter E. Uta H. Sven M. Richard N. Oyane S. Pallesen F. Holsten Y. Inoue M. Fujita N. Emura K. Kuroda N. Uchimura A. Johnston J. Astbury G. Kennedy K. Hoedlmoser M. Schabus T. Pecherstorfer S. Moser G. Gruber P. Anderer W. Klimesch N. Naidoo M. Ferber A. Pack D. Neu O. Mairesse G. Hoffmann A. Dris L. Lambrecht P. Linkowski P. Verbanck O. Le Bon N. Matsuura M. Yamao N. Adachi R. Aritomi Y. Komada H. Tanaka S. Shirak University College London London UK University College London London USA Warwick Medical School London UK Warwick Medical School Coventry USA Radboud University Nijmegen Nijmegen Netherlands Department of Neurology University of Chile Santiago Chile Neurology Service Hospital del Salvador Santiago Chile Family Health Center Juan Pablo II Santiago Chile Department of Biomedicine Section on Physiology Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care University of Bergen Bergen Norway Norwegian Competence Center for Sleep Disorders Haukeland University Hospital Bergen Norway Department of Child and Adolescent Psychiatry Haukeland University Hospital Univeristy of Bergen Bergen Norway Department of Biomedicine University of Bergen Bergen Norway US Army Medical Research and Materiel Command Fort Detrick Frederick USA Walter Reed Army Institute of Research Silver Spring USA Department of Behavioral Biology Walter Reed Army Institute of Research Silver Spring USA Faculty Department of Pulmonology Pamukkale University Medical Denizli Turkey University of Ballarat Ballarat Australia Sanofi-Aventis Recherche et Developpement Paris France Brigham and Women’s Hospital Harvard Medical School Boston USA The University of Queensland Adelaide Australia School of Psychology University of Queensland Brisbane Australia The University of Queensland Brisbane Australia Victoria University Melbourne Australia Department of Psychology Victoria University Melbourne Australia School of Psychology Victoria University Melbourne Australia Philips-Universität Marburg Germany Philips-Universitaet-Marburg Marburg Germany Instituto de Investigaciones del Sueno Madrid Spain Universitätsklinik für Neurologie Innsbruck Austria Universitaetsklinik fuer Neurologie Innsbruck Austria IMEREM GmbH Nuernberg Germany Schwarz Pharma AG Monheim Germany

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