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International Conference on Data engineering

作者： Daehoon Kim Jae-Gil Lee Byung Suk Lee Department of Knowledge Service Engineering KAIST Korea Department of Computer Science University of Vermont U.S.A.

Social curation services are emerging social media platforms that enable users to curate their contents according to the topic and express their interests at the topic level by following curated collections of other users' contents rather than the users themselves. The topic-level information revealed through this new feature far exceeds what existing methods solicit from the traditional social networking services, to greatly enhance the quality of topic-sensitive influence modeling. In this paper, we propose a novel model called the topical influence with social curation (TISC) to find influential users from social curation services. This model, formulated by the continuous conditional random field, fully takes advantage of the explicitly available topic-level information reflected in both contents and interactions. In order to validate its merits, we comprehensively compare TISC with state-of-the-art models using two real-world data sets collected from Pinterest and ***. The results show that TISC achieves higher accuracy by up to around 80% and finds more convincing results in case studies than the other models. Moreover, we develop a distributed learning algorithm on Spark and demonstrate its excellent scalability on a cluster of 48 cores.

关键词： Data models Media Social network services Predictive models Graphical models Computational modeling Heart

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Determining performance boundaries on high-level system specifications 16

Determining performance boundaries on high-level system spec...

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19th International Workshop on Software and Compilers for Embedded Systems, SCOPES 2016

作者： Van Teijlingen, Wouter Van Leuken, Rene Galuzzi, Carlo Kienhuis, Bart Delft University of Technology Circuits and Systems Mekelweg 4 Delft Netherlands Maastricht University Department of Data Science and Knowledge Engineering Bouillonstraat 8-11 Maastricht Netherlands Leiden University Leiden Institute of Advanced Computer Science Niels Bohrweg 1 Leiden Netherlands

ISBN: (纸本)9781450343206

We can significantly reduce the time required to realize designs if it is possible to find limits to the performance of an embedded system, solely based on high-level system specifications. For that purpose, we present in this paper the cprof profiler, which determines the number of clock cycles needed to execute a C-program in hardware. The cprof tool is based on the Clang compiler front-end to parse C-programs and to produce instrumented source code for the profiling. Using cprof, we determine a lower and upper bound limit for all 29 cases of the PolyBench/C benchmark suite. The lower and upper bound are determined using the absolute performance estimations assuming all statement are mapped onto the same processing resource and unbounded performance estimations assuming unlimited resources. We also compared the clock cycles found by cprof with RTL implementations for all 29 Polybench/C cases and found that cprof determines with 1.2% accuracy the correct number of clock cycles. It does this in a fraction of the time compared to the time needed to do a full RTL simulation. © 2016 ACM.

关键词： Program compilers

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Extreme Large Margin Distribution Machine and its applications for biomedical datasets

Extreme Large Margin Distribution Machine and its applicatio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhiyong Yang Jingcheng Lu Taohong Zhang Department of Computer University of Science and Technology Beijing (USTB) Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China

ISBN: (纸本)9781509016129

Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin theory show that, besides maximizing the minimum margin of a given training dataset, it is also necessary to optimization the margin distribution to boost the overall generalization ability. Correspondingly, a novel SVM-like algorithm called Large Margin Distribution Machine (LDM) realizes this idea by maximizing the average of margin and minimizing the variance of margin simultaneously. And a series of applications has been reported thereafter. There is another well-known machine learning algorithm called Extreme Learning Machine (ELM) which shares similar framework with SVM. It is believed in this paper ELM could also benefit from the virtues of margin distribution optimization. Bearing this in mind, a novel algorithm called Extreme Large Margin Distribution Machine(ELDM) is proposed in this paper by bridging the advantages of ELM and LDM. And an efficient extension of ELDM for multi-class classifications under One vs. All Scheme is proposed subsequently. Finally, the experiment results on both benchmark datasets and biomedical classification datasets show the effectiveness of our proposed algorithm.

关键词： Heart Glass Training Support vector machines Algorithm design and analysis Diabetes Kernel

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Women empowerment towards developing India

Women empowerment towards developing India

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： A. Priyadharshini R. Thiyagarajan V. Kumar T. Radhu Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Shreenivasa Engineering College Dharmapuri Tamil Nadu India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem Tamil Nadu India

ISBN: (纸本)9781509041787

India is a developing country and the citizens of India are dreaming that within 2020, India will become a well-developed nation. Indian government has started Start-up India, Make in India to improve our nation, but still women in India are facing lots of obstacle towards their improvement. Despite this, women are having high exposure and they are working in all fields like defense, engineering, medical, etc. In order to make all women in the nation to attain a standard Women Empowerment is needed. Women Empowerment focuses on empowering each and every woman in the country to make them independent in all aspects in the society, to be aware on the rights and to make them equip about physical security. This paper focuses on describing the issues that women are facing in their daily life, schemes available for Women Empowerment in India and Self Help Group which is successfully running in the state of Tamil Nadu, suggestions on Self Help Group for future enhancement and a case study of Women Empowerment Cell.

关键词： Government Training Industries Economic indicators Monitoring

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Erratum to: The use of data-derived label hierarchies in multi-label classification

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Journal of Intelligent Information Systems 2016年第2期48卷 475-476页

作者： Gjorgji Madjarov Dejan Gjorgjevikj Ivica Dimitrovski Sašo Džeroski Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

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Curating a Semantic Bibliographic Catalog

Curating a Semantic Bibliographic Catalog

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International Conference on Semantic Computing

作者： H. S. Bhargav Gangadhar Akalwadi K. Kishan Kavi Mahesh Department of Computer Science and Engineering PES Institute of Technology Bangalore India KAnOE - Centre for Knowledge Analytics and Ontological Engineering PES University Bangalore India

Large online libraries are now common across the Web. However these libraries are usually fragmented and are not semantically connected thereby making their search and access difficult. Also there are no large bibliographic Linked Open Datasets available for use in research and analytics. This paper shows how to create a large, comprehensive, RDF triple store of semantic data about books. The primary aim of this work is to establish a linked relation between all the available books in the world and connect them to the already available linked datasets. The Open Library dataset, which has over 45 million records is first serialized by converting it into a triple format and then linked together using predicates from different ontologies. A simple endpoint for a semantic book search engine called BookLOD is created to demonstrate the utility of the dataset.

关键词： Semantics Libraries Resource description framework Ontologies Vocabulary Search engines

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Information Diffusion Model and Hadoop Implementation Research on Online Social Network

Information Diffusion Model and Hadoop Implementation Resear...

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2016 International Conference on computer, Mechatronics and Electronic engineering (CMEE 2016)

作者： Hong-song CHEN Yue HU Department of Computer Science University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Institute of information engineering Chinese Academy of Sciences University of Chinese Academy of Sciences

Social networks are currently gotten increasing focus with the growth of online social network services, such as Facebook, Sina Weibo. One major challenge for social network research is how to build information diffusion model to describe the complex information diffusion model. For this purpose, information diffusion models are researched. To show the importance of network topology, how to identify the most important spreaders in the network is a key scientific problem. Different kinds of network centrality measure methods are analyzed. The role of a potential edge in social network is analyzed. The relationship between network topology structure and information diffusion dynamics model is analyzed. Pagerank algorithm for Sina Weibo users are implemented in Hadoop cloud computing platform. Experiment result shows that Pagerank algorithm can be implemented in Hadoop cloud platform to recognize the important spreaders in social network. Big data in social network can be analyzed in the platform.

关键词： Social network Information diffusion model Hadoop Network topology

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on computer, Communication and Computational sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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