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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yu-Lun Hsieh Shih-Hung Liu Yung-Chun Chang Wen-Lian Hsu Department of Computer Science National Chengchi University Taiwan Social Networks and Human-Centered Computing Program Academia Sinica Taiwan Institute of Information Science Academia Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Departmem of Information Management National Taiwan University Taiwan

ISBN: (纸本)9781467396073

In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, for document categorization using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. Using a Chinese news corpus containing over 100,000 articles and five topics, we provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively categorize a document into the predefined topics. Results demonstrate that our method can achieve the best performances compared to several other approaches.

关键词： Computational modeling Manuals

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A fast noise resilient anomaly detection using GMM-based collective labelling

A fast noise resilient anomaly detection using GMM-based col...

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science and Information Conference (SAI)

作者： Elnaz Bigdeli Bijan Raahemi Mahdi Mohammadi Stan Matwin School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada Telfer School of Management Knowledge Discovery and Data Mining lab University of Ottawa E. Ottawa ON Canada Department of Computing Dalhousie University Halifax NB Canada Polish Academy of Sciences Warsaw Poland

Anomaly detection algorithms face several challenges including computational complexity and resiliency to noise in input data. In this paper, we propose a fast and noise-resilient cluster-based anomaly detection method using collective labelling approach. In the proposed Collective Probabilistic Anomaly Detection method, first instead of labelling each new sample (as normal or anomaly) individually, the new samples are clustered, then labelled. This collective labelling mitigates the negative impact of noise by relying on group behaviour rather than individual characteristics of incoming samples. Second, since grouping and labelling new samples may be time-consuming, we summarize clusters using Gaussian Mixture Model (GMM). Not only does GMM offer faster processing speed; it also facilitates summarizing clusters with arbitrary shape, and consequently, reducing the memory space requirement. Finally, a modified distance measure, based on Kullback-Liebner method, is proposed to calculate the similarity among clusters represented by GMMs. We evaluate the proposed method on various datasets by measuring its false alarm rate, detection rate and memory requirement. We also add different levels of noise to the input datasets to demonstrate the performance of the proposed collective anomaly detection method in the presence of noise. The experimental results confirm superior performance of the proposed method compared to individually-based labelling techniques in terms of memory usage, detection rate and false alarm rate.

关键词： Noise Shape Training Gaussian mixture model Clustering algorithms Support vector machines

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A probabilistic approach to selecting units for speech synthesis based on acoustic similarity

A probabilistic approach to selecting units for speech synth...

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2014 20th National Conference on Communications, NCC 2014

作者： Babu, Anjana Krishnan, K. Raghava Sao, Anil K. Murthy, Hema A. School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781479923618

Most unit selection synthesisers sound quite natural when the database consists of a number of realisations of the same sound unit from a large number of contexts. A common problem observed with these synthesisers is unexpected prosody when a new context is presented in the text. The objective of this paper is to address this issue and select appropriate units that are relevant to a specific context. Text-to-speech synthesisers propose a number of different features based on the linguistic context to select units. The key contribution in this paper is that the acoustic context rather than the linguistic context is crucial for improving naturalness. A probabilistic framework is proposed for selecting units based on an acoustic framework. Reducing the variability in acoustic context improves both naturalness and intelligibility. Since the context is only specified by acoustics, it can be applied to any language and perhaps even multilingual synthesis. The proposed approach has been tested on 2 Indian languages. An improvement of up to 21.9% in DMOS and 73.93% in WER relative to the conventional system that uses linguistic criteria is observed. © 2014 IEEE.

关键词： Linguistics

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Direct measurements of magnetic and electric optical responses from silicon nanoparticles

Direct measurements of magnetic and electric optical respons...

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International Seminar Day on Diffraction

作者： Dmitry V. Permyakov Ivan S. Sinev Dmitry L. Markovich Pavel Ginzburg Anton K. Samusev Pavel A. Belov Vytautas Valuckas Arseniy I. Kuznetsov Boris S. Luk'yanchuk Andrey E. Miroshnichenko Dragomir N. Neshev Yuri S. Kivshar ITMO University St. Petersburg Russia School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Electrical and Computer Engineering National University of Singapore Singapore Data Storage Institute A*STAR (Agency for Science Technology and Research) Singapore Nonlinear Physics Centre Australian National University Canberra Australia

We report on our experimental and numerical studies of optically-induced magnetic and electric dipole resonances of spherical silicon nanoparticles. We combine polarization-resolved dark-field spectroscopy and near-field scanning optical microscopy and demonstrate experimentally that electromagnetic fields scattered by nanoparticles exhibit strong sensitivity to the probing beam polarization both in far- and near-field regions. We suggest that our complex experimental approach can be employed as a powerful tool for analysing nanoscale photonic systems possessing both electric and magnetic dipole responses.

关键词： Magnetic resonance imaging Nanoparticles Magnetic moments Silicon Optical polarization Optical diffraction Optical scattering

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Methods for Evaluating Medical Tests and Biomarkers

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Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A. Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Sabine Van Huffel Jan Verbakel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O’Sullivan Rafael Perera Brian Nicholson Hannah L. Bromley Tracy E. Roberts Adele Francis Denniis Petrie G. Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G. Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

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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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ZZ→ℓ+ℓ−ℓ′+ℓ′− cross-section measurements and search for anomalous triple gauge couplings in 13 TeV pp collisions with the ATLAS detector

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Physical Review D 2018年第3期97卷 032005-032005页

作者： M. Aaboud G. Aad B. Abbott O. Abdinov B. Abeloos S. H. Abidi O. S. AbouZeid N. L. Abraham H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya S. Adachi L. Adamczyk J. Adelman M. Adersberger T. Adye A. A. Affolder Y. Afik T. Agatonovic-Jovin C. Agheorghiesei J. A. Aguilar-Saavedra S. P. Ahlen F. Ahmadov G. Aielli S. Akatsuka H. Akerstedt T. P. A. Åkesson E. Akilli A. V. Akimov G. L. Alberghi J. Albert P. Albicocco M. J. Alconada Verzini S. C. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander T. Alexopoulos M. Alhroob B. Ali M. Aliev G. Alimonti J. Alison S. P. Alkire B. M. M. Allbrooke B. W. Allen P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. A. Alshehri M. I. Alstaty B. Alvarez Gonzalez D. Álvarez Piqueras M. G. Alviggi B. T. Amadio Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders J. K. Anders K. J. Anderson A. Andreazza V. Andrei S. Angelidakis I. Angelozzi A. Angerami A. V. Anisenkov N. Anjos A. Annovi C. Antel M. Antonelli A. Antonov D. J. Antrim F. Anulli M. Aoki L. Aperio Bella G. Arabidze Y. Arai J. P. Araque V. Araujo Ferraz A. T. H. Arce R. E. Ardell F. A. Arduh J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster L. J. Armitage O. Arnaez H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Artz S. Asai N. Asbah A. Ashkenazi L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay K. Augsten G. Avolio B. Axen M. K. Ayoub G. Azuelos A. E. Baas M. J. Baca H. Bachacou K. Bachas M. Backes P. Bagnaia M. Bahmani H. Bahrasemani J. T. Baines M. Bajic O. K. Baker E. M. Baldin P. Balek F. Balli W. K. Balunas E. Banas A. Bandyopadhyay Sw. Banerjee A. A. E. Bannoura L. Barak E. L. Barberio D. Barberis M. Barbero T. Barillari M-S Barisits J. T. Barkeloo T. Barklow N. Barlow S. L. Barnes B. M. Barnett R. M. Barnett Z. Barnovska-Blenessy A. Baroncelli G. Barone A. J. Barr L. Barranco Navarro F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos A. Basalaev A. B Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Ontario Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene Oregon USA Department of Physics Stockholm University Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb Illinois USA Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas - LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston Massachusetts USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Italy Dipartimento di Fisica Un

Measurements of ZZ production in the ℓ+ℓ−ℓ′+ℓ′− channel in proton–proton collisions at 13 TeV center-of-mass energy at the Large Hadron Collider are presented. The data correspond to 36.1 fb−1 of collisions collected by the ATLAS experiment in 2015 and 2016. Here ℓ and ℓ′ stand for electrons or muons. Integrated and differential ZZ→ℓ+ℓ−ℓ′+ℓ′− cross sections with Z→ℓ+ℓ− candidate masses in the range of 66 GeV to 116 GeV are measured in a fiducial phase space corresponding to the detector acceptance and corrected for detector effects. The differential cross sections are presented in bins of twenty observables, including several that describe the jet activity. The integrated cross section is also extrapolated to a total phase space and to all standard model decays of Z bosons with mass between 66 GeV and 116 GeV, resulting in a value of 17.3±0.9[±0.6(stat)±0.5(syst)±0.6(lumi)] pb. The measurements are found to be in good agreement with the standard model. A search for neutral triple gauge couplings is performed using the transverse momentum distribution of the leading Z boson candidate. No evidence for such couplings is found and exclusion limits are set on their parameters.

关键词： Electroweak interaction W & Z bosons Particle data analysis

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Example-Specific Density Based Matching Kernels for Scene Classification Using Support Vector Machines

Example-Specific Density Based Matching Kernels for Scene Cl...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Abhijeet Sachdev Veena Thenkanidiyoor A.D. Dileep C. Chandra Sekhar School of Computing and Electrical Engineering Indian Institute of Technology Mandi Kamand Himachal Pradesh India Dept. of Comput. Sci. & Eng. Nat. Inst. of Technol. Goa Ponda India Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai Tamilnadu India

In this paper, we propose the example-specific density based matching kernel (ESDMK) for classification of scene images represented as sets of local feature vectors. The proposed kernel is computed between the pair of examples, represented as sets of local feature vectors, by matching the estimates of example-specific densities computed at every local feature vector in those two examples. In this work, the number of local feature vectors of an example among the K nearest neighbors of a local feature vector is considered as an estimate of the example-specific density. The minimum of the two example-specific densities, one for each example, at a local feature vector is considered as the matching score. The ESDMK is then computed as the sum of the matching score computed at every local feature vector in a pair of examples. We also propose the spatial ESDMK (SESDMK) to include spatial information present in the scene images while matching the pair of scene images. Each of the scene images is divided spatially into a fixed number of regions. Then the SESDMK is computed as a combination of region specific ESDMKs that match the corresponding regions. We study the performance of the support vector machine (SVM) based classifiers using the proposed ESDMKs for scene classification and compare with that of the SVM-based classifiers using the state-of-the-art kernels for sets of local feature vectors.

关键词： Kernel Support vector machines Visualization Histograms Feature extraction Semantics Maximum likelihood estimation

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Legal and social aspects of cyber crime in Greece

Communications in Computer and Information Science

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Communications in computer and Information science 2014年 441卷 154-164页

作者： Papathanasiou, Anastasios Papanikolaou, Alexandros Vlachos, Vasileios Chaikalis, Konstantinos Dimou, Maria Karadimou, Magdalini Katsoula, Vaia Cyber Crime Prosecution Subdivision Financial Police and Cyber Crime Unit Hellenic Police Athens Greece Department of Computer Science and Engineering Technological Educational Institute of Thessaly Larissa Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece Data Protection Authority Athens Greece

ISBN: (纸本)9783319117096

The increased use of personal computers and other personal devices with the ability to connect to the Internet, as well as the availability of public WiFi hotspots, enabled people to make extensive use of the offered Internet services, thus rendering them a tempting target for cyber criminals. Furthermore, cyber crime has a transnational character and can simultaneously affect individuals at different geographical locations, thus requiring international cooperation in order to be fought effectively. This paper presents the various international efforts against cyber crime that involve Greece, along with the relevant legal framework and some planned developments for the future. © Springer International Publishing Switzerland 2014.

关键词： Cybersecurity

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Erratum: Bright nanoscale source of deterministic entangled photon pairs violating Bell's inequality

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Scientific reports 2017年第1期7卷 7751页

作者： Klaus D Jöns Lucas Schweickert Marijn A M Versteegh Dan Dalacu Philip J Poole Angelo Gulinatti Andrea Giudice Val Zwiller Michael E Reimer Applied Physics Department Royal Institute of Technology Albanova University Centre Roslagstullsbacken 21 106 91 Stockholm Sweden. klausj@kth.se. Kavli Institute of Nanoscience Delft University of Technology Lorentzweg 1 2628CJ Delft The Netherlands. klausj@kth.se. Applied Physics Department Royal Institute of Technology Albanova University Centre Roslagstullsbacken 21 106 91 Stockholm Sweden. Kavli Institute of Nanoscience Delft University of Technology Lorentzweg 1 2628CJ Delft The Netherlands. Quantum optics Quantum Nanophysics and Quantum Information Faculty of Physics University of Vienna Boltzmanngasse 5 1090 Vienna Austria. Institute for Quantum Optics and Quantum Information Austrian Academy of Science Boltzmanngasse 3 1090 Vienna Austria. National Research Council of Canada Ottawa K1A 0R6 Canada. Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria piazza Leonardo da Vinci 32 20133 Milano Italy. Micro Photon Devices via Stradivari 4 39100 Bolzano Italy. Institute for Quantum Computing and Department of Electrical & Computer Engineering University of Waterloo Waterloo N2L 3G1 Canada.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

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