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19th Annual Conference of the Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2016

作者： Bertero, Dario Siddique, Farhad Bin Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781509035168

We present and discuss an ongoing data collection and annotation effort to build a large corpus on speech emotion detection. We collected 207 hours of public speech data from TED talks. We highlight the expected relation between the API output emotion distribution and the common features of a TED talk. We then employed manual annotators to improve the quality of the annotations, building a two-level annotation process where to a main emotion label we add multiple secondary emotion descriptors. Additional annotations were also obtained through crowdsourcing, and we provide an analysis of the agreement between multiple annotators. We conducted a comparison between the automatic and manual labeling, obtaining an average accuracy of 28.4% of the automatic multiclass annotation API, as well as automatic classification with a CNN, reaching more than 60% average accuracy in various experimental settings. We also discuss various active learning methods we are using to select the samples to be annotated in order to obtain more relevant data at a faster pace. A large speech emotion detection corpus will enable more accurate emotion detection systems, which can then be integrated into dialog systems to recognize and react to the user emotion. © 2016 IEEE.

关键词： Speech recognition

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Sentential paraphrasing as black-box machine translation

Sentential paraphrasing as black-box machine translation

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2016 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2016

作者： Napoles, Courtney Callison-Burch, Chris Post, Matt Center for Language and Speech Processing Johns Hopkins University United States Computer and Information Science Department University of Pennsylvania United States Human Language Technology Center of Excellence Johns Hopkins University United States

We present a simple, prepackaged solution to generating paraphrases of English sentences. We use the Paraphrase Database (PPDB) for monolingual sentence rewriting and provide machine translation language packs: Prepackaged, tuned models that can be downloaded and used to generate paraphrases on a standard Unix environment. The language packs can be treated as a black box or customized to specific tasks. In this demonstration, we will explain how to use the included interactive webbased tool to generate sentential paraphrases. © NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, Proceedings of the Demonstrations Session. All rights reserved.

关键词： Machine translation

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Local training for PLDA in speaker verification 19

Local training for PLDA in speaker verification

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19th Annual Conference of the Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2016

作者： Zhao, Chenghui Li, Lantian Wang, Dong Pu, April Pachira Beijing China Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology China Center for Speech and Language Technologies Research Institute of Information Technology China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781509035168

PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labeled development data, which is highly expensive in most cases. A possible approach to mitigate the problem is various unsupervised adaptation methods, which use unlabeled data to adapt the PLDA scattering matrices to the target domain. In this paper, we present a new 'local training' approach that utilizes inaccurate but much cheaper local labels to train the PLDA model. These local labels discriminate speakers within a single conversion only, and so are much easier to obtain compared to the normal 'global labels'. Our experiments show that the proposed approach can deliver significant performance improvement, particularly with limited globally-labeled data. © 2016 IEEE.

关键词： Speech recognition

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Pushing for weighted tree automata

arXiv

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arXiv 2017年

作者： Hanneforth, Thomas Maletti, Andreas Quernheim, Daniel Universität Potsdam Human Sciences Faculty Department Linguistik Karl-Liebknecht-Str. 24–25 Potsdam14476 Germany Universität Leipzig Faculty of Mathematics and Computer Science Institute of Computer Science PO box 100 920 Leipzig04009 Germany Universität Stuttgart Institute for Natural Language Processing Pfaffenwaldring 5b Stuttgart70569 Germany

A weight normalization procedure, commonly called pushing, is introduced for weighted tree automata (wta) over commutative semifields. The normalization preserves the recognized weighted tree language even for nondeterministic wta, but it is most useful for bottom-up deterministic wta, where it can be used for minimization and equivalence testing. In both applications a careful selection of the weights to be redistributed followed by normalization allows a reduction of the general problem to the corresponding problem for bottom-up deterministic unweighted tree automata. This approach was already successfully used by Mohri and Eisner for the minimization of deterministic weighted string automata. Moreover, the new equivalence test for two wta M and M′ runs in time O((|M| + |M′|) log (|Q| + |Q′|)), where Q and Q′ are the states of M and M′, respectively, which improves the previously best run-time O(|M| · |M′|).MSC Codes 68Q45, 68Q25 © 2017, CC BY.

关键词： Automata theory

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Demographer: Extremely Simple Name Demographics 1

Demographer: Extremely Simple Name Demographics

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EMNLP 2016 1st Workshop on Natural language Processing and Computational Social science, NLP + CSS 2016

作者： Knowles, Rebecca Carroll, Josh Dredze, Mark Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Qntfy CrownsvilleMD21032 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781945626265

The lack of demographic information available when conducting passive analysis of social media content can make it difficult to compare results to traditional survey results. We present DEMOGRAPHER,1 a tool that predicts gender from names, using name lists and a classifier with simple character-level features. By relying only on a name, our tool can make predictions even without extensive user-authored content. We compare DEMOGRAPHER to other available tools and discuss differences in performance. In particular, we show that DEMOGRAPHER performs well on Twitter data, making it useful for simple and rapid social media demographic inference. ©2016 Association for Computational Linguistics.

关键词： Surveys

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An integration between information systems engineering and software engineering theories towards engineering a novel framework of web-based systems success for institutions based on students’ perceptions

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Journal of Physics: Conference Series 2018年第1期1019卷 012081-012081页

作者： Ibrahim Eskandar Ibrahim Fadhel Syed Zulkarnain Syed Idrus Amani Ali Elmetwaly Ali Ibrahim Mazni Omar Ahmed Ali Ahmed Bahashwan Ali Ali Musaed Al-Ansi Department of Computer and Information Systems in National Institute of Administrative Science (NIAS) Aden Yemen. School of Human Development and Techno-communication University Malaysia Perlis Perlis Malaysia. School of Human Development and Techno-communication University Malaysia Perlis Perlis Malaysia. Green Advanced Computing and Technology (GREAT) Research Group - CEGeoGTech University Malaysia Perlis Perlis Malaysia. School of Human Development and Techno-communication University Malaysia Perlis Perlis Malaysia. College of Science and Arts School of Computing University Utara Malaysia Kedah Malaysia. School of Computer & Communication University Malaysia Perlis Perlis Malaysia. College of Business Administration Shaqra University Kingdom of Saudi Arabia.

Large numbers of dissatisfying systems users, organizations and higher rate of systems failure is still a problem. In targeting this failure, measure of systems success categorized as a solution to increase the rate of success in future systems initiatives. Success measure of systems is an important part of software’s engineering and information systems engineering to improve systems performance. However, studies showed a lack of research done on systems success evaluation in the universities of world, Arab region and Yemeni as special. Systems especially the web-based faces high rate of failure in Arab region, it was observed that many funded system projects failed, it was also, noted that in Yemen systems were completed and implemented without success measuring. Data analysis and interpretation for the web based information systems (WIS) success framework related to higher education can be taken as a latest phenomenon for the incorporation of theories of TAM, DM 2003 and software engineering ISO 25010. Moreover, this is also responsive and effective for assessing computer anxiety and management support aspects to explain satisfaction, loyalty and net impact. The present study is enthusiastic to outline the need for highlighting student perspective where exists dearth of research in this regard and dire need for engineering framework that works as a tool for organizations to success measure of their systems. Though, prior scholarly work has outlined significant role and importance of designing quality university WIS framework from the aspect of WIS developers not from the side of users. Henceforth, the present study aims to strive for highlighting the perception of users (students) regarding to universities WIS.

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Homophily and minority size explain perception biases in social networks

arXiv

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arXiv 2017年

作者： Lee, Eun Karimi, Fariba Wagner, Claudia Jo, Hang-Hyun Strohmaier, Markus Galesic, Mirta Department of Mathematics University of North Carolina Chapel Hill27599 United States Department of Computational Social Science GESIS Cologne50667 Germany Institute for Web Science and Technologies University of Koblenz-Landau Koblenz Germany Asia Pacific Center for Theoretical Physics Pohang37673 Korea Republic of Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Computer Science Aalto University EspooFI-00076 Finland HumTec Institute RWTH Aachen University Germany Santa Fe Institute Santa FeNM United States Harding Center for Risk Literacy Max Planck Institute for Human Development Berlin Germany

People’s perceptions about the frequency of attributes in their social networks sometimes show false consensus, or overestimation of the frequency of own attributes, and sometimes false uniqueness, or underestimation of the frequency. Here we show that both perception biases can emerge solely from the structural properties of social networks. Using a generative network model, we show analytically that perception biases depend on the level of homophily and its asymmetric nature, as well as on the size of minority group. Model predictions correspond to empirical data from a cross-cultural survey study and to numerical calculations on six real-world networks. We also show in what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This study advances our understanding of the impact of network structure on perception biases and offers a quantitative approach for addressing related issues in society. Copyright © 2017, The Authors. All rights reserved.

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Global brain dynamics during social exclusion predict subsequent behavioral conformity

arXiv

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arXiv 2017年

作者： Wasylyshyn, Nick Hemenway, Brett Garcia, Javier O. Cascio, Christopher N. O'Donnell, Matthew Brook Raymond Bingham, C. Simons-Morton, Bruce Vettel, Jean M. Falk, Emily B. Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen Proving GroundMD21005 Annenberg School for Communication University of Pennsylvania PhiladelphiaPA19104 Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 Department of Bioengineering University of Pennsylvania PhiladelphiaPA19104 University of Michigan Transportation Research Institute Ann ArborMI48109 Eunice Kennedy Shriver National Institute on Child Health and Human Development BethesdaMD20892 Department of Psychological and Brain Sciences University of California Santa Barbara Santa BarbaraCA93106 Marketing Department Wharton School University of Pennsylvania University of Pennsylvania PhiladelphiaPA19104 Department of Psychology University of Pennsylvania PhiladelphiaPA19104

Individuals react differently to social experiences;for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (anterior cingulate cortex, anterior insula). Using cross-validated machine learning, this measure of global network connectivity during exclusion predicts the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW) Vila Real, Portugal. 01-03 December 2016 Abstracts

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BMC SPORTS science MEDICINE AND REHABILITATION 2017年第SUPPL 1期9卷 5-5页

作者： [Anonymous] GAIA Association of Electronic and Information Technologies in the Basque Country Bilbao Spain Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal INESC TEC and Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Vienna University of Technology Vienna Austria motion3D GmbH Vienna Austria Department of Psychology Masaryk University Faculty of Arts Brno Czech Republic Universidade Nova de Lisboa NOVA IMS Lisboa Portugal NOVA IMS Lisboa Portugal Department of Sport Sciences and CIDESD University of Beira Interior Covilhã Portugal Department of Sport Sciences University of Beira Interior and Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal Department of Sport Sciences University of Beira Interior Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal INESC TEC and University of Trás-os-Montes and Alto Douro Vila Real Portugal Digital Sports Group Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Institute for Factory Automation and Production Systems Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Fisio Lógic Centro de Fisioterapia Lisboa Portugal Faculdade de Motricidade Humana Universidade de Lisboa Laboratório de Comportamento Motor Estrada da Costa 1499-002 Cruz Quebrada Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) and Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência (INESC TEC) Vila Real Portugal INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal

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SHREC'16 track: Shape retrieval of low-cost RGB-D captures 9

SHREC'16 track: Shape retrieval of low-cost RGB-D captures

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

作者： Pascoal, Pedro B. Proença, Pedro Gaspar, Filipe Dias, Miguel Sales Ferreira, Alfredo Tatsuma, Atsushi Aono, Masaki Logoglu, K. Berker Kalkan, Sinan Temizel, Alptekin Li, Bo Johan, Henry Lu, Yijuan Seib, Viktor Link, Norman Paulus, Dietrich ISCTE - Instituto Universitário de Lisboa ISTAR-IUL Lisbon Portugal Microsoft Language and Development Center Lisbon Portugal INESC-ID Técnico Lisboa Universidade de Lisboa Portugal Surrey Space Centre University of Surrey United Kingdom Department of Computer Science and Engineering Toyohashi University of Technology Japan Informatics Institute Middle East Technical University Ankara Turkey Department of Computer Engineering Middle East Technical University Ankara Turkey Department of Mathematics and Computer Science University of Central Missouri Warrensburg United States Fraunhofer IDM NTU Singapore Singapore Department of Computer Science Texas State University San Marcos United States University of Koblenz-Landau Universitätsstr. 1 Koblenz56070 Germany

ISBN: (纸本)9783038680048

RGB-D cameras allow to capture digital representations of objects in an easy and inexpensive way. Such technology enables ordinary users to capture everyday object into digital 3D representations. In this context, we present a track for the Shape Retrieval Contest, which focus on objects digitized using the latest version of Microsoft Kinect, namely, Kinect One. The proposed, track encompasses a dataset of two hundred objects and respective classification. © 2016 The Eurographics Association.

关键词： Classification (of information)

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