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11th IEEE International Conference on cognitive Informatics and cognitive Computing, ICCI*CC 2012

作者： Wang, Yingxu Berwick, Robert C. Dept. of Electrical and Computer Engineering Schulich School of Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Dept. of Brain and Cognitive Science Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States

ISBN: (纸本)9781467327930

cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words. © 2012 IEEE.

关键词： Semantics

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A formal measurement of the cognitive complexity of texts in cognitive linguistics

A formal measurement of the cognitive complexity of texts in...

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11th IEEE International Conference on cognitive Informatics and cognitive Computing, ICCI*CC 2012

作者： Wang, Yingxu Berwick, Robert C. Luo, Xiangfeng Lei, Jingsheng Dept. of Electrical and Computer Engineering Schulich School of Engineering University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada Dept. of Brain and Cognitive Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 United States Dept. of Computer Science Shanghai University Shanghai China School of Computer and Information Engineering Shanghai University of Electrical Power Shanghai China

ISBN: (纸本)9781467327930

The cognitive complexity of texts in natural languages is a fundamental measure of the properties of syntax and semantics in textual comprehension, processing, and search. This paper presents a formal metrics of text comprehension complexity in cognitive linguistics. Both objective and subjective aspects of text comprehension and their complexity are formally modeled. A formal language model is established that characterizes the discourse of natural languages. The mathematical models of cognitive complexity of texts and their comprehension are rigorously described. On the basis of the cognitive and mathematical models of cognitive linguistics, the measurement of cognitive complexity of texts is quantitatively established and tested by a set of case studies. A wide range of applications of the measurement of textual complexity are identified in cognitive linguistics and contemporary web technologies such as search engines, online document retrieval, natural language processing, cognitive linguistics, cognitive computing, cognitive machine learning, and computing with words. © 2012 IEEE.

关键词： Semantics

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{\cal U}Boost: Boosting with the Universum

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2012年第4期34卷 825-832页

作者： Chunhua Shen Peng Wang Fumin Shen Hanzi Wang Australian Center for Visual Technologies and School of Computer Science University of Adelaide North Terrace SA Australia School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China Cognitive Science Department School of Information Science and Technology and Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen Fujian China School of Computer Science University of Adelaide SA Australia

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.

关键词： Boosting Training Educational institutions Optimization Algorithm design and analysis Histograms Accuracy

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Investigation of smoking related features in spatio-spectral domain on resting-state fMRI data using nonnegative matrix factorization

Investigation of smoking related features in spatio-spectral...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyu Ding Jong-Hwan Lee Seong-Whan Lee Department of Computer Science and Engineering Korea University Seoul South Korea Department of Brain and Cognitive Engineering Korea University Seoul South Korea

Smoking is a typical exemplar in drug addiction researches for the wide range of tobacco users. The presented study uses functional magnetic resonance imaging (fMRI) to explore the resting-state (RS) neural mechanisms associated with smoking deprivation. We propose a novel analysis approach which applies nonnegative matrix factorization (NMF) algorithm on spatially concatenated two group datasets to investigate smoking related RS features in spatio-spectral domain. The NMF algorithm decomposed the magnitude spectra of fMRI time series into distinct frequency-specific basis functions and corresponding nonnegative spatial maps. After a two sample T-test on the z-scored spatial maps between groups, representative feature regions such as insula, anterior cingulate cortex and precuneus were found to be associated with smoking. These regions were consistent with that revealed by previous literatures studying in spatio-temporal domain. It indicates that our proposed analysis method provides another option for exploring neural mechanism differences between two groups, which might be used in a range of applications.

关键词： Matrix decomposition Algorithm design and analysis Magnetic resonance imaging Transmission line matrix methods Drugs Time series analysis Band pass filters

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Is the clustering coefficient a measure for fault tolerance in wireless sensor networks?

Is the clustering coefficient a measure for fault tolerance ...

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IEEE International Conference on Communications (ICC)

作者： Matthias R. Brust Damla Turgut Carlos H. C. Ribeiro Marcus Kaiser Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA Computer Science Division Technological Institute of Aeronautics Brazil School of Computing Science University of Newcastle UK Department of Brain and Cognitive Sciences Seoul National University South Korea

Distributed systems such as the Internet and wireless sensor networks must provide a high degree of resilience against errors and attacks. Besides steps that increase reliability of data and resources of the network, the topology structure itself plays a crucial role in the efficacy of the fault-tolerance behavior. The network topology is a supportive factor to reduce or avoid malfunction behavior of the system after a strike on a strategic node or a random failure of a node. For a self-organizing topology with numerous nodes, it is necessary to have a local fault tolerance measure instead of collecting information of the entire network to adjust the topology locally when needed. The local clustering coefficient determines the degree of the connectedness of the node's neighbors. The correlation between the clustering coefficient and fault tolerance is an open research problem. In this paper, we propose the clustering coefficient as a local metric for fault tolerance, in particular for wireless sensor networks. We describe how to increase the clustering coefficient by (a) exclusively adding and (b) exclusively removing links to a wireless sensor network topology. Simulation results indicate that the clustering coefficient is correlated to the fault tolerance of the system.

关键词： Fault tolerance Fault tolerant systems Wireless sensor networks Network topology Topology Ad hoc networks Optimization

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A formal measurement of the cognitive complexity of texts in cognitive linguistics

A formal measurement of the cognitive complexity of texts in...

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IEEE International Conference on cognitive Informatics

作者： Yingxu Wang Robert C. Berwick Xiangfeng Luo Jingsheng Lei Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Dept. of Brain and Cognitive Science & Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Dept. of Computer Science Shanghai University Shanghai China School of Computer and Information Engineering Shanghai University of Electrical Power Shanghai China

关键词： Complexity theory Semantics Syntactics Pragmatics Mathematical model Natural languages Computational modeling

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On formal models for cognitive linguistics

On formal models for cognitive linguistics

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IEEE International Conference on cognitive Informatics

作者： Yingxu Wang Robert C. Berwick International Institute of Cognitive Informatics and Cognitive Computing (ICIC) University of Calgary Calgary Alberta Canada T2N 1N4 Dept. of Brain and Cognitive Science & Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA

关键词： Grammar Semantics Pragmatics Computational modeling Natural languages Speech Mathematical model

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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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Tricks for time series

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7700 LECTURE NO卷 339-341页

作者： Müller, Klaus-Robert Department of Computer Science Technische Universität Berlin Franklinstr. 28/29 10587 Berlin Germany Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-713 Korea Republic of

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Introduction

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7700 LECTURE NO卷 1-5页

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