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9th IET data Fusion and Target Tracking Conference: Algorithms and Applications, DF and TT 2012

作者： Schikora, M. Gning, A. Mihaylova, L. Cremers, D. Koch, W. Streit, R. Fraunhofer FKIE Dept. Sensor Data and Information Fusion Wachtberg Germany University of Lancaster School of Computing and Communications United Kingdom University College London Department of Computer Science United Kingdom Technical University of Munich Department of Computer Science Germany Metron Inc. Reston VA United States

ISBN: (纸本)9781849196246

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

关键词： Distributed computer systems

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Efficient approximation of the maximal preference scores by lightweight cubic views

Efficient approximation of the maximal preference scores by ...

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15th International Conference on Extending database Technology, EDBT 2012

作者： Chen, Yueguo Cui, Bin Du, Xiaoyong Tung, Anthony K. H. Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China Department of Computer Science Peking University Beijing China School of Information Renmin University of China Beijing China School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781450307901

Given a multi-features data set, a best preference query (BPQ) computes the maximal preference score (MPS) that the tuples in the data set can achieve with respect to a preference function. BPQs are very useful in applications where users want to efficiently check whether many individual data sets contain tuples that are of interest to them. Although a BPQ can be naïvely answered by issuing a top-1 query and computing the score from the returned tuple, doing so might require to load a larger number of tuples externally. In this paper, we address the problem of efficient processing BPQs by using lightweight cubic (3-dimensional) views. With these in-memory views, the MPSs of BPQs can be efficiently estimated with an error bound guaranteed, by paying only a small number of I/Os. Extensive experimental results over real-life data sets show that our approximate solution can achieve the efficiency of up to three orders of magnitude compared to exact solutions, with certain accuracy guaranteed. © 2012 ACM.

关键词： best preference score materialized views preference query top-k query

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In what ways does policy on academic integrity, copyright and privacy need to respond in order to accommodate assessment with Web 2.0 tools?

In what ways does policy on academic integrity, copyright an...

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23rd Australasian Conference on information Systems, ACIS 2012

作者： Richardson, Joan Hamilton, Margaret Gray, Kathleen Waycott, Jenny Thompson, Celia School of Business Information Systems RMIT University Melbourne Australia School of Computer Science RMIT University Melbourne Australia Health and Biomedical Informatics Research Unit Melbourne School of Medicine The University of Melbourne Australia Department of Computing and Information Systems The University of Melbourne Australia School of Languages and Linguistics The University of Melbourne Australia

ISBN: (纸本)9781741561722

Increasingly social web technologies, such as blogging and micro-blogging, audio and video podcasting, photo/video, social bookmarking, social networking, wiki writing or virtual worlds are being used as forms of authoring or content creation to support students' learning in higher education. As Web 2.0 teaching practice is characterised by open access to information and collaborative networks there are both familiar and novel challenges for policy-makers in higher education institutions. The Government 2.0 Taskforce heralded legislative and practice changes necessary because of Web 2.0. We reflect on the qualitative feedback received from innovative higher education practitioners using Web 2.0 to assess student work. This indicates a need for information policy review to accommodate the cultural shift towards information exchange and communication across traditional institutional boundaries. Issues involved when implementing Web 2.0 assessments are identified to highlight requisite areas for policy improvement in higher education, in particular for academic integrity, copyright and privacy policies. Joan Richardson, Margaret Hamilton, Kathleen Gray, Jenny Waycott, Celia Thompson © 2012.

关键词： Copyrights

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Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

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1st International Conference on Health information science, HIS 2012

作者： Xie, Juanying Lei, Jinhu Xie, Weixin Gao, Xinbo Shi, Yong Liu, Xiaohui School of Computer Science Shaanxi Normal University Xi'an 710062 China School of Electronic Engineering Xidian University Xi'an 710071 China School of Information Engineering Shenzhen University Shenzhen 518060 China CAS Research Centre of Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100080 China School of Information Systems Computing and Mathematics Brunel University London UB8 3PH United Kingdom

ISBN: (纸本)9783642293603

This paper proposes hybrid feature selection algorithms to build the efficient diagnostic models based on a new accuracy criterion, generalized F-score (GF) and SVM. The hybrid algorithms adopt Sequential Forward Search (SFS), and Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, with SVM to accomplish hybrid feature selection with the new accuracy criterion to guide the procedure. We call them as modified GFSFS, GFSFFS and GFSBFS, respectively. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the efficient classifiers. To get the best and statistically meaningful classifiers, we not only conduct 10-fold cross validation experiments on training subset, but also on the whole erythemato-squamous diseases datasets. Experimental results show that our proposed hybrid methods construct efficient diagnosis classifiers with high average accuracy when compared with traditional algorithms. © 2012 Springer-Verlag.

关键词： Feature Selection

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Categorization of computing education resources with utilization of crowdsourcing 12

Categorization of computing education resources with utiliza...

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12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12

作者： Chen, Yinlin Bogen II, Paul Logasa Hsieh, Haowei Fox, Edward A. Cassel, Lillian N. Department of Computer Science Virginia Tech. Blacksburg VA United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge TN 37931 United States School of Library and Information Science University of Iowa Iowa City IA 52242 United States Department of Computing Sciences Villanova University Villanova PA 19085 United States

ISBN: (纸本)9781450311540

The Ensemble Portal harvests resources from multiple heterogeneous federated collections. Managing these dynamically increasing collections requires an automatic mechanism to categorize records in to corresponding topics. We propose an approach to use existing ACM DL metadata to build classifiers for harvested resources in the Ensemble project. We also present our experience with utilizing the Amazon Mechanical Turk platform to build ground truth training data sets from Ensemble collections. © 2012 ACM.

关键词： Digital libraries

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Box-particle PHD filter for multi-target tracking

Box-particle PHD filter for multi-target tracking

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International Conference on information Fusion

作者： Marek Schikora Amadou Gning Lyudmila Mihaylova Daniel Cremers Wolfgang Koch Department Sensor Data and Information Fusion Fraunhofer FKIE Wachtberg Germany Department of Computer Science University College London UK School of Computing and Communications University of Lancaster UK Department Computer Science Technical University of Munich Germany

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small particle number makes this approach attractive for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes methods from the field of interval analysis. The theoretical derivation of the box-PHD filter is presented followed by a comparative analysis with a standard sequential Monte Carlo (SMC) version of the PHD filter. To measure the performance objectively three measures are used: inclusion, volume and the optimum subpattern assignment metric. Our studies suggest that the box-PHD filter reaches similar accuracy results, like a SMC-PHD filter but with much considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the box-PHD filter even outperforms the classical SMC-PHD filter.

关键词： Atmospheric measurements Particle measurements Time measurement Covariance matrix Target tracking Uncertainty Standards

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Practical privacy for value-added applications in vehicular ad hoc networks 5th

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5th International Conference on Internet and Distributed computing Systems, IDCS 2012

作者： Zhang, Lei Wu, Qianhong Qin, Bo Domingo-Ferrer, Josep Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China UNESCO Chair in Data Privacy Dept. of Comp. Eng. and Maths Universitat Rovira i Virgili TarragonaCatalonia Spain Key Lab. of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University School of Computer China Department of Maths School of Science Xi’an University of Technology China

ISBN: (纸本)9783642348822

Advances in mobile networking and information processing technologies have triggered vehicular ad hoc networks (VANETs) for traffic safety and value-added applications. Most efforts have been made to address the security concerns while little work has been done to investigate security and privacy for value-added applications in VANETs. To fill this gap, we propose a value-added application, specifically, a security and privacy preserving location-based service (LBS) scheme for VANETs. For each LBS transaction, the scheme provides authentication, integrity and non-repudiation for both the service provider and the user. A user can obtain the service in an anonymous way and hence user privacy is well protected. However, a tracing procedure can be invoked to find malicious users, thereby efficiently preventing users from abusing the anonymity provided by the system. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Location based services

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On the performance of the Box Particle Filter for extended object tracking using laser data

On the performance of the Box Particle Filter for extended o...

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Workshop on Sensor data Fusion: Trends, Solutions, Applications (SDF)

作者： Nikolay Petrov Martin Ulmke Lyudmila Mihaylova Amadou Gning Marek Schikora Monika Wieneke Wolfgang Koch School of Computing and Communications Lancaster University UK Department of Sensor Data and Information Fusion Fraunhofer FKIE Wachtberg Germany Department of Computer Science University College London UK

This paper considers the challenging task of realtime extended object tracking using cluttered measurements from laser range scanners. The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor. The problem is expressed as the joint estimation of both state and parameters of an extended target. Circularly and elliptically shaped targets are considered. Promising results are presented.

关键词： Atmospheric measurements Particle measurements Vectors Target tracking Shape Uncertainty Time measurement

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Joint Analysis of BICEP2/Keck Array and Planck data

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Physical Review Letters 2015年第10期114卷 101301-101301页

作者： P. A. R. Ade N. Aghanim Z. Ahmed R. W. Aikin K. D. Alexander M. Arnaud J. Aumont C. Baccigalupi A. J. Banday D. Barkats R. B. Barreiro J. G. Bartlett N. Bartolo E. Battaner K. Benabed A. Benoît A. Benoit-Lévy S. J. Benton J.-P. Bernard M. Bersanelli P. Bielewicz C. A. Bischoff J. J. Bock A. Bonaldi L. Bonavera J. R. Bond J. Borrill F. R. Bouchet F. Boulanger J. A. Brevik M. Bucher I. Buder E. Bullock C. Burigana R. C. Butler V. Buza E. Calabrese J.-F. Cardoso A. Catalano A. Challinor R.-R. Chary H. C. Chiang P. R. Christensen L. P. L. Colombo C. Combet J. Connors F. Couchot A. Coulais B. P. Crill A. Curto F. Cuttaia L. Danese R. D. Davies R. J. Davis P. de Bernardis A. de Rosa G. de Zotti J. Delabrouille J.-M. Delouis F.-X. Désert C. Dickinson J. M. Diego H. Dole S. Donzelli O. Doré M. Douspis C. D. Dowell L. Duband A. Ducout J. Dunkley X. Dupac C. Dvorkin G. Efstathiou F. Elsner T. A. Enßlin H. K. Eriksen E. Falgarone J. P. Filippini F. Finelli S. Fliescher O. Forni M. Frailis A. A. Fraisse E. Franceschi A. Frejsel S. Galeotta S. Galli K. Ganga T. Ghosh M. Giard E. Gjerløw S. R. Golwala J. González-Nuevo K. M. Górski S. Gratton A. Gregorio A. Gruppuso J. E. Gudmundsson M. Halpern F. K. Hansen D. Hanson D. L. Harrison M. Hasselfield G. Helou S. Henrot-Versillé D. Herranz S. R. Hildebrandt G. C. Hilton E. Hivon M. Hobson W. A. Holmes W. Hovest V. V. Hristov K. M. Huffenberger H. Hui G. Hurier K. D. Irwin A. H. Jaffe T. R. Jaffe J. Jewell W. C. Jones M. Juvela A. Karakci K. S. Karkare J. P. Kaufman B. G. Keating S. Kefeli E. Keihänen S. A. Kernasovskiy R. Keskitalo T. S. Kisner R. Kneissl J. Knoche L. Knox J. M. Kovac N. Krachmalnicoff M. Kunz C. L. Kuo H. Kurki-Suonio G. Lagache A. Lähteenmäki J.-M. Lamarre A. Lasenby M. Lattanzi C. R. Lawrence E. M. Leitch R. Leonardi F. Levrier A. Lewis M. Liguori P. B. Lilje M. Linden-Vørnle M. López-Caniego P. M. Lubin M. Lueker J. F. Macías-Pérez B. Maffei D. Maino N. Mandolesi A. Mangilli M. Maris P. G. Martin E. Martínez-González S. Masi P. Mason S. Matarrese K. G. Megerian School of Physics and Astronomy Cardiff University Queens Buildings The Parade Cardiff CF24 3AA United Kingdom Institut d’Astrophysique Spatiale CNRS (UMR8617) Université Paris-Sud 11 Bâtiment 121 Orsay France Department of Physics Stanford University Stanford California 94305 USA California Institute of Technology Pasadena California USA Harvard-Smithsonian Center for Astrophysics 60 Garden Street MS 42 Cambridge Massachusetts 02138 USA Laboratoire AIM IRFU/Service d’Astrophysique–CEA/DSM–CNRS–Université Paris Diderot Bâtiment 709 CEA-Saclay F-91191 Gif-sur-Yvette Cedex France SISSA Astrophysics Sector via Bonomea 265 34136 Trieste Italy Université de Toulouse UPS-OMP IRAP F-31028 Toulouse cedex 4 France CNRS IRAP 9 Avenue colonel Roche BP 44346 F-31028 Toulouse cedex 4 France Joint ALMA Observatory Vitacura Santiago Chile Instituto de Física de Cantabria (CSIC-Universidad de Cantabria) Avenida de los Castros s/n Santander Spain APC AstroParticule et Cosmologie Université Paris Diderot CNRS/IN2P3 CEA/lrfu Observatoire de Paris Sorbonne Paris Cité 10 rue Alice Domon et Léonie Duquet 75205 Paris Cedex 13 France Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California USA Dipartimento di Fisica e Astronomia G. Galilei Università degli Studi di Padova via Marzolo 8 35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova via Marzolo 8 I-35131 Padova Italy University of Granada Departamento de Física Teórica y del Cosmos Facultad de Ciencias Granada Spain University of Granada Instituto Carlos I de Física Teórica y Computacional Granada Spain Institut d’Astrophysique de Paris CNRS (UMR7095) 98 bis Boulevard Arago F-75014 Paris France UPMC Université de Paris 06 UMR7095 98 bis Boulevard Arago F-75014 Paris France Institut Néel CNRS Université Joseph Fourier Grenoble I 25 rue des Martyrs Grenoble France Department of Physics and Astronomy University College London L

We report the results of a joint analysis of data from BICEP2/Keck Array and Planck. BICEP2 and Keck Array have observed the same approximately 400 deg2 patch of sky centered on RA 0 h, Dec. −57.5°. The combined maps reach a depth of 57 nK deg in Stokes Q and U in a band centered at 150 GHz. Planck has observed the full sky in polarization at seven frequencies from 30 to 353 GHz, but much less deeply in any given region (1.2 μK deg in Q and U at 143 GHz). We detect 150×353 cross-correlation in B modes at high significance. We fit the single- and cross-frequency power spectra at frequencies ≥150 GHz to a lensed-ΛCDM model that includes dust and a possible contribution from inflationary gravitational waves (as parametrized by the tensor-to-scalar ratio r), using a prior on the frequency spectral behavior of polarized dust emission from previous Planck analysis of other regions of the sky. We find strong evidence for dust and no statistically significant evidence for tensor modes. We probe various model variations and extensions, including adding a synchrotron component in combination with lower frequency data, and find that these make little difference to the r constraint. Finally, we present an alternative analysis which is similar to a map-based cleaning of the dust contribution, and show that this gives similar constraints. The final result is expressed as a likelihood curve for r, and yields an upper limit r0.05<0.12 at 95% confidence. Marginalizing over dust and r, lensing B modes are detected at 7.0σ significance.

关键词： STOKES equations FREQUENCIES of oscillating systems POWER spectra GRAVITATIONAL waves

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Box-particle intensity filter

Box-particle intensity filter

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9th IET data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications

作者： M. Schikora A. Gning L. Mihaylova D. Cremers W. Koch R. Streit Department of Computer Science University College London UK Department Sensor Data and Information Fusion Fraunhofer FKIE Wachtberg Germany School of Computing and Communications University of Lancaster UK Department of Computer Science Technical University of Munich Germany Metron Systems Inc. Reston VA USA

ISBN: (纸本)9781622761951

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Further more, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

关键词： Multi-Target Tracking Box Particle Filters Poisson Point Processes Intensity Filter Interval Measurements Filters Boxes data association Unknown False alarms

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