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IEEE International Conference on Cloud computing Technology and science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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bandicoot: a python toolbox for mobile phone metadata

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Yves-Alexandre De Montjoye Luc Rocher Alex Sandy Pentland Google MPI for Intelligent Systems MIT Media Lab. Cambridge MA and Imperial College London Dept. of Computing and Data Science Institute London UK Université catholique de Louvain ICTEAM Louvain-la-Neuve Belgium MIT Media Lab. Cambridge Cambridge MA

bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license.

关键词： CDR feature engineering mobile phone metadata python visualization

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Coal mine safety evaluation method based on incomplete labeled data stream classification

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Open Cybernetics and Systemics Journal 2014年第1期8卷 918-923页

作者： Gang, Sun Huaping, Zhou Kelei, Sun Data Mining and Intelligent Computing Laboratory Hefei University of Technology Hefei China School of Computer and Information Fuyang Teachers College Fuyang China School of Computer Science and Engineering Anhui University of Science and Technology Huainan China

Monitoring data in coal mine is essentially data stream, and missing coal mine monitoring data is caused by harsh coal mine environment, therefore coal mine safety evaluation can be seen as incomplete labeled data stream classification. The method is proposed for unlabeled data and concept drift in incomplete labeled data stream in this paper that uses semi-supervised learning method based on k-Modes algorithm and incremental decision tree model and concept drift detection mechanism based on clustering concept-cluster. Experimental results show the method can better label unlabeled data and detect concept drift in incomplete labeled data stream, and it has better classification accuracy for incomplete labeled data stream, and it provides a new practical approach for coal mine safety evaluation. © Gang et al.

关键词： Coal mines

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Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians

Sequential pattern mining of electronic healthcare reimburse...

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IEEE International Conference on Big data

作者： Kunal Malhotra Tanner C. Hobson Silvia Valkova Laura L. Pullum Arvind Ramanathan Computational Science and Engineering Oak Ridge National Laboratory Tennessee USA College of Computing Georgia Institute of Technology Georgia USA IMS Government Solutions Pennsylvania USA Health Data Sciences Institute Oak Ridge National Laboratory Tennessee USA

We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). We show that EHRCs are correlated with disease incidence estimates published by the Centers for Disease Control. Further, by analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with autism spectrum disorder (ASD), heart disease (HD) and breast cancer (BC) using sequential pattern mining algorithms. Our analyses reveal that in contrast to treating HD and BC, clinical procedures for ASD diagnoses are highly varied leading up to and after the ASD diagnoses. The discovered clinical procedure sequences also reveal significant differences in the overall costs incurred across different parts of the US, indicating a lack of consensus amongst practitioners in treating ASD patients. We show that a data-driven approach to understand clinical trajectories using EHRC can provide quantitative insights into how to better manage and treat patients. Based on our experience, we also discuss emerging challenges in using EHRC datasets for gaining insights into the state of contemporary healthcare delivery and practice in the US.

关键词： data mining Trajectory Diseases High definition video Electronic mail

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Unresolved Gamma-Ray Sky through its Angular Power Spectrum

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Physical Review Letters 2018年第24期121卷 241101-241101页

作者： M. Ackermann M. Ajello L. Baldini J. Ballet G. Barbiellini D. Bastieri R. Bellazzini E. Bissaldi R. D. Blandford R. Bonino E. Bottacini J. Bregeon P. Bruel R. Buehler E. Burns S. Buson R. A. Cameron R. Caputo P. A. Caraveo E. Cavazzuti S. Chen G. Chiaro S. Ciprini D. Costantin A. Cuoco S. Cutini F. D’Ammando P. de la Torre Luque F. de Palma A. Desai S. W. Digel N. Di Lalla M. Di Mauro L. Di Venere F. Fana Dirirsa C. Favuzzi A. Franckowiak Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Giglietto F. Giordano M. Giroletti D. Green I. A. Grenier L. Guillemot S. Guiriec D. Horan G. Jóhannesson M. Kuss S. Larsson L. Latronico J. Li I. Liodakis F. Longo F. Loparco P. Lubrano J. D. Magill S. Maldera D. Malyshev A. Manfreda M. N. Mazziotta I. Mereu P. F. Michelson W. Mitthumsiri T. Mizuno M. E. Monzani A. Morselli I. V. Moskalenko M. Negro E. Nuss M. Orienti E. Orlando M. Palatiello V. S. Paliya D. Paneque M. Persic M. Pesce-Rollins V. Petrosian F. Piron T. A. Porter G. Principe S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer D. Serini C. Sgrò E. J. Siskind G. Spandre P. Spinelli D. J. Suson H. Tajima M. Takahashi J. B. Thayer L. Tibaldo D. F. Torres E. Troja T. M. Venters G. Vianello K. Wood M. Yassine G. Zaharijas S. Ammazzalorso N. Fornengo M. Regis Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson South Carolina 29634-0978 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay F-91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia “G. Galilei” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 I-35122 Padova Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 F-34095 Montpellier France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 F-91128 Palaiseau France NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Science and Technology (CRESST) and NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 I-20133 Milano Italy Italian Space Agency Via del Politecnico snc 00133 Roma Ita

The gamma-ray sky has been observed with unprecedented accuracy in the last decade by the Fermi -large area telescope (LAT), allowing us to resolve and understand the high-energy Universe. The nature of the remaining unresolved emission [unresolved gamma-ray background (UGRB)] below the LAT source detection threshold can be uncovered by characterizing the amplitude and angular scale of the UGRB fluctuation field. This Letter presents a measurement of the UGRB autocorrelation angular power spectrum based on eight years of Fermi-LAT Pass 8 data products. The analysis is designed to be robust against contamination from resolved sources and noise systematics. The sensitivity to subthreshold sources is greatly enhanced with respect to previous measurements. We find evidence (with ∼3.7σ significance) that the scenario in which two classes of sources contribute to the UGRB signal is favored over a single class. A double power law with exponential cutoff can explain the anisotropy energy spectrum well, with photon indices of the two populations being 2.55±0.23 and 1.86±0.15.

关键词： Gamma ray astronomy

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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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Time difference calculation based on signal starting point detection

Time difference calculation based on signal starting point d...

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International Conference on Modelling, Identification and Control (ICMIC)

作者： Wan-zhen Zhou Yu Ling Yong-qiang Zhang Wei-dong Wu School of Information Science & Engineering Hebei University of Science and Technology China Doctor's degree from China people's Liberation Army Ordnance Engineering college in 2006 Research interests include the search on Artificial intelligence data mining and IOT Research interests include IOT and cloud computing Research areas include the research on IOT and distributed computing Research interests include the research on IOT and the embedded system

The research of the high-precision sound source localization system which is based on time difference of arrival (TDOA) of different sensors positioning system has increasingly become popular research. The outcome of the traditional generalized cross-correlation function (GCC) time delay estimation algorithm which is under the condition of the intense noise, has an apparent error, therefore, we can't guarantee the accuracy of the positioning system. In this paper, we put forward a spectral entropy-double threshold endpoint detection algorithm, through obtaining the exactly starting time of source sound to get a time difference, then improving it. Experiments show that the algorithm has the characteristic of simple process, small amount of computation and accuracy. It is much suitable for DSP and small system time difference estimation module.

关键词： Entropy Detection algorithms Conferences Signal processing algorithms Information science Electronic mail Microphones

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Appearance-order-based schema matching

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Journal of computing science and Engineering 2014年第2期8卷 94-106页

作者： Ding, Guohui Cao, Keyan Wang, Guoren Han, Dong Department of Computer Science Shenyang Aerospace University Shenyang China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China National Marine Data and Information Service Tianjin China

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance. © 2014. The Korean Institute of Information Scientists and Engineers.

关键词： data integration

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Search for gamma-ray emission from local primordial black holes with the Fermi large area telescope

arXiv

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arXiv 2018年

作者： Ackermann, M. Atwood, W.B. Baldini, L. Ballet, J. Barbiellini, G. Bastieri, D. Bellazzini, R. Berenji, B. Bissaldi, E. Blandford, R.D. Bloom, E.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Charles, E. Chekhtman, A. Cheung, C.C. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Conrad, J. Costantin, D. D’Ammando, F. de Palma, F. Digel, S.W. Di Lalla, N. Di Mauro, M. Di Venere, L. Favuzzi, C. Fegan, S.J. Focke, W.B. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guillemot, L. Guiriec, S. Horan, D. Jóhannesson, G. Johnson, C. Kensei, S. Kocevski, D. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Magill, J.D. Maldera, S. Malyshev, D. Manfreda, A. Mazziotta, M.N. McEnery, J.E. Meyer, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ojha, R. Omodei, N. Orienti, M. Orlando, E. Ormes, J.F. Palatiello, M. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Piron, F. Principe, G. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ritz, S. Sánchez-Conde, M. Sgrò, C. Siskind, E.J. Spada, F. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thayer, J.G. Thayer, J.B. Torres, D.F. Tosti, G. Troja, E. Valverde, J. Vianello, G. Wood, K. Wood, M. Zaharijas, G. Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Santa Cruz Institute for Particle Physics Department of Physics Department of Astronomy and Astrophysics University of California at Santa Cruz Santa CruzCA95064 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Laboratoire AIM CEA-IRFU CNRS Université Paris Diderot Service d’Astrophysique CEA Saclay Gif sur YvetteF-91191 France Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy California State University Los Angeles Department of Physics and Astronomy Los AngelesCA90032 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France NASA Goddard Space Flight Center GreenbeltMD20771 United States INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy Italian Space Agency Via del Politecnico snc Roma00133 Italy College of Science George Mason University Fairfax Naval Researc

Black holes with masses below approximately 1015 g are expected to emit gamma rays with energies above a few tens of MeV, which can be detected by the Fermi Large Area Telescope (LAT). Although black holes with these masses cannot be formed as a result of stellar evolution, they may have formed in the early Universe and are therefore called Primordial Black Holes (PBHs). Previous searches for PBHs have focused on either short timescale bursts or the contribution of PBHs to the isotropic gamma-ray emission. We show that, in case of individual PBHs, the Fermi LAT is most sensitive to PBHs with temperatures above approximately 16 GeV and masses 6×1011 g, which it can detect out to a distance of about 0.03 pc. These PBHs have a remaining lifetime of months to years at the start of the Fermi mission. They would appear as potentially moving point sources with gamma-ray emission that becomes spectrally harder and brighter with time until the PBH completely evaporates. In this paper, we develop a new algorithm to detect the proper motion of a gamma-ray point sources, and apply it to 318 unassociated point sources at high galactic latitude in the third Fermi-LAT source catalog (3FGL). None of unassociated point sources with spectra consistent with PBH evaporation show significant proper motion. Using the non-detection of PBH candidates, we derive a 99% confidence limit on PBH evaporation rate in the vicinity of the Earth ρPBH 3 pc−3yr−1. This limit is similar to the limits obtained with ground-based gamma-ray observatories. Copyright © 2018, The Authors. All rights reserved.

关键词： Gamma rays

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