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Multivariate Spearman's p for Aggregating Ranks Using Copulas

Journal of Machine Learning Research 2016年 17卷 1-30页

作者： Bedo, Justin Ong, Cheng Soon Walter and Eliza Hall Institute 1G Royal Parade ParkvilleVIC3052 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Data61 CSIRO 7 London Circuit CanberraACT2601 Australia Research School of Computer Science Australian National University Department of Electrical and Electronic Engineering VIC3010 Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's p, which measures correlation between a set of ranked lists. Multivariate Spearman's p is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's κ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008. © 2016 Justin Bed}o and Cheng Soon Ong.

关键词： Benchmarking

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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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Simulate Human Saccadic Scan-Paths in Target Searching

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International Journal of Intelligence science 2016年第1期6卷 1-9页

作者： Lijuan Duan Jun Miao David M. W. Powers Jili Gu Laiyun Qing Beijing Key Laboratory of Trusted Computing Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data College of Computer Science and Technology Beijing University of Technology Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Engineering & Maths Flinders University of South Australia Adelaide South Australia Beijing Samsung Telecom R&D Center Beijing China University of Chinese Academy of Sciences Beijing China

Human saccade is a dynamic process of information pursuit. There are many methods using either global context or local context cues to model human saccadic scan-paths. In contrast to them, this paper introduces a model for gaze movement control using both global and local cues. To test the performance of this model, an experiment is done to collect human eye movement data by using an SMI iVIEW X Hi-Speed eye tracker with a sampling rate of 1250 Hz. The experiment used a two-by-four mixed design with the location of the targets and the four initial positions. We compare the saccadic scan-paths generated by the proposed model against human eye movement data on a face benchmark dataset. Experimental results demonstrate that the simulated scan-paths by the proposed model are similar to human saccades in term of the fixation order, Hausdorff distance, and prediction accuracy for both static fixation locations and dynamic scan-paths.

关键词： Saccadic Scan-Paths Eye Movement Fixation Locations Dynamic Scan-Paths

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Modeling parameter interactions in ranking SVM 15

Modeling parameter interactions in ranking SVM

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhang, Yaogong Xu, Jun Lan, Yanyan Guo, Jiafeng Xie, Maoqiang Huang, Yalou Cheng, Xueqi College of Computer and Control Engineering Nankai University China CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China College of Software Nankai University China

ISBN: (纸本)9781450337946

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Σ(i,j)αij(xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = (υi,υj), where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM. © 2015 ACM.

关键词： Information retrieval

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Security risk assessment based on bayesian multi-step attack graphs

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Journal of Computational Information Systems 2015年第11期11卷 3911-3918页

作者： Yang, Yunxue Jin, Shuyuan Fang, Binxing CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Security risk assessment is fundamental to the security of network systems. Attack graphs have been widely used to evaluate the likelihood of possible multi-step attacks that exploit multiple vulnerabilities in this field. The aim of this paper is to propose a dynamic risk assessment framework which considers the probability of success of a vulnerability exploitation and captures characteristics of risks for assets that are related to a particular IT environment. We use Bayesian networks to establish the model and Bayesian inference techniques of forward and backward propagation to obtain risk values of assets. We then show how the proposed model combines environmental changes in a network system making dynamic risk assessment. Finally, experimental results show that the advance of the proposed model when compared to existing work. ©, 2015, Binary Information Press. All right reserved.

关键词： Bayesian networks

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A new evolving clustering algorithm for online data streams

A new evolving clustering algorithm for online data streams

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Clauber Gomes Bezerra Bruno Sielly Jales Costa Luiz Affonso Guedes Plamen Parvanov Angelov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Federal University of Rio Grande do Norte - UFRN Natal RN BR Chair of Excellence Carlos III University Madrid Spain Data Science Group School of Computing and Communications Lancaster United Kingdom

In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to “start from scratch” (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging.

关键词： Clustering algorithms Mathematical model Shape Electronic mail Real-time systems Training Conferences

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Chinese wall security policies information flows in business cloud

Chinese wall security policies information flows in business...

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IEEE International Conference on Big data

作者： Tsau-Young T. Y. Lin Institute of Data Science and Computing San Jose State University and GrC Society San Jose

A Business Cloud is defined to be a collection of company datasets that are stored on the "Cloud". For simplicity, we have assumed: Each company only has one dataset. There are information flows among these datasets. Within such an environment Chinese Wall Security Policy (CWSP) is revisited. Based on the "physical" view of Brewer and Nash, the Chinese Wall policy that regulates "Conflict of Interest" binary Relation, denoted by CIR among company datasets, is investigated. CIR has found to be anti-reflexive, symmetric and anti-transitive, in other words, the compliment of CIR is an equivalence relation. The main theorem states that, from the owner (a company) view, a dataset under CWSP will neve flow into enemies hands (companies in conflicts). More generally, if CIR is anti-reflexive and symmetric, then there are Aggressive Chinese Wall that surrounds the company datasets of those companies who are in conflict, and a Conservative Chinese Wall that that surrounds the company datasets whose owner are friends; note that the set friends is a subset of "not in conflict". The main theorem states that no information of those company datasets that are stored within the Aggressive Chinese Wall may never penetrate the Wall, similar conclusion for Conservative Chinese Wall. However those company datasets that are outside these two walls may penetrate the two walls.

关键词： Yttrium Companies Security Mathematical model Big data

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Challenges in Chinese knowledge graph construction

Challenges in Chinese knowledge graph construction

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International Conference on data Engineering Workshops

作者： Chengyu Wang Ming Gao Xiaofeng He Rong Zhang Shanghai Key Laboratory of Trustworthy Computing Data Science and Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9781479984435

The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and relation extraction are highly depending on language itself, data sources largely determine the way the data are processed, relations are extracted, and ultimately how knowledge graphs are formed, which deeply involves the analysis of lexicon, syntax and semantics of the content. Currently, much progress has been made for knowledge graphs in English language. In this paper, we discuss the challenges facing Chinese knowledge graph construction because Chinese is significantly different from English in various linguistic perspectives. Specifically, we analyze the challenges from three aspects: data sources, taxonomy derivation and knowledge extraction. We also present our insights in addressing these challenges.

关键词： Encyclopedias Internet Electronic publishing Taxonomy Semantics data mining

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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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Book Review: Quantitative Methods and Socio-Economic Applications in GIS, 2nd edition, F. Wang, CRC Press, Boca Raton, FL, 301 pages, 2015, £76.99 $119.95 ISBN 9781466584723

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Applied Spatial Analysis and Policy 2015年第2期9卷 283-285页

作者： Higgs, Gary GIS Research Centre and Wales Institute of Social and Economic Research Data and Methods (WISERD) Faculty of Computing Engineering & Science University of South Wales Pontypridd UK

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