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2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014

作者： Mazloom, Masoud Li, Xirong Snoek, Cees G. M. ISLA Faculty of Science University of Amsterdam Science Park 904 1098 XH Amsterdam Netherlands Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China

ISBN: (纸本)1595930361

An emerging topic in multimedia retrieval is to detect a complex event in video using only a handful of video examples. Different from existing work which learns a ranker from positive video examples and hundreds of negative examples, we aim to query web video for events using zero or only a few visual examples. To that end, we propose in this paper a tag-based video retrieval system which propagates tags from a tagged video source to an unlabeled video collection without the need of any training examples. Our algorithm is based on weighted frequency neighbor voting using concept vector similarity. Once tags are propagated to unlabeled video we can rely on off-the-shelf language models to rank these videos by the tag similarity. We study the behavior of our tag-based video event retrieval system by performing three experiments on web videos from the TRECVID multimedia event detection corpus, with zero, one and multiple query examples that beats a recent alternative. Copyright 2014 ACM.

关键词： Image retrieval

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Dense matter with eXTP

arXiv

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arXiv 2018年

作者： Watts, Anna L. Yu, Wenfei Poutanen, Juri Zhang, Shu Bhattacharyya, Sudip Bogdanov, Slavko Ji, Long Patruno, Alessandro Riley, Thomas E. Bakala, Pavel Baykal, Altan Bernardini, Federico Bombaci, Ignazio Brown, Edward Cavecchi, Yuri Chakrabarty, Deepto Chenevez, Jérôme Degenaar, Nathalie Santo, Melania Del Salvo, Tiziana Di Doroshenko, Victor Falanga, Maurizio Ferdman, Robert D. Feroci, Marco Gambino, Angelo F. Ge, MingYu Greif, Svenja K. Guillot, Sebastien Gungor, Can Hartmann, Dieter H. Hebeler, Kai Heger, Alexander Homan, Jeroen Iaria, Rosario in’t Zand, Jean Kargaltsev, Oleg Kurkela, Aleksi Lai, Xiaoyu Li, Ang Li, XiangDong Li, Zhaosheng Linares, Manuel Lu, FangJun Mahmoodifar, Simin Méndez, Mariano Miller, M. Coleman Morsink, Sharon Nättilä, Joonas Possenti, Andrea Prescod-Weinstein, Chanda Qu, JinLu Riggio, Alessandro Salmi, Tuomo Sanna, Andrea Santangelo, Andrea Schatz, Hendrik Schwenk, Achim Song, LiMing Šrámková, Eva Stappers, Benjamin Stiele, Holger Strohmayer, Tod Tews, Ingo Tolos, Laura Török, Gabriel Tsang, David Urbanec, Martin Vacchi, Andrea Xu, RenXin Xu, Ypeng Zane, Silvia Zhang, Guobao Zhang, ShuangNan Zhang, Wenda Zheng, ShiJie Zhou, Xia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Shanghai Astronomical Observatory Shanghai200030 China Tuorla Observatory Department of Physics and Astronomy University of Turku Väisäläntie 20 PiikkiöFIN-21500 Finland Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-10691 Sweden Institute of High Energy Physics CAS Beijing100049 China Tata Institute of Fundamental Research Mumbai400005 India Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Institut für Astronomie und Astrophysik Tübingen Universität Tübingen Sand 1 TübingenD-72076 Germany Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands Research Center for Computational Physics and Data Processing Silesian University in Opava Bezrucovo nam. 13 OpavaCZ-74601 Czech Republic Physics Department Middle East Technical University Ankara06531 Turkey INAF Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio CatoneI-00078 Italy New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Dipartimento di Fisica Enrico Fermi University of Pisa PisaI-56127 Italy INFN Italian National Institute for Nuclear Physics Pisa Section Pisa56127 Italy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom MIT Kavli Institute for Astrophysics and Space Research CambridgeMA02139 United States DTU Space Technical University of Denmark Elektrovej 327-328 Kgs Lyngby2800 Denmark INAF/IASF Palermo via Ugo La Malfa 153 PalermoI-90146 Italy Universita degli Studi di Palermo Dipartimento di Fisica e Chimica via Archira 36 Palermo90123 Italy

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry (eXTP) mission for determining the nature of dense matter;neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of science, the eXTP mission is expected to be launched in the mid 2020s. Copyright © 2018, The Authors. All rights reserved.

关键词： Pulsars

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Rail Inspection Meets Big data: Methods and Trends

Rail Inspection Meets Big Data: Methods and Trends

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International Conference on Network-Based Information Systems (NBIS)

作者： Qingyong Li Zhangdui Zhong Zhengping Liang Yong Liang Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China College of Computer Science and Software Engineering Shenzhen University Communication & Power Utilization Technology Subcompany State Grid Electric Power Research Institute State Grid Beijing China

Rail inspection is one of the most important tasks for rail industry, in order to guarantee the safety of railway systems and control their cost. Rail are systematically inspected for defects using various non-destructive evaluation (NDE) techniques, which include ultrasonic inspection, visual detection, magnetic flux leakage method, acoustic emission inspection etc. The data obtained by these NDE devices are going to increase in both quality and quantity, therefore big data has emerged as a potential challenge for rail inspection. This paper reviews the advanced NDE techniques for rail inspection, and brings forward a new framework of rail inspection based on big data, according to the characteristics of inspection data.

关键词： Rails Inspection Acoustics Surface cracks Visualization Surface treatment Big data

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A fast noise resilient anomaly detection using GMM-based collective labelling

A fast noise resilient anomaly detection using GMM-based col...

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science and Information Conference (SAI)

作者： Elnaz Bigdeli Bijan Raahemi Mahdi Mohammadi Stan Matwin School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada Telfer School of Management Knowledge Discovery and Data Mining lab University of Ottawa E. Ottawa ON Canada Department of Computing Dalhousie University Halifax NB Canada Polish Academy of Sciences Warsaw Poland

Anomaly detection algorithms face several challenges including computational complexity and resiliency to noise in input data. In this paper, we propose a fast and noise-resilient cluster-based anomaly detection method using collective labelling approach. In the proposed Collective Probabilistic Anomaly Detection method, first instead of labelling each new sample (as normal or anomaly) individually, the new samples are clustered, then labelled. This collective labelling mitigates the negative impact of noise by relying on group behaviour rather than individual characteristics of incoming samples. Second, since grouping and labelling new samples may be time-consuming, we summarize clusters using Gaussian Mixture Model (GMM). Not only does GMM offer faster processing speed; it also facilitates summarizing clusters with arbitrary shape, and consequently, reducing the memory space requirement. Finally, a modified distance measure, based on Kullback-Liebner method, is proposed to calculate the similarity among clusters represented by GMMs. We evaluate the proposed method on various datasets by measuring its false alarm rate, detection rate and memory requirement. We also add different levels of noise to the input datasets to demonstrate the performance of the proposed collective anomaly detection method in the presence of noise. The experimental results confirm superior performance of the proposed method compared to individually-based labelling techniques in terms of memory usage, detection rate and false alarm rate.

关键词： Noise Shape Training Gaussian mixture model Clustering algorithms Support vector machines

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Privilege separation in browser extensions based on web workers

Privilege separation in browser extensions based on web work...

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4th International Conference on Materials science and Information Technology, MSIT 2014

作者： Yu, Chun Mei Sun, Jian Hua Chen, Hao Xu, Xiang Hua College of Information Science and Engineering Hunan University Changsha China Zhejiang Provincial Key Lab of Data Storage and Transmission Technology Hangzhou Dianzi University China

ISBN: (纸本)9783038351733

In this paper, we propose to use Web Workers [1], isolated parallel threads in current browser working in the background, to enforce privilege separation in chrome extensions. Our tests show that our design is applicable to most chrome extensions (those not using the jquery library) and achieves a reduction in TCB up to 77 percent for our examples. Our implementation shows that our design is efficient enough to enforce privilege separation in chrome extensions and not needs to modify the browser or learn new high-level languages. © (2014) Trans Tech Publications, Switzerland.

关键词： Separation

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Supernovae 2016bdu and 2005gl, and their link with SN 2009ip-like transients: another piece of the puzzle

arXiv

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arXiv 2017年

作者： Pastorello, A. Kochanek, C.S. Fraser, M. Dong, S. Elias-Rosa, N. Benetti, S. Cappellaro, E. Tomasella, L. Drake, A.J. Hermanen, J. Reynolds, T. Shappee, B.J. Smartt, S.J. Chambers, K.C. Huber, M.E. Smith, K. Stanek, K.Z. Filippenko, A.V. Christensen, E.J. Denneau, L. Djorgovski, S.G. Flewelling, H. Gall, C. Gal-Yam, A. Geier, S. Heinze, A. Holoien, T.W.-S. Isern, J. Kangas, T. Kankare, E. Koff, R.A. Llapasset, J.-M. Lowe, T.B. Lundqvist, P. Magnier, E.A. Mattila, S. Morales-Garoffolo, A. Mutel, R. Nicolas, J. Ochner, P. Ofek, E.O. Prosperi, E. Rest, A. Sano, Y. Stalder, B. Stritzinger, M.D. Taddia, F. Terreran, G. Tonry, J.L. Wainscoat, R.J. Waters, C. Weiland, H. Willman, M. Young, D.R. Zheng, W. INAF-Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Department of Astronomy Ohio State University 140 West 18th Avenue ColumbusOH43210 United States Ohio State University 191 W. Woodruff Ave. ColumbusOH43210 United States School of Physics O’Brien Centre for Science North University College Dublin Belfield Dublin 4 Ireland Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Royal Society – Science Foundation Ireland University Research Fellow Kavli Institute for Astronomy and Astrophysics Peking University Yi He Yuan Road 5 Hai Dian District Beijing100871 China Astronomy Department California Institute of Technology PasadenaCA91125 United States Tuorla Observatory Department of Physics and Astronomy University of Turku Väisäläntie 20 PiikkiöFI-21500 Finland Nordic Optical Telescope Apartado 474 Santa Cruz de La PalmaE-38700 Spain Carnegie Observatories 813 Santa Barbara Street PasadenaCA91101 United States Hubble Fellow and Carnegie-Princeton Fellow Astrophysics Research Centre School of Mathematics and Physics Queen’s University Belfast BelfastBT7 1NN United Kingdom Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Astronomy University of California BerkeleyCA94720-3411 United States Lunar and Planetary Lab Department of Planetary Sciences University of Arizona TucsonAZ85721 United States Dark Cosmology Centre Niels Bohr Institute University of Copenhagen Juliane Maries Vej 30 Copenhagen2100 Denmark Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus CDK-8000 Denmark Department of Particle Physics and Astrophysics Faculty of Physics Weizmann Institute of Science Rehovot76100 Israel Cuesta de San José s/n Breña Baja La PalmaE-38712 Spain Instituto de Astrofísica de Canarias Vía Láctea s/n La Laguna TenerifeE-38200 Spain US Department of Energy Computatio

Supernova (SN) 2016bdu is an unusual transient resembling SN 2009ip. SN 2009ip-like events are characterized by a long-lasting phase of erratic variability which ends with two luminous outbursts a few weeks apart. The second outburst is significantly more luminous (about 3 mag) than the first. In the case of SN 2016bdu, the first outburst (Event A) reached an absolute magnitude Mr ≈ −15.3 mag, while the second one (Event B) occurred over one month later and reached Mr ≈ −18 mag. By inspecting archival data, a faint source at the position of SN 2016bdu is detectable several times in the past few years. We interpret these detections as signatures of a phase of erratic variability, similar to that experienced by SN 2009ip between 2008 and mid-2012, and resembling the currently observed variability of the luminous blue variable SN 2000ch in NGC 3432. Spectroscopic monitoring of SN 2016bdu during the second peak initially shows features typical of a SN IIn. One month after the Event B maximum, the spectra develop broad Balmer lines with P Cygni profiles and broad metal features. At these late phases, the spectra resemble those of a typical Type II SN. All members of this SN 2009ip-like group are remarkably similar to the Type IIn SN 2005gl. For this object, the claim of a terminal SN explosion is supported by the disappearance of the progenitor star. The similarity with SN 2005gl suggests that all members of this family may finally explode as genuine SNe, although the unequivocal detection of nucleosynthesised elements in their nebular spectra is still missing. Copyright © 2017, The Authors. All rights reserved.

关键词： Supernovae

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Single trial classification for mobile BCI a multiway kernel approach 8

Single trial classification for mobile BCI a multiway kernel...

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8th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2015

作者： Billiet, Lieven Hunyadi, Borbála Matic, Vladimir Van Huffel, Sabine Verleysen, Michel De Vos, Maarten STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium Medical IT IMinds Leuven3001 Belgium Institute for Information and Communication Technologies Electronics and Applied Mathematics Université Catholique de Louvain Place du levant 3 Louvain-La-Neuve1348 Belgium Methods in Neurocognitive Psychology Lab Department of Psychology Cluster of Excellence Hearing4all Carl von Ossietzky University Oldenburg Germany Research Center Neurosensory Science Carl von Ossietzky University Oldenburg Germany

ISBN: (纸本)9789897580697

Subspace methods have been applied in various application fields to obtain robust results. Using multilinear algebra, they can also be applied on structured tensorial data. This work combines this principle with the power of non-linear kernels to investigate its merits in single trial classification for a mobile BCI ERP classification task. The accuracy difference with regard to more conventional vector kernels is evaluated for sitting and walking condition, increasing training data set and averaging over multiple trials. The study concludes that in general, the tensorial approach does not yield any advantage, though it might for specific subjects.

关键词： Vectors

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A filtering algorithm for scattered point cloud based on curvature features classification

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Journal of Information and Computational science 2015年第2期12卷 525-532页

作者： Gu, Xiaoying Liu, Yongshan Wu, Qiong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

In order to remove the noise of three-dimensional scattered point cloud, and to smooth point cloud model effectively, an algorithm which uses different filtering strategies on different feature regions of a point cloud model is proposed. The algorithm firstly estimates the normal vectors of a point cloud model by using covariance analysis method, then estimates Gaussian curvatures of the point cloud model by using parabolic fitting method, and uses the average Gaussian curvature in the k-neighborhood as the threshold, to divide the point cloud model into flat neighborhood type regions and mutant neighborhood type regions, and, we use improved median filtering algorithm and bilateral filtering algorithm on different feature regions, so as to achieve the purpose of smoothing scattered point cloud. Experimental results show that it has a very good smoothing effect and maintains the detail features of the point cloud model, which has proved the algorithm is simple and effective. Copyright © 2015 Binary Information Press.

关键词： Three dimensional computer graphics

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Source separation improves music emotion recognition 14

Source separation improves music emotion recognition

引用

2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014

作者： Xu, Jieping Li, Xirong Hao, Yun Yang, Gang Multimedia Computing Lab. School of Information Renmin University of China Beijing 100872 China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China Graduate School of Library and Information Science University of Illinois at Urbana-Champaign United States

ISBN: (纸本)1595930361

Despite the impressive progress in music emotion recognition, it remains unclear what aspect of a song, i.e., singing voice and accompanied music, carries more emotional information. As an initial attempt to answer the question, we introduce source separation into a standard music emotion recognition system. This allows us to compare systems with and without source separation, and consequently reveal the influence of singing voice and accompanied music on emotion recognition. Classification experiments on a set of 267 songs with *** annotations verify the new finding that source separation improves song music emotion recognition. Copyright is held by the owner/author(s).

关键词： Source separation

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Novel way to research nonlinear feedback shift register

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science China(Information sciences) 2014年第9期57卷 161-174页

作者： ZHAO DaWei PENG HaiPeng LI LiXiang HUI SiLi YANG YiXian Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications Zhejiang Provincial Key Lab of Data Storage and Transmission Technology Hangzhou Dianzi University National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Shandong Provincial Key Laboratory of Computer Network Shandong Computer Science Center

In this paper,we regard the nonlinear feedback shift register(NLFSR)as a special Boolean network,and use semi-tensor product of matrices and matrix expression of logic to convert the dynamic equations of NLFSR into an equivalent algebraic *** on them,we propose some novel and generalized techniques to study ***,a general method is presented to solve an open problem of how to obtain the properties(the number of fixed points and the cycles with different lengths)of the state sequences produced by a given NLFSR,i.e.,the analysis of a given *** then show how to construct all 22n-(l-n)/22n-lshortest n-stage feedback shift registers(nFSR)and at least 22n-(l-n)-1/22n-l-1shortest n-stage nonlinear feedback shift registers(nNLFSR)which can output a given nonperiodic/periodic sequence with length ***,we propose two novel cycles joining algorithms for the construction of full-length ***,two algorithms are presented to construct 22n-2-1different full-length nNLFSRs,respectively.

关键词： nonlinear feedback shift register semi-tensor product Boolean network full-length nNLFSR binary sequence

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