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An investigation into content based video processing in cloud computing paradigm

An investigation into content based video processing in clou...

2013 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013

作者： Mustafa, Rashed Zhu, Dingju Laboratory for Smart Computing and Information Science Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering University of Chittagong Bangladesh Shenzhen Public Platform for Triple-play Video Transcoding Center Shenzhen China School of Computer Science South China normal University Guangzhou China

ISBN: (纸本)1601322534

Finding Obscenity from videos is a crucial issue due to social and ethical reasons of using online resources. Dating back to two decades the research on this ground has been started. Most of the works are based on image based skin color detection which is not a suitable method for finding obscenity from videos. The reason for this is that, there are many skin like pixels such as beach photos, human skin like animal, skin colored painting that enables false positive (FPR) and false negative rate (FNR). Moreover frame checking time is also a performance factor. In addition all works performed well on some particular set of data. In this paper some aspects of finding obscenity from videos is described delineating strength, weakness and possible extensions of prior works. Introducing some new features and incorporation of multiple classifiers and transfer learning will lead the work more robust. In addition traditional multimedia cloud computing has been investigated in this paper following some extensions. © 2013 CSREA Press. All rights reserved.

关键词： Cloud computing

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"Judgments on video applications for required contents" - Using Evidential Reasoning (ER) Algorithm

"Judgments on video applications for required contents" - Us...

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2013 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013

ISBN: (纸本)1601322534

Searching desired videos is now challenging due to scattered collection of large amounts of videos all over the world. It is very difficult to find appropriate video applications for different video contents required by different users from repositories. This paper contributes prioritizing videos using Multi criteria Decision Analysis Approach (MCDA) which is denoted as Evidential Reasoning Method (ER). In this research a simple case study presented over some classification of videos where particular object/person need to be identified. It has been shown that the approach best-prioritized shot-Stock videos for selecting particular object/person and ER approach is more efficient than Analytic Hierarchical Process (AHP). © 2013 CSREA Press. All rights reserved.

关键词： Hierarchical systems

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Application of Hopfield Neural Networks Approach in Solar Energy Product Conceptual Design

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computer Aided Drafting,Design and Manufacturing 2013年第2期23卷 48-52页

作者： XIA Zhi-qiu WANG Ling REN Na WEI Xiao-peng ZHANG Qiang ZHAO Ting-ting Department of Computer Science Collage of EngineeringUniversity of Leicester The archives Dalian University Department of Information Engineering Shenyang Institute of Engineering Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education

A new product conceptual design approach is put forward based on Hopfield neural networks models. By research on the mechanisms of Hopfield neural networks, the associative simulation approaches are proposed. The approach is given by Hebb learn- ing law, Hopfield neural networks and crossover and mutation. The calculating models and the calculating formulas for the concep- tual design are put forward. Finally, an example for the conceptual design of a solar energy lamp is given. The better results are ob- tained in the conceptual design.

关键词： Hopfield neural networks conceptual design solar energy

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Loop Transforming for Reducing Data Alignment on Multi-Core SIMD Processors

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Journal of Signal Processing Systems 2013年第2期74卷 137-150页

作者： Wang, Yi Pan, Linfeng Shao, Zili Guan, Yong Guo, Minyi Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Information Engineering Capital Normal University Beijing China

Multimedia SIMD extensions are commonly employed today to speed up media processing. When performing vectorization for SIMD architectures, one of the major issues is to handle the problem of memory alignment. Prior study focused on either vectorizing loops with all memory references being properly aligned, or introducing extra operations to deal with the misaligned memory references. On the other hand, multi-core SIMD architectures require coarse-grain parallelism. Therefore, it is an important problem to study how to parallelize and vectorize loop nests with the awareness of data misalignments. This paper presents a loop transformation scheme that maximizes the parallelism of outermost loops, while the misaligned memory references in innermost loops are reduced. The basic idea of our technique is to align each level of loops in the nest, considering the constraint of dependence relations. To reduce the data misalignments, we establish a mathematical model with a concept of offset-collection and propose an effective heuristic algorithm. For coarser-grain parallelism, we propose some rules to analyze the outermost loop. When transformations are applied, the inner loops are involved to maximize the parallelism. To avoid introducing more data misalignments, the involved innermost loop is handled from other levels of loops. Experimental results show that 7 % to 37 % (on average 18.4 %) misaligned memory references can be reduced. The simulations on CELL show that 1.1x speedup can be reached by reducing the misaligned data, while 6.14x speedup can be achieved by enhancing the parallelism for multi-core.

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Converting continuous-space language models into N-gram language models for statistical machine translation

Converting continuous-space language models into N-gram lang...

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2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013

作者： Wang, Rui Utiyama, Masao Goto, Isao Sumita, Eiichro Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai200240 China

ISBN: (纸本)9781937284978

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, CSLMs have not been used in the first pass decoding of SMT, because using CSLMs in decoding takes a lot of time. In contrast, we propose a method for converting CSLMs into back-off n-gram language models (BNLMs) so that we can use converted CSLMs in decoding. We show that they outperform the original BNLMs and are comparable with the traditional use of CSLMs in reranking. © 2013 Association for Computational Linguistics.

关键词： Decoding

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Corrigendum to “A novel approach to collaborative testing in a crowdsourcing environment” in the Journal of Systems and Software 86 (2013) 2143–2153

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Journal of Systems and Software 2014年 87卷 137-137页

作者： Yuan-Hsin Tung Shian-Shyong Tseng Wei-Tek Tsai Department of Computer Science and Information Engineering National Chiao Tung University Taiwan ROC Telecommunication Laboratory Chunghwa Telecom Co. Ltd. Taiwan ROC Department of Applied Informatics and Multimedia Asia University Taiwan ROC School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

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An empirical study on word segmentation for Chinese machine translation

An empirical study on word segmentation for Chinese machine ...

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14th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013

作者： Zhao, Hai Utiyama, Masao Sumita, Eiichiro Lu, Bao-Liang MOE-Microsoft Key Laboratory of Intelligent Computing and Intelligent System Shanghai Jiao Tong University #800 Dongchuan Road Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University #800 Dongchuan Road Shanghai 200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto 619-0289 Japan

ISBN: (纸本)9783642372551

Word segmentation has been shown helpful for Chinese-to-English machine translation (MT), yet the way different segmentation strategies affect MT is poorly understood. In this paper, we focus on comparing different segmentation strategies in terms of machine translation quality. Our empirical study covers both English-to-Chinese and Chinese-to-English translation for the first time. Our results show the necessity of word segmentation depends on the translation direction. After comparing two types of segmentation strategies with associated linguistic resources, we demonstrate that optimizing segmentation itself does not guarantee better MT performance, and segmentation strategy choice is not the key to improve MT. Instead, we discover that linguistical resources such as segmented corpora or the dictionaries that segmentation tools rely on actually determine how word segmentation affects machine translation. Based on these findings, we propose an empirical approach that directly optimize dictionary with respect to the MT task for word segmenter, providing a BLEU score improvement of 1.30. © 2013 Springer-Verlag.

关键词： Machine translation

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2D and 3D palmprint information, PCA and HMM for an improved person recognition performance

2D and 3D palmprint information, PCA and HMM for an improved...

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作者： Meraoumia, Abdallah Chitroub, Salim Bouridane, Ahmed Laboratoire de Génie Électrique Faculté de Science et de la Technologie et des Sciences de la Matière Université Kasdi Merbah de Ouargla Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty Telecommunication Department Electronics and Computer Science Faculty University of Science and Technology of Houari Boumedienne Algiers Algeria School of Computing Engineering and Information Sciences Northumbria University Pandon Building Newcastle upon Tyne United Kingdom

Biometric systems based on a single source of information suffer from limitations such as the lack of uniqueness, non-universality of the chosen biometric trait, noisy data and spoof attacks. Multimodal biometrics are relatively new systems that overcome those problems. These systems fuse information from multiple sources in order to achieve the better person recognition performance. In this paper, the 2D and 3D information of palmprint are integrated in order to construct an efficient multimodal biometric system based on fusion at matching score level and at feature extraction level. The observation vectors are created independently either from the original data of the two modalities (2D and 3D palmprint) or from their rotation invariant variance measures applied on textures. On each modality (or its corresponding invariant texture), we have applied the Principal Component Analysis (PCA) for reducing dimension of the feature vector. We have also used the multi-scale wavelet decomposition for each modality and the results of decomposition are combined and compressed using PCA for selecting the feature vectors. Subsequently, we have used the Hidden Markov Model (HMM) for modeling the feature vectors. Finally, Log-likelihood scores are used for palmprint evaluation. We note that the selected principal components of two modalities are fused at feature level and at matching score level. The proposed scheme is tested and evaluated using PolyU 2D and 3D palmprint database of 250 persons. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. © 2013 - IOS Press and the author(s). All rights reserved.

关键词： Hidden Markov models

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Erratum to: Measurement of the inclusive jet cross-section in proton-proton collisions at s = 7 TeV using 4.5 fb −1 of data with the ATLAS detector

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Journal of High Energy Physics 2015年第9期2015卷 1-39页

作者： G. Aad B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams J. Adelman S. Adomeit T. Adye T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen F. Ahmadov G. Aielli H. Akerstedt T. P. A. Åkesson G. Akimoto A. V. Akimov G. L. Alberghi J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport J. Almond A. Aloisio A. Alonso F. Alonso C. Alpigiani A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis I. Angelozzi P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi A. Antonaki M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella R. Apolle G. Arabidze I. Aracena Y. Arai J. P. Araque A. T. H. Arce J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster O. Arnaez V. Arnal H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Asai N. Asbah A. Ashkenazi B. Åsman L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay B. Auerbach K. Augsten M. Aurousseau G. Avolio G. Azuelos Y. Azuma M. A. Baak A. Baas C. Bacci H. Bachacou K. Bachas M. Backes M. Backhaus J. Backus Mayes E. Badescu P. Bagiacchi P. Bagnaia Y. Bai T. Bain J. T. Baines O. K. Baker P. Balek F. Balli E. Banas Sw. Banerjee A. A. E. Bannoura V. Bansal H. S. Bansil L. Barak S. P. Baranov E. L. Barberio D. Barberis M. Barbero T. Barillari M. Barisonzi T. Barklow N. Barlow B. M. Barnett R. M. Barnett Z. Barnovska A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos V. Bartsch A. Bassalat A. Basye R. L. Bates J. R. Batley M. Battaglia M. Battistin F. Bauer H CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States Institute of Physics Academia Sinica Taipei Taiwan LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater United States Department of Physics and Astronomy Michigan State University East Lansing United States Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel CERN Geneva Switzerland Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton United States Department of Physics Yale University New Haven United States Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland Department of Physics Boston University Boston United States Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN

It was found that the non-perturbative corrections calculated using Pythia with the Perugia 2011 tune did not include the effect of the underlying event. The affected correction factors were recomputed using the Pythia 6.427 generator. These corrections are applied as baseline to the NLO pQCD calculations and thus the central values of the theoretical predictions have changed by a few percent with the new corrections. This has a minor impact on the agreement between the data and the theoretical predictions. Figures 2 and 6 to 13, and all the tables have been updated with the new values. A few sentences in the discussion in sections 5.2 and 9 were altered or removed.

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