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arXiv 2017年

作者： Li, Qiong Zhang, Xiaowang Feng, Zhiyong School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

In this paper, we propose a plugin-based framework for RDF stream processing named PRSP. Within this framework, we can employ SPARQL query engines to process C-SPARQL queries with maintaining the high performance of those engines in a simple way. Taking advantage of PRSP, we can process large-scale RDF streams in a distributed context via distributed SPARQL engines. Besides, we can evaluate the performance and correctness of existing SPARQL query engines in handling RDF streams in a united way, which amends the evaluation of them ranging from static RDF (i.e., RDF graph) to dynamic RDF (i.e., RDF stream). Finally, within PRSP, we experimently evaluate the correctness and the performance on YABench. The experiments show that PRSP can still maintain the high performance of those engines in RDF stream processing although there are some slight differences among them. Copyright © 2017, The Authors. All rights reserved.

关键词： Engines

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Double Time Slot RFID Anti-collision Algorithm based on Gray Code

Double Time Slot RFID Anti-collision Algorithm based on Gray...

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ISCC 2017-the 5th International Conference on Information Science and Cloud Computing

作者： Hongwei Deng Lang Li Hui Wang School of Computer Science and Technology Hengyang Normal University School of Information Science and Engineering Central South University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

To solve the radio frequency identification problem,an anti-collision algorithm based on the Gray code(BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance with Gray code regulations and enhances the identification speed through efficient division and branching of the collision tags. The results of simulation and subsequent analysis of the proposed algorithm confirm that it effectively reduces the number of collisions and transmission delays,enhances the throughput rate,and increases the tag identification efficiency.

关键词： tag Double Time Slot RFID Anti-collision Algorithm based on Gray Code

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Building ontology for different emotional contexts and multilingual environment in opinion mining 28

Building ontology for different emotional contexts and multi...

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28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016

作者： Tao, Wan Liu, Tao Yu, Wei Huang, Gan School of Computer and Information Science Anhui Polytechnic University Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu China

ISBN: (纸本)189170639X

With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g., reviews, forum discussions, blogs, microblogs, comments, and postings in social network sites) for decision making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ontology DEMLOnto would help in identifying the opinion features associated with the contextual environment, which may change along with applications. We built the ontology according to ontology engineering. It was developed on the platform Protégé by using OWL. Copyright © 2016 by KSI Research Inc. and Knowledge Systems Institute Graduate School.

关键词： Sentiment analysis

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Measurements of azimuthal anisotropy of nonprompt D⁰ mesons in PbPb collisions at s_NN=5.02TeV

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2024年 850卷

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Rejeb Sfar, H. Van Mechelen, P. Bols, E.S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Taliercio, A. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Torres Da Silva De Araujo, F. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Thakur, S. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Monti, F. Sharma, R. Tao, Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics Univer

Measurements of the elliptic (v2) and triangular (v3) azimuthal anisotropy coefficients are presented for [Formula presented] mesons produced in [Formula presented] hadron decays (nonprompt [Formula presented] mesons)... 详细信息

Measurements of the elliptic (v₂) and triangular (v₃) azimuthal anisotropy coefficients are presented for [Formula presented] mesons produced in [Formula presented] hadron decays (nonprompt [Formula presented] mesons) in lead-lead collisions at s_NN=5.02TeV. The results are compared with previously published charm meson anisotropies measured using prompt [Formula presented] mesons. The data were collected with the CMS detector in 2018 with an integrated luminosity of 0.58nb⁻¹. Azimuthal anisotropy is sensitive to the interactions of quarks with the hot and dense medium created in heavy ion collisions. Comparing results for prompt and nonprompt [Formula presented] mesons can assist in understanding the mass dependence of these interactions. The nonprompt results show lower magnitudes of v₂ and v₃ and weaker dependences on the meson transverse momentum and collision centrality than those found for prompt [Formula presented] mesons. The results are in agreement with theoretical predictions that include a mass dependence in the interactions of quarks with the medium. © 2024

关键词： CMS Flow Heavy flavor Heavy ions QPG

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Study of azimuthal anisotropy of ϒ(1S) mesons in pPb collisions at s_NN = 8.16 TeV

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2024年 850卷

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Rejeb Sfar, H. Van Mechelen, P. Bols, E.S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Niedziela, M. Rendón, C. Roskas, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Taliercio, A. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Torres Da Silva De Araujo, F. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Thakur, S. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics Uni

The azimuthal anisotropy of [Formula presented] mesons in high-multiplicity proton-lead collisions is studied using data collected by the CMS experiment at a nucleon-nucleon center-of-mass energy of 8.16TeV. The [Formula presented] mesons are reconstructed using their dimuon decay channel. The anisotropy is characterized by the second Fourier harmonic coefficients, found using a two-particle correlation technique, in which the [Formula presented] mesons are correlated with charged hadrons. A large pseudorapidity gap is used to suppress short-range correlations. Nonflow contamination from the dijet background is removed using a low-multiplicity subtraction method, and the results are presented as a function of [Formula presented] transverse momentum. The azimuthal anisotropies are smaller than those found for charmonia in proton-lead collisions at the same collision energy, but are consistent with values found for [Formula presented] mesons in lead-lead interactions at a nucleon-nucleon center-of-mass energy of 5.02 TeV. © 2024 The Author(s)

关键词： CMS Flow Heavy ion pPb Upsilon v2

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Suffix type string matching algorithms based on multi-windows and integer comparison

Suffix type string matching algorithms based on multi-window...

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作者： Fan, Hongbo Shi, Shupeng Zhang, Jing Dong, Li Department of Computer Science Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Laboratory of Yunnan Kunming Yunnan650500 China

In this paper, 3 classic suffix type algorithms: QS, Tuned BM and BMHq were improved by reducing the average cost of basic operations. Firstly, the multi-windows method was used to let the calculations of the jump distance run in parallel and pipelining. Secondly, the comparison unit was increased to integer to reduce the total number and the average cost of comparisons. Especially for BMHq, the jump distance was increased by good prefix rule and the operations to get the jump distance were simplified by unaligned integer read. Thus, 3 algorithms named QSMI, TBMMI and BMHqMI were presented. These algorithms are faster than other known algorithms in many cases. © Springer International Publishing Switzerland 2016.

关键词： Cost reduction

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Revisiting benchmarking principles and methodologies for big data benchmarking 6th

Revisiting benchmarking principles and methodologies for big...

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6th Workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2015

作者： Zhao, Liutao Gao, Wanling Jin, Yi Key Laboratory of Cloud Computing Technology and Application Beijing Computing Center Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319290058

Benchmarking as yardsticks for system design and evaluation, has developed a long period and plays a pivotal role in many domains, such as database systems and high performance computing. Through prolonged and unremitting efforts, benchmarks on these domains have been reaching their maturity gradually. However, in terms of emerging scenarios of big data, its different properties in data volume, data types, data processing requirements and techniques, make that existing benchmarks are rarely appropriate for big data systems and further make us wonder how to define a good big data benchmark. In this paper, we revisit successful benchmarks in other domains from two perspectives: benchmarking principles which define fundamental rules, and methodologies which guide the benchmark constructions. Further, we conclude the benchmarking principle and methodology on big data benchmarking from a recent open-source effort – BigDataBench. © Springer International Publishing Switzerland 2016.

关键词： Benchmarking

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Clothing Co-Segmentation Based on HOG Features and E-SVM Classifier 6

Clothing Co-Segmentation Based on HOG Features and E-SVM Cla...

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6th International Conference on Digital Home, ICDH 2016

作者： Zhang, Jianhong Liu, Li Huang, Dongyan Fu, Xiaodong Huang, Qingsong Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781509044009

To improve the accuracy of clothing segmentation, a novel co-segmentation method is proposed in this paper based on HOG(Histogram of Oriented Gradients) features and E-SVM(Exemplar Support Vector Machine) classifier. The cosegmentation method uses an auxiliary dataset and is implemented with three steps: superpixel grouping, E-SVM classifier training, and segmentation propagation. Firstly, image input by user is segmented in superpixel, such that the images in auxiliary dataset can be divided into multi-regions. Secondly, some regions are selected to a positive segmentation and HOG information is used to train E-SVM classifiers. Finally, the cloth is segmented into user image with E-SVM classifiers and segmentation propagation. Experimental results show that the proposed method can segment clothing images with high accuracy. © 2016 IEEE.

关键词： Superpixels

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Describing images with Ontology-Aware dictionary learning 22nd

Describing images with Ontology-Aware dictionary learning

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Zhang, Chengyue Han, Yahong School of Computer Science and Technology Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

ISBN: (纸本)9783319276700

In this paper, we focus on the generation of contextual descriptions for images by learning an ontology-aware dictionary. Ontology deals with questions concerning what entities exist and how such entities can be related with a hierarchy. Thus, if we incorporate the semantic hierarchies of visual concepts into a learned visual dictionary, which consists of visual atoms, we can generate contextual descriptions of testing images through the reconstruction. This paper proposes to learn the ontology-aware dictionary by integrating hierarchical dictionary learning and multi-task regression into a joint framework. By utilizing a hierarchical regularization term defined on the multiple semantic categories, the hierarchical structures are introduced into the multi-task regression. The joint optimization of the sparse coding and multi-task regression makes the semantic hierarchies embedded into the learned dictionary. Experiments on two benchmark datasets show the better performance of the proposed algorithm. Examples of the ontology-aware dictionary and generated image descriptions successfully demonstrate the effectiveness of the proposed framework. © Springer International Publishing Switzerland 2016.

关键词： Ontology

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Research and application of online monitoring fault diagnosis for infrared image and visible image registration 6

Research and application of online monitoring fault diagnosi...

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2016 6th International Workshop on computer Science and Engineering, WCSE 2016

作者： Huang, Jianqiang Wu, Huan Wang, Xiaoying Yi, Zhengming Department of Computer Technology and Application State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining Qinghai810016 China

ISBN: (纸本)9789811100086

To find the hidden trouble in substation accident early, it is necessary to build a infrared temperature monitoring and warning system, which can collect infrared image of substation electrical equipment automatically and circularly, through extracting the temperature information of the objective device to distinguish fault. In order to solve the positional deviation of the image, which caused by the mechanical transmission, leads to that it is difficult for systems to monitoring and positioning problem on interested equipment, so it needs to use corrected method of image registration, firstly segmenting object and defining configuration for image, and extracting the target which is detected, using suitable image registration method to register the deviation between a sequence of image. So that the system can get the temperature of the equipment parts in time and accurately, and ensure the reliable operation of the monitoring system. The infrared image and the visual image registration need to be registered and the fault region in the visible light image need to be marked for detecting the fault zone accurately. In the implementation, we use the matching method for the registration of infrared image and visible light image which base on the trunk line pair ofreference1.

关键词： Monitoring

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