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arXiv 2017年

作者： Zhang, Jie Li, Qingyang Caselli, Richard J. Ye, Jieping Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Neurology Mayo Clinic ScottsdaleAZ United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases on longitudinal data has drawn great interest from computer vision researchers. The current state-of-the-art models for many image classification tasks are based on the Convolutional Neural Networks (CNN). However, a key challenge in applying CNN to biological problems is that the available labeled training samples are very limited. Another issue for CNN to be applied in computer aided diagnosis applications is that to achieve better diagnosis and prognosis accuracy, one usually has to deal with the longitudinal dataset, i.e., the dataset of images scanned at different time points. Here we argue that an enhanced CNN model with transfer learning for the joint analysis of tasks from multiple time points or regions of interests may have a potential to improve the accuracy of computer aided diagnosis. To reach this goal, we innovate a CNN based deep learning multi-task dictionary learning framework to address the above challenges. Firstly, we pretrain CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks. Then, we propose a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), for learning different tasks by using shared and individual dictionaries and generating the sparse features required to predict the future cognitive clinical scores. We apply our new model in a publicly available neuroimaging cohort to predict clinical measures with two different feature sets and compare them with seven other state-of-the-art methods. The experimental results show our proposed method achieved superior results. Copyright © 2017, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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Computing and Software for Big Science 2024年第1期8卷 1-36页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P.S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Mechelen, P. Van Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Faham, H. El Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Onsem, G. P. Van Putte, S. Van Vannerom, D. Clerbaux, B. Das, A.K. De Lentdecker, G. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F.A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Bossche, N. Van Den Linden, J. van der Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sh. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. De Souza, S. Fonseca De Souza, R. Gomes Martins, J. Herrera, C. Mora Mundim, L. Nogima, H. Pinheiro, J.P. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T. R. Fernandez Perez Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos 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 Nanjing Normal University Nanjing China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang 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 Faculty of Science University of Split 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 Biophysic

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. © The Author(s) 2024.

关键词： CMS Machine learning Offline and computing

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The CMS Statistical Analysis and Combination Tool: Combine

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Computing and Software for Big Science 2024年第1期8卷 1-45页

作者： Zhokin, A. Zhizhin, I. Zarubin, A. Yuldashev, B.S. Voytishin, N. Vorotnikov, G. Vorobyev, A. Volkov, P. Uzunian, A. Uvarov, L. Toropin, A. Tlisova, I. Teryaev, O. Terkulov, A. Tcherniaev, E. Sulimov, V. Sosnov, D. Smirnov, V. Slabospitskii, S. Skovpen, Y. Shulha, S. Shmatov, S. Shalaev, V. Savrin, V. Savina, M. Radchenko, O. Popov, V. Polikarpov, S. Perfilov, M. Perelygin, V. Palichik, V. Oreshkin, V. Obraztsov, S. Nikitenko, A. Murzin, V. Matveev, V. Malakhov, A. Makarenko, V. Lychkovskaya, N. Levchenko, P. Lanev, A. Krasnikov, N. Kozyrev, A. Korenkov, V. Konstantinov, D. Kodolova, O. Klyukhin, V. Kirsanov, M. Kirpichnikov, D. Kirakosyan, M. Kim, V. Karneyeu, A. Karjavine, V. Kachanov, V. Ivanov, Y. Gribushin, A. Gorbunov, I. Golutvin, I. Golubev, N. Golovtcov, V. Gninenko, S. Gavrilov, V. Gavrilov, G. Dudko, L. Dubinin, M. Druzhkin, D. Dimova, T. Dermenev, A. Chistov, R. Chekhovsky, V. Chadeeva, M. Bunichev, V. Budkouski, D. Borshch, V. Boos, E. Blinov, V. Babaev, A. Azarkin, M. Aushev, T. Andreev, Yu. Andreev, V. Alexakhin, V. Afanasiev, S. Warden, A. Vetens, W. Tsoi, H.F. Teague, D. Smith, W.H. Sharma, V. Shang, V. Savin, A. Pinna, D. Pétré, L. Parida, G. Mondal, S. Mohammadi, A. Mallampalli, A. Sreekala, J. Madhusudanan Loveless, R. Lanaro, A. Koraka, C.K. Herve, A. Herndon, M. He, H. Galloni, C. Everaerts, P. De Bruyn, I. Dasu, S. Bose, T. Black, K. Banerjee, S. Aravind, A. Karchin, P.E. Bhattacharya, S. Neu, C. Ledovskoy, A. Hirosky, R. Hakala, J. Cox, B. Cardwell, B. Viinikainen, J. Velkovska, J. Tuo, S. Sheldon, P. Romeo, F. Melo, A. Elayavalli, R. Kunnawalkam Johns, W. Gurrola, A. Greene, S. Chen, Y. Appelt, E. Volobouev, I. Peltola, T. Mankel, A. Lee, S.W. Lamichhane, K. Kazhykarim, Y. Hussain, A. Hegde, V. Gogate, N. Damgov, J. Akchurin, N. Safonov, A. Rathjens, D. Overton, D. Mueller, R. Luo, S. Kim, H. Kamon, T. Huang, T. Gilmore, J. Eusebi, R. Bouhali, O. Akhter, T. Ahmad, M. Aebi, D. Spanier, S. Nibigira, E. Lee, L. Karunarathna, N. Kanuganti, A.R. Holmes, T. Higginbotham, S. Fiorendi, S. Delannoy 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 Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture University of Split Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Universidad San Francisco de Quito Quito Ecuador Egyptian Network of High Energy Physics Academy of Scientific Research and Technology of the Arab Republic of Egypt Cairo Egypt Center for High

This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide. © CERN, for the benefit of the CMS Collaboration 2024.

关键词： CMS Combine Higgs Higgs boson Statistics

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Measurement of the Higgs boson production via vector boson fusion and its decay into bottom quarks in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第1期2024卷 1-54页

作者： Hayrapetyan, A. 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. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. 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. 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. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Agapitos, A. Ban, Y. Levin, A. 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 and 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

A measurement of the Higgs boson (H) production via vector boson fusion (VBF) and its decay into a bottom quark-antiquark pair (bb¯) is presented using proton-proton collision data recorded by the CMS experiment at s = 13 TeV and corresponding to an integrated luminosity of 90.8 fb⁻¹. Treating the gluon-gluon fusion process as a background and constraining its rate to the value expected in the standard model (SM) within uncertainties, the signal strength of the VBF process, defined as the ratio of the observed signal rate to that predicted by the SM, is measured to be μ_Hbb¯^qqh=1.01_−0.46^+0.55. The VBF signal is observed with a significance of 2.4 standard deviations relative to the background prediction, while the expected significance is 2.7 standard deviations. Considering inclusive Higgs boson production and decay into bottom quarks, the signal strength is measured to be μ_Hbb¯^incl.=0.99_−0.41^+0.48, corresponding to an observed (expected) significance of 2.6 (2.9) standard deviations. © The Author(s) 2024.

关键词： B Physics Hadron-Hadron Scattering Higgs Physics

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Panspermia of GeoData in support systems for design & execution of operational procedures 3

Panspermia of GeoData in support systems for design & execut...

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3rd International Conference on Mathematics and computers in Sciences and in Industry, MCSI 2016

作者： Tsavdaridis, George Koukoutsis, Elias Karadimas, Nikolaos V. Div. of Communication Electronic and Information Engineering School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Informatics Mathematics and Engineering Sciences Hellenic Army Academy Vari Greece

ISBN: (纸本)9781509009732

In the present paper, the desired Data/Info that the operational officers need is examined, both during the design phase and during the execution of operational missions. This information has been developed and organized by several military and civilian authorities and organizations at national and international level, but there are severe problems in acquiring external collections of Data/Info. In addition, the observatories of information and repositories of data have collapsed by their large volume. The most significant note is the fact that the desired Data/Info is directly or indirectly georeferenced, very high volume, complex, diversified, with internal correlations and therefore requires special handling. Operational officers' duty is supported by useful and powerful tools such as Geographical Information Systems, which resolve complex geographical problems in order to take accurate and timely decisions. Nowadays, organizing of Support Systems for designing, monitoring and controlling of operational procedures follow ad hoc heuristic methodologies and forms of internal architectures, which are not able to handle large volume of data core (information), to achieve upgrading, thematic augmentation or shift and do not present the required desirable inner interoperability. The complexity of these Systems does not, also, permit the increasing or modification of the range of the data core (information) functions. There are many technical difficulties in organizing large sets of Data/Info in a single (even composite) Information System. It is imperative, by a systematic and orthological way to construct suitable organized georeferenced data cores (and programs) in order to support the operational procedures for a significant number of different missions. It is possible a general, common and adaptable core program to be constructed based on the aforementioned core data. ©2016 IEEE.

关键词： Interoperability

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Web Based Application for Decision Support System with ELECTRE Method

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Journal of Physics: Conference Series 2018年第1期1028卷

作者： Ahmad Yanie Abdurrozaq Hasibuan I Ishak M Marsono Syamsuddin Lubis N Nurmalini M Mesran Surya Darma Nasution Robbi Rahim Heri Nurdiyanto Ansari Saleh Ahmar Department of Electrical Engineering Universitas Harapan Medan Medan Indonesia Department of Industrial Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Computer System STMIK Triguna Dharma Medan Indonesia Department of Information System STMIK Triguna Dharma Medan Indonesia Department of Managemnt Informatics AMIK Intelcom Global Indo Kisaran Kisaran Indonesia Department of Informatics Engineering STMIK Budi Darma Medan Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Perlis Malaysia Department of Informatics Engineering STMIK Dharma Wacana Metro Lampung Indonesia Department of Statistics Universitas Negeri Makassar Makassar Indonesia

Decision Support System is a system commonly used to assist management in assisting decision-making in top managerial sections, Multi-Criteria Decision Making (MCDM) is one of many decision-making methods that can be used to select the best alternative from a number of alternatives based on certain criteria, one of the methods that can be utilized is the Elimination Et Choix Traduisant la Realite (ELECTRE) method that works based on the concept of outranking using pairwise comparisons of each alternative based on appropriate criteria, this paper applies the ELECTRE method in a web based application that can be employed for input and output dynamic for alternative, criteria, user values and also a fast ranking process, the choice of web based application because there is many research about decision support system but only few that applied to application, and this research tries to applied ELECTRE method to web based application.

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Efficient query answering over expressive inconsistent description logics 25

Efficient query answering over expressive inconsistent descr...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Tsalapati, Eleni Stoilos, Giorgos Stamou, Giorgos Koletsos, George School of Electrical and Computer Engineering Greece Department of Informatics National Technical University of Athens Greece Athens University of Economics and Business Greece

Inconsistent-tolerant semantics, like the IAR and ICAR semantics, have been proposed as means to compute meaningful query answers over inconsistent Description Logic (DL) ontologies. In the current paper we present a framework for scalable query answering under both the IAR and ICAR semantics, which is based on highly efficient data saturation systems. Our approach is sound and complete for ontologies expressed in the lightweight DL DL-Lite, but for more expressive DLs the problem is known to be intractable, hence our algorithm only computes upper approximations. Nevertheless, its structure motivates a new type of ICAR like semantics which can be computed in polynomial time for a very large family of DLs. We have implemented our techniques and conducted an experimental evaluation obtaining encouraging results as both our IAR- and ICAR-answering approaches are far more efficient than existing available IAR-based answering systems.

关键词： Ontology

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Designing architecture of information dashboard system to monitor implementation performance of economic census 2016 in Statistics Indonesia 4

Designing architecture of information dashboard system to mo...

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4th International Conference on Information and Communication Technology, ICoICT 2016

作者： Zulyadi Lubis, Adyanata Hayadi, B. Herawan School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Faculty Pasir Pengarian University Riau Indonesia

ISBN: (纸本)9781467398794

Sensus Ekonomi 2016 (SE2016-Economy Census 2016) which will be held in 2016 is an important and massive activity. Badan Pusat Statistik (BPS-Statistics Indonesia) taking main responsibility needs to arrange strategic and appropriate actions for the success of the event. One of the actions that can be used to support SE2016 implementation is by providing information about progress of SE2016. Therefore in this paper, it will be presented a design of architecture of information dashboard system used to monitor SE2016 implementation. The result of this research expected can be used by BPS as reference and baseline to develop advanced information dashboard of SE2016 implementation. © 2016 IEEE.

关键词： Surveys

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Combination Base64 Algorithm and EOF Technique for Steganography

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： Robbi Rahim Heri Nurdiyanto Rahmat Hidayat Ansari Saleh Ahmar Dodi Siregar Andysah Putera Utama Siahaan Ilham Faisal Sayuti Rahman Diana Suita Ahmad Zamsuri Dahlan Abdullah Darmawan Napitupulu Muhammad Ikhsan Setiawan S Sriadhi School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics STMIK Dharma Wacana Metro Lampung Indonesia Department of Information Technology Politeknik Negeri Padang Indonesia Departement of Statistics Universitas Negeri Makasar Indonesia Department of Informatics Engineering Universitas Harapan Medan Indonesia Department of Informatics Universitas Harapan Medan Medan Indonesia Department of Informatics Universitas Lancang Kuning Pekanbaru Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Indonesia Department of Civil Engineering Narotama University Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia

The steganography process combines mathematics and computer science. Steganography consists of a set of methods and techniques to embed the data into another media so that the contents are unreadable to anyone who does not have the authority to read these data. The main objective of the use of base64 method is to convert any file in order to achieve privacy. This paper discusses a steganography and encoding method using base64, which is a set of encoding schemes that convert the same binary data to the form of a series of ASCII code. Also, the EoF technique is used to embed encoding text performed by Base64. As an example, for the mechanisms a file is used to represent the texts, and by using the two methods together will increase the security level for protecting the data, this research aims to secure many types of files in a particular media with a good security and not to damage the stored files and coverage media that used.

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Dynamical system with Markov parameters for modelling system security of threats in cyberspace

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AIP Conference Proceedings 2019年第1期2116卷

作者： Miroslava Růžičková Irada Dzhalladova Josef Diblík 1University of Białystok Faculty of Mathematics and Informatics K. Ciołkowskiego 1M 15-245 Białystok Poland 2National University of Economics Department of Computer Mathematics and Information Security Kyiv 03068 Peremogy 54/1 Ukraine 3Brno University of Technology Faculty of Electrical Engineering and Communication Department of Mathematics Technická 3058/10 61600 Brno Czech Republic

The moment equations are derived for a system of linear difference equations with Markov switching. One example illustrates the use of such systems for modeling the security of threats in cyberspace.

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