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Teaching in the Age of Covid-19—The New Normal

Postdigital science and Education 2022年第3期4卷 877-1015页

作者： Jandrić, Petar Martinez, Ana Fuentes Reitz, Charles Jackson, Liz Grauslund, Dennis Hayes, David Lukoko, Happiness Onesmo Hogan, Michael Mozelius, Peter Arantes, Janine Aldous Levinson, Paul Ozoliņš, Jānis John Kirylo, James D. Carr, Paul R. Hood, Nina Tesar, Marek Sturm, Sean Abegglen, Sandra Burns, Tom Sinfield, Sandra Stewart, Georgina Tuari Suoranta, Juha Jaldemark, Jimmy Gustafsson, Ulrika Monzó, Lilia D. Kokić, Ivana Batarelo Kihwele, Jimmy Ezekiel Wright, Jake Kishore, Pallavi Stewart, Paul Alexander Bridges, Susan M. Lodahl, Mikkel Bryant, Peter Kaur, Kulpreet Hollings, Stephanie Brown, James Benedict Steketee, Anne Prinsloo, Paul Hazzan, Moses Kayode Jopling, Michael Mañero, Julia Gibbons, Andrew Pfohl, Sarah Humble, Niklas Davidsen, Jacob Ford, Derek R. Sharma, Navreeti Stockbridge, Kevin Pyyhtinen, Olli Escaño, Carlos Achieng-Evensen, Charlotte Rose, Jennifer Irwin, Jones Shukla, Richa SooHoo, Suzanne Truelove, Ian Buchanan, Rachel Urvashi, Shreya White, E. Jayne Novak, Rene Ryberg, Thomas Arndt, Sonja Redder, Bridgette Mukherjee, Mousumi Komolafe, Blessing Funmi Mallya, Madhav Devine, Nesta Sattarzadeh, Sahar D. Hayes, Sarah Zagreb University of Applied Sciences Zagreb Croatia University of Wolverhampton Wolverhampton UK University West Trollhättan Sweden Kansas City Kansas Community College Kansas City USA Education University of Hong Kong Hong Kong China University College of Northern Denmark (UCN) Aalborg Denmark Further Education Worcester UK Institute of Development Studies University of Dar Es Salaam Dar es Salaam Tanzania School of Psychology National University of Ireland Galway Galway Ireland Mid Sweden University Östersund/Stockholm Sweden Victoria University Melbourne Australia Fordham University New York City USA College of Philosophy and Theology University of Notre Dame Australia Fremantle Australia Faculty of History and Philosophy University of Latvia Riga Latvia University of South Carolina Columbia USA Département des sciences de l’éducation Université du Québec en Outaouais Gatineau Canada Faculty of Education and Social Work University of Auckland Auckland New Zealand School of Architecture Planning and Landscape University of Calgary Calgary Canada Centre for Professional and Educational Development London Metropolitan University London UK Auckland University of Technology Auckland New Zealand Faculty of Social Sciences Tampere University Tampere Finland Department of Education Mid Sweden University Sundsvall Sweden Department of Applied Educational Science Umeå University Umeå Sweden Attallah College of Educational Studies Chapman University Orange USA Department of Pedagogy Faculty of Humanities and Social Sciences University of Split Split Croatia Department of Education Mzumbe University Morogoro Tanzania Center for Learning Innovation University of Minnesota Rochester Rochester USA Jindal Global Law School O.P. Jindal Global University Sonipat India MIMA School of Art and Design Teesside University Middlesbrough UK The University of Hong Kong Hong Kong China Dania Academy Randers Denmark University of Sydney Sydney Australia J

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Measurement of the and tH production rates in the H → decay channel using proton-proton collision data at = 13 TeV

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Journal of High Energy Physics 2025年第2期2025卷 1-75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. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. 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. Gomes De Souza, R. Macedo, M. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. 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. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles 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 de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina 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 Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City U.S.A. Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong 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 Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Hangzhou

An analysis of the production of a Higgs boson (H) in association with a top quark-antiquark pair ( $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ ) or a single top quark (tH) is presented. The Higgs boson decay into a bottom quark-antiquark pair (H → $$ \textrm{b}\overline{\textrm{b}} $$ ) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at $$ \sqrt{s} $$ = 13 TeV in 2016–2018, which correspond to an integrated luminosity of 138 fb−1. The observed $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate relative to the standard model expectation is 0.33 ± 0.26 = 0.33 ± 0.17(stat) ± 0.21(syst). Additionally, the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of $$ {19.3}_{-6.0}^{+9.2} $$ . Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels.

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Combination of Measurements of the Top Quark Mass from Data Collected by the ATLAS and CMS Experiments at s=7 and 8 TeV

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Physical Review Letters 2024年第26期132卷 261902-261902页

作者： A. Hayrapetyan A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic P. S. Hussain M. Jeitler N. Krammer A. Li D. Liko I. Mikulec J. Schieck R. Schöfbeck D. Schwarz M. Sonawane S. Templ W. Waltenberger C.-E. Wulz M. R. Darwish T. Janssen P. Van Mechelen E. S. Bols J. D’Hondt S. Dansana A. De Moor M. Delcourt H. El Faham S. Lowette I. Makarenko D. Müller A. R. Sahasransu S. Tavernier M. Tytgat S. Van Putte D. Vannerom B. Clerbaux G. De Lentdecker L. Favart D. Hohov J. Jaramillo A. Khalilzadeh K. Lee M. Mahdavikhorrami A. Malara S. Paredes L. Pétré N. Postiau L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer M. De Coen D. Dobur Y. Hong J. Knolle L. Lambrecht G. Mestdach C. Rendón A. Samalan K. Skovpen N. Van Den Bossche J. van der Linden L. Wezenbeek A. Benecke G. Bruno C. Caputo C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre J. Lidrych P. Mastrapasqua K. Mondal T. T. Tran S. Wertz G. A. Alves E. Coelho C. Hensel T. Menezes De Oliveira A. Moraes P. Rebello Teles M. Soeiro W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza R. Gomes De Souza J. Martins C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro A. Sznajder M. Thiel A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores P. G. Mercadante S. F. Novaes B. Orzari Sandra S. Padula A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Shopova G. Sultanov A. Dimitrov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka S. Keshri S. Thakur T. Cheng Q. Guo T. Javaid L. Yuan Z. Hu J. Liu K. Yi G. M. Chen H. S. Chen M. Chen F. Iemmi C. H. Jiang A. Kapoor H. Liao Z.-A. Liu R. Sharma J. N. Song J. Tao C. Wang J. Wang Z. Wang H. Zhang A. Agapitos Y. Ban A. Levin C. Li Q. Li Y. Mao S. J. Qian X. Sun D. Wang H. Yang L. Zhang C. Zhou Z. You N. Lu G. Bauer X. Gao D. Leggat H. Okawa Y. Zhang Z. Lin C. Lu M. Xiao C. Avila D. A. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Indian Institute of Technology Madras Madras India APC Université Paris Cité CNRS/IN2P3 Paris France Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Korea University Seoul Korea 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 IJCLab Université Paris-Saclay CNRS/IN2P3 91405 Orsay France 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 Department of Physics Oxford University Oxford United Kingdom School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MOE) SKLPPC Shanghai China Department of Physics Chinese University of Hong Kong Shatin N.T. Hong Kong China Department of Physics University of Michigan Ann Arbor Michigan USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei China Department of Physics Nanjing University Nanjing 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 Zhe

A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The datasets used correspond to an integrated luminosity of up to 5 and 20 fb−1 of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exploit both the semileptonic and hadronic decays of the top quark, and a measurement using events enriched in single top quark production via the electroweak t channel. The combination accounts for the correlations between measurements and achieves an improvement in the total uncertainty of 31% relative to the most precise input measurement. The result is mt=172.52±0.14(stat)±0.30(syst) GeV, with a total uncertainty of 0.33 GeV.

关键词： Top quark Hadron colliders

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Search for and production in the final state using proton-proton collisions at

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The European Physical Journal C 2024年第7期84卷 1-33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. Van Mechelen, P. Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. 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, A. Ban, Y. Levin, A Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerp Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Brussels 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 de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina 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 Nanjing Normal University Nanjing China The University of Iowa Iowa City USA Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong 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 Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai China Zhejiang University Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechan

A search for $${\text {Z}{}{}} {\text {Z}{}{}} $$ and $${\text {Z}{}{}} {\text {H}{}{}} $$ production in the $${\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} {\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} $$ final state is presented, where H is the standard model (SM) Higgs boson. The search uses an event sample of proton-proton collisions corresponding to an integrated luminosity of 133 $$\,\text {fb}^{-1}$$ collected at a center-of-mass energy of 13 $$\,\text {Te}\hspace{-.08em}\text {V}$$ with the CMS detector at the CERN LHC. The analysis introduces several novel techniques for deriving and validating a multi-dimensional background model based on control samples in data. A multiclass multivariate classifier customized for the $${\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} {\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} $$ final state is developed to derive the background model and extract the signal. The data are found to be consistent, within uncertainties, with the SM predictions. The observed (expected) upper limits at 95% confidence level are found to be 3.8 (3.8) and 5.0 (2.9) times the SM prediction for the $${\text {Z}{}{}} {\text {Z}{}{}} $$ and $${\text {Z}{}{}} {\text {H}{}{}} $$ production cross sections, respectively.

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of Information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Efficiently Inferring Top-k Elephant Flows based on Discrete...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kun Xie Jiazheng Tian Xin Wang Gaogang Xie Jigang Wen Dafang Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

关键词： Inference algorithms Binary codes Data models Partitioning algorithms Indexes Computational modeling

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Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution

Online Internet Anomaly Detection With High Accuracy: A Fast...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaocan Li Kun Xie Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang Zheng Qin Department of Electrical and Computer Engineering State University of New York at Stony Brook USA College of Computer Science and Electronics Engineering Hunan University Changsha China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

关键词： Anomaly detection Monitoring Matrix decomposition Microsoft Windows Principal component analysis Data models

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Actuation attacks on constrained linear systems: A set-theoretic analysis

arXiv

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arXiv 2019年

作者： Trodden, P.A. Maestre, J.M. Ishii, H. Department of Automatic Control Ystems Engineering University of Sheffield Sheffield United Kingdom Departmento de Ingenieria de Sistemas y Automatica University of Seville Seville Spain Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This paper considers a constrained discrete-time linear system subject to actuation attacks. The attacks are modelled as false data injections to the system, such that the total input (control input plus injection) satisfies hard input constraints. We establish a sufficient condition under which it is not possible to maintain the states of the system within a compact state constraint set for all possible realizations of the actuation attack. The developed condition is a simple function of the spectral radius of the system, the relative sizes of the input and state constraint sets, and the proportion of the input constraint set allowed to the attacker. Copyright © 2019, The Authors. All rights reserved.

关键词： Linear systems

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Advancing neutron and photon shielding for space technology and electronic protection: Investigation of Nylon-Based and non-nylon-based polymers as soft protection materials through PHITS code

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Radiation Physics and Chemistry 2025年 237卷

作者： Hessa Alkarrani Ghada Almisned Hesham M.H. Zakaly Gokhan Kilic Shams A.M. Issa Duygu Sen Baykal Antoaneta Ene H.O. Tekin Research Institute for Medical and Health Sciences University of Sharjah Sharjah 27272 Sharjah United Arab Emirates Department of Medical Diagnostic Imaging College of Health Sciences University of Sharjah 27272 Sharjah United Arab Emirates Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Institute of Physics and Technology Ural Federal University 620002 Ekaterinburg Russia Department of Physics and Technical Sciences Western Caspian University Baku Azarbaijan Eskisehir Osmangazi University Faculty of Science Department of Physics TR-26040 Eskisehir Türkiye Department of Physics Faculty of Science University of Tabuk Tabuk Saudi Arabia Istanbul Nisantasi University Faculty of Engineering and Architecture Mechatronics Engineering 34398 Istanbul Türkiye Department of Chemistry Physics and Environment INPOLDE Research Center Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street 800008 Galati Romania Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul 34396 Türkiye

This study investigates the neutron and photon attenuation properties of various Nylon-Based and Non-Nylon-Based Polymers as soft protection materials for space technology and related applications. We analyzed the material densities, mass and linear attenuation coefficients, half-value layers, mean free paths, effective atomic numbers, effective electron densities, fast neutron effective removal cross-sections, and energy absorption buildup factor values of several polymers through PHITS code and other computational tools. Nylon Type 11 (Rilsan) emerged as a standout, offering substantial resistance to neutron radiation, highlighting its potential in sectors where neutron shielding is paramount. Conversely, Polyvinyl Chloride (PVC), characterized by its unique composition, demonstrated superior gamma-ray attenuation capabilities, making it an attractive option for environments predominantly exposed to gamma radiation. Our results showed that the efficacy of PVC in gamma shielding and Nylon Type 11's (Rilsan) notable neutron attenuation, demonstrating their potential as lightweight and environmentally sustainable alternatives for specific applications where lead's heavy weight poses a challenge. While lead remains superior in gamma shielding due to its high atomic number and density, the tested polymers offer practical advantages in applications requiring weight reduction and environmental considerations.

关键词： Neutron PHITS Polymers Radiation protection Space radiation Transmission

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A Pipelining Loop Optimization Method for Dataflow architecture

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Journal of computer science & technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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