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Understanding perceived COVID-19 changes, collectivism, and social exclusion: A cross-cultural study in 32 countries

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International Journal of Intercultural Relations 2025年 107卷

作者： Zhou, Xiaoyu English, Alexander Scott Wei, Liuqing Yudiarso, Ananta Dash, Arobindu Tipandjan, Arun Biddle, Ashley Nam, Benjamin H. Boonroungrut, Chinun Chettiar, Cicilia Daniele, Paolini Dubrov, Dmitrii Grigoryev, Dmitry Šakan, Dušana Oliver, Eliza Medosevic - Korjenic, Elma Fabricio Licoa Campos, Adolfo Novaes, Felipe Maricchiolo, Fridanna Navarro-Carrillo, Ginés Belen, Hacer Gunawan, Hendrik Jiang, Huang Hofhuis, Joep Kunst, Jonas R. Park, Joonha Neto, Jose Candido Pereira Huang, Kaiyue Addington-Lefringhausen, Katharina Hossain, Kazi Nur Martínez-Buelvas, Laura Yousefi, Mahdi Yuki, Masaki F. Falavarjani, Mehrdad Schwarzenthal, Miriam Klimek-Tulwin, Monika Kosakowska-Berezecka, Natasza Geeraert, Nicolas Khieowan, Nuannut Chobthamkit, Phatthanakit Sun, Qian Cowden, Richard G. Castro, Rita Thomson, Robert Tong, Rongtian Malik, Sadia Lins, Samuel Batić Očovaj, Sanja Wang, Shuang Aquino, Sibele D. Kulich, Steve Farid, Tahir Alves, Tales Talhelm, Thomas Jovanović, Veljko Yeung, Victoria Wai Lan Li, Xiaoyuan Zhang, Xinyi Computational Communication Research Center Beijing Normal University Zhuhai China School of Journalism and Communication Beijing Normal University Beijing China School of Psychology Wenzhou-Kean University China Department of Psychology Hubei University China Faculty of Psychology University of Surabaya Indonesia Department of Social Sciences International University of Business Agriculture and Technology China International centre for psychological counseling and social research China Social Sciences University of Hawaiʻi Leeward Community College United States School of Education Shanghai International Studies University China Faculty of Education Silpakorn University Thailand Department of Psychology Maniben Nanavati Women's College India Faculty of Human Sciences Education and Sport Pegaso University Italy Center for Sociocultural Research HSE University Russian Federation Department of Psychology Union University Faculty of Law and Business Studies dr Lazar Vrkatić Serbia School of Psychology University of Oslo Norway Department of Psychology University of Sarajevo University of Sarajevo Bosnia and Herzegovina Literature Research Institute Shanghai International Studies University China Department of Psychology Pontifical Catholic University of Rio de Janeiro Brazil Department of Education University of Roma Tre Italy Department of Social Psychology University of Granada Spain Educational Sciences Psychological Counseling and Guidance Bursa Uludag University Turkey Faculty of Social and Behavioural Sciences Utrecht University Netherlands Institute for Language Materials Development Shanghai Foreign Language Education Press China Amsterdam School of International Business (AMSIB) Amsterdam University of Applied Sciences Germany Department of Commuication and Culture BI Norwegian Business School Norway of Education Kyoto University Japan Pontifical Catholic University of Rio de Janeiro Brazil Department of Literature and Media Yulin Normal University

This cross-cultural study examines the relationship between perceived COVID-19 changes due to the impacts on everyday life by the pandemic with social exclusion and across 32 cultures and their levels in collectivism. With data from the start of the first outbreak from March 2020 to June 2020 (N = 9245), multilevel analysis indicated that as individuals perceive greater daily life changes induced by the pandemic, they experience heightened levels of social exclusion, with this association being particularly pronounced in less collectivistic cultures. These findings underscore the importance of considering cultural context in understanding responses to crises such as the COVID-19 pandemic, with implications for culturally sensitive interventions aimed at promoting social inclusion across diverse cultural contexts. © 2025 Elsevier Ltd

关键词： Collectivism Perceived COVID-19 changes Perceived social exclusion

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SPOT: A low cost intelligent parking system for urban malls 3

SPOT: A low cost intelligent parking system for urban malls

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3rd International Conference on Informatics and Computing, ICIC 2018

作者： Olowolayemo, Akeem Alarood, Ala Abdulsalam Yap, Shee Niang Mantoro, Teddy Dept of Cognitive Science Faculty of Cognitive Science Human Development University Malaysia Sarawak Malaysia Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Faculty Science and Technology Sampoerna University Jakarta Indonesia

ISBN: (纸本)9781538669204

Manual searching for vacant parking spaces in shopping malls is a tedious process, especially during the weekends and public holidays. It is rather troublesome for consumers to drive around without any directions in search for an empty parking space. Previous studies have confirmed that majority of the drivers experience difficulties in finding a vacant parking space in multilevel car parks and often spend more than 10 minutes searching for parking lots on weekends and public holidays. Current solutions available are probably expensive to implement in terms of hardware and software requirements for shopping malls in developing economies such as Malaysia. Furthermore, the process of payment of parking fee at an Autopay machine is not only bothersome but avoidable. Although leaving a car park may not be as tedious as finding a vacant parking lot, it is still considered a time-consuming activity to most drivers. If given the chance, most people would choose to avoid the trouble of walking to an Autopay machine due to finding incessant long queues or shortage of change at the machine. Advanced societies have adopted Touch'n'Go system which is more convenient than the conventional Autopay system. However, developing economies like Malaysia are still using the manual parking as well as Autopay machine. This study presents a low cost intelligent parking system for developing countries shopping malls to mitigate the above challenges. The system provides an added safety and security measures to prevent crimes such as thefts, kidnapping, vandalism and accidents. The system proposed takes advantage of the fact that smartphones have become pervasive. © 2018 IEEE.

关键词： Smart city

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Energy Conservation Based on Destination-Sequenced Distance-Vector Protocol in Intelligent Internet of Things

Energy Conservation Based on Destination-Sequenced Distance-...

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computer science and Electronic Engineering Conference (CEEC)

作者： Khattab M Ali Alheeti Laith Al-Jobouri Duaa Al-Dosary Muzhir Shaban Al-ani College of Computer Sceinces and Information Technology University Of Anbar Iraq School of Engineering Arts Science and Technology University of Suffolk United Kingdom College of Computer Sceinces and Information Technology University of Anbar Iraq Department of Information Technology University of Human Development Sulaymaniyah Iraq

The Intelligent Internet of Things (IoT) caught a significant attention in research area and plays a major role in the advancement of technologies to expand the connectivity of electronics devices. Recent studies expected that in 2020 there will be more than 50 million devices connected to IoT. Any technology expansion will always encounter several issues in matters of the driven techniques and energy consumption. One of the performance enhancement techniques for internet of things is power saving. In this paper, a new power saving method is presented to reduce devices power consumption in IoT. The system proposed can save energy by putting node or device into sleep mode depending on the trace file extracted of the network, this can be considered as a new intelligent feature. Our approach proved that a significant enhancement can be done to the node performance in IoT.

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Speaker adapted beamforming for multi-channel automatic speech recognition

arXiv

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arXiv 2018年

作者： Menne, Tobias Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

This paper presents, in the context of multi-channel ASR, a method to adapt a mask based, statistically optimal beamforming approach to a speaker of interest. The beamforming vector of the statistically optimal beamformer is computed by utilizing speech and noise masks, which are estimated by a neural network. The proposed adaptation approach is based on the integration of the beamformer, which includes the mask estimation network, and the acoustic model of the ASR system. This allows for the propagation of the training error, from the acoustic modeling cost function, all the way through the beamforming operation and through the mask estimation network. By using the results of a first pass recognition and by keeping all other parameters fixed, the mask estimation network can therefore be fine tuned by retraining. Utterances of a speaker of interest can thus be used in a two pass approach, to optimize the beamforming for the speech characteristics of that specific speaker. It is shown that this approach improves the ASR performance of a state-of-the-art multi-channel ASR system on the CHiME-4 data. Furthermore the effect of the adaptation on the estimated speech masks is discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost functions

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Teaching in the Age of Covid-19—1 Year Later

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Postdigital science and Education 2021年第3期3卷 1073-1223页

作者： Jandrić, Petar Hayes, David Levinson, Paul Christensen, Line Lisberg Lukoko, Happiness Onesmo Kihwele, Jimmy Ezekiel Brown, James Benedict Reitz, Charles Mozelius, Peter Nejad, Harry G. Martinez, Ana Fuentes Arantes, Janine Aldous Jackson, Liz Gustafsson, Ulrika Abegglen, Sandra Burns, Tom Sinfield, Sandra Hogan, Michael Kishore, Pallavi Carr, Paul R. Batarelo Kokić, Ivana Prinsloo, Paul Grauslund, Dennis Steketee, Anne Achieng-Evensen, Charlotte Komolafe, Blessing Funmi Suoranta, Juha Hood, Nina Tesar, Marek Rose, Jennifer Humble, Niklas Kirylo, James D. Mañero, Julia Monzó, Lilia D. Lodahl, Mikkel Jaldemark, Jimmy Bridges, Susan M. Sharma, Navreeti Davidsen, Jacob Ozoliņš, Jānis (John) Bryant, Peter Escaño, Carlos Irwin, Jones Kaur, Kulpreet Pfohl, Sarah Stockbridge, Kevin Ryberg, Thomas Pyyhtinen, Olli SooHoo, Suzanne Hazzan, Moses Kayode Wright, Jake Hollings, Stephanie Arndt, Sonja Gibbons, Andrew Urvashi, Shreya Forster, Daniella J. Truelove, Ian Mayo, Peter Rikowski, Glenn Stewart, Paul Alexander Jopling, Michael Stewart, Georgina Tuari Buchanan, Rachel Devine, Nesta Shukla, Richa Novak, Rene Mallya, Madhav Biličić, Eva Sturm, Sean Sattarzadeh, Sahar D. Philip, Abey P. Redder, Bridgette White, E. Jayne Ford, Derek R. Allen, Quaylan Mukherjee, Mousumi Hayes, Sarah Zagreb University of Applied Sciences Zagreb Croatia University of Wolverhampton Wolverhampton UK Further Education Worcester UK Fordham University New York City USA Department of Communication and Psychology Aalborg University Aalborg Denmark Institute of Development Studies University of Dar es Salaam Dar es Salaam Tanzania Department of Education Mzumbe University Morogoro Tanzania Umeå School of Architecture Umeå University Umeå Sweden Kansas City Kansas Community College Kansas City USA Department of Computer and System Science (DSV) Mid Sweden University Östersund/Stockholm Sweden Jindal Institute of Behavioural Sciences O. P. Jindal Global University Haryana India Katedralskolan (Lund)/University West Trollhättan Sweden Victoria University Melbourne Australia Education University of Hong Kong Hong Kong China Department of Applied Educational Science Umeå University Umeå Sweden School of Architecture Planning and Landscape University of Calgary Calgary Canada Centre for Professional and Educational Development London Metropolitan University London UK School of Psychology National University of Ireland Galway Galway Ireland Jindal Global Law School O.P. Jindal Global University Sonipat India Département des sciences d l’éducation Université du Québec en Outaouais Gatineau Canada Department of Pedagogy Faculty of Humanities and Social Sciences University of Split Split Croatia University of South Africa Pretoria South Africa University College of Northern Denmark (UCN) Aalborg Denmark School of Education Millikin University Decatur Il USA Attallah College of Educational Studies Chapman University Orange USA Science Education Department Adekunle Ajasin University Ondo Nigeria Curriculum and Pedagogy College of Teacher Education Zhejiang Normal University Jinhua China Faculty of Social Sciences Tampere University Tampere Finland Faculty of Education University of Auckland Auckland New Zealand School of Social Science Education and

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A Learning Model to Improve Learning Outcome on Experiential Learning in a Multi-Phase Internship: a Case Study of the Internship Program of a Thai University

A Learning Model to Improve Learning Outcome on Experiential...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Chanakarn Kingkaew Thepchai Supnithi Thanaruk Theeramunkong Kai Morita Koji Tanaka Mitsuru Ikeda School of Knowledge Science Japan Advanced Institute of Science and Technology Japan School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Thailand School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thailand Department of Psychological Science College of Informatics and Human Communication Kanazawa Institute of Technology Nonoichi Japan

The internship is one of the crucial learning processes that provide a unique experience to combine what they learned in university courses and how to use it in the real society during their university lives as it provides the opportunity to learn through work experience offered by the external organization. Although learner can learn career skills through the internship, most learner cannot make good reflection and cannot learn how to learn from experience. We think the cause of this problem is that most learner cannot reflect their experience and cannot clear the abstract concept and apply it to the next trial. This implies that those learners can improve their learning through internship by acquiring experiential learning skills. In the internship in general, the mentor has the important education roles. One is to foster career skills learning, and the other is to promote experiential learning. However, in most case, the mentor is very good at career skill teaching, but most of the mentor is not well prepared to support experiential learning. In response to the above problems, to support learners to realized good learning, we provide a learning environment for the learner to support experiential learning. A guideline for the learner to let them aware of the critical concept of internship and experiential learning. Also, we provide a web system to practice reflective writing to understand and able to apply experiential learning concepts. In this paper, as the preliminary research to develop learning environment, the analysis of the learner learning activity of the current internship program and show the prototype of the conceptualization of the educational concepts to support the experiential learning.

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Authors’ Response to Peer Reviews of “Emergence of the First Strains of SARS-CoV-2 Lineage B.1.1.7 in Romania: Genomic Analysis”

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JMIRx Med 2021年第3期2卷

作者： Andrei Lobiuc Mihai Dimian Olga Sturdza Roxana Filip Mihai Covasa Department of Human Health and Development College of Physical Exercise and Sport Stefan cel Mare University of Suceava Suceava Romania Department of Computers Electronics and Automation College of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan cel Mare University of Suceava Suceava Romania Suceava County Emergency Hospital Suceava Romania

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Digital Twin technology advancing Industry 4.0 and Industry 5.0 across Sectors

Results in Engineering

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Results in Engineering 2025年

作者： Ocident Bongomin Mwewa Chikonkolo Mwape Nonsikelelo Sheron Mpofu Brendah Kembabazi Bahunde Richard Kidega Ibrahim Luqman Mpungu Godias Tumusiime Cynthia Awuor Owino Yannick Marnaigue Goussongtogue Aregawi Yemane Proscovia Kyokunzire Clement Malanda Jimmy Komakech Dan Tigalana Onesmas Gumisiriza George Ngulube Department of Mechanical Engineering Faculty of Engineering and Survey Ndejje University P.O. Box 7088 Kampala Uganda Department of Manufacturing Textile and Industrial Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Egypt Solid Waste Management Center of Excellence Faculty of Engineering Ain Shams University Cairo 11566 Egypt Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhoftstraße 1a Witzenhausen 37213 Germany Department of Crop Inspection and Certification Ministry of Agriculture Animal Industry and Fisheries P.O Box 102 Entebbe Uganda Department of Mining Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Mining and Mineral Processing Engineering School of Mines and Engineering Taita Taveta University P.O. Box 635-80300 Voi Kenya Department of Technical Teacher and Instructor Education School of Education Kyambogo University P.O. Box 1 Kyambogo Kampala Uganda Department of Polymer Textile and Industrial Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University P.O. Box 7062 Kampala Uganda Department of Civil and Structural Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Department of Industrial Engineering School of Mechanical and Industrial Engineering Mekelle University Mekelle Ethiopia Department of Polymer Engineering Graduate School Chonnam National University Gwangju 61186 Republic of Korea Department of Applied Studies Malawi University of Science and Technology P.O. Box 5196 Limbe Malawi College of Education and Human Development Western Michigan University 1903 W Michigan USA Department of Oil and Gas Industry Facility Design and Construction Faculty of Pipeline Construction Ufa State Petroleum Technological University 450064 Street K

Digital Twin (DT) technology is transforming industrial systems by integrating physical assets with digital models, enabling real-time monitoring, predictive analytics, and process optimization, particularly within the framework of Industry 4.0 (I4.0). As the global industrial landscape shifts toward Industry 5.0 (I5.0), DTs are increasingly being redefined to support human-centric innovation, sustainability, and system resilience. This review examines the evolving role of DTs in bridging the efficiency-driven goals of I4.0 with the inclusive, sustainable objectives of I5.0. It explores ten enabling technologies such as artificial intelligence (AI), internet of things, blockchain, cloud and edge computing, and extended reality, while discussing both the opportunities and challenges posed by I5.0. The study emphasizes key principles of DTs, including real-time synchronization, feedback mechanisms, and lifecycle integration. A detailed sectorial analysis across manufacturing, infrastructure, energy, transportation, mining, agriculture, and healthcare illustrates how DTs are being applied in diverse contexts to enhance operational efficiency, product quality, and decision-making. The mapping of applications by country, sector, and industrial focus reveals growing trends toward I5.0 in areas such as logistics and infrastructure. Common application domains include monitoring, optimization, prediction, and decision support. Despite their potential, DT adoption faces challenges including high implementation costs, data integration issues, cybersecurity concerns, and lack of standardization. The review discusses these barriers alongside the importance of validation and security for trusted deployment. It concludes by identifying future directions, including cognitive twins, industrial metaverse integration, and ethical AI. DTs are positioned as foundational technologies for advancing sustainable, resilient, and human-centered industrial systems.

关键词： Digital Twins Industry 4.0 Industry 5.0 human-Centric Innovation Sustainability Artificial Intelligence Predictive Analytics

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ACOUSTIC MODELING OF SPEECH WAVEFORM BASED ON MULTI-RESOLUTION, NEURAL NETWORK SIGNAL PROCESSING

ACOUSTIC MODELING OF SPEECH WAVEFORM BASED ON MULTI-RESOLUTI...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zoltán Tüske Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781538646595

Recently, several papers have demonstrated that neural networks (NN) are able to perform the feature extraction as part of the acoustic model. Motivated by the Gammatone feature extraction pipeline, in this paper we extend the waveform based NN model by a second level of time-convolutional element. The proposed extension generalizes the envelope extraction block, and allows the model to learn multi-resolutional representations. Automatic speech recognition (ASR) experiments show significant word error rate reduction over our previous best acoustic model trained in the signal domain directly. Although we use only 250 hours of speech, the data-driven NN based speech signal processing performs nearly equally to traditional handcrafted feature extractors. In additional experiments, we also test segment-level feature normalization techniques on NN derived features, which improve the results further. However, the porting of speech representations derived by a feed-forward NN to a LSTM back-end model indicates much less robustness of the NN front-end compared to the standard feature extractors. Analysis of the weights in the proposed new layer reveals that the NN prefers both multi-resolution and modulation spectrum representations.

关键词： ASR neural network time-signal waveform modulation spectrum feature representation modulation spectra waveforms time signal Speech recognition Neural network aquifer storage and recovery Feature extraction Speech signal processing Multi-resolution Acoustic models

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Predicting risk of dyslexia with an online gamified test

arXiv

引用

arXiv 2019年

作者： Rello, Luz Baeza-Yates, Ricardo Ali, Abdullah Bigham, Jeffrey P. Serra, Miquel Department of Information Systems and Technology IE University Madrid Spain KCCIS Northeastern University at Silicon Valley San JoseCA United States School of Computer Science University of Washington SeattleWA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Cognition and Development University of Barcelona Barcelona Spain

Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and associated predictive machine learning model. In a study with more than 4,300 participants, our model correctly detected nearly 80% of participants with dyslexia. Our work shows that dyslexia can be screened using a computational approach. An online screening tool based on our methods has already been used by more than 200,000 people. Copyright © 2019, The Authors. All rights reserved.

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