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arXiv 2021年

作者： Wu, Haibin Li, Xu Liu, Andy T. Wu, Zhiyong Meng, Helen Lee, Hung-Yi The Graduate Institute of Communication Engineering National Taiwan University Taiwan The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The Shenzhen International Graduate School Tsinghua University China

Previous works have shown that automatic speaker verification (ASV) is seriously vulnerable to malicious spoofing attacks, such as replay, synthetic speech, and recently emerged adversarial attacks. Great efforts have been dedicated to defending ASV against replay and synthetic speech;however, only a few approaches have been explored to deal with adversarial attacks. All the existing approaches to tackle adversarial attacks for ASV require the knowledge for adversarial samples generation, but it is impractical for defenders to know the exact attack algorithms that are applied by the in-the-wild attackers. This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms. Inspired by self-supervised learning models (SSLMs) that possess the merits of alleviating the superficial noise in the inputs and reconstructing clean samples from the interrupted ones, this work regards adversarial perturbations as one kind of noise and conducts adversarial defense for ASV by SSLMs. Specifically, we propose to perform adversarial defense from two perspectives: 1) adversarial perturbation purification and 2) adversarial perturbation detection. The purification module aims at alleviating the adversarial perturbations in the samples and pulling the contaminated adversarial inputs back towards the decision boundary. Experimental results show that our proposed purification module effectively counters adversarial attacks and outperforms traditional filters from both alleviating the adversarial noise and maintaining the performance of genuine samples. The detection module aims at detecting adversarial samples from genuine ones based on the statistical properties of ASV scores derived by a unique ASV integrating with different number of SSLMs. Experimental results show that our detection module helps shield the ASV by detecting adversarial samples. Both purification and detection methods are helpful for defending against different kinds of

关键词： Supervised learning

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Shaping the Metaverse into Reality: A Holistic Multidisciplinary Understanding of Opportunities, Challenges, and Avenues for Future Investigation

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Data Processor for Better Business Education 2023年第3期63卷 735-765页

作者： Alex Koohang Jeretta Horn Nord Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Eugene Cheng-Xi Aw Abdullah Mohammed Baabdullah Dimitrios Buhalis Tat-Huei Cham Charles Dennis Vincent Dutot Yogesh K. Dwivedi Laurie Hughes Emmanuel Mogaji Neeraj Pandey Ian Phau Ramakrishnan Raman Anshuman Sharma Marianna Sigala Akiko Ueno Lai-Wan Wong a School of Computing Middle Georgia State University Macon GA USA b Department of Management Science and Information Systems Oklahoma State University Stillwater OK USA c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiad School of Finance and Economics Nanchang Institute of Technology Nanchang People’s Republic of Chinau Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Malaysia c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiad School of Finance and Economics Nanchang Institute of Technology Nanchang People’s Republic of Chinae School of Economics and Management Yunnan Normal University Kunming People’s Republic of Chinau Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Malaysia f Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emiratesg Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysia h Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia i Department of Tourism and Hospitality Bournemouth University Poole UK c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiaj Tashkent State University of Economics Tashkent Uzbekistan k Business School Middlesex University London UK l EM Normandie Business School Métis Lab Clichy France m Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Swansea UKn Department of Management Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune India m Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Swansea UK o School of Management & Marketing University of Greenwich London UK p National Institute of Industrial Engineering Mum

ABSTRACTABSTRACTThe term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

关键词： Metaverse marketing tourism hospitality manufacturing operations management education the retailing industry banking services healthcare human resource management

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Future-proofing geotechnics workflows: accelerating problem-solving with large language models

arXiv

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arXiv 2023年

作者： Wu, Stephen Otake, Yu Mizutani, Daijiro Liu, Chang Asano, Kotaro Sato, Nana Baba, Hidetoshi Fukunaga, Yusuke Higo, Yosuke Kamura, Akiyoshi Kodama, Shinnosuke Metoki, Masataka Nakamura, Tomoka Nakazato, Yuto Saito, Taiga Shioi, Akihiro Takenobu, Masahiro Tsukioka, Keigo Yoshikawa, Ryo Research Organization of Information and Systems The Institute of Statistical Mathematics Tokyo Japan The Graduate University for Advanced Studies Department of Statistical Science Tokyo Japan Department of Civil and Environmental Engineering Tohoku University Miyagi Japan Department of Urban Management Graduate School of Engineering Kyoto University Kyoto Japan Coastal Development Institute of Technology Tokyo Japan Civil Engineering Group Urban and Civil Project Department NIKKEN SEKKEI LTD Tokyo Japan Disaster Prevention Solution Department KOZO KEIKAKU ENGINEERING Inc. Tokyo Japan Port and Harbor Department National Institute for Land and Infrastructure Management Yokosuka Japan Railway Technical Research Institute Tokyo Japan

The integration of Large Language Models (LLMs) like ChatGPT into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making. This paper delves into the innovative application of LLMs in geotechnical engineering, as explored in a hands-on workshop held in Tokyo, Japan. The event brought together a diverse group of 20 participants, including students, researchers, and professionals from academia, industry, and government sectors, to investigate practical uses of LLMs in addressing specific geotechnical challenges. The workshop facilitated the creation of solutions for four different practical geotechnical problems as illustrative examples, culminating in the development of an academic paper. The paper discusses the potential of LLMs to transform geotechnical engineering practices, highlighting their proficiency in handling a range of tasks from basic data analysis to complex, multimodal problem-solving. It also addresses the challenges in implementing LLMs, particularly in achieving high precision and accuracy in specialized tasks, and underscores the need for expert oversight. The findings demonstrate LLMs' effectiveness in enhancing efficiency, data processing, and decision-making in geotechnical engineering, suggesting a paradigm shift towards more integrated, data-driven approaches in this field. This study not only showcases the potential of LLMs in a specific engineering domain, but also sets a precedent for their broader application in interdisciplinary research and practice, where the synergy of human expertise and artificial intelligence redefines the boundaries of problem-solving. © 2023, CC BY-NC-SA.

关键词： Geotechnical engineering

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Affecting factors on perceived usefulness of area-business continuity management: A perspective from employees in industrial areas in Thailand 12

Affecting factors on perceived usefulness of area-business c...

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12th ACEH International Workshop on Sustainable Tsunami Disaster Recovery: Sharing Experience, Knowledge and Culture 2019, AIWEST-DR 2019

作者： Meechang, K. Leelawat, N. Tang, J. Ino, E. Kodaka, A. Chintanapakdee, C. Watanabe, K. Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Disaster and Risk Management Information Systems Research Group Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Advanced Disaster Prevention Engineering Center Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan Graduate School of System Design and Management Keio University Kanagawa223-8526 Japan Department of Civil Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Department of Architecture Civil Engineering and Industrial Management Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan

The 2011 Thailand Floods heavily impacted 7 industrial complexes, in which 56.7% were Japanese companies. Many notable companies received severe damage until they had to cease their production. Area Business Continuity management (Area-BCM) implemented in Thailand stems from this disaster which causes both private and public sectors to think about their business sustainability. The Area-BCM project is an on-going implementation in Thailand aiming to enhance collaboration among stakeholders in industrial areas for coping with upcoming threats. One of the most significant factors before launching a plan is to understand individual attitudes and perceptions pertaining to the Area-BCM project for the best practice, effective and continuous outcomes. This study aims to investigate various factors that affect the perceived usefulness (PU) about implementing Area-BCM. Our proposed research model is developed aligning with the behavioral model and factors influencing flood mitigation consisting of Subjective norms, Experience, Worry about flooding, and Flood hazard knowledge. Questionnaires were distributed to employees in the industrial areas which were flooded in 2011. The developed model was tested by Partial Least Square Structural Equation Modeling (PLS-SEM). The results consequently show that subjective norms and flood hazard knowledge significantly influence perceived usefulness. This can be implied that, in an organization, major thoughts of related people could shape individual perceptions about using a disaster management plan. Moreover, the governmental and local authorities should be a significant force, that helps support plan implementation and educate people about disaster knowledge. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Disasters

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Dramatic reduction in thermal conductivity by high-density dislocations in SrTiO3

arXiv

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arXiv 2025年

作者： Ding, Jinxue Zhang, Jiawen Dong, Jinfeng Higuchi, Kimitaka Nakamura, Atsutomo Lu, Wenjun Sun, Bo Fang, Xufei Institute for Applied Materials Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Institute of Materials and Systems for Sustainability Nagoya University Aichi Nagoya464-8601 Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka560−8531 Japan Tsinghua SIGS and Guangdong Provincial Key Laboratory of Thermal Management Engineering & Materials Tsinghua University Shenzhen518055 China

Decreasing thermal conductivity is important for designing efficient thermoelectric devices. Traditional engineering strategies have focused on point defects and interface design. Recently, dislocations as line defects have emerged as a new tool for regulating thermal conductivity. In ceramics-based thermoelectric materials, the key challenge lies in achieving sufficiently high-density dislocations to effectively scatter phonons, as the typical dislocation density in ceramics after bulk deformation is constrained to ~1012 m−2. In this work, we adopted the mechanical imprinting method and achieved a dislocation density of ~1015 m−2 in single-crystal SrTiO3, which is known for its room-temperature plasticity and acts as a promising material for thermoelectric applications. Using the time-domain thermoreflectance (TDTR) method, we measured a ~50% reduction in the thermal conductivity over a broad temperature range (80−400 K) with the engineered high-density dislocations. These results suggest that tuning dislocations could offer a new path to minimizing thermal conductivity for engineering thermoelectric materials. © 2025, CC BY.

关键词： Single crystals

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Analysis of Vlog Transcripts using Topic Modeling, Summarizer, and Cluster Analysis

Analysis of Vlog Transcripts using Topic Modeling, Summarize...

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Frontiers in Education (FIE) Conference

作者： Upmanyu Singh Tiffany-Rose Sikorski Erica Wortham Ekundayo Shittu Columbian College of Arts and Sciences The George Washington University Washington D.C Graduate School of Education and Human Development The George Washington University Washington D.C GW Innovation Center School of Engineering and Applied Science The George Washington University Washington D.C. Engineering Management & Systems Engineering The George Washington University Washington D.C

This paper demonstrates the use of natural language processing (NLP) models to analyze qualitative data for engineering education research purposes. Three NLP models are applied: topic modelling, which identifies salient keywords in text; summarizer, which extracts and concatenates sentences with unique meaning from a text; and cluster analysis, which groups texts together based on similar word sequences. The study applied these techniques to short video logs, or vlogs, collected as part of a case study of undergraduate engineering students' exposure to a social innovation curriculum. The curriculum aimed to teach students to use their engineering skills to identify and address issues that cause suffering for marginalized communities. In the vlogs, students responded to prompts asking them to describe what social issues they are passionate about, how they relate to these issues, and how they would propose exploring and addressing those issues. Vlogs were transcribed, pre-processed, and then examined with each technique to identify patterns within and across the prompts. Widely-recognized limitations of NLP techniques included the potential loss of important contextual information and the dependence on large volumes of data to produce valid and reliable results. Despite these limitations, the combination of three techniques was effective for locating high priority transcripts within the data corpus, identifying themes within and across vlogs, and supporting longitudinal analysis of student responses. Previous literature has documented the utility of topic modeling and other NLP techniques to analyze large volumes of written text on, for example, course evaluations or student writing assignments. Importantly, this study demonstrates the novel and meaningful application of topic modelling, summarizer, and cluster analysis to analyze a relatively small corpus of transcript data. Given these results, we are optimistic about the potential for NLP approaches to complement

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Government COVID-19 Responses and Subsequent Influences on NO2Variation in Ayutthaya, Thailand

Government COVID-19 Responses and Subsequent Influences on N...

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First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Akira Kodaka Natt Leelawat Jing Tang Yasushi Onda Naohiko Kohtake Graduate School of System Design and Management Keio University Kanagawa Japan Disaster and Risk Management Information Systems Research Group Chulalongkorn University Bangkok Thailand Faculty of Engineering International School of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9781728187600

ISBN: (纸本)9781728187617

The control of economic activities by governments for the purpose of minimizing COVID-19 spread influenced atmospheric conditions. Satellite-based technology is promising to monitor these changes and even non-specialists in geospatial analysis can use it through cloud-based open platforms. This study aims to give scientific interpretation on the causes of the atmospheric changes based on these governmental controls on human activity. The nitrogen dioxide (NO 2 ) levels were monitored in Ayutthaya Province in Thailand in an urban area including industrial zones during January 1 to April 30, 2020. An analysis was conducted with Google Earth Engine by using four datasets including NO 2 data from Sentinel-5P. The mean value of NO 2 density in 2020 decreased 12.5% compared with that in 2019. The closure of industrial facilities by the government seemed to be a key cause of the phenomena. The decline was statistically tested and resulted in a significant difference between the two periods. As the new daily cases decreased to single digits, the difference became non-significant from May 1, 2020. Single timeseries analysis on NO 2 density change enabled visualization of the brief impact of responses and policies made by the government and subsequent economic activities; however, further atmospheric and industrial activity data are required for further assessment.

关键词： COVID-19 Economics Social networking (online) Government Urban areas Monitoring MODIS

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Non-Negative Tensor Factorization of Infant Spontaneous Movements: A Pilot Study for ASD Risk Evaluation of Newborn Infants

Non-Negative Tensor Factorization of Infant Spontaneous Move...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yonei, Rikuya Furui, Akira Doi, Hirokazu Shimatani, Koji Hayashi, Hideaki Yamamoto, Midori Sakurai, Kenichi Mori, Chisato Tsuji, Toshio Hiroshima University Graduate School of Advanced Science and Engineering 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima739-8527 Japan Nagaoka University of Technology Department of Information and Management Systems Engineering 1603-1 Kamitomioka Niigata Nagaoka940-2188 Japan Prefectural University of Hiroshima Faculty of Health and Welfare 1-1 Gakuen-machi Hiroshima Mihara723-0053 Japan Osaka University Institute for Datability Science 2-8 Yamadaoka Osaka Suita565-0871 Japan Chiba University Center for Preventive Medical Sciences Department of Nutrition and Metabolic Medicine 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Center for Preventive Medical Sciences Department of Sustainable Health Science 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Graduate School of Medicine Department of Bioenvironmental Medicine 1-8-1 Inohana Chuo-ku Chiba260-8670 Japan

ISBN: (纸本)9798331531614

Early detection of infants with autism spectrum disorder (ASD) can lead to effective developmental support. In clinical practice, early screening of 18-month-old infants is implemented using a parent-completed questionnaire. However, research has suggested that signs of ASD may also appear in movement characteristics during the first few months of age. In this paper, we propose a method to evaluate infant movement from videos based on non-negative tensor factorization (NTF) and apply it to ASD risk assessment in the neonatal period. The proposed method applies NTF to pose data estimated from videos, and decomposes the infant's movements into multiple components while considering the linkage between body parts. In the experiment, we evaluated the effectiveness of the proposed method using 36 low-risk infants and 13 high-risk infants for ASD, with the aim of applying the method to ASD risk assessment. The results showed that the proposed method captured the tendency for infants to perform different movements depending on their risk level. Machine learning analysis revealed that the proposed method identified ASD risk with an accuracy exceeding 70%, which was comparable or superior to the existing video evaluation method based on heuristically designed indicators. © 2025 IEEE.

关键词： Non-negative matrix factorization

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Non-Negative Tensor Factorization of Infant Spontaneous Movements: A Pilot Study for ASD Risk Evaluation of Newborn Infants

Non-Negative Tensor Factorization of Infant Spontaneous Move...

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IEEE/SICE International Symposium on System Integration

作者： Rikuya Yonei Akira Furui Hirokazu Doi Koji Shimatani Hideaki Hayashi Midori Yamamoto Kenichi Sakurai Chisato Mori Toshio Tsuji Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Faculty of Health and Welfare Prefectural University of Hiroshima Hiroshima Japan Institute for Datability Science Osaka University Osaka Japan Department of Sustainable Health Science Center for Preventive Medical Sciences Chiba University Chiba Japan Department of Nutrition and Metabolic Medicine Center for Preventive Medical Sciences Chiba University Chiba Japan Department of Bioenvironmental Medicine Graduate School of Medicine Chiba University Chiba Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Early detection of infants with autism spectrum disorder (ASD) can lead to effective developmental support. In clinical practice, early screening of 18-month-old infants is implemented using a parent-completed questionnaire. However, research has suggested that signs of ASD may also appear in movement characteristics during the first few months of age. In this paper, we propose a method to evaluate infant movement from videos based on non-negative tensor factorization (NTF) and apply it to ASD risk assessment in the neonatal period. The proposed method applies NTF to pose data estimated from videos, and decomposes the infant’s movements into multiple components while considering the linkage between body parts. In the experiment, we evaluated the effectiveness of the proposed method using 36 low-risk infants and 13 high-risk infants for ASD, with the aim of applying the method to ASD risk assessment. The results showed that the proposed method captured the tendency for infants to perform different movements depending on their risk level. Machine learning analysis revealed that the proposed method identified ASD risk with an accuracy exceeding 70%, which was comparable or superior to the existing video evaluation method based on heuristically designed indicators.

关键词： Couplings Pediatrics Autism Tensors Pose estimation System integration Machine learning Risk management Spectrogram Videos

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Competitive Advantage Model Through Knowledge management systems Implementation to Optimize Business Sustainability 2021

Competitive Advantage Model Through Knowledge Management Sys...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Wahyu Sardjono Erma Lusia Hargo Utomo Samudra Sukardi Aninda Rahmasari Yuri Delano Regent Montororing Information Systems Management Department BINUS Graduate Program BINA NUSANTARA University Indonesia Research Capacity Building Research & Development BINA NUSANTARA University Indonesia Department of Management Faculty of Economic and Business Gajahmada University Indonesia Graduate Program Faculty of Economy and Business University of Indonesia Indonesia International Relations Department Faculty of Humanities BINA NUSANTARA University Indonesia Industrial Engineering Study Program Bhayangkara University Indonesia

ISBN: (纸本)9781450384971

Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhile, the SRI model needs to be developed to see the business sustainability through implementation knowledge management system in the organization. The data collection method was carried out, starting with building a research instrument based on knowledge management theory and continued with observation and distributing questionnaires to respondents who were involved in the business process of managing SRI. The data analysis method was used to process the data from the questionnaire, factor analysis method was used and the method to build the model used was the regression analysis method. The results obtained from this study are the discovery of 4 (four) factors with a number of indicators that are grouped on each formed factor, which can then be made and built a readiness model that reflects current conditions, so that it can be used to build predictions in the future related to readiness. implementation of knowledge management system development in order to support increased understanding of the service recovery index in the organization.

关键词： sustainability knowledge management service recovery index business factor analysis

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