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作者： Huang, Ying Zhu, Fujin Porter, Alan L. Zhang, Yi Zhu, Donghua Guo, Ying School of Information Management Wuhan University Wuhan430072 China Ku Leuven Leuven3000 Belgium School of Management and Economics Beijing Institute of Technology Beijing100081 China Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Search Technology Inc. NorcrossGA30092 United States Program in Science Technology and Innovation Policy Georgia Institute of Technology AtlantaGA30332 United States Business School China University of Political Science and Law Beijing100088 China

Technological innovation is a dynamic process that spans the life cycle of an idea, fromscientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)-namely global MPA and global key-route main analysis-for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Decision making

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Constructing a Minimized Connected Dominating Set Based on the Decentralized Nomination Algorithm in Mobile Ad Hoc Networks

SSRN

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SSRN 2023年

作者： Hsieh, Hui-Ching Chiang, Mao-Lun Lin, Tzu-Ling Liao, Lu-Wen Chen, Ze-Yu Department of New Media Communication Hsing Wu University New Taipei City Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung404336 Taiwan Department of Intelligent Production Engineering National Taichung University of Science and Technology Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Inspired by the backbone architecture of wired networks, in this study, we applied the concept of a virtual backbone to the routing scheme in a mobile ad hoc network (MANET) to develop an effective routing method. Using connected dominating set (CDS)-based methods to construct a virtual backbone network can effectively improve routing performance in a MANET. The CDS virtual backbone network we developed in the present study can respond quickly and identify routing paths through these CDS members as the network topology changes. Meanwhile, the nodes that are not CDS members can enter the power-saving mode to minimize unnecessary power consumption. Wireless nodes in a MANET can move freely within the network;therefore, CDS virtual backbones are easy to deconstruct. Researchers have developed centralized and decentralized CDS algorithms to reconstruct network topologies. When previously developed centralized CDS algorithms are applied, the system requires the information of all nodes in the network to reconstruct the virtual backbone. Therefore, the information exchange costs of such algorithms before refactoring are high. By contrast, decentralized CDS algorithms can use local information to reconstruct virtual backbones quickly. Yet, decentralized algorithms produce larger CDSs than centralized algorithms. To improve upon the performance of previously developed decentralized algorithms, we developed and herein present a new decentralized CDS algorithm called APP-CDS that can quickly construct a virtual backbone with the smallest possible CDS for use in MANET environments. © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Human-Computer Interaction and Knowledge Transfer: Study of E-Learning Achievement

Human-Computer Interaction and Knowledge Transfer: Study of ...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Doni Purnama Alamsyah Norfaridatul Ahkmaliah Othman Johan Muliadi Kerta Doni Morika Yudi Ramdhani Creativepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Design Interior Department School of Design Bina Nusantara University Jakarta Indonesia Information System Program Satu University Bandung Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance.

关键词： Human computer interaction Analytical models Electronic learning Data models Informatics Knowledge transfer

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Improving Team Winning Rate through Experiential and Observational Learning in E-Sports Games: A Multilevel Analysis 42

Improving Team Winning Rate through Experiential and Observa...

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42nd International Conference on information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Zhu, Jiantao Xiong, Bingqing Cai, Zhao Lim, Eric School of Management University of Science and Technology of China Jinzhai Road 96 Anhui Hefei China University of Science and Technology of China-City University of Hong Kong Joint Program Jinzhai Road 96 Anhui Hefei China Nottingham University Business School China University of Nottingham Ningbo China 199 Taikang East Road Ningbo China School of Information Systems and Technology Management UNSW Business School SydneyNSW2052 Australia

ISBN: (纸本)9781713893608

Despite participating in a growing number of tournaments, but can e-sports players continuously learn and improve? This question is deeply connected with experiential learning theory since players could acquire experience through repetitive participation but with increasingly smaller marginal returns. Observational learning is touted as a complementary learning mechanism by drawing on external sources of knowledge (or others) to accelerate learning progress. This study hence advances a dynamic and integrated research model that scrutinizes the moderating influence of peer- and rival-based observational learning on the relationship between experiential learning and team performance. Analyzing data gathered on 8,410 teams and 29,078 players in e-Sports games, we reveal that peer-based observational learning attenuates the negative effect of experiential learning on players’ incremental absorptive capacity, whereas rival-based observational learning reinforces the negative relationship between experiential learning and players’ incremental absorptive capacity. Players’ incremental absorptive capacity, in turn, enhances team performance. © 2021 42nd International Conference on information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Sports

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Integrating educational robot and low-cost self-made toys to enhance STEM learning performance for primary school students

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Behaviour and information technology 2024年第8期43卷 1614-1635页

作者： Hu, Chih-Chien Yang, Yu-Fen Cheng, Ya-Wen Chen, Nian-Shing Department of Information Management Tatung University Taipei Taiwan Department of Applied Foreign Languages National Yunlin University of Science and Technology Douliou Taiwan Department of Early Childhood Education Asia University Taichung Taiwan Institute for Research Excellence in Learning Sciences and Program of Learning Sciences National Taiwan Normal University Taipei Taiwan

The application of STEM (science, technology, Engineering, and Math) education in solving real-world problems is challenging. To tackle this challenge, a project-based learning approach that integrates robots with cost-effective self-made toys to problem-solving was adopted to assist rural primary school students to apply STEM skills. The purpose of this study was to evaluate the effects of the project-based learning approach which includes lecturing and hands-on activities on primary school students’ STEM learning outcomes and attitudes. An experiment was conducted with 25 primary school students who voluntarily participated in a STEM summer-camp programme. Students were asked to assemble a paper house equipped with an IoT control module and LED light switches, and write and edit robot scripts to produce a robot-based storytelling narrative using the paper house they made as a context. The results show that the project-based learning approach was an effective approach for cultivating primary school students’ STEM knowledge and skills as evidenced from the post-written test. The relationships between the students’ STEM learning outcomes and attitudes were also confirmed by a clustering analysis. Students who had higher learning attitudes also achieved higher STEM learning outcomes;the findings are also supported by the feedback from the open-ended questionnaire items. This study suggests that integrating low-cost self-made toys and robots in project-based learning activities is an effective and practical approach to enhance primary school students’ learning outcomes and learning attitudes in STEM education. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Toys

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction

Using LSTM and Regression Analysis for Railway Passenger Tra...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Fu Jie Tey Chih-Chi Tuan Cheng-En Cai Ming-Xuan Wu Yun-Sheng Wu Tin-Yu Wu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan

ISBN: (纸本)9781665492331

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data.

关键词： Temperature distribution Solid modeling Government Transportation Training data Predictive models Market research

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia Computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) Computer-aided diagnosis (CAD)

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