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IEEE Conference on Wireless Communications and Networking

作者： Lindong Zhao Jingyue Tang Mingzhe Chen Liang Zhou Weihua Zhuang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

关键词： Performance evaluation Knowledge engineering computer aided instruction Costs Distance learning Scalability Graph neural networks Data models Internet of Things Interchannel interference

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Machine Learning-Based DC to AC Dual Quasi Z-source Converter for Distribution Generation Applications 4

Machine Learning-Based DC to AC Dual Quasi Z-source Converte...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Uppara, Rajesh Priya, S. Department of EEE Siddaganga Institute of Technology Karnataka Tumakuru India

ISBN: (纸本)9798331530402

Using artificial neural networks to simulate a dual QZS converter high step up dc to ac converter is the main goal of this work. The standard converter of QZS with PI controller could be modified by incorporating an ANN controller, diode, inductor, and two capacitors to resemble the dual QZSconverter. The recommended dual QZS converter achieves all the benefits of a standard QZS converter, including continuous input current, voltage boost, and enhanced reliability. Furthermore, the recommended dual QZS converter reduces shoot through duty cycle by 30% without adding additional switches to the typical QZS converter, all at the same voltage boost factor. Acquired in both shoot through and non-shot through modes, simulation results are shown along with a description of the dual QZS converter. © 2025 IEEE.

关键词： computer aided instruction

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Detecting Model Poisoning Attacks Via Dummy Symbol Insertion for Secure Over-the-Air Federated Learning

Detecting Model Poisoning Attacks Via Dummy Symbol Insertion...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Hang Zhou Yi-Han Chiang Caijuan Chen Xiaoyan Wang Yusheng Ji Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Department of Electrical and Electronic Systems Engineering Osaka Metropolitan University Osaka Japan Graduate School of Science and Engineering Ibaraki University Hitachi Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

With the rapid advancement of intelligent IoT services, the need for high-quality and efficient wireless communication has become increasingly critical for information exchanges and collaborations among intelligent edge devices such as autonomous mobile robots (AMRs), robots in smart factories, and surveillance cameras, etc., Federated Learning (FL) is a promising distributed learning framework by exploring the computation capability on edge devices while protecting data privacy of users. On the other hand, Over-the-Air (OTA) computation techniques is a new paradigm of integrated computation and communication. OTA based FL (OTA-FL) can enhance spectrum efficiency by leveraging the superposition properties of wireless channels for model aggregations. The implementation of OTA-FL on edge devices can further protect user data privacy for clients, improve the throughput of inter-device communications, and enhance the distributed learning performance. However, despite these advantages, the convergence of OTA-FL systems remain vulnerable to model poisoning attacks, presenting significant challenges. In this study, we address these vulnerabilities by proposing a two-phase detection mechanism that secures OTA-FL system while preserving high spectral efficiency. Our simulation results demonstrate that under varying conditions, our approach can make distributed learning more secure and communication-efficient.

关键词： computer aided instruction Data privacy Federated learning Distance learning Wireless networks Computational modeling Surveillance Symbols Throughput Convergence

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Determinant Factors Analysis of Behavior Intention using Massive Open Online Course (MOOC) as a Learning Platforms for Generation Z

Determinant Factors Analysis of Behavior Intention using Mas...

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International Conference on Educational and Information Technology (ICEIT)

作者： Kevin Tanaka Avril Angelin Yakob Utama Chandra Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

The Massive Open Online Courses (MOOC) has gained traction as a widely utilized online education platform among higher education institutions globally. MOOCs are digital courses accessible to a wide audience, typically offered for free or at a low cost. It can also be a type of online distance learning platform that attracts numerous learners globally by offering exceptional educational resources from esteemed academic experts. In this study, the researchers aim to know factors that affect the Behavioral Intention in MOOC. The data for this research were collected through a questionnaire completed by 521 respondents to address the research question: “What are the factors that determine Behavioral Intention in MOOCs?”. This research was conducted using quantitative methods. The result of the research below shows that 3 factors have a significant impact on Behavioral Intention. There are Perceived Ease of Use, Perceived Usefulness, and Satisfaction. In addition, there are factors such as Course Content Quality, Course Instructor Quality, Course Design Quality, Course Relevance, Learner-Learner Interaction, and MOOC Performance that also influence the other factors (Perceived Ease of Use, Perceived Usefulness, and Satisfaction) which also affect behavioral intentions. However, Learner-Instructor Interaction does not have a significant impact, so further research is needed to ensure the influence on the usefulness and ease of use of MOOC. This study also includes PLSpredict to identify the most influential factors affecting learners' behavioral intention.

关键词： computer aided instruction Electronic learning Costs Education Information technology Best practices

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Towards a Virtual Reality-Based Educational System for Teaching Single-Layer Perceptron Concepts

Towards a Virtual Reality-Based Educational System for Teach...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Navid Khezrian Forouzan Farzinnejad Jens Grubert Coburg University of Applied Sciences and Arts

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

We present a Virtual Reality (VR) educational system for teaching single-layer perceptron concepts, offering an immersive and interactive approach to enhance traditional learning methods. The VR platform gamifies neural network training by simulating a logical OR gate in a factory-inspired environment, aiming at bridging abstract theories with practical applications. In a user study with eight participants, the system received positive but also critical feedback for its interactivity and usability, achieving a System Usability Scale score of 64.69. As an initial study on teaching machine learning in VR, this work highlights VR's potential to enhance comprehension and engagement through experiences.

关键词： Training computer aided instruction Three-dimensional displays Conferences Neural networks Machine learning User interfaces Logic gates Usability Immersive learning

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Aspect-Based Sentiment Analysis of Student Reviews on MOOC Platforms

Aspect-Based Sentiment Analysis of Student Reviews on MOOC P...

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Advanced Research in Computing (ICARC), International Conference on

作者： A.D.M.B.B. Dissanayake Pumudu A. Fernando Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The rapid growth of Massive Open Online Courses (MOOCs), particularly in the post-COVID-19 era, has transformed education and led to a substantial increase in student-generated reviews. However, manual analysis of these reviews is impractical due to their sheer volume, and traditional binary sentiment analysis lacks the granularity needed to extract actionable insights. This study proposes a novel framework leveraging Aspect-Based Sentiment Analysis (ABSA) to classify MOOC reviews into seven critical aspects. The model was trained on a 100,000-review dataset from Coursera and a manually annotated 10,000-review dataset from Udemy. Various machine learning techniques were evaluated, with the final model integrating Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM). The SVM achieved notable performance, with accuracy rates of 93.7% for sentiment prediction and 88.64% for aspect classification on the Coursera dataset, and 73.91% for sentiment prediction and 66.95% for aspect classification on the Udemy dataset. These results underscore the model's efficacy in delivering a comprehensive and platform-independent approach to analyzing MOOC reviews, providing valuable insights for educators and course developers.

关键词： Support vector machines Deep learning computer aided instruction Sentiment analysis Analytical models Electronic learning Accuracy Reviews Refining Long short term memory

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Centroid-Based Aspect Sentiment Analysis for MOOC Learner Reviews

Centroid-Based Aspect Sentiment Analysis for MOOC Learner Re...

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Learning and Technology Conference (L&T)

作者： Naila Marir Akila Sarirete Tayeb Brahimi Effat College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Developing comprehensive analytics for Massive Open Online Courses (MOOCs) is essential for improving course design and enhancing learner engagement. In this work, we introduce MOOCSense, a multi-stage sentiment analysis module designed to analyze MOOC learner reviews and contribute to generating detailed MOOC analytics. In the first stage, we employ a mapping algorithm that extracts key MOOC-specific terms and central semantic phrases from the reviews. In the second stage, we propose a novel Centroid-Based Learning approach combined with the BERT (CLB) model to capture both implicit and explicit sentiment polarity in learner reviews, leveraging BERT’s deep contextual understanding of natural language. By focusing on the central semantics of each review, our approach uncovers the emotional drivers behind learner engagement or dissatisfaction. This dual-stage module enables more accurate sentiment association with specific course aspects, enriching MOOC analytics with valuable insights. Experimental results demonstrate the effectiveness of our approach across various MOOC datasets, achieving an accuracy of 92%, making it a promising solution for generating in-depth learning analytics and supporting course improvement strategies.

关键词： computer aided instruction Sentiment analysis Electronic learning Accuracy Reviews Semantics Transfer learning Sustainable development Long short term memory Context modeling

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Laugh at Your Own Pace: Basic Performance Evaluation of Language Learning Assistance by Adjustment of Video Playback Speeds Based on Laughter Detection

arXiv

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arXiv 2025年

作者： Nishida, Naoto Nozaki, Hinako Shizuki, Buntarou University of Tsukuba Ibaraki Japan

Among various methods to learn a second language (L2), such as listening and shadowing, Extensive Viewing involves learning L2 by watching many videos. However, it is difficult for many L2 learners to smoothly and effortlessly comprehend video contents made for native speakers at the original speed. Therefore, we developed a language learning assistance system that automatically adjusts the playback speed according to the learner’s comprehension. Our system judges that learners understand the contents if they laugh at the punchlines of comedy dramas, and vice versa. Experimental results show that this system supports learners with relatively low L2 ability (under 700 in TOEIC Score in the experimental condition) to understand video contents. Our system can widen learners’ possible options of native speakers’ videos as Extensive Viewing material. © 2025, CC BY-NC-SA.

关键词： computer aided instruction

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QCI Robot with Generative AI Knowledge Graph for Taiwanese/Japanese Co-Learning Model Application 29th

QCI Robot with Generative AI Knowledge Graph for Taiwanese/J...

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29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Lee, Yu-Hsiang Kubota, Naoyuki Sato-Shimokawara, Eri Obo, Takenori Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Mechanical Systems Engineering Tokyo Metropolitan University Hachioji Japan Department of Computer Science Tokyo Metropolitan University Hachioji Japan

ISBN: (纸本)9789819645954

This paper proposes a Quantum Computational Intelligence (QCI) robot with a Generative AI (GAI) knowledge graph (KG) for Taiwanese and Japanese co-learning model applications. During the 2024 IEEE CIS Summer School on QCI at Tokyo Metropolitan University (TMU) in Japan, we organized lectures and a hands-on workshop on QCI for young students to learn and experience QCI using the QCI&AI-FML learning tool and robot. Learners first observe, study, research, utilize, understand, and explain what they have learned in the heart-sutra-based human and machine co-learning model. We transcribed the collected multimodal data using the OpenAI Whisper models into English or Japanese texts. The GAI Knowledge Graph (GAIKG) agent generates the knowledge graph with concepts, relations, and communities for the transcribed texts by providing prompts with Large Language Models (LLMs), such as Trustworthy AI Dialogue Engine (TAIDE). The Sentence BERT (SBERT) similarity agent computes the similarity between the collected texts and the golden standard provided by the English/Japanese domain experts. Next, CI domain experts constructed the CI model based on the generated data from these two agents to infer the performance of the generated knowledge graph and deployed the model to the QCI robot. Finally, the QCI robot evaluates how well the generative knowledge graph leverages human and machine learning according to the constructed CI model. In addition, the QCI robot evaluates the performance of the generative knowledge graph in co-learning English/Japanese based on the constructed CI model and quantum fuzzy inference engine. In the future, we will extend the QCI robot to more countries for young students to co-learn CI, QCI, and Taiwanese/Japanese languages with smart machines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer aided instruction

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A Massive Open Online Course (MOOC) on High-Performance Parallel Computing for *** Web-learning Platform

A Massive Open Online Course (MOOC) on High-Performance Para...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Giuliano Laccetti Marco Lapegna Ilaria Merciai dept. of Mathematics and Applications Univ. of Naples Federico II Naples Italy Federica Web Learning Univ. of Naples Fedrico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users to learn at their own pace and on their own schedule. In this contribution, we describe the design and the implementation of a Massive Open Online Course on Parallel Computing and High-Performance Computing, developed for Federica Web Learning: the University Center for innovation, experimentation, and dissemination of multimedia teaching at the University of Naples Federico II.

关键词： Training computer aided instruction Technological innovation Schedules Electronic learning High performance computing Multimedia computing Parallel processing Character recognition Videos

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