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Metaverse for Education: Developments, Challenges, and Future Direction

computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients Data Heterogeneity

Towards Robust Federated Learning: Investigating Poisoning A...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdenour Soubih Seyyid Ahmed Lahmer Mohammed Abuhamad Tamer Abuhmed College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago USA

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions.

关键词： Training Toxicology Federated learning Computational modeling Data models Information management Security Reliability Faces Resilience

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Menstrual cycle inspired latent diffusion model for image augmentation in energy production

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Scientific Reports 2025年第1期15卷 1-19页

作者： Mahmoud, Gamal M. Elbaz, Mostafa Said, Wael Elsonbaty, Amira A. Department of Electrical Engineering Pharos University in Alexandria Alexandria Egypt Department of Computer Science Faculty of Computers and Informatics Kafrelsheikh University Kafrelsheikh Egypt Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt Communication and Electronics Department High Institute of Engineering and Technology New Damietta 34517 Egypt

In the energy production domain, image classification is critical for monitoring, diagnostics, and operational optimization tasks. Latent diffusion models (LDMs) have shown potential in generating diverse images during the augmentation process based on text input. However, they are hindered by pixel integrity, texture consistency, and mode collapse. This paper introduces menstrual cycle-inspired latent diffusion model (MCI-LDM), a novel framework that addresses these challenges with three key modifications. First, a menstrual cycle-inspired metaheuristic algorithm is integrated to improve generated images’ pixel integrity and structural coherence. Second, an adaptive attention mechanism is employed to dynamically focus on critical regions during image generation, ensuring that fine details are preserved. Third, a multi-scale feature enhancement module is incorporated to capture global structures and local textures, mitigating mode collapse and enhancing overall image quality. Extensive experiments were conducted on five energy-related datasets, demonstrating the superior performance of MCI-LDM in terms of image augmentation, diversity, and generation accuracy. The results highlight the efficiency of the proposed model, making it a valuable tool for improving image classification and data augmentation in energy sector applications. MCI-LDM outperforms LDM by generating more diverse images, with a higher Inception Score (7.1 vs. 5.4) and a lower Fréchet Inception Distance (22.5 vs. 35.2), indicating better quality and variation. Additionally, MCI-LDM preserves image integrity more effectively, achieving superior PSNR (32.7 dB vs. 28.5 dB) and SSIM (0.92 vs. 0.78). © The Author(s) 2025.

关键词： Energy production Image augmentation Latent diffusion model MCI-LDM Menstrual cycle-inspired metaheuristic

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An Adaptable Methodology for Generating Realistic Annotated Synthetic Data with 3d Gaussian Splatting and Modeling Software

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SSRN 2025年

作者： Losada del Olmo, Juan Jesús López-De-Teruel, Pedro Enrique Ruiz, Alberto Department of Computer Engineering and Technology University of Murcia Murcia30100 Spain Department of Informatics and Systems University of Murcia Murcia30100 Spain Bleecker Technologies Murcia30100 Spain

The increasing demand for large annotated datasets in computer vision underscores the need for scalable synthetic data generation methods, as traditional approaches often lack adaptability or offer limited annotations. To address these challenges, this work introduces a comprehensive methodology that leverages 3D Gaussian splatting and Blender to generate photorealistic synthetic datasets with automatic annotations, suitable for training deep learning computer vision models in multiple tasks, including object detection, pose estimation, semantic and instance segmentation, depth estimation, and surface normal estimation. The proposed pipeline can model any scenario by reconstructing high-fidelity 3D environments, integrating synthetic objects through diverse modeling techniques, and applying procedural generation to vary lighting, camera angles, and object placements. With all annotations generated automatically, this approach facilitates the efficient creation of extensive annotated datasets, thereby eliminating manual labeling efforts. To validate the effectiveness of our synthetic data, we train computer vision models exclusively on these datasets and evaluate their performance in real applications, demonstrating their generalization capability and practical relevance. The results confirm that our methodology provides a scalable, flexible, and high-quality alternative to traditional data collection, bridging the gap between synthetic and real-world datasets while offering a powerful tool for both research and industrial applications. Our code is publicly accessible at https://***/juanjesus-ldo/scene_generator. © 2025, The Authors. All rights reserved.

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Performance and emissions of gas turbine engines fueled with karanja oil-based biofuel blends: a machine learning approach using Lasso regression

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Aircraft engineering and Aerospace Technology 2025年

作者： Aldehim, Ghadah Allafi, Randa Alkharashi, Abdulwhab A, Sagai Francis Britto J, Vijayalakshmi A, Sumaiya Begum M.C, Sashikkumar Velusamy, Priya Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Mechanical Engineering Rohini College of Engineering and Technology Palkulam India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering Centre for Water Resources Anna University Chennai India Department of Civil Engineering GMR Institute of Technology Rajam India

Purpose: This study aims to investigate the performance and emission characteristics of gas turbine engines operating on biofuel blends derived from karanja oil as a potential alternative to conventional Jet-A fuel. Design/methodology/approach: The study tested three biofuel blends: JA20 (20% karanja oil, 80% Jet-A), JA30 (30% karanja oil, 70% Jet-A) and JA40 (40% karanja oil, 60% diesel). Engine performance parameters, including thrust output, thrust-specific fuel consumption (TSFC) and turbine inlet temperature (TIT), were measured at engine speeds ranging from 30,000 to 80,000 rpm. Exhaust emissions of carbon monoxide (CO), carbon dioxide (CO2) and nitrogen oxides (NOx) were analyzed using a gas analyzer. Additionally, a Lasso regression model was used to predict micro gas turbine (MGT) performance and emissions based on experimental data. Findings: Increasing the karanja oil content in the fuel blends reduced overall thrust output, increased TSFC and lowered TIT compared to Jet-A fuel. The JA40 blend exhibited the most significant performance reduction, with a 20% decrease in thrust output, a 7.5% increase in TSFC and a 4.1% decrease in TIT. However, biofuel blends consistently resulted in lower CO, CO2 and NOx emissions compared to Jet-A fuel, with reductions of up to 36%, 6.9% and 13.6%, respectively, for the JA40 blend. The Lasso regression model effectively captured the influence of engine speed and fuel blend composition on emissions, achieving an R² of 0.95 for CO2 and 0.94 for CO predictions. Originality/value: This study provides insights into the feasibility of using karanja oil-based biofuels in gas turbine engines, demonstrating their potential to reduce emissions while highlighting tradeoffs in performance. The use of a Lasso regression model for predicting emissions offers a novel approach to assessing biofuel performance in MGTs. © 2025, Emerald Publishing Limited.

关键词： Polynomial regression

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Analysis of the Performance of Transformers and Ensemble Schemes for Twitter US Airlines Sentiment Analysis 24

Analysis of the Performance of Transformers and Ensemble Sch...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Konstantinos Kyritsis Isidoros Perikos Charalampos M. Liapis Vaggelis Kapoulas Michael Paraskevas Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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A Convolutionaly-Informed XLM-RoBERTa for Classifying Short Text Inquires in Greek School Network 24

A Convolutionaly-Informed XLM-RoBERTa for Classifying Short ...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Charalampos M. Liapis Isidoros Perikos Konstantinos Kyritsis Vaggelis Kapoulas Michael Paraskevas Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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Self-Supervised Hyperbolic Spectro-Temporal Graph Convolution Network for Early 3D Behavior Prediction

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Peng Lai, Qin Li, Haibo Zhao, Chong Wang, Qicong Meng, Hongying Xiamen University Department of Computer Science and Technology School of Informatics Xiamen361102 China Brunel University London Department of Electronic and Electrical Engineering UxbridgeUB8 3PH United Kingdom

3D human behavior is a highly nonlinear spatiotemporal interaction process. Therefore, early behavior prediction is a challenging task, especially prediction with low observation rates in unsupervised mode. To this end, we propose a novel self-supervised early 3D behavior prediction frame-work that learns graph structures on hyperbolic manifold. Firstly, we employ the sequence construction of multi-dynamic key information to enlarge the key details of spatio-temporal behavior sequences, addressing the high redundancy between frames of spatio-temporal interaction. Secondly, for capturing dependencies among long-distance joints, we explore a unique graph Laplacian on hyperbolic manifold to perceive the subtle local difference within frames. Finally, we leverage the learned spatio-temporal features under different observation rates for progressive contrast, forming self-supervised signals. This facilitates the extraction of more discriminative global and local spatio-temporal information from early behavior sequences in unsupervised mode. Extensive experiments on three behavior datasets have demonstrated the superiority of our approach at low to medium observation rates. © 2016 IEEE.

关键词： Self-supervised learning

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``Impressively Scary:' Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications 25

``Impressively Scary:' Exploring User Perceptions and Reacti...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Jack West Bengisu Cagiltay Shirley Zhang Jingjie Li Kassem Fawaz Suman Banerjee Department of Computer Sciences University of Wisconsin-Madison Madison Wisconsin USA School of Informatics University of Edinburgh Edinburgh United Kingdom Electrical and Computer Engineering University of Wisconsin-Madison Madison Wisconsin USA

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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