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Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories

Social Sciences and Humanities Open 2024年 10卷

作者： Abbas Shah, Syed Faisal Mazhar, Tehseen Shahzad, Tariq khan, Muhammad Amir Ghadi, Yazeed Yasin Hamam, Habib Department of Computer Science & Information Technology Virtual University of Pakistan Lahore 54000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), create realistic digital experiences by overlaying digital elements. This study aims to highlight the benefits of utilizing VR technology in education, particularly in universities and higher education institutions, by integrating VR with educational theories to create engaging and interactive learning experiences that enhance understanding and retention. This review includes a comprehensive analysis of 103 works, synthesizing existing literature to evaluate the suitability of various educational approaches and theories for implementation in VR-based educational applications. The findings emphasize VR's potential to transform learning by providing immersive experiences that bridge different domains and encourage engagement. By integrating learning theory into VR development, meaningful learning environments can be created, improving student understanding and retention across diverse learning settings. The study concludes that the combination of technology and educational theories provides a novel approach to motivate students and foster deep learning in VR laboratories. © 2024 The Authors

关键词： Augmented reality Educational theories Engineering education Immersive technology Interactive learning VR laboratories

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Graph learning: A survey

arXiv

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arXiv 2021年

作者： Xia, Feng Sun, Ke Yu, Shuo Aziz, Abdul Wan, Liangtian Pan, Shirui Liu, Huan School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia School of Software Dalian University of Technology Dalian116620 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this *** Codes 68T07 © 2021, CC BY.

关键词： Graphic methods

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Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

arXiv

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arXiv 2021年

作者： Zheng, Chuanpan Fan, Xiaoliang Pan, Shirui Jin, Haibing Peng, Zhaopeng Wu, Zonghan Wang, Cheng Yu, Philip S. Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Computer Science and Technology Department Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen361005 China School of Information and Communication Technology Griffith University Australia Centre for Artificial Intelligence FEIT University of Technology Sydney Australia Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Multi-method analysis of emotional and affective states

Multi-method analysis of emotional and affective states

引用

Progress in Applied Electrical Engineering (PAEE)

作者： Adrian Luckiewicz Bogdan Ruszczak Mariusz Pelc Dariusz Mikołajewski Adam Sudoł Barbara Grochowicz Edward J. Gorzelanczyk Patryk Mendon Aleksandra Kawala-Sterniu Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

关键词： Electrical engineering Brain Data processing Hemodynamics Functional near-infrared spectroscopy

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Evolutionary dynamics in stochastic nonlinear public goods games

arXiv

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arXiv 2024年

作者： Zhu, Wenqiang Wang, Xin Wang, Chaoqian Liu, Longzhao Hu, Jiaxin Zheng, Zhiming Tang, Shaoting Zheng, Hongwei Dong, Jin School of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Beijing Academy of Blockchain and Edge Computing Beijing100085 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China State Key Lab of Software Development Environment Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Faculty of Business The Hong Kong Polytechnic University Hung Hom Kowloon999077 Hong Kong

Understanding the evolution of cooperation in multiplayer games is of vital significance for natural and social systems. An important challenge is that group interactions often leads to nonlinear synergistic effects. However, previous models mainly focus on deterministic nonlinearity where the arise of synergy or discounting effect is determined by certain conditions, ignoring uncertainty and stochasticity in real-world systems. Here, we develop a probabilistic framework to study the cooperative behavior in stochastic nonlinear public goods games. Through both analytical treatment and Monte Carlo simulations, we provide comprehensive understanding of social dilemmas with stochastic nonlinearity in both well-mixed and structured populations. We find that increasing the degree of nonlinearity makes synergy more advantageous when competing with discounting, thereby promoting cooperation. Interestingly, we show that network reciprocity loses effectiveness when the probability of synergy is small. Moreover, group size exhibits nonlinear effects on group cooperation regardless of the underlying structure. Our findings thus provide novel insights into how stochastic nonlinearity influences the emergence of prosocial behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear simulations

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

引用

IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning

SSRN

引用

SSRN 2023年

作者： Goli, Rohan Hubig, Nina Min, Hua Gong, Yang Sittig, Dean F. Rennert, Lior Robinson, David Biondich, Paul Wright, Adam Nøhr, Christian Law, Timothy Faxvaag, Arild Weaver, Aneesa Gimbel, Ronald Jing, Xia School of Computing College of Engineering Computing and Applied Science Clemson University ClemsonSC United States Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA United States School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States Department of Public Health Sciences College of Behavioral Social and Health Sciences Clemson University ClemsonSC United States General Practitioner/Independent Consultant Cumbria United Kingdom Clem McDonald Biomedical Informatics Center Regenstrief Institute Department of Pediatrics Indiana University School of Medicine IndianapolisIN United States Vanderbilt University Medical Center NashvilleTN United States Department of Planning Faculty of Engineering Aalborg University Aalborg Denmark Ohio Musculoskeletal and Neurologic Institute Ohio University AthensOH United States Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway

Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for data labeling requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised KP identification framework using minimal labeled data based on hierarchical attention over the documents and domain adaptation. Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play a role in realtime KP identification as a complementary approach to human experts’ effort. © 2023, The Authors. All rights reserved.

关键词： Interoperability

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PAMI-Anonymous Password Authentication Protocol for Medical Internet of Things

PAMI-Anonymous Password Authentication Protocol for Medical ...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Yousheng Zhou Yang Luo Mohammad S. Obaidat P. Vijayakumar Xiaojun Wang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Computing and Informatics University of Sharjah Sharjah UAE King Abdullah II School of Information Technology University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China University College of Engineering Tindivanam Tindivanam India School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728181059

With the continuous maturity of Internet of Things (IoT) technology, it has begun to be frequently used in all walks of life to improve people's work efficiency and living standards. The wide use of IoT in the medical field makes it convenient for patients to obtain medical services, and also enables doctors to obtain patients' physical conditions more timely and accurately, so as to formulate more efficient treatment plans. However, when people enjoy the convenience of medical IoT, how to ensure the security of communication and privacy of patients are all problems that cannot be ignored. In order to achieve secure access the network, this paper proposes an anonymous password authenticated key exchange protocol for medical Internet of Things (PAMI), where only a low-entropy password is required to realize the mutual authentication between medical device and telemedicine server, so as to negotiate a high-entropy session key. The security of PAMI is formally proved under the standard model, and the experiment based performance comparison demonstrates that it is more efficient than the existing similar schemes.

关键词： Privacy Protocols Telemedicine Authentication Passwords Medical services Internet of Things

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

引用

Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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HVoR-tree: Indexing highest order Voronoi cells using VoR-tree 8

HVoR-tree: Indexing highest order Voronoi cells using VoR-tr...

引用

8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019

作者： Zakirah, Daraswati Alamri, Sultan Adhinugraha, Kiki Maulana Aladalah, Mohammed School of Computing Telkom University Bandung Indonesia College of Computing and Informatics SEU Dammam Saudi Arabia Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9781538676844

Index is a data structure to improve searching and minimizing effort to read the actual data. In spatial data structure, there are various objects can be considered, such as point, regular polygon, and irregular polygon. Irregular polygon is the shape of Voronoi Diagram which is used to divide a region into smaller regions based on its nearest neighbor. Voronoi Diagram has three orders, namely: (1) First Order Voronoi Diagram, (2) Higher Order Voronoi Diagram (HOVD), (3) Highest Order Voronoi Diagram (HSVD). Index uses Minimum Bounding Rectangle (MBR) in HSVD can cause high level of overlapping. The highest level of overlapping found in the HSVD, while the lowest is found in the First Order. VoR-tree (Voronoi R-Tree) is used to minimize overlapping. Previous studies used VoR-tree only to observed First Order Voronoi Diagram. This paper presents an index HVoR-Tree, which uses the VoR-tree structure effectively used in Highest Order Voronoi Diagram (HSVD). © 2019 IEEE.

关键词： Data structures

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