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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Cheng, J. Komissarov, I. Chen, G. Chakraborty, D. Kutcher, S. Chen, H. Trivedi, S. Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Brimrose Technology Corp SparksMD21152 United States

We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cdo * Mg0.03Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited photor... 详细信息

ISBN: (纸本)9781957171258

关键词： X ray detectors

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Design of Model Predictive Control-based Optimal Charging Algorithm

Design of Model Predictive Control-based Optimal Charging Al...

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Chang-Yan Lee Guan-Jhu Chen Yi-Hua Liu Yu-Shan Cheng Yi-Feng Luo Chun-Liang Liu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Taiwan Ocean University Keelung Taiwan Graduate Institute of A.I. Cross-Disciplinary Tech National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Lithium-ion (Li-ion) batteries are used in a multitude of applications including portable consumer electronics, electric vehicles, and large-scale energy storage systems. The development of an optimal charging algorithm for the management of charging time and temperature rise is crucial for maximizing the life of Li-ion batteries. Thus, we introduced a charging algorithm based on model predictive control. The incorporation of the model in battery optimization control offers significant benefits, particularly in computational efficiency. A comparative analysis with the commonly used constant current-constant voltage (CC-CV) charging method indicated that the proposed algorithm enhanced charging speed and reduced temperature rise significantly.

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Heteroassociative Mapping with Self-Organizing Maps for Probabilistic Multi-output Prediction

Heteroassociative Mapping with Self-Organizing Maps for Prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rewbenio A. Frota Marley M.B.R. Vellasco Guilherme A. Barreto Candida Menezes de Jesus PETROBRAS Rio de Janeiro Brazil Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Graduate Program in Teleinformatics Engineering Center of Technology Federal University of Ceará (UFC) Fortaleza Brazil

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recent years, Representation Learning (RepL) has experienced a surge, especially for cross-modal applications with mixed-type data. Most current cross-modal RepL approaches rely mainly on supervised deep learning models, with unsupervised models playing auxiliary roles. In this paper, we present a fully unsupervised approach to demonstrate the capabilities of such models for cross-modal RepL. Our method jointly learns representations into topologically coherent cross-modal heteroassociative mappings. We apply the proposed framework to a yet to be solved multi-output prediction problem in petroleum geoscience: generating a complete set of regular petrophysical well logs from acoustic borehole images in highly heterogeneous Brazilian pre-salt reservoirs.

关键词： Self-organizing feature maps Representation learning Deep learning Neural networks Geoscience Reservoirs Probabilistic logic

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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IEEE International Symposium on Biomedical Imaging

作者： J. Mason Harding Haatef Pourmotabbed Yamin Li Kimberly Rogge-Obando Kate Wang Sarah E. Goodale Shiyu Wang Camden Bibro Bergen Allee Caroline Martin Victoria L. Morgan Dario J. Englot Catie Chang Department of Electrical and Computer Engineering Vanderbilt University Department of Biomedical Engineering Vanderbilt University Department of Computer Science Vanderbilt University Neuroscience Graduate Program Vanderbilt University Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center Department of Neurological Surgery Vanderbilt University Medical Center Department of Radiology Vanderbilt University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.

关键词： Neuroimaging Brain Fluctuations Epilepsy Functional magnetic resonance imaging Electroencephalography Pattern recognition Hemodynamics Spatial resolution Signal resolution

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High Spatial Resolution Terahertz Time-Domain Imaging of Murine Pancreatic Ductal Adenocarcinoma Tissues

High Spatial Resolution Terahertz Time-Domain Imaging of Mur...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627-1299 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

We developed a terahertz time-domain spectroscopy system to generate high-resolution 2-dimensional images of paraffin-embedded murine pancreatic ductal adenocarcinoma tissues using the refractive index and absorption ... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection

Quantum-Trained Convolutional Neural Network for Deepfake Au...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Chu-Hsuan Abraham Lin Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Department of Electrical and Electronic Engineering Imperial College London London UK Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Wells Fargo New York NY USA

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.

关键词： Training Deepfakes Quantum computing Accuracy Neural networks Signal processing Feature extraction Convolutional neural networks Speech processing Testing

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Mental Stress Analysis using the Power Spectrum of fNIRS Signals

Mental Stress Analysis using the Power Spectrum of fNIRS Sig...

引用

2022 International Conference on electrical and Computing Technologies and Applications, ICECTA 2022

作者： Katmah, Rateb Al-Shargie, Fares Tariq, Usman Babiloni, Fabio Al-Mughairbi, Fadwa Al-Nashash, Hasan American University of Sharjah Biomedical Engineering Graduate Program Sharjah United Arab Emirates American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sapienza Rome Department of Molecular Medicine Rome Italy United Arab Emirates University College of Medicines and Health Sciences Al-Ain United Arab Emirates

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

Mental stress is one of the major health problems in modern societies. Detecting mental stress at its early stage and developing a method of stress mitigation is very important for safety, health and quality of life. In this study, we employed a modified Stroop Color Word Task (SCWT) with time constrains and negative feedback about the performance, together with Binaural Beats Stimulation (BBs), to induce three varied levels of mental stress. Level one was based on SCWT alone and level two was based on SCWT+time-pressure and level three was by utilizing SCWT+time-pressure+BBs. We then quantified the level of stress using a NASA TLX questionnaire and functional near-infrared spectroscopy (fNIRS). The fNIRS signals were then analyzed using the Welch power spectrum density (PSD) to investigate the discrepancy in brain activity across the three levels of mental stress. We found that the mean PSD value in the frequency range of 0.2 to 0.5 Hz has significantly changed between the three stress levels. These findings indicate that the fNIRS power spectrum analysis method could be used as a robust marker for mental stress. © 2022 IEEE.

关键词： Power spectrum

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Statistical 3D Device Simulation of Full Fluctuations of Gate-All-Around Silicon Nanosheet MOSFETs at Sub-3-nm Technology Nodes

Statistical 3D Device Simulation of Full Fluctuations of Gat...

引用

2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Kola, Sekhar Reddy Li, Yiming Chen, Chieh-Yang Chuang, Min-Hui National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

We for the first time study characteristic fluctuation of gate-all-around silicon nanosheet MOSFETs induced by random dopants fluctuation (RDF), interface trap fluctuation (ITF), and work function fluctuation (WKF), and their combinations. The explored devices affected by the WKF and its combination result in sizeable variability. Because of enigmatical interactions among the aforementioned random factors, overestimated variabilities occur if we only consider one random source each time, compared with the statistical 3D device simulation for full RDF, ITF and WKF at the same time. Notably, 10.78% and 103% overestimations are found for the threshold voltage and off-state current fluctuations. © 2022 IEEE.

关键词： Nanosheets

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Mental Stress Assessment Using fNIRS and LSTM

Mental Stress Assessment Using fNIRS and LSTM

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Mental stress is a significant factor in the development of a wide variety of psychological, emotional, behavioral, and physical illnesses. It is critical to accurately quantify mental stress, which needs reliable neuroimaging to monitor stress levels. In this work, we used a modified Stroop Color Word Task (SCWT) with time constraints and negative feedback to elicit two distinct degrees of stress in the workplace. We then used salivary alpha amylase (SAA) concurrently with functional near-infrared spectroscopy (fNIRS) to quantify the level of stress. We propose Long Short-Term Memory (LSTM) to decode the two classes of mental stress based on the fNIRS time series. Five-fold cross validation, two LSTM layers, as well as many trainable characteristics were used to construct the network. The induced mental stress increased the level of salivary alpha amylase significantly (p © 2022 IEEE.

关键词： Long short-term memory

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