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A deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues

Neural Computing and Applications 2025年第8期37卷 6283-6303页

作者： Pannattee, Peerawat Shimada, Shogo Yem, Vibol Nishiuchi, Nobuyuki Graduate School of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Graduate School of Science and Technology Degree Program in Systems and Information Engineering University of Tsukuba Ibaraki3058573 Japan

Effective development of virtual reality (VR) applications is heavily reliant on the evaluation of user experience (UX). However, traditional methods such as questionnaires have inherent limitations which hinder their ability to capture nuanced behavioral responses and impede the agility of VR content creation: They are time-consuming, burdensome for users, and require significant human effort for interpretation. This study introduces an automated framework aimed at addressing the limitations of questionnaire-based evaluations to assess UX in VR. Our primary focus is to validate the concept of this framework through assessment of the sense of presence (SOP), a crucial psychological perception with significant impact on VR UX. Our proposed framework utilizes a deep neural network (DNN) to analyze patterns in multimodal behavioral cues, including facial expressions, head movements, and hand movements, to predict scores from the Igroup Presence Questionnaire (IPQ). Additionally, we introduce two statistical profiles: the Visual Entropy Profile (VEP), which offers insights into visual complexity by depicting scene entropy, and the Experiential Presence Profile (EPP), which is designed to capture users’ historical SOP levels to enable personalized baseline and sensitivity estimation. The proposed framework achieves a significant correlation between actual and predicted IPQ scores, with a Spearman’s rank correlation coefficient of 0.7303, showcasing the potential of DNNs in analyzing complex behavioral signals and automating SOP assessment. This study represents a pioneering effort in leveraging DNNs for the automatic assessment of SOP and paves the way for future advances in automatically assessing VR UX and unlocking new opportunities in the field. © The Author(s) 2025.

关键词： Virtual environments

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A New Credit Scoring Model to Reduce Potential Predatory Lending: A Design Science Approach 30th

A New Credit Scoring Model to Reduce Potential Predatory Len...

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Advances in systems Engineering - Proceedings of the 30th International Conference on systems Engineering, ICSEng 2023

作者： Zakowska, Anna Center for Information Systems and Technology Program Claremont Graduate University ClaremontCA United States

ISBN: (纸本)9783031405785

This research examines the potential impact of using a new credit scoring model that incorporates factors beyond the traditional FICO credit score model to address the issue of predatory lending and financial inclusion for "credit invisible," "credit unscorable," "unbankable," and "underbankable" individuals. The lack of credit history for such borrowers creates a challenge for lenders to assess their creditworthiness, leading to the use of unfair lending practices. The research analyzed a dataset containing credit reports and loan information for 1000 applicants to investigate the shortcoming of the FICO score and the impact of new attributes in a new credit scoring model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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EFFECTS OF REFRACTIVE INDEX AND ASPECT RATIO ON THE PARTICLE IMAGES IN A LEVITATED DROPLET

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Multiphase Science and technology 2024年第1期36卷 13-26页

作者： Gatete, Eugene Kaneko, Akiko Shen, Biao University of Tsukuba Systems and Information Engineering Graduate School of Science and Technology Degree Program of Engineering Mechanics and Energy 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan University of Tsukuba Faculty of Engineering of Information Engineering and Systems 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan

A technique using droplets suspended by ultrasound has attracted attention as one of the containerless processing methods. While this can avoid contamination from the container, it is known that ultrasonic levitation creates flow fields inside and outside the droplet. For more precise droplet control, it is desirable to elucidate the internal flow of the droplet, and measurements of the internal flow have been performed using the particle image velocimetry (PIV). The aim of this study is to elucidate the internal flow field behavior by solving optical problems and improving the accuracy of velocity field measurements in levitated droplets. The fluid that is to be investigated is scattered with small tracer particles and illuminated by a laser to capture the flow on a created laser sheet. The curvature distortion is successfully visualized using the PIV approach toward distortion correction using calibration methods. The curvature distortion illustration was performed based on the refractive index and aspect ratio of the simulated droplet in acrylic materials. The fluid flow, affected by droplet curvature and refractive index, has been visualized for both levitated and simulated droplets. The experimental results showed that the droplet curvature can be distorted in two types such as radial and tangential distortions and increase as the refractive index and aspect ratio increases. © 2024 by Begell House, Inc.

关键词： Refractive index

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Systematic Analysis of Student Academic Performance Prediction: Prediction Model, Optimization, Data Augmentation, and Educational Policy Implications

Systematic Analysis of Student Academic Performance Predicti...

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2024 International Conference on information technology systems and Innovation, ICITSI 2024

作者： Setiawan, Ismail Gernowo, Rahmat Warsito, Budi Universitas Diponegoro Department of Information System Semarang Indonesia Universitas 'Aisyiyah Surakarta Bachelor of Information Systems and Technology Program Faculty of Science and Technology Surakarta Indonesia Universitas Diponegoro Department of Statistics Semarang Indonesia

ISBN: (纸本)9798331511470

Predicting student academic performance is a critical challenge in higher education that aims to improve the quality of learning and optimize support for students. The main issues faced are how to choose the right prediction model, utilize optimization methods, and improve prediction accuracy. This paper presents a systematic review of the literature related to various prediction models, ranging from traditional statistical models to machine learning and ensemble/hybrid models, and discusses optimization techniques such as evolutionary algorithms. In addition, determinants of academic performance such as motivation, student engagement, and institutional support are identified as important variables in predicting learning outcomes. The datasets used, model evaluation methods, and technologies and tools that support the analysis process are also discussed in detail. The results of this review show that a hybrid approach that combines machine learning and optimization techniques is able to provide more accurate predictions compared to traditional models. This paper also highlights existing limitations, including challenges in model generalization, and provides policy recommendations to improve the effectiveness of academic performance prediction and its implementation in educational practice. © 2024 IEEE.

关键词： Prediction models

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A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control systems

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 584-602页

作者： Boqian Li Shiping Wen Zheng Yan Guanghui Wen Tingwen Huang Australian AI Institute Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China Science Program Texas A&M University at QatarDoha 23874Qatar IEEE

This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control *** problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)*** CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control *** objectives imply important properties including controllability,convergence,and robustness of control *** this framework,optimal control corresponds to the minimal solution to a constrained QP *** uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of *** recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this ***,we provide research directions that are significant for the advance of knowledge in this area.

关键词： Control barrier function(CBF) control Lyapunov function(CLF) nonlinear-affine control systems

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Urgency Detection of Events Through Twitter Post: A Research Overview 4

Urgency Detection of Events Through Twitter Post: A Research...

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4th International Conference on Electrical Engineering and Computer Science, ICECOS 2024

作者： Edlim, Frederick William Edo, Gregorius Wiratama, Rangga Kurnia Putra Mahmudin, Riyan Solihin, Andi Ariyanto, Amelia Devi Putri Purwitasari, Diana Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Widya Husada University Information Technology and Systems Study Program Semarang Indonesia

ISBN: (纸本)9798350368253

Indonesia is located in volatile Pacific fire ring and stands as one of the most global disaster-prone areas. The government and public are in constant vigilance of the potential threats. Social media generates large amounts of data from its users and is one of the prime candidates to be used as a disaster monitoring and response system. Several studies have demonstrated the effectiveness of using such an approach for developing disaster monitoring systems. but due to the number of messages generated, filtering is needed to extract useful information from the message. Hence, it's needed to detect an urgency from a message. This review, which was carried out following the PRISMA model, focused on the detection of the urgency of Indonesian Twitter during crisis events. From studies selected, several recommendations are suggested for future endeavor in this topic. Future systems are advised to employ three or four class schemes rather than binary class to gain more nuanced insights into tweet urgency. On-Event training and testing approaches are suitable for frequently occurring events, whereas out-of-event approaches offer broader applicability for real-world scenarios. Both machine learning and deep learning methods, including multimodal approaches, have demonstrated efficacy in urgency detection. Addressing pre-processing challenges, such as abbreviations and slang, is critical, and ensuring consistency in manually annotated data is essential. Additionally, the selection of appropriate word embeddings, such as fastText, IndoBERT, or crisis-specific embeddings, is crucial for achieving optimal model performance. © 2024 IEEE.

关键词： Tweets

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A Note on Even-Sized Spectral Clustering 13

A Note on Even-Sized Spectral Clustering

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Miyakawa, Hiroki Endo, Yasunori Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Ibaraki Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan

ISBN: (纸本)9798350373332

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples. © 2024 IEEE.

关键词： Unsupervised learning

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data 8

Optimizing Random Forest Classification Using Chi-Square and...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Andri Yunis, Roni Tanti Universitas Mikroskil Undergraduate Program of Computer Science Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Systems Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Technology Department Medan Indonesia

ISBN: (纸本)9798350342604

Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model's ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates. © 2023 IEEE.

关键词： Students

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Blockchain technology to Enhance Performance of Drugs Supply Chain in the Era of Digital Transformation in Egypt 3rd

Blockchain Technology to Enhance Performance of Drugs Supply...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Moussa, Aya Mohammed A. El-Gamal, Fatma El-Zahraa A. El Fetouh, Ahmed Saleh A. Business Information Technology Program Faculty of Computer and Information Mansoura University Mansoura Egypt Information Technology Department Faculty of Computer and Information Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Medicine is one of the products that must be delivered to the patient safely, undamaged, and during its validity period. However, the risks of the drug supply chain have become increasingly apparent. To address these risks and due to the rapid advances in the technology field, various digital transformation trials around the globe start to be considered in order to replace the traditional drug supply chain until its delivery to the patient. Therefore, the aim of this paper is to contribute in the digital transformation goal of the drug supply chain in Egypt. Thus, this paper proposes a trusted automatic system, based on the blockchain and Internet of Things (IoT) technologies to enhance the performance of the drug supply chain and accordingly make the drug manufacturing and distribution system in Egypt more efficient and reliable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Internet of things

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Examining Gen-Z Intention to Participate in Deposit Refund systems: A Convergent Mixed-Method Study in Indonesia 7

Examining Gen-Z Intention to Participate in Deposit Refund S...

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7th International Conference on Vocational Education and Electrical Engineering, ICVEE 2024

作者： Gunawan, Muhamad Rachmat Samopa, Febriliyan Mukhlason, Ahmad Institut Teknologi Sepuluh Nopember Doctoral Program in Technology Management Interdiciplinary School of Management and Technology Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331505103

The rising environmental challenges in waste management have prompted an urgent need for effective recycling initiatives. This study investigates the intention of Generation Z (Gen-Z) in Indonesia to participate in a Deposit Refund System (DRS), a system aimed at promoting recycling behaviors. Using a convergent mixed-method approach, quantitative data from 228 respondents were analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between intention, distrust, environmental concern, and behavioral intention. Qualitative data analysis, employing vector embedding and k-means clustering, revealed key patterns and themes in Gen-Z's attitudes. Integration of qualitative and quantitative results through PLS-SEM provided a comprehensive understanding of Gen-Z motivations towards environmental sustainability. The results show that environmental concern positively impacts behavioral intention, while distrust was found to be statistically insignificant. This study offers practical and academic insights for policymakers to design more effective DRS initiatives. Future research could explore the long-term behavioral shifts resulting from DRS participation and the role of digital platforms in enhancing engagement. © 2024 IEEE.

关键词： Deposit Refund System Distrust Environmental Concern Waste Management

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