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Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

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Interactive technology and Smart Education 2024年第2期22卷 304-331页

作者： Wang, Chun-Chia Chen, Hsuan-Chu Hung, Jason C. School of Information and Design Chang Jung Christian University Tainan Taiwan Department of Digital Media Design Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

Purpose: This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. Design/methodology/approach: This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings: The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value: Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning. © 2024, Emerald Publishing Limited.

关键词： Affective computing Convolutional neural networks Digital game-based learning Eye-tracking technology Transfer learning

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Predicting Gold Prices with Rolling Average Representation and Machine Learning 1

Predicting Gold Prices with Rolling Average Representation a...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Kannan, Rajkumar Andres, Frederic Muthu, Kalaiarasi Sonai Anutariya, Chutiporn Department of Computer Science Tiruchirappalli India National Institute of Informtics Digital Content and Media Science Tokyo Japan Multimedia University Faculty of Information Science and Technology Malaysia Asian Institute of Technology School of Engineering and Technology Pathum Thani Thailand

ISBN: (纸本)9798350348637

Price prediction and forecasting have been important topics that have been long studied in the financial world. Commodity price prediction is one such highly popular application in stock markets across the world. Among all commodities such as gold, silver, platinum and others, gold has continued to be the prime and forefront commodity which decides the price movements of other commodities. Moving average approach and its variants are the important technical indicators which help financial institutions, investors and traders to understand the movement of gold prices throughout the year. Some of the popular technical indicators are simple moving averages, cumulative moving averages and exponential moving averages. This paper proposes a unique gold price prediction methodology by building popular ensemble machine learning models utilizing all three types of moving average values as feature representation and next day price as the target for prediction. The predicted values based on the three moving average features are presented to users, investors and traders, so that investors and traders will know the gold price for the next day. Extensive experiments are conducted on the Indian gold price daily data from December 2011 to March 2024 by comparing six prominent machine learning models and ARIMA model. We also present a detailed study of different feature importance. Experimental results on the gold price daily data have confirmed that polynomial regression models have given superior performance in terms of regression accuracy and error metrics namely MSE, RMSE and MAE © 2024 IEEE.

关键词： Prediction models

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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FARE: A Deep Learning-Based Framework for Radar-Based Face Recognition and Out-of-Distribution Detection

FARE: A Deep Learning-Based Framework for Radar-Based Face R...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sabri Mustafa Kahya Boran Hamdi Sivrikaya Muhammet Sami Yavuz Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) School of Computation Information and Technology Technical University of Munich

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this work, we propose a novel pipeline for face recognition and out-of-distribution (OOD) detection using shortrange FMCW radar. The proposed system utilizes RangeDoppler and micro Range-Doppler Images. The architecture features a primary path (PP) responsible for the classification of in-distribution (ID) faces, complemented by intermediate paths (IPs) dedicated to OOD detection. The network is trained in two stages: first, the PP is trained using triplet loss to optimize ID face classification. In the second stage, the PP is frozen, and the IPs—comprising simple linear autoen-coder networks—are trained specifically for OOD detection. Using our dataset generated with a 60 GHz FMCW radar, our method achieves an ID classification accuracy of 99.30% and an OOD detection AUROC of 96.91%.

关键词： Training Accuracy Face recognition Pipelines Radar detection Radar Smart homes Signal processing Reliability Speech processing

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Soliton Solutions for Coupled Nonlinear Generalized Zakharov Equations with Anti-cubic Nonlinearity Using Various Techniques

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International Journal of Applied and Computational Mathematics 2024年第1期10卷 1-30页

作者： AlQahtani, Salman A. Alngar, Mohamed E. M. New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology and Information Cairo 11585 Egypt

This study introduces novel exact solutions for the coupled nonlinear generalized Zakharov equations with anti-cubic nonlinearity. Utilizing a variety of mathematical approaches, including the extended trial function method, the rational exp (-ψ(ξ))-expansion method, and three well-known auxiliary equation methods, a spectrum of solutions is derived. These solutions encompass dark solitons, bright solitons, singular solitons, Jacobi elliptic functions, periodic waves, and rational solutions. The unique contribution lies in the use of a phase variable θ(ξ) instead of a phase constant, providing fresh insights into wave behavior. A comparative analysis underscores the originality of the derived solutions, setting this work apart from prior studies. These findings advance our understanding of nonlinear dynamics and mathematical modeling, with potential applications in various domains. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Coupled nonlinear generalized Zakharov equations Extended trial function method Rational exp(- ψ(ξ))-expansion method Soliton solutions Three famous auxiliary equation methods

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Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor Antenna for Water Characterization Technique 13

Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor...

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13th IEEE International Conference on System engineering and technology, ICSET 2023

作者： Hadi, Aya Khalid Mosleh, Mahmood Farhan Azeez, Adham R. Abd-Alhameed, Raed A. University of Middle Technique Department of Computer Techniques Engineering Baghdad Iraq University of Information Technology and Communications Department of Media Technology and Communications Engineering College of Engineering Baghdad Iraq University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom

ISBN: (纸本)9798350340891

This article presents a miniaturized size Microstrip-patch sensor structure based on meander-line slot for water-quality and salinity measurement applications. The HFSS-software tool is employed to design and simulate the proposed sensor. The proposed sensor is printed on 60× 50× 1mm3 a dielectric material of FR-4 type. The meander-line slot resonator is inserted in the patch-sensor for miniaturize with the sensitivity enhancement. In order to contain the water sample that wants to be tested, a receptacle with dimensions of 47.75× 50× 4mm3 is mounted on the sensor structure. The receptacle is made from PTFE-Teflon dielectric material. The miniaturize sensor is noticed resonant at 2.74 GHz for unloaded-state. Furthermore, it's found resonant at;2.44 GHz, 2.49 GHz, and 2.5 GHz for DI-water, fresh-water and sea-water respectfully. The miniaturized-sensor is showed good sensing values that equals to 0.901 for DI-water, 0.751 for fresh-water, and 0.721 for Sea-water. Finally, a good agreement is showed between the measurement results and those from simulation. © 2023 IEEE.

关键词： Dielectric materials

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Developing an Emotional Interaction Design for Social Anxiety Disorders (SAD) 25th

Developing an Emotional Interaction Design for Social Anxiet...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Chien, HsiangTing Huang, Hsin-Yi Chang, Teng-Wen Shih, Chi-Chi Li, Jia-Rong Wang, Zih-Syuan Chang, Ching-Chih Department of Digital Media Design National Yunlin University of Science and Technology Yunlin Douliu Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliu Taiwan

ISBN: (纸本)9783031359910

This study focuses on using books in libraries as an interactive medium, aiming to help individuals with social anxiety disorder alleviate their anxiety and establish positive social experiences. The research background and literature review explore the relationship between social anxiety disorder and books, utilizing narrative frameworks and mirroring psychotherapy techniques to design an experience related to books, book reviews, and social experiences. Through case studies, needs exploration, scenario mapping systems, and interactive process design, we provide an innovative approach for individuals with social anxiety disorder who desire socialization, allowing them to find like-minded partners and engage in social interactions with reduced anxiety interference. Finally, this study summarizes system design, interface design, and user testing and evaluation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reviews

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RFOOD: Real-time Facial Authentication and Out -of-distribution Detection with Short-range FMCW Radar

RFOOD: Real-time Facial Authentication and Out -of-distribut...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Boran Hamdi Sivrikaya Eckehard Steinbach Department of Computer Engineering Technical University of Munich School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Out-of-distribution (OOD) detection is critical for the safe deployment of modern neural network architectures, as it aims to identify samples outside the training domain. In this paper, we introduce RFOOD, a novel OOD detection framework designed for real-time, privacy-preserving facial authentication using low-cost frequency-modulated continuous-wave (FMCW) radar. RFOOD employs both range-Doppler and micro range- Doppler images to enhance the detection accuracy. The architecture consists of a multi-encoder multi-decoder Body Part (BP) and Intermediate Linear Encoder-Decoder (ILED) components. This design allows the system to accurately classify a single individual's face as in-distribution (ID) while identifying all other faces as OOD. On our dataset collected with 60 GHz short-range FMCW radar, RFOOD achieves an Area Under the Receiver Operating Characteristic (AUROC) curve of 94.13 % and a False Positive Rate of 18.12% at a True Positive Rate of 95 % (FPR95). Additionally, RFOOD outperforms state-of-the-art OOD detection methods in common OOD detection metrics and operates in real-time.

关键词： Measurement Training Accuracy Neural networks Radar detection Authentication Receivers Feature extraction Real-time systems Faces

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