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12th International Conference on Information and Education technology, ICIET 2024

作者： Shiri, Farhad Mortezapour Perumal, Thinagaran Mustapha, Norwati Mohamed, Raihani Ahmadon, Mohd Anuaruddin Bin Yamaguchi, Shingo University Putra Malaysia Faculty Of Computer Science And Information Technology Malaysia Graduate School Of Sciences And Technology For Innovation Yamaguchi University Japan

ISBN: (纸本)9798350371772

The advent of online learning has revolutionized the educational landscape, significantly enhancing global access to and affordability of education. However, a critical challenge in this domain is the efficacy of online classes, particularly in the context of the feedback loop between instructors and students. A pivotal aspect of this challenge is the assessment of student engagement and other affective states in online learning environments, a task that is inherently complex due to its nuanced nature. In this paper, we introduce a novel spatio-temporal hybrid deep learning model that combines the strengths of ConvNeXtLarge with an ensemble of gated recurrent unit (GRU and Bi-GRU) models to detect and classify affective states of students from video data. This model adeptly handles the multi-label, multi-class nature of the problem, employing four parallel fully connected (Dense) layers for effective classification. Our model's performance is evaluated using the DAiSEE dataset, a publicly available resource that captures a range of student affective states in an online learning environment. The proposed model demonstrates promising results in identifying four key affective states of boredom, engagement, confusion, and frustration, with accuracy rates of 54.89%, 56.46%, 70.18%, and 79.32% respectively. These findings underscore the efficacy of our model in accurately gauging student affective states, thereby offering significant insights into the potential of spatio-temporal hybrid networks in this realm. The study demonstrates the feasibility of employing such models to enhance the quality of online education. © 2024 IEEE.

关键词： Students

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RNN-EdgeQL: An auto-scaling and placement approach for SFC

RNN-EdgeQL: An auto-scaling and placement approach for SFC

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作者： Pandey, Suman Choi, Minji Yoo, Jae-Hyoung Hong, James Won-Ki School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang Korea Republic of Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement. © 2022 John Wiley & Sons Ltd.

关键词： Recurrent neural networks

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Development of a Visual Analysis Tool for Seabird Behavior 1

Development of a Visual Analysis Tool for Seabird Behavior

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Onuki, Tomoya Misue, Kazuo University of Tsukuba Graduate School of Science and Technology Ibaraki Japan University of Tsukuba Department of Computer Science Ibaraki Japan

ISBN: (纸本)9798350335569

Biologging data are used to analyze animal behavior. Visualization of such data facilitates analysis;however, the data are often large, and designing visual representations and interactions is challenging. Another problem is that few tools can comprehensively support analysis. The authors aim to develop an integrated analysis tool to support seabird researchers in data analysis. To support the analysis of large datasets, they developed an automatic behavior classification function and visual representation of seabird behavior in a spatiotemporal manner. The usefulness of the tool was confirmed through interviews with seabird researchers. © 2023 IEEE.

关键词： Data visualization

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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2023 International Conference on Informatics Engineering, science and technology, INCITEST 2023

作者： Tarigan, Masmur Gaol, Ford Lumban Mauritsius, Tuga Budiharto, Widodo Esa Unggul University Bina Nusantara University Computer Science Department / Computer Science Graduate Program Jakarta Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Management Bina Nusantara University Department of Information System Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317862

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process. © 2023 IEEE.

关键词： Genetic algorithms

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Multi-Field Distributions Generation for Complex Objects Using a Deep Learning-Based Image Processing Method

Multi-Field Distributions Generation for Complex Objects Usi...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Wu, Chenhao Yang, Shuojin Huang, Zhongwei Zou, Yuntao Liu, Jiang Graduate School of Fundamental Science and Engineering Waseda University Tokyo Japan Tsinghua University Department of Computer Science and Technology Beijing China Macau University of Science and Technology Faculty of Innovation Engineering China School of Energy and Power Engineering Huazhong University of Science and Technology Hubei China

ISBN: (纸本)9798350378214

Accurately and rapidly determining the field distribution for complex objects is crucial, especially in the energy sector. Computational Fluid Dynamics (CFD) methods have been employed to simulate thermal field flows by numerically solving the Navier-Stokes (NS) equations. This methodology requires the implementation of complex procedures, such as the establishment of the computational domain, the discretization of the partial differential equation, and so forth. Consequently, it is both time-consuming and labor-intensive. This paper presents a novel approach for predicting thermal field distributions using a deep learning-based image processing method. Generative Adversarial Networks (GANs), which are known for their ability to generate complex images, present a new opportunity for simulating thermal field distributions. In contrast to conventional CFD techniques, GANs are capable of directly generating thermal field distribution images from input object images without the necessity of solving NS equations, thereby significantly simplifying the process. The approach is based on the StarGAN v2 architecture, which has been shown to be efficient for generating complex images. The method is applied to a dataset constructed using traditional CFD methods, and the results demonstrate that the generated thermal field images closely match the original images, with a Structural Similarity Index (SSIM) value exceeding 0.92, this confirms the effectiveness and precision of the proposed method. © 2024 IEEE.

关键词： Navier Stokes equations

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Analysis of the effects of a game-based review system integrated with the hierarchy of learning on learning outcomes in an elementary social science course

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Interactive Learning Environments 2023年第6期31卷 4000-4020页

作者： Wu, Ting-Ting Sung, Tien-Wen Graduate School of Technological and Vocational Education National Yunlin University of Science and Technology Douliou Taiwan College of Computer Science and Mathematics Fujian University of Technology Fuzhou China

Social science education aims to enable people to acquire the knowledge and abilities necessary to lead a normal social life. However, social science is often neglected in basic education compared with languages and mathematics, thus affecting students’ learning motivation and achievements. In this study, a digital game-based review system was designed for fourth-grade students in Taiwan taking a social science course. The students could review the unit content through the completion of game tasks. The concepts of the forgetting curve, hierarchy of learning, and formative assessment were incorporated into the game design to solidify memory and optimize learning effectiveness. The effects of the game on learning effectiveness, motivation, attitude, and interest were investigated, and learning behavior patterns were assessed through lag sequential analysis. The findings may serve as a reference for improving elementary school social science education. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： forgetting curve Game-based review hierarchy of learning learning behavior social science education

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SUPPORTING FARMING SMART DOCUMENTATION SYSTEM by MODULAR BLOCKCHAIN SOLUTIONS

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and Engineering 2022年第1期5卷 1-26页

作者： Arsyad, Andi Arniaty Widayat, Irawan Widi Köppen, Mario Departement of Computer Science and Systems Engineering Graduate School of Creative Informatics Kyushu Institute of Technology Japan

For more than a decade, various farm-specific models have been developed by collaborating and integrating sensing technologies as a step toward successful data-farm documentation and effective decision-making. However, the stored and gathered data continues to rely on cloud infrastructure or centralized platform control, which is particularly vulnerable to threats such as data tampering, data distortion, confidentiality, and manipulation, which caused the farm product data difficult to trace to its provenance. The objective study in this paper proposes a farm transaction model by demonstrating a flow of farm transaction simulation implicated by Modular Block Chain(MBC) sensing instrument with an array of sensors, controllers, networking hardware, computing equipment, and internal memory functions to enhance data integrity and security farm object. Based on the proposed model, a proof-of-concept experimental system called Encapsulating Block Mesh (EBM) integrates blockchain technology with the specific application case of cocoa production has been implemented. Results have shown that farm objects represented by MBC take turn recording information on the process of generating, transacting, and consuming a farm product and encrypting it into a block was validated and linked in the EBM with the hash of transaction data that connected to each cocoa farm object in a simulation environment. The findings from this study are twofold: the approach has been shown to be feasible and effective, but also capable to be expanded to other stages of the food supply chain, such as manufacturing, supermarkets, food consumption (consumers), and recycling in real-world environments. © 2018 by the authors.

关键词： Food supply

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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