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

作者： Choi, Wan Chong Choi, Iek Chong Illinois Institute Of Technology Department Of Computer Science United States Macao Polytechnic University Faculty Of Applied Sciences China School Of Education City University Of Macau China

ISBN: (纸本)9798350371772

In an era where technological skills are essential, integrating coding curriculum within Macao's primary education is becoming increasingly prevalent. However, fostering programming skills among young learners is challenging, particularly when low motivation impedes learning. ***, a block-based programming website, has recently seen increasing use in universities, high schools, and primary schools. It incorporates gamification elements, allowing students to learn programming through a game-based challenge. This study employed a quasi-experimental approach to explore the impact of *** on enhancing students' motivation to learn coding. The participants comprised 20 grade three students who received three weeks of instruction, with six lessons each week, each lasting 40 minutes. The effectiveness of the *** intervention was assessed using the Instructional Materials Motivation Survey (IMMS), which measures the dimensions of the ARCS motivation model: Attention, Relevance, Confidence, and Satisfaction. The findings revealed a significant boost in learning motivation across all ARCS dimensions following the *** course. The study also demonstrated a strong correlation between these dimensions within the block-based coding curriculum provided by ***, suggesting the value of such platforms in motivating and improving programming education in primary schools. © 2024 IEEE.

关键词： Curricula

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Educational Robotics: A Comparison Between the Thymio and NAO Robot

Educational Robotics: A Comparison Between the Thymio and NA...

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IEEE Education Engineering (EDUCON)

作者： Hafsa Al Ansari Department of Computer Science Huddersfield University University of Technology and Applied Science

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

As the use of technology increases throughout the world, we are witnessing some significant developments in a variety of domains. We integrate technology with different sciences and produce a useful invention. One of these inventions is the robot. A robot has been developed to assist humans in their work, faster and more efficiently to be used in a place where people cannot reach, or where it might cause a harm to them. This research paper presents a comparative analysis of two prominent educational robotics, Thymio and NAO robots. We explore their benefits, features, and how they're used in learning. By looking at their network models, we highlight what makes each robot unique. Through a combination of empirical analysis and theoretical discourse, this research contributes to a deeper understanding of educational robotics and offers guidance for future developments and applications in educational settings.

关键词： Technological innovation Educational robots Engineering education Robots

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Integrated Interaction Journey and Privacy Risk Assessment: A Graph Model

Integrated Interaction Journey and Privacy Risk Assessment: ...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Alabsi, Maha Gill, Asif Bandara, Madhushi Faculty of Engineering and IT School of Computer Science University of Technology Sydney Sydney Australia Applied College Taibah University Al-Madinah Al-Munawarrah Madina Saudi Arabia

Smart airports involve several applications and stakeholders to facilitate passenger journey. Passengers interact with those stakeholders and share their personal information using the smart airport applications. While the use of smart airport applications offers several benefits, it also puts passengers' personal information at risk. This draws our attention to the need for identifying and understanding privacy risks with a view to protect passenger information at smart airports. Our earlier systematic literature review study revealed a gap in modelling passenger information privacy risks in the context of smart airports. This paper aims to address this gap by developing an ontology for interaction journey and privacy risk assessment modeling. The contribution of the proposed ontology is to bring new knowledge and understanding of privacy risks in the contemporary smart airport context. The development and evaluation of the proposed framework follows the Design science Research (DSR) method along with the ontology development techniques. The proposed ontology aims to assist privacy experts in modelling and analyzing privacy risks relevant to passenger information in the smart airport context. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Differential privacy

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Comparative Analysis of Compromise Approaches in Multiple Rankings Decision-Making: A Case Study on Performance of Electric Cars 28th

Comparative Analysis of Compromise Approaches in Multiple Ra...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Paradowski, Bartosz Gajewski, Mariusz Wilczynski, Andrzej Salabun, Wojciech National Institute of Telecommunications National Research Institute ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

As civilization advances, decision-making problems become increasingly complex, posing significant challenges in deriving optimal solutions. The field of multi-criteria decision analysis (MCDA) has evolved to address these complexities;however, single-solution approaches are often inadequate in multi-decision-maker scenarios, necessitating group decision-making methods. This study proposes an alternative compromise solution for evaluating electric car performance, examining various compromise approaches, including voting methods like Borda and Copeland counts, and more advanced techniques such as Iterative Compromise Ranking Analysis (ICRA), Improved Borda, HQ compromise, Dominance-directed graph, and Rank position methods. These methods were assessed using two ranking correlation coefficients to evaluate their effectiveness in multi-decision-maker scenarios. Results indicate that while minor differences in rankings were observed based on the chosen compromise method, these differences could influence decision-making outcomes. The analysis highlights the importance of selecting methods that align with specific needs and expectations. Future research should focus on simulation-based studies to better understand the characteristics of each method and apply these methods to real-world case studies to determine their suitability in various contexts. © 2024 The Authors.

关键词： Optimal systems

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IoDseC++: authenticated key exchange protocol for cloud-enable internet of drone communication

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9529-9542页

作者： Amin, Ruhul Jayaswal, Sakshita Sureshkumar, Venkatasamy Rathore, Balram Jha, Ananya Abdussami, Mohammad Department of Computer Science and Engineering IIIT Naya raipur Sector 24 Chhattisgarh Naya Raipur493661 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamilnadu Coimbatore641004 India

In recent times, sensor node or smart camera can be embedded in the drones for the collection of important data with the help of Internet technology. Several security threats/vulnerabilities may hamper the system while collecting data. Hence, strong protection against security attacks is essential in drone based data collection system. In this work, we have considered a drone based cloud enable architecture for collecting and sharing information. The aim of this paper is to design a lightweight security protocol which will provide secure communication and mutual authentication among the entities. The proposed protocol establishes temporary session key for each session in order to make the complete system security attacks free. We have simulated the proposed protocol for measuring security strength using Scyther simulation whose results show all the claimed parameters are private during protocol run. It also ensures the correctness of mutual authentication. The informal analysis confirms strong protection on all security threats related to proposed architecture. The performance analysis has also made which shows that the protocol is lightweight and achieves better efficiency in comparison with related published works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Drones

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Collaborative Task Offloading with Digital Twin in Multi-Vehicle and Multi-Edge Environments

Collaborative Task Offloading with Digital Twin in Multi-Veh...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gu, Anqi Wu, Huaming Wang, Yixiao Li, Ruidong Tang, Chaogang Tianjin University Center for Applied Mathematics Tianjin300072 China Kanazawa University Institute of Science and Engineering Kanazawa920-1192 Japan China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China

ISBN: (纸本)9798350351255

In recent years, the effective utilization of edge servers to assist vehicles in handling compute-intensive and latency-sensitive tasks has emerged as a pivotal concern in Vehicular Edge Computing (VEC). In this paper, we adopt a cooperative approach that leverages the collective capabilities of multiple edge servers. This strategy is designed to effectively manage tasks and alleviate the computational burden imposed on these servers. Specifically, Graph Neural Network (GNN) is applied to extract and classify features such as the geographical locations and communication statuses of multiple edge servers, enabling the selection of the most suitable servers for collaborative task execution. We have utilized solar energy for local computing, effectively achieving environmental protection and reducing the local energy burden on vehicles. Moreover, a novel edge attraction formula is defined to refine the rationality of clustering. In addition, Deep Reinforcement Learning (DRL) is employed to make real-time offloading decisions. To ensure experimental accuracy while mitigating costs, we establish a corresponding digital twin environment to acquire experimental data. By conducting a comparative analysis against three other baseline methods, we effectively reduce task completion time and thus meet the stringent demands of time-sensitive tasks. © 2024 IEEE.

关键词： Deep reinforcement learning

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Abrol, Monika Pandey, Rajendra P. Pawar, Rahul Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes;however, using a combination of those strategies has been visible to enhance the classification performance. © 2024 IEEE.

关键词： characteristic classification development function strategies

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The Impact of CLIP Encoders and CNN Architectures on Model Efficiency: A Case Study on Hate Speech Detection 32

The Impact of CLIP Encoders and CNN Architectures on Model E...

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Kälin, Christof Rushe, Ellen Kull, Stefan McCarren, Andrew School of Computing Dublin City University Dublin 9 Ireland Lucerne School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

In this paper, the impact of model architecture, model size and overall efficiency of deep vision-language models is explored, focusing on the trade-off between performance and resource utilisation. Specifically, the effectiveness of Contrastive Language-Image Pre-training (CLIP) encoders combined with Convolutional Neural Networks (CNNs) are evaluated for this task. To this end, a study is performed using the Facebook Hateful Memes Dataset (HMD) to train and evaluate the Hate-CLIPper architecture and its variations, alongside smaller scale extentions using convolutional layers. Though the evaluation demonstrates that Hate-CLIPper shows the strongest performance, the reduced versions of Hate-CLIPper’s cross-fusion mechanism see a large decrease in parameters without a comparably large decrease in performance. This suggests that the addition of parameters to these models may ultimately lead to diminishing returns in terms of performance. We also show that downscaling a smaller version of HateCLIPper leads to a larger reduction in performance than its larger-scale counterparts, suggesting a non-linear relationship between the number of parameters and performance gains. The question of overparameterisation in Hate-CLIPper is therefore raised, highlighting the importance of balancing model complexity with training efficiency. Through this case study, this research contributes to the development of more efficient methods for automatic hateful meme detection which, in extension, can improve content moderation practices and reduce the spread of online hate speech. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Zeba Rana, Nadim Khan, Haneef Alam, Md Imran College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer and Network Engineering Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350347050

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the 'edge' of your local network, rather than transferred away. technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems. © 2023 IEEE.

关键词： Edge computing

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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