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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Optimizing Energy and QoS in VANETs through Approximate Computation on Heterogeneous MPSoC

Optimizing Energy and QoS in VANETs through Approximate Comp...

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Emerging Smart technologies and Applications (eSmarTA), International Conference on

作者： Ahmed A. J. Al-Hchaimi Abdul Hadi M. Alaidi Yousif Raad Muhsen Mohammed F. Alomari Nasri Bin Sulaiman Mamika Ujianita Romdhini Dept. of Electromechanical Systems Engineering Southern Technical University Basra Iraq Dept. of Computer Science and Information Technology Universiti Tenaga National Kuala Lumpur Malaysia Dept. of Computer Science Universiti Putra Malaysia Kuala Lumpur Selangor Malaysia Dept. of Electrical and Electronic Engineering Universiti Putra Malaysia Kuala Lumpur Malaysia Dept. of Mathematics University of Mataram Mataram Indonesia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

This paper proposes a new algorithm called Dynamic Optimization of Energy and QoS in VANETs (DOEQ-VANET), designed to maximize the energy efficiency and Quality of Service (QoS) of the Vehicular Ad-hoc Networks (VANETs). Through exploiting the heterogeneity of Multiprocessor System-on-Chip (MPSoC) employing an approximate computation, DOEQ-VANET is capable of dynamically adjusting the computational accuracy for non-critical applications which results in energy conservation without the risk of propagating the misinformation. Task assignment and scheduling are continuously dynamically adjusted as they respond to network fluctuation, vehicle dynamics and energy availability. The simulation result and validation proved the power of the proposed algorithm, which would mark a large advancement toward more green and smart vehicular networks within the field of Intelligent Transport systems.(ITS). This algorithm is crucial for improving the sustainability and efficiency of future transportation systems.

关键词： Heuristic algorithms Green products Vehicular ad hoc networks Transportation Quality of service Approximation algorithms Prediction algorithms

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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International Conference on information and Communication systems.(ICICS)

作者： Suboh Alkhushayni Zakaria Alomari Du'a Al-Zaleq Dept. Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Dept. Computer Science New York Institute of Technology Vancouver BC Canada Dept. Robotics and Artificial Intelligence Engineering Al-Ahliyya Amman University Amman Jordan

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution.

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Federated Trustworthy AI Architecture for Smart Cities 8

Federated Trustworthy AI Architecture for Smart Cities

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Utomo, Sapdo John, A. Rouniyar, Adarsh Hsu, Hsiu-Chun Hsiung, Pao-Ann Grad. Institute of Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan National Chung Cheng University Dept. of Information Management Chiayi County Taiwan

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

As the number of smart citizens or users grows, several governments are eager to develop smart cities. All stakeholders, including intelligent users, demand an AI system that is trustworthy. To be considered trustworthy, an AI system must meet seven key criteria: (1) human agency and oversight, (2) robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, nondiscrimination, and fairness, (6) societal and environmental well-being, and (7) accountability. By merging the existing trustworthy AI framework from KServe and Federated Learning with a more reliable model aggregation protocol than earlier studies, we introduced the federated trustworthy Artificial Intelligence (FTAI) architecture. With the integration of FedPSO and AIF360, we updated the FedCS technique. The proposed architecture meets all seven of the essential requirements to a high degree of satisfaction. This paper also includes a detailed explanation of this claim. The scenario demonstrates that the new global model comes with clear measures of fairness metrics, ensuring that the model is devoid of bias. © 2022 IEEE.

关键词： Smart city

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models 25

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Nahar, Khalid M.O. Ebrahim Megdad, Mohammad Al Mamlook, Rabia Emhamed Aljaidi, Mohammad Nahar, Besan K.M. Nahar, Mohammad K. M. Anbarkhan, Samar Hussni Khafajeh, Hayel Amer, Waleed Arab Open University Faculty of Computer Studies P.O. Box 84901 Riyadh11681 Saudi Arabia Yarmouk University Computer Science Department Irbid21163 Jordan University of Zawia Dept. of Mechanical and Industrial Engineering Zawia Libya Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Faculty of Medicine Irbid Jordan Jordan University of Science and Technology Faculty of Applied Medical Science Irbid21163 Jordan Northern Border University Information Systems Department Rafha76322 Saudi Arabia Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa13110 Jordan Zarqa University Faculty of Information Technology Department of Cybersecurity Zarqa13110 Jordan

ISBN: (纸本)9798331540012

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses. © 2024 IEEE.

关键词： COVID-19

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Fully passive measurement-device-independent quantum key distribution

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Physical Review Applied 2024年第6期21卷 064056-064056页

作者： Jinjie Li Wenyuan Wang Hoi-Kwong Lo Department of Physics HK Institute of Quantum Science & Technology Dept. of Electrical & Computer Engineering Centre for Quantum Information and Quantum Control (CQIQC) Dept. of Physics Quantum Bridge Technologies Inc. 100 College Street Toronto ON M5G 1L5 Canada

A recently proposed fully passive quantum key distribution (QKD) removes all source modulator side channels. In this work, we combine fully passive sources with measurement-device-independent (MDI) QKD to simultaneously remove side channels from source modulators and detectors. We show a numerical simulation of passive MDI QKD, and we obtain an acceptable key rate while achieving much better implementation security, as well as ease of implementation, compared with a recently proposed fully passive twin-field QKD, paving the way towards more secure and practical QKD systems. We prove that a fully passive protocol is compatible with MDI QKD and we propose a novel idea that can improve the sifting efficiency.

关键词： Optical quantum information processing Photonics Quantum communication, protocols & technology Quantum cryptography

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Online Task Scheduling Algorithm with Complex Dependencies in Edge Computing 12

Online Task Scheduling Algorithm with Complex Dependencies i...

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12th International Conference on Identification, information and Knowledge in the internet of Things, IIKI 2021

作者： Shi, Lei Ma, Zhaoxing Fan, Yuqi Shi, Yi Ding, Xu Li, Zhehao School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Procince Hefei University of Technology Hefei 230009 China Virginia Tech Dept. of ECE BlacksburgVA24061 United States

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Dependent task Edge computing Online scheduling

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth systems.sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Dense Residual Networks for Gaze Mapping on Indian Roads

arXiv

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arXiv 2022年

作者： Kapoor, Chaitanya Kumar, Kshitij Vishnoi, Soumya Ramanathan, Sriram Dept. of Electrical and Electronics Engineering Bits Pilani Dept. of Computer Science and Information Systems Bits Pilani

In the recent past, greater accessibility to powerful computational resources has enabled progress in the field of Deep Learning and computer Vision to grow by leaps and bounds. This in consequence has lent progress to the domain of Autonomous Driving and Navigation systems. Most of the present research work has been focused on driving scenarios in the European or American roads. Our paper draws special attention to the Indian driving context. To this effect, we propose a novel architecture, DR-Gaze, which is used to map the driver's gaze onto the road. We compare our results with previous works and state-of-the-art results on the DGAZE dataset. Our code will be made publicly available upon acceptance of our paper. © 2022, CC BY.

关键词： Navigation systems

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FPGA-based Low Power Acceleration of HARK Sound Source Localization

FPGA-based Low Power Acceleration of HARK Sound Source Local...

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IEEE Cool Chips

作者： Zirui Lin Katsutoshi Itoyama Kazuhiro Nakadai Hideharu Amano Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.

关键词： Location awareness Program processors Power demand Parallel processing Energy efficiency Matrix decomposition Robots

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