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2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021

作者： Muñoz Cardona, John Edison Dautenhahn, Kerstin Systems Design Engineering Department University of Waterloo WaterlooON Canada Electrical and Computer Engineering System Design Engineering Departments University of Waterloo WaterlooON Canada

ISBN: (纸本)9781450382908

Games have been used extensively to study human behavior. Researchers in the field of human-robot interaction (HRI) are becoming more aware of the importance of designing compelling and playful games to study interrelationships among players. Despite the growing interest, the use of game design techniques in the creation of playful experiences for HRI experiments is still in its infancy and more multidisciplinary activities should be promoted to foster the convergence between game research and HRI. This workshop aims at discussing the value of using iterative game design techniques to integrate playful experiences using social robots for HRI experiments. More concretely, we want to explore tools, approaches and methods used in previous experiences for appropriate design of interactive games in HRI. Furthermore, based on previous research, a taxonomy for game design using social robots will be presented and attendees will have access to hands-on material created to facilitate the design of interactive games considering important aspects of the robotic systems to maximize the fun experience. We hope this workshop will bring to HRI researchers, game designers, roboticists, and technology enthusiasts enlightening thoughts and ideas to confront the often complicated and time-demanding process of designing compelling games for HRI experiments. © 2021 Owner/Author.

关键词： Human robot interaction

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed S.Salama Mohamed I.Eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)*** presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Predictive Analytics for Learning Performance in First-Year University Programming Course 10

Predictive Analytics for Learning Performance in First-Year ...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Kartiwi, Mira Gunawan, Teddy Surya Yusoff, Nelidya Md International Islamic University Malaysia Department of Information Systems Kuala Lumpur53100 Malaysia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

The increasing demand for programming skills has highlighted the need for effective teaching strategies to support student success in programming courses. Despite significant advancements in learning analytics, predictive models explicitly tailored to programming courses remain underexplored. This research aims to develop a machine learning model to predict student performance in programming courses offered within IT programs by analyzing gender, type of activity (readings, coding exercises, assignments), and frequency of access to different activities. Our study utilizes log data of the asynchronous learning activities in the learning management systems of the students enrolled in programming courses. We employ machine learning techniques, decision trees, gradient boosting machines (GBM), and logistic regression to build robust predictive models. In this study, the decision tree model outperformed logistic regression (77.77%) and gradient boosting machine (GBM) (86.57%) by achieving the highest accuracy of 89.09% and excelling in predicting 'Poor' student performance with a recall of 90.67%, establishing it as the most effective model for this predictive analysis. The findings from this research offer actionable insights for educators, enabling early intervention for at-risk students and developing tailored teaching strategies to enhance student performance through strategically provisioning the learning materials in programming courses. This study contributes to the growing knowledge of learning analytics and provides a foundation for future research in predictive modeling for diverse educational contexts. © 2024 IEEE.

关键词： Predictive analytics

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An Implementation of Answer Code Validation Program for Code Writing Problem in Java Programming Learning Assistant System

An Implementation of Answer Code Validation Program for Code...

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11th International Conference on information and Education Technology, ICIET 2023

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Mon, Khin Thet Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Okayama University Dept. of Information and Communication Systems Okayama Japan Tokyo University of Agriculture and Technology Dept. of Computer and Information Science Tokyo Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665465489

To assist Java programming learning of novice students, we have developed the web-based Java Programming Learning Assistant System (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal. © 2023 IEEE.

关键词： Students

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Translating Natural Language Specifications into Access Control Policies by Leveraging Large Language Models 6

Translating Natural Language Specifications into Access Cont...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent systems, and Applications, TPS-ISA 2024

作者： Lawal, Sherifdeen Zhao, Xingmeng Rios, Anthony Krishnan, Ram Ferraiolo, David San AntonioTX United States University of Texas Department of Information Systems and Cyber Security San AntonioTX United States Department of Electrical & Computer Engineering San AntonioTX United States National Institute of Standards and Technology GaithersburgMD United States

ISBN: (纸本)9798350386745

This paper investigates the application of large language models (LLMs) for the automated translation and information extraction of access control policies from a natural language source. Prior research in this domain have predominantly relied on manual methods, traditional natural language processing (NLP), or a hybrid approach involving machine learning and artificial neural networks combined with NLP techniques. We demonstrate a significant advancement by leveraging the power of LLMs to achieve improved efficiency and accuracy in these tasks. Our study focuses on applying cutting-edge prompt engineering techniques designed to optimize LLM performance in the specific context of access control policy information extraction. The findings highlight the potential of LLMs to streamline the process of converting human-readable requirements into formal, machine-interpretable policies, ultimately contributing to the automation and security of access control systems. © 2024 IEEE.

关键词： Natural language processing systems

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Towards Sustainable Buildings: Chilled Water System Analysis & Efficiency Modeling

Towards Sustainable Buildings: Chilled Water System Analysis...

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Global Power, Energy and Communication Conference (GPECOM)

作者： Wiam Ayrir Ahmed. M. Helmi Simon Abongmbo Driss Benhaddou Electrical Engineering Networks and Telecommunication Systems Department National School of Applied Sciences Ibn Tofail University Kenitra Morocco Computer Engineering Department College of Engineering and Information Technology Buraydah Private Colleges Buraydah KSA Computer and Systems Engineering Faculty of Engineering Zagazig University Zagazig Egypt Electrical and Computer Engineering Department University of Houston Houston Texas USA Engineering Technology Department University of Houston Houston Texas USA

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

Optimizing the performance of heating, ventilation, and air conditioning (HVAC) systems constitutes a significant research topic in energy efficiency that brings together various disciplines. In particular, campus-like buildings use HVAC system implemented in a central plant as a form of the chiller system. The main objective is to minimize the energy consumption of these systems in different types of buildings and increase their efficiency while maintaining optimal interior comfort. This paper offers a thorough analysis and evaluation of a university’s chilled water system performance. The insights gained shed light on key optimization perspectives. Additionally, this paper employs regression modeling using Random Forest to analyze the HVAC system, aiming to understand the importance of features such as consumption, outside temperature, and other relevant factors in the chilled water system’s energy efficiency.

关键词： Energy consumption Analytical models HVAC Temperature System performance systems operation Buildings

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Computation Offloading in Air-Ground Integrated Vehicular Edge Computing Networks

Computation Offloading in Air-Ground Integrated Vehicular Ed...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Li, Shichao Chen, Hongbin Tan, Fangqing Zhang, Ning Lin, Siyu S. Quek, Tony Q. School of Information and Communication Guilin University of Electronic Technology Guilin541004 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B3P4 Canada School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

ISBN: (纸本)9798350370218

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving, where the tasks can be offloaded to the high altitude platform (HAP) through an unmanned aerial vehicle (UAV) as a relay. In this paper, we investigate a joint access equipment selection and resource allocation problem, with the objective of minimizing the total task offloading delay in the air-ground integrated VEC network. Considering the formulated problem is a non-convex problem, we decompose it into three subproblems: access equipment selection, joint bandwidth and computation resource allocation, and UAV trajectory design. Considering the first subproblem is an integer programming problem, the many-to-one matching method is utilized to solve it. Meanwhile, we utilize the CVX package and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize coalition game method to deal with it. Based on the solutions of the three subproblems, the joint computation offloading and resource allocation (JCORA) algorithm has been proposed. Simulation results show that the proposed JCORA algorithm can reduce the total task offloading delay compared with other algorithms. © 2023 IEEE.

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IoT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Python

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Repairing Schemes for Tamo-Barg Codes

Repairing Schemes for Tamo-Barg Codes

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IEEE International Symposium on information Theory

作者： Han Cai Ying Miao Moshe Schwartz Xiaohu Tang School of Information Science and Technology Southwest Jiaotong University Chengdu China Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan Department of Electrical and Computer Engineering McMaster University Hamilton Ontario Canada School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We study the problem of repairing erasures in locally repairable codes beyond the code locality under the rack-aware model. We devise two repair schemes to reduce the repair bandwidth for Tamo-Barg codes under the rack-aware model, by setting each repair set as a rack. The first repair scheme provides optimal repair bandwidth for one rack erasure. We then establish a cut-set bound for locally repairable codes under the rack-aware model. Using this bound we show that our second repair scheme is optimal. Furthermore, we consider the partial-repair problem for locally repairable codes under the rack-aware model, and introduce both repair schemes and bounds for this scenario.

关键词： Codes Bandwidth Maintenance engineering

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Securing Digital Media in the Age of AI and Deepfakes: A Hardware-Based Solution

Securing Digital Media in the Age of AI and Deepfakes: A Har...

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IEEE Southeastcon

作者： Eric Scott Sisson Steven C. Puckett Dept. of Electrical and Computer Engineering Mississippi State University Starkville AL USA Computer Science and Information Systems University of North Alabama Florence AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The current capabilities of deepfake and Artificial Intelligence (AI) technologies make it difficult to distinguish what is original from what is fabricated. Electronic documents can be digitally signed to verify and authenticate their information, making them admissible as evidence in court. In contrast, images and videos from Internet of Things (IoT) devices-such as police body cameras, security cameras, and smartphones are susceptible to deepfake manipulation, which can undermine their authenticity in legal proceedings. This research hypothesizes that digitally signing image and video data utilizing hardware cryptographic coprocessors could render them admissible in court. Utilizing our processes will also identify if any manipulation of the image data has occurred. Our approach addresses a critical need in the justice system to combat the challenges deepfakes pose and could pave the way for enhanced trust in digital evidence, bolstering the integrity of the judicial process.

关键词： Deepfakes Law enforcement Media Cameras Hardware Cryptography Internet of Things Artificial intelligence Digital signatures Coprocessors

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