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14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Speaking from Experience: Co-designing E-textile Projects with Older Adult Fiber Crafters 23

Speaking from Experience: Co-designing E-textile Projects wi...

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17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Jelen, Ben Lazar, Amanda Harrington, Christina Pradhan, Alisha Siek, Katie A. Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States College of Information Studies University of Maryland College ParkMD United States HCI Institute Carnegie Mellon University College ParkMD United States School of Informatics Computing Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9781450399777

Researchers support race, gender, and age diverse groups of people to create with maker electronics. These groups include older adults, who are often overlooked as not interested or capable of learning new technologies due to ageist stereotypes. One approach, often involving e-textiles, leverages crafting as a bridge to broaden participation in making. We investigated ways to broaden participation in maker electronics for older adults by remotely co-designing e-textile projects with 6 older adult crafters over the course of 5 workshop sessions for a total of 45 hours. We developed a deeper understanding of their practices, identifying a Planner-Improviser Spectrum for how they approached their craft, and created medium fidelity prototypes. Our design implications draw on our participants' crafting experience and their experience in the workshop to highlight what e-textile toolkit designers can learn from skilled older adult crafters, such as selecting familiar materials, supporting aesthetic goals, and making electronics more attainable. © 2023 ACM.

关键词： Older adults electronic toolkits maker electronics remote workshop co-design crafting crafters crafting technology.

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Systematic Literature Review Langchain Proposed 25

Systematic Literature Review Langchain Proposed

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25th International Electronics Symposium, IES 2023

作者： Asyrofi, Rakha Dewi, Mutia Rahmi Lutfhi, Muhammad Irfan Wibowo, Prasetyo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350314731

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review. © 2023 IEEE.

关键词： Software testing

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peiyuan Guan Chen Chen Ziru Chen Lin X. Cai Xing Hao Amir Taherkordi Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge UK Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA School of Information Science and Technology Northwest Univesity Xi’an China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

关键词： Wireless communication Simulation Serverless computing Bandwidth Quality of service Containers Heterogeneous networks Delays Global communication Edge computing

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CEASEFIRE: An AI-Powered System for Combating Illicit Firearms Trafficking

CEASEFIRE: An AI-Powered System for Combating Illicit Firear...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Cani, Jorgen Mademlis, Ioannis Mancuso, Marina Paternoster, Caterina Adamakis, Emmanouil Margetis, George Chambon, Sylvie Crouzil, Alain Lechelek, Loubna Dede, Georgia Evangelatos, Spyridon Lalas, George Mignet, Franck Linardatos, Pantelis Kentrotis, Konstantinos Gierszal, Henryk Tyczka, Piotr Karagiorgou, Sophia Pantelis, George Stavropoulos, Georgios Votis, Konstantinos Papadopoulos, Georgios Th. Harokopio University of Athens Department of Informatics and Telematics Greece Università Cattolica del Sacro Cuore - Transcrime Italy Institute of Computer Science Foundation for Research and Technology Hellas Greece Universite de Toulouse Cnrs Irit France Netcompany-Intrasoft Thales France Exus Software Single Member Limited United Kingdom Itti Poland Ubitech Ltd Centre for Research and Technology Hellas Greece

ISBN: (纸本)9798350362480

Modern technologies have enabled illicit firearms trafficking to partially merge with cybercrime, while also allowing its off-line aspects to become increasingly complex. The online trade of firearms, their components, 3D blueprints and illicit substances carried out by criminals on both the surface Web and dark Web is increasingly difficult to address as a consequence of the exponential growth in the amount of information disseminated on the Internet. On the other hand, law enforcement agencies are confronted with significant challenges that require the development of sophisticated technological solutions capable of processing large volumes of data, identifying relevant information in a timely manner and creating networks of connections between potential criminal groups. This article presents a real-world practical system, namely the CEASEFIRE one, powered by advanced artificial intelligence technologies that can assist law enforcement personnel in addressing the above described challenges. © 2024 IEEE.

关键词： Crime

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Clustering Based User Allocation in 5G Networks∗

Clustering Based User Allocation in 5G Networks∗

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2021 International Symposium on Networks, computers and Communications, ISNCC 2021

作者： Bouras, Christos Kalogeropoulos, Rafail Sina, Ioannis Computer Technology Institute Press 'Diophantus201D Univ. Of Patras Greece Patras Greece Computer Engineering And Informatics Dept.

ISBN: (纸本)9780738113166

Machine learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictates an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize machine learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and machine learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes machine learning techniques. © 2021 IEEE.

关键词： Wireless networks

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Enhancing Secure Cross-Border Collaboration among Law Enforcement Agencies for Facial Biometric Search

Enhancing Secure Cross-Border Collaboration among Law Enforc...

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Electronic Engineering, Information technology & Education (EEITE), International Conference in

作者： Kyriaki Miniadou Asterios Leonidis Georgios Th. Papadopoulos Constantine Stephanidis Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Computer Science University of Crete Heraklion Greece

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Addressing the growing challenge of combating international crime involves the development of secure and efficient methodologies that allow Law Enforcement Agencies to exchange information seamlessly, without being hindered by time-consuming bureaucratic processes. In this context, we present a solution centered on facial biometric search methodologies. Our approach underscores the importance of employing accurate and reliable methods to assess image data similarity, particularly in the domain of facial images, which pose unique challenges due to subtle variations. We propose a comprehensive solution that harnesses hashing techniques and homomorphic encryption. By doing so, our approach ensures secure data exchange while safeguarding confidentiality and integrity. We firmly believe that our approach will substantially improve collaboration in law enforcement efforts and make significant contributions to global security.

关键词： Data privacy Law enforcement Scalability Education Collaboration Fingerprint recognition Regulation

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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in Computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Azran, Guy Danesh, Mohamad H. Albrecht, Stefano V. Keren, Sarah Taub Faculty of Computer Science Technion Israel Institute of Technology Israel School of Computer Science McGill University Canada School of Informatics University of Edinburgh United Kingdom

Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward machines (RMs), state machine abstractions that induce subtasks based on the current task’s rewards and dynamics. Our method provides agents with symbolic representations of optimal transitions from their current abstract state and rewards them for achieving these transitions. These representations are shared across tasks, allowing agents to exploit knowledge of previously encountered symbols and transitions, thus enhancing transfer. Empirical results show that our representations improve sample efficiency and few-shot transfer in a variety of domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Application of Parallel Signal Processing Using Reverse Blockchain technology in Conditions of Network Instability

Application of Parallel Signal Processing Using Reverse Bloc...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Volodymyr Khilenko Andrii Zinchenko Oleksii Stepanov Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing.

关键词： Analytical models Heuristic algorithms Instruction sets Signal processing algorithms Process control Parallel processing Control systems Smart systems Data models Blockchains

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