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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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An Architecture for Predictive Resource Allocation in Cloud Capacity Management Using Neural Networks

An Architecture for Predictive Resource Allocation in Cloud ...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Amaral, Janete Lima, Alberto Sampaio De Souza, Jose Neuman Moura, Jose Antao Beltrao Federal University of Ceará Computer Science Department Cear Fortaleza Brazil Federal University of Ceará Teleinformatics Engineering Department CE Fortaleza Brazil Federal University of Ceará Computer Science Department CE Fortaleza Brazil Federal University of Campina Grande Department of Systems and Computing Paraiba Campina Grande Brazil

ISBN: (纸本)9798350304602

Cloud service providers (CSP) offer shared environments to meet customers' fluctuating requirements. The challenge is to use mechanisms capable of optimizing the use of resources and ensuring that the performance of these services continues to comply with quality metrics and the respective Service Level Agreements (SLA). CSPs must offer autonomous mechanisms to readily promote resource scalability, while customers must be confident in the performance and costs involved. In Cloud Capacity Management, the target of research in Business-driven IT Management (BDIM), predictive approaches have already been proposed to overcome the limitations of reactive methods. Such techniques have yet to demonstrate satisfactory results. This research suggests an Autonomous Architecture for Predictive Resource Allocation in Cloud Capacity Management using Neural Networks, combining reactive and predictive approaches for resource scheduling. A deep learning-based method supported predictive resource provisioning through the SLSTM (Stacked long short-term memory) neural network. This proposal adopted an autonomic approach using the MAPE-K (Monitor-Analyze-Plan-Execute over a Knowledge base) loop. A comparison between statistical methods, classical neural networks LSTM (Long short-term memory), and SLSTM demonstrated the proposal's feasibility. © 2023 IEEE.

关键词： Long short-term memory

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Is Cybersecurity Winning: A CrossSectional Study in Fiji

Is Cybersecurity Winning: A CrossSectional Study in Fiji

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2023 IEEE Asia-Pacific Conference on Computer Science and Data engineering, CSDE 2023

作者： Reddy, Pritika Goundar, Munil Shiva Khan, Shaireen Shabnam Sharma, Bibhya Fiji National University Department of Computing Science and Information Systems Suva Fiji Health Information and Intelligence Unit Aspen Medical Lautoka Fiji School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Fiji

ISBN: (纸本)9798350341072

The advent of the Internet, as well as the increasing use of various web applications, social media platforms, and the Internet of Things, are posing online threats to people. Moreover, with the growing dependence on Industry 4.0 operations, cybersecurity awareness and practices have become more important to organizations and countries than ever before. The current study intends to evaluate cybersecurity practices and awareness at a local education service provider in Fiji. A quantitative methodology comprising an online survey was administered to collect responses from its students who are employees from 22 different organizations in Fiji. The results of the study showed that the employees in Fiji have an average awareness of cyber hygiene. The highly positive correlation value between employee response to cybersecurity practices and awareness indicates that some organizations follow good cyber hygiene. The results from this study indicate that organizations in Fiji need to be more vigilant to defend themselves from the raising cyber threats and vulnerabilities. © 2023 IEEE.

关键词： Cybersecurity

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Estimation of the Cycling Cadence Using Low-Cost Inertial Sensors Mounted on a Bicycle

Estimation of the Cycling Cadence Using Low-Cost Inertial Se...

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Cloudification of the Internet of Things (CIoT)

作者： Hrvoje Mihaldinec Luka Klaić Hrvoje Džapo Department of Electronic Systems and Information Processing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

With a rising interest in micro-mobility in the context of green technologies and increased awareness of the importance of regular physical activity, bicycles and e-bikes have gained significant popularity in everyday life. Equipping a bike with smart sensors may provide additional data to complement personal fitness trackers. Previous research has shown that cycling cadence can be measured by IMUs, however, studies were based on expensive commercially available motion tracking development kits, tested in laboratory setup environments. The goal of this research was to investigate the possibilities of measuring the cycling cadence by means of lowcost and easy-to-integrate MEMS inertial measurement units (IMUs) and signal processing algorithms, suitable for running on low-power microcontroller architectures. Moreover, the measurement system was evaluated in realistic conditions on a paved and a pebble road to prove that cheaper IMU sensor alternatives may still achieve satisfactory results in the estimation of cycling parameters in real-life outdoor conditions.

关键词： Micromechanical devices Wireless communication Wireless sensor networks Measurement units Tracking Roads Estimation

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Metaheuristic-Based Lagrangian Relaxation for Total Weighted Tardiness Minimization 21st

Metaheuristic-Based Lagrangian Relaxation for Total Weighte...

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21st Polish Control Conference, PCC 2023

作者： Smutnicki, Czeslaw Rudy, Jaroslaw Idzikowski, Radoslaw Bożejko, Wojciech Department of Computing Engineering Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783031351723

In this paper, a Lagrangian relaxation approach to the problem of minimization of the total weighted tardiness is presented. The approach can be used both to solve the original problem as well as to obtain a high-quality lower bound for the branch-and-bound method. The problem was further decomposed into upper and lower problems. For the upper problem, an approach using a local-search metaheuristic is proposed instead of a subgradient method from the literature. Several variants of both methods are then implemented and compared through a numerical example using OR library problem instances. The results prove the viability of the metaheuristic approach as with little calibration it is able to match or outperform a refined subgradient method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Branch and bound method

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Unified Theory Acceptance and Use of Technology Against Unplugged Computational Thinking Activities

Unified Theory Acceptance and Use of Technology Against Unpl...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Ahmad, Izzah Sakinah Sim, Tze Ying Talib, Corrienna Abdul School of Engineering and Technology Sunway University Department of Computing and Information Systems Bandar Sunway Malaysia School of American Education Sunway University Bandar Sunway Malaysia School of Education Universiti Teknologi Malaysia Department of Educational Sciences Mathematics and Creative Multimedia Skudai Malaysia

ISBN: (纸本)9798350302684

Occasionally, there may be a low impact on learning engagement between teachers and students due to, for example, poor internet connection or simply because students have lost their focus. One of the methods to improve learning engagement is through a research framework called Unified Theory Acceptance and Use of Technology (UTAUT). This paper adopted 17 UTAUT questions to compare with research variables (pre-post surveys and genders). The user acceptance and use of technology or UTAUT consisted of Likert scale (Strongly disagree to Strongly Agree) questions, computed to generate total scores for each participant. Then, the scores will be compared to each variable to investigate the relationships between these variables against the UTAUT. This study could be implemented via online learning, but in the end, the offline (unplugged) method was the only method to deliver the learning activities. There is limited evidence discussing UTAUT within the area of offline learning;hence, this paper will focus on another spectrum regarding the UTAUT study. Instead of mobile (online) learning engagement, this paper studies the relationship between unplugged learning with user acceptance and the use of technology. Spearman correlation indicated that the unplugged learning approach has a significant positive relationship with user acceptance and use of technology. It revealed that students may also demonstrate an interest in online learning engagement and be able to comprehensively accept the online learning approach. In contrast, Mann-Whitney U test analysis on genders revealed no significant difference. The findings could contribute to another noteworthy discussion regarding the validity of user acceptance and the use of technology against the unplugged learning approach. © 2023 IEEE.

关键词： Students

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Sports Events Recognition Using Multi Features and Deep Belief Network

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Computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices 7

Peer to Peer Federated Learning: Towards Decentralized Machi...

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7th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Aristeidis Karras, Christos Giotopoulos, Konstantinos C. Tsolis, Dimitrios Oikonomou, Konstantinos Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Federated Learning (FL) is an emerging technique that assures user privacy and data integrity in distributed machine learning environments. To perform so, chunks of data are trained across edge devices and a high performance cluster server maintains a local copy without exchanging it with other parties. In this work, we investigate a FL scenario in a real-world case study using 5, 10 and 20 Raspberry Pi devices acting as clients. Under this setup, we employ the widely known FedAvg algorithm which trains each client for several local epochs and then the weight of each model is aggregated. Moreover we perform experiments across imbalanced and noisy data so as to explore scalability and robustness based on real-world datasets were noise is present and we also propose two innovative algorithms where the FL scenario is considered as a peer-to-peer formulation. Ultimately, to ensure that each device is not oversampled a client-balancing Dirichlet sampling algorithm with probabilistic guarantees is proposed. © 2022 IEEE.

关键词： Computer aided instruction

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Soft computing Based Metaheuristic Algorithms for Resource Management in Edge computing Environment

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Computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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Computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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