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检索条件"机构=Dept. of Computing and Informatics"

共 512 条记录，以下是1-10 订阅

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model 37

Mobile Learning Technologies as drivers of School Enrolment ...

引用

37th International BCS Human-Computer Interaction Conference, BCS HCI 2024

作者： Muazu, Fatima Ahmad Adedoyin, Festus Dogan, Huseyin Whittington, Paul Mavengere, Nicholas Dept. of Computing & Informatics Bournemouth University United Kingdom

This study addresses the imperative challenge of enhancing school enrolment in South Africa by investigating the dynamics of mobile learning technologies, internet access, and key socio-economic variables. Spanning 35 years (1998–2022), the research draws from the National Digital and Future Skills Strategy, aiming to provide insights into the factors influencing educational access. Employing an analytical framework that integrates Autoregressive Distributed Lag (ARDL), dynamic ARDL (dynARDL) simulations, and Kernel-based Regularized Least Squares (KRLS) machine learning, the study finds that economic prosperity, as represented by real GDP per capita, positively influences secondary school enrolment. Mobile phone subscribers emerge as a significant driver, emphasizing the transformative potential of digital technologies. Surprisingly, an inverse relationship between internet users and enrolment prompts a reassessment of the role of internet access in education. The dynARDL simulations introduce counterfactual shocks, highlighting the positive impact of a 10% increase in mobile subscribers and the nuanced consequences of changes in internet users. KRLS analysis reinforces the significance of economic indicators, digital technologies, and human development in shaping enrolment. Drawing policy implications, the study advocates targeted investments in digital infrastructure, strategic approaches to internet access optimization, and policies fostering sustainable economic growth. © Muazu et al.

关键词： Adversarial machine learning

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Leveraging Deep Learning for Access Control and Data Security in Cloud Environments 4

Leveraging Deep Learning for Access Control and Data Securit...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Omogbehin, Blessing Ifeoluwa Sigwele, Tshiamo Semong, Thabo Botswana International University of Science and Technology Dept. of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Cloud computing emerged as a promising model for delivering services over the internet, offering users a pay-as-you-use structure that grants access to infrastructures (Infrastructure as a service), platforms for running various programs (Platform as a Service) and software (Software as a Service) at minimal cost. Surprisingly, as cloud environments evolve and expand, access control has become a critical concern to ensure that the data stored in these systems maintains confidentiality, integrity, and availability. The increasing complexity of cloud architecture highlights the urgent need for a robust access control model, as the consequences of inadequate protection could be disastrous. This article provides a novel approach to addressing the vulnerabilities in cloud security, emphasizing the need for enhanced access control measures. It investigates how deep learning can provide a defense-in-dept. strategy to detect and prevent unauthorized access and data breaches by analyzing current gaps in cloud security research. Through a systematic analysis, this study aims to bridge these gaps, offering insights into how deep learning models can strengthen cloud environments against potential threats. By combining deep learning with traditional methods, the model dynamically optimizes access control decisions and strengthens data confidentiality. Results show that the proposed outperforms current works with accuracy of 94.39% that demonstrates considerable gains in data security, resilience, and overall performance that effectively addresses anomalies in cloud environments. © 2024 IEEE.

关键词： Cloud security

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UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claims 7

UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claim...

引用

7th Fact Extraction and VERification Workshop, FEVER 2024

作者： Sevgili, Özge Nikishina, Irina Yimam, Seid Muhie Semmann, Martin Biemann, Chris Language Technology Group Dept. of Informatics & Hub of Computing and Data Science Universität Hamburg Germany

ISBN: (纸本)9798891761728

This paper presents UHH’s approach developed for the AVeriTeC shared task. The goal of the challenge is to verify given real-world claims with evidences from the Web. In this shared task, we investigate a Retrieval-Augmented Generation (RAG) model, which mainly contains retrieval, generation, and augmentation components. We start with the selection of the top 10k evidences via BM25 scores, and continue with two approaches to retrieve the most similar evidences: (1) to retrieve top 10 evidences through vector similarity, generate questions for them, and rerank them or (2) to generate questions for the claim and retrieve the most similar evidence, again, through vector similarity. After retrieving the top evidences, a Large Language Model (LLM) is prompted using the claim along with either all evidences or individual evidence to predict the label. Our system submission, UHH, using the first approach and individual evidence prompts, ranks 6th out of 23 systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

引用

2023 IEEE International Conference on computing, ICOCO 2023

作者： Khan, Ijaz Zabil, Mohammed Hazli Mohamed Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparment of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. © 2023 IEEE.

关键词： Students

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Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

Towards the Selection of Distance Metrics for k-NN Classifie...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Khan, Ijaz Mohamed Zabil, Mohammed Hazli Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics' Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparmtne of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier. © 2023 IEEE.

关键词： Forecasting

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Self-Adaptation in IoT Systems for Smart Cities

Self-Adaptation in IoT Systems for Smart Cities

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2023 Symposium on Internet of Things, SIoT 2023

作者： Nogueira, Bruno C. Motta, Rebeca C. Delicato, Flavia C. Batista, Thais V. Institute of Computing - Uff Niterói Brazil Ufrn Informatics and Applied Mathematics Dept. Natal Brazil

ISBN: (纸本)9798350329438

Smart Cities (SC) leverage digital technologies to enhance the effectiveness of urban services and improve the quality of life. Since it is a recent field of development, several challenges are afoot. An important part of this challenge concerns adaptive behavior, which is paramount to SC solutions, to respond to contextual changes and carry out tasks with minimal human interaction. Given this relevance, we performed a literature review to characterize the platforms, application types, and adaptation aspects for SC and discuss the challenges to building self-adaptive systems SC. The reviews resulted in 23 papers analyzed, from which the findings of the field's research potential, with a particular emphasis on developing SC solutions and the difficulties that must be resolved before they can be built. © 2023 IEEE.

关键词： Internet of things

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model 24

Mobile Learning Technologies as drivers of School Enrolment ...

引用

Proceedings of the 37th International BCS Human-Computer Interaction Conference

作者： Fatima Ahmad Muazu Festus Adedoyin Huseyin Dogan Paul Whittington Nicholas Mavengere Dept. of Computing & Informatics Bournemouth University UK

关键词： Mobile Learning

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Unified Blockchain-Based Electronic Dental Record Ecosystem for Secure Data Exchange 6

Unified Blockchain-Based Electronic Dental Record Ecosystem ...

引用

6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Allan, Ahmad Ziad Nouh Albahnasawi, Asil M.Y. Mokhamed, Takua Abbas, Sohail Moufti, Mohammad Adel Talib, Manar Abu Dept. of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Dept. of Restorative Dentistry College of Dental Medicine University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350351538

The blockchain technology implemented with Electronic Dental Records (EDRs) provides a solid ground for enhancing data exchange in the field of dentistry. Implementing blockchain technology into EDRs permits decentralization and easy collaboration among dental clinics and laboratories, eventually improving data security and the data exchange processes across various organizations. Traditional EDR systems that exist nowadays face challenges related to data security, integrity, and interoperability, which can affect effective collaboration and comprehensive patient care. To mitigate these issues, the research community is actively looking at blockchain technology as a viable solution due to its decentralized, secure, and transparent nature. Our research work introduces a blockchain-based system that enables dentists to securely store patient EDRs on the Hyperledger Fabric blockchain. Our study aims to enable secure sharing of patient data between different organizations, prioritizing patient consent. By utilizing both off-chain and on-chain storage for various types of data, including text, images, and radiographs, and by implementing smart contracts, we ensure the storage of data securely and ease the communication between organizations for sharing of data between them. © 2024 IEEE.

关键词： Electronic health record

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A FRAMEWORK FOR DIGITAL TWIN COLLABORATION

A FRAMEWORK FOR DIGITAL TWIN COLLABORATION

引用

2024 Winter Simulation Conference, WSC 2024

作者： Hua, Zhengchang Djemame, Karim Tziritas, Nikos Theodoropoulos, Georgios Shenzhen China School of Computing University of Leeds Leeds United Kingdom Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece SUSTech Shenzhen China

ISBN: (纸本)9798331534202

Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework’s effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system’s objectives across various test cases and show scalability for large deployments. © 2024 IEEE.

关键词： Prediction models

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Cybersecurity and Smart Cities: Current Status and Future

Cybersecurity and Smart Cities: Current Status and Future

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2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology, ICES and T 2023

作者： Habib, Muhammad Yasir Qureshi, Haseeb Ahmad Khan, Shujahat Ali Mansoor, Zara Chishti, Abdul Rehman The Islamia University of Bahawalpur Faculty of Engineering Dept. of Electronic Engineering Pakistan Bournemouth University Faculty of Computing Dept. of Computing and Informatics Bournemouth United Kingdom Charles Darwin University College of Engineering IT and Environment Darwin Australia

ISBN: (纸本)9781665455602

A Smart city implements the latest IoT and information and communication technologies (ICT) to improve the quality of urban city administrations, decrease expenditures, asset management and interconnect citizens of a Smart city. Smart cities offer numerous advantages like improved energy productivity, management, healthcare facilities, efficient transport systems, proper waste and water management, and individual security. Nonetheless, this reliance on ICT and IoT technologies makes a Smart city prone to digital cyber assaults. These technologies are vulnerable to many security issues like Information theft, Eaves-dropping attack, Denial of service, Communication delays, Data manipulation, IoT security attacks, Communication interception, Jamming, Sensor failure, insecure API, and Remote exploitation. This research study intends to address opinions on cybersecurity technologies, vulnerabilities, and cybercrime awareness based on the systematic literature review 'PRISMA Model' as our research method and help researchers and practitioners to look for innovative Smart City solutions. Our research endeavors to momentarily depict the central ideas of digital security and protection issues related to Smart city areas and uncover digital cyber-Attacks that focus on Smart city communities in the literature. In brief, the focus of this research is to explore and review the aspects of Smart city cybersecurity issues, Smart city vulnerabilities related to information security, and provide a comprehensive research framework that will help the researchers and practitioners explore this area of research. © 2023 IEEE.

关键词： Cybersecurity

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