2828 research outputs found
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Exploring Changes in Inclusive Education Policies and Practices for Children with Cerebral Palsy in a Private School in Dubai: A Comparative Case Study of 2011 and 2024
No full textCerebral palsy is a group of permanent movement disorders caused by early brain damage, affecting a child’s motor skills and coordination. As a result, children with cerebral palsy often encounter barriers to accessing quality education. Inclusive education plays a vital role in overcoming these barriers, ensuring equitable access to learning opportunities for all children. Despite the importance of inclusive education, there is a lack of extensive research on inclusive practices for children with cerebral palsy in Dubai, and whether such practices have progressed over time. This study explores the evolution of inclusive education policies and practices for children with cerebral palsy in Dubai, comparing the experiences of two children—Child A and Child B—attending Year 1 at the same private school in 2011 and 2024. Using a comparative case study approach, data were collected through semi-structured interviews, field observations, and document analysis. The findings highlight progress in policy development, accessibility, teacher support, and social inclusion. However, despite these advancements, similar challenges persisted for both children, such as insufficient teacher training, limited awareness of cerebral palsy, lack of external agency support, and the financial burden on families, which continue to hinder the full implementation of inclusive practices. This study contributes to global efforts towards inclusive education by demonstrating how policy evolution and systemic changes can enhance educational opportunities for children with cerebral palsy, providing valuable lessons for countries aiming to meet international goals like Sustainable Development Goal 4 (quality education) and build more inclusive, equitable education systems
Using XAI Techniques to Detect Targeted Data Poisoning Attacks on Healthcare Applications of Machine Learning Systems
No full textThis research study explores the application of Explainable Artificial Intelligence (XAI) methods for detecting targeted data poisoning attacks in healthcare machine learning systems. As machine learning becomes increasingly integrated into critical fields like healthcare, the integrity and security of training data have become paramount concerns. Data poisoning attacks, which manipulate training datasets to influence model behaviour, pose a significant threat to the reliability and effectiveness of these systems. Our study presents a novel approach that leverages XAI techniques, particularly focusing on global explanations of selected features, to identify signs of data manipulation. We propose a method of monitoring the impact level of carefully chosen features as an indicator of potential poisoning, using predetermined thresholds to trigger warnings when unusual patterns are detected. The research methodology involves applying global explanation method, to measure and monitor features impact in healthcare datasets, then explore the effectiveness of this approach using a case study on hypothyroid diagnosis, where data poisoning could lead to delayed treatment with potentially life-threatening consequences. Research findings suggest that XAI techniques can provide valuable insights into the behaviour of machine learning models, enabling more effective detection of subtle, targeted poisoning attacks. However, we also acknowledge limitations, including the need for some prior knowledge of potential attack goals and the risk of false positives or negatives
The Influence of Intelligent Automation on Senior Project Practitioners’ Roles and Responsibilities for a Successful Implementation of Intelligent Automation Services in the UAE Public Sector
No full textThis study investigates the influence of intelligent automation (IA) on the roles and responsibilities of senior project practitioners (SPPs) in the UAE public sector, aiming to provide a comprehensive framework for successful implementation. By focusing on public sector challenges, this research bridges theoretical gaps between IA, project management, and governance while providing practical strategies for implementation. The findings equip public sector organizations and SPPs with tools to foster innovation and resilience in an era of technological advancement. With the rapid evolution of IA, it is imperative for senior project practitioners to adapt to new organisational processes that facilitate efficient project management. Using a quantitative survey-based approach, responses from 291 SPPs were analysed through exploratory factor analysis (EFA) and regression modelling to identify key drivers of IA readiness. The findings underscore SPPs' need to transition into mentorship roles, focusing on human interactions and addressing skill gaps within their teams. This research fills a vital gap in the existing literature by concentrating specifically on the UAE public sector, offering insights into government agencies' unique challenges and opportunities. The developed conceptual framework serves as a foundation for future research and practice in the realm of IA within the UAE public sector. This study bridges theoretical gaps by integrating the Technology Acceptance Model (TAM) into understanding IA adoption, linking its implications to project management and public sector governance. Practically, it equips UAE public sector organizations and SPPs with strategies to implement IA, providing essential tools to drive enhanced organizational performance and productivity
Leveraging Network Traffic Byte-Streams for Machine Learning Based Early Botnet Attack Detection
No full textBotnet attacks can overwhelm networks and severely affect the availability of services. Anomaly based detection techniques using machine learning are effective against zero-day attacks. However, they require complex data preprocessing and feature extraction which can affect the early detection of botnet attacks. In this paper we propose a novel approach, for early detection of botnet attacks using machine learning models that learn from byte representation of raw network traffic flows. The study departs from the traditional approach of network-based intrusion detection which relies on flow statistics and other hand-crafted features. We discuss our framework which includes light weight network traffic pre-processing, transformation, and model training. We used the CTU-13 dataset to evaluate the proposed byte-based botnet detection system. Our results show that byte-based representation can provide an effective and ultra lightweight means of developing network intrusion detection systems that can match the performance of traditional approaches, while also enabling early detection of botnet attacks. In our experiments we achieved accuracy of 99.9% consistently across different byte stream sizes for the Decision Tree and Logistic Regression classifiers
The Determinants of Cryptocurrency Adoption: An Integrated Framework for the UAE
No full textThis study investigates the factors influencing cryptocurrency adoption intentions among consumers in the United Arab Emirates (UAE), a country positioning itself as a global blockchain and digital asset hub. Despite policy-driven advancements such as the Emirates Blockchain Strategy and the establishment of regulatory bodies such as Virtual Assets Regulatory Authority (VARA), cryptocurrency adoption among UAE residents remains limited. To address this gap, the research integrates the Uncertainty Framework, Perceived Risk Theory, and UTAUT2 to develop a holistic model examining how uncertainty, perceived risk, and technology acceptance shape consumer behaviour. The study adopts a quantitative, cross-sectional research design, employing a structured online questionnaire distributed to a sample of 450 UAE residents. Using Partial Least Squares Structural Equation Modelling (PLS-SEM) via WarpPLS 8.0, the analysis evaluates the measurement and structural models for reliability, validity, and predictive power. The findings reveal that perceived information asymmetry, technological uncertainty, regulatory ambiguity, and the intangibility of cryptocurrency services significantly influence perceived risk, which in turn affects the adoption intentions. Technological constructs from the UTAUT2, such as performance expectancy, effort expectancy, social influence, and trust, also emerge as critical factors shaping the adoption behaviour. This study contributes theoretically by extending the UTAUT2 with the risk and uncertainty constructs, and offers practical implications for regulators, developers, and marketers seeking to increase consumer engagement with cryptocurrencies in high-risk, innovation-driven environments like the UAE. The results underscore the importance of regulatory clarity, consumer education, and trust-building initiatives in bridging the gap between national blockchain ambitions and individual-level adoption
A Holistic Exploration of Quality Assurance Mechanisms at Systemic and Institutional Levels: Conceptualisation, Perception and Practices in Pakistani Higher Education Institutions
No full textThis thesis explores Quality Assurance (QA) mechanisms at systemic and institutional levels in Pakistani Higher Education Institutions (HEIs), focusing particularly on stakeholder conceptualisation, perceptions, and QA practices. Grounded in frameworks such as the World Culture Theory, Neo-Institutional Theory, Stakeholder Theory, Total Quality Management (TQM), and Theory of Change (ToC), this study examines the coherence between the National Agenda and National Policies in relation to Quality Assurance (QA) in higher education. This research investigates the interplay between global QA benchmarks and local QA standards, specifically analysing how Pakistan's QA mechanisms align with the European Standards and Guidelines (ESG). Furthermore, it examines the alignment between External Quality Assurance (EQA) and Internal Quality Assurance (IQA) practices through the Precepts, Standards, and Guidelines (PSG) QA framework, evaluating its relevance to both local needs and international compatibility. Using a mixed-methods approach, data were gathered through document analysis, surveys, interviews, and focus groups, with the sample comprising representatives from the Higher Education Commission (HEC), QA practitioners, full-time faculty, and postgraduate students across eight universities in Pakistan. The study employed quantitative sampling with quality practitioners (n=80), faculty (n=158), and students (n=204) to ensure representative data. For qualitative analysis, participants included HEC members, quality practitioners, faculty, and postgraduate students, who contributed through focus groups (n=32) and open-ended questions (n=22), providing diverse insights across various disciplines and institutions. Quantitative data were analysed using SPSS, whereas qualitative data were processed using NVivo. The research identifies significant themes, including EQA and IQA practices, evaluation processes, actors involved in QA, and Continuous Improvement Processes. The findings highlight significant systemic and institutional challenges impeding the effectiveness of QA mechanisms, including insufficient training and empowerment of QA staff, as well as inadequate review processes. Furthermore, governance constraints, limited resources, and the inefficient allocation of funds hinder the successful implementation of QA frameworks. These factors often lead to regulatory compliance, rather than promoting a culture of continuous improvement. The two major contributions of this research are the development of the Quality Assurance Review Tool (QART), a comprehensive assessment instrument designed to advocate and offer practical insights into effective QA applications. In addition, the findings led to the development of a Theory of Change (ToC) action plan that offers strategic insights for policymakers and institutions to ensure and enhance QA systems in Pakistan's Higher Education Institutions, ensuring alignment with global standards, while addressing local challenges
A Novel Versatile Framework for Enabling Early Detection of Evolving Network-based Cyberattacks
No full textNetwork-based cyber-attacks have been increasing in scale, frequency and sophistication, posing significant threats to nation states and organizations worldwide. Researchers have proposed various anomaly-based solutions to detect such attacks and address the shortcomings of traditional signature-based methods. However, these solutions either require complex preprocessing to extract network flow statistics or depend on hand-crafted features from domain expertise, thus adding computational overhead that limits the ability for early attack detection. To address these limitations, this thesis proposes a novel framework called FPAC (Flexible Parser Anonymizer Converter) which is designed to enable early detection of different types of attacks by processing only the first few packets of network flows. The study departs from established methods that rely on flow statistics and hand-crafted features by introducing innovative techniques for processing and learning from raw network traffic bytes. In the thesis, two attack detection scenarios i.e. Botnet and Low-rate Denial of Service (LDoS), and four different low overhead techniques i.e. Histogram of Oriented Gradients (HOG), entropy byte histogram, byte-based feature learning, and representation learning from bytes, were used to demonstrate the applicability of the FPAC framework for early attack detection. Experiments were performed to validate the FPAC approach using the CTU botnet and the UTSA 2021 LDoS datasets. For botnet attack detection, the byte-based feature learning techniques with Decision Trees (DT) and Extreme Gradient Boosting (XGB) performed optimally, achieving 99.9% accuracy with fast detection times ranging from 0.006 to 0.026 seconds. Image-based approaches using HOG and entropy byte histogram also achieved 99.4% and 100% accuracy, respectively, while incurring reduced overheads compared to related works. The 1D CNN model matched the best byte-based results with 99.9% accuracy, validating the role of deep learning within the FPAC framework. For LDoS attack detection, which is inherently more challenging due to its subtle nature, all four lightweight techniques employed in this thesis performed favourably compared to existing approaches. The byte-based method again delivered the best results, achieving 95.8% accuracy. Image-based techniques attained accuracies of 88.9% for HOG and 92.1% for entropy byte histogram with XGB, while the representation learning from bytes approach using 1D CNN achieved 95.6% accuracy. These results outperform computationally expensive methods reported in related works, showcasing that the FPAC framework achieves high detection performance with very low overheads while also generalizing effectively across different network attack types.
Keywords: network-based attacks, early attack detection, machine learning, representation learning, botnet, LDoS, HOG, entropy byte histogram
Examining the Role of School Leadership Styles in Promoting School’s Performance and Academic Success in UAE: A Systematic Review-Based Analysis
No full textAcademic success in schools today depends on the effectiveness of the leadership styles of school leaders. Amidst the accelerated change such as e-learning and evolving student demands, successful leadership affects the performance of both teachers and students. This study examines the influence of different leadership styles on the performance of schools through a systematic literature review, focusing on research from 2013 to 2024. The methodology involves a critical review of relevant literature on the effects of different forms of leadership approaches in schools. The analysis synthesizes evidence, establish trends among different learning settings, and assess determinants of effective leadership. The paper contributes to the debate on guidelines for assisting school leaders understand and apply the most effective leadership styles in varying situations. It proposes leadership approaches that optimize teacher satisfaction, student achievement, and school success. These include models such as transformational and democratic leadership, which induce motivation, shared decision making, along with innovation. This study contributes to the discourse on how school administrators can map current plans to institutional objectives and community aspirations within UAE
Digital Transformation in Construction Sector; Assessing the Impact on Projects Performance & Productivity
No full textDigital transformation is a mega trend around the globe and considered to be as a critical success factor for countries, business, and organizations. This study intended to explore the impact of digital transformation on project performance productivity in the construction sector. A secondary data approach was executed to gather the data to address the research question and accomplish its objectives. Thae main finding of this research that there is a positive correlation between digital transformation and projects performance and productivity its construction sector. Moreover, the research presented a statistical findings on the digital transformation impact in the terms of cost, time, quality, and error in construction projects
The Impact of Organizational Learning on Business Continuity Through the Moderating Effect of Organizational Culture: A Study of UAE’s Public Sector Organizations
No full textDespite the UAE’s vast growth, its public sector remains essential to its stability, where effective crisis management and fast change are unique challenges. It is crucial to understand the relationship between organizational learning (OL), business continuity (BC), and organizational culture (OC). This paper examines the OL and BC relationship through the moderating role of OC, specifically the hierarchical culture that is applied in the UAE's public sector. The study aims to expand OL and BC literature. Empirical data was collected using the questionnaire method. The study hypothesized that OL has a positive impact on BC and that OC moderates the relationship between OL and BC. The study findings indicate that the OL process has a direct and positive impact on BC in the context of the UAE public sector. However, the study reveal that the OC does not moderate the relationship between OL and BC, which sheds light on the complexity of this relationship and can guide future research. Thus, the study findings have the potential to assist UAE public sector organizations in developing their OL activities, fostering an OC, and enhancing effective strategies for BC