Digital Commons Network^™

Enhancement Of Rainfall Prediction And Satellite Precipitation Estimates: A Machine Learning Approach For United Arab Emirates, Faisal Baig

Dissertations

The accurate estimation of rainfall in arid regions presents a significant challenge due to sparse ground observations and complex atmospheric dynamics. This dissertation investigates various aspects of rainfall estimation and prediction in the United Arab Emirates (UAE), employing Satellite Precipitation Products (SPPs) and Machine Learning (ML) techniques. The research is structured around four key articles, each addressing different facets of the overarching theme. The first publication examines rainfall variability, consistency, and concentration in the UAE by comparing satellite precipitation products with rain gauge observations, highlighting the discrepancies between the two data sources. The second publication evaluates the performance of precipitation …

School Leadership For The Practice Of Inclusive Education In Elementary Schools In The United Arab Emirates, Aysha Saeed Al Nuaimi

Dissertations

The number of students with disabilities in regular classrooms is increasing. In 2020, the United Arab Emirates (UAE) introduced a new policy with the goal of promoting inclusive education in all public schools. Although the UAE government is committed to the education of children with disabilities, the role of school leaders is vital to the successful implementation of inclusive education in schools. While school leaders have been found to be the main agents in steering inclusive practices, their contribution to practices in schools in the UAE context remains empirically unresearched. The objective of this study was to explore elementary school …

Cybersecurity Education And Continuous Learning Towards Uae Ncsp Fulfilment, Saleh Hamad Aldaajeh

Dissertations

This dissertation delves into enhancing cybersecurity education by aligning academic curricula with national cybersecurity strategic plan (NCSP) objectives, emphasizing the crucial role of Higher Education Institutions (HEIs) in developing a skilled cybersecurity workforce. Analyzing ten NCSPs, it identifies strategic themes and gaps between national goals and HEI offerings. The study reviews NCSP guidelines, international cybersecurity indices, and literature, including the NICE-NIST framework, to develop a framework that bridges the educational gap, improving learning outcomes and arming students with vital skills, knowledge, and competencies. Furthermore, it introduces a platform for continuous cybersecurity learning, employing micro-credentials, blockchain technology, and AI-driven systems. Based …

An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan

Dissertations

Autonomous Vehicles (AVs) are transforming next-generation autonomous mobility. These vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance overall mobility. They achieve higher levels of Autonomous Driving (AD) by integrating sensors, communication technologies, computation, and Artificial Intelligence (AI). Apart from these advancements, significant gaps remain in integrating heterogeneous technologies and disciplines essential for optimizing AD. Therefore, the current solution approaches lack the capability to exploit intelligent road infrastructures and effectively orchestrate perceptual services for complex driving scenarios. The main objective of this dissertation is to address these challenges by proposing a novel end-to-end intelligent framework …

Investigating Parental Roles In Developing Preschool-Aged Children’S Emergent Literacy In English, Their Second Language, Through Storybook Reading In A Sample Of Kuwaiti Homes, Fajr M. Alkouz

Dissertations

Using a convergent mixed methods design, this study explored home emergent literacy environments actualized by six mothers of young preschool-aged children living in Kuwait. It investigated the role of these Kuwaiti mothers in developing their bilingual preschool-aged children’s print knowledge, oral language, and phonological awareness, as three areas associated with Emergent Literacy (EL). In particular, the study explored the influence of parental storybook reading on the development of bilingual children’s EL skills in English, their Second Language (L2). Employing purposive sampling, this case study obtained statistical quantitative results from parents who responded to a questionnaire about their home literacy experiences …

On The Projections And Unitary Groups Of Unital C*-Algebras, Fouzia Shaheen

Dissertations

H. Dye proved that the unitary group in a factor determines the algebraic type of that factor. Al-Rawashdeh, Booth and Giordano established that, for a large class of simple unital C*-algebras, an isomorphism between the unitary groups induces an isomorphism between their K₀-ordered group and 1-groups. Then using the results of Dadarlat-Elliot-Gong and Kirchberg-Phillips, the C*-algebras are isomorphic. Dye introduced special projections P_i,j (a) of the matrix algebra M_n(A), and he used it as a main tool to establish his results in the case of von Neumann factors. Precisely, in case of von Neumann algebra, …

Transforming Fermentation Residues From Date Fruit Pomace For The Synthesis Of Bioadsorbent Material In An Integrated Biorefinery Framework, Sabeera Haris

Dissertations

In the pursuit of enhanced waste management solutions, this comprehensive research delves into the untapped potential of Date Fruit Pomace (DFP), an abundant byproduct from the date syrup industry that is currently underutilized, leading to environmental concerns. The study emphasizes the need for proper waste management and valorization strategies to mitigate the environmental impact of DFP and promote sustainability within the date processing industry. The initial phase involves characterizing DFP from Emirati varieties, highlighting its compositional and functional properties. The analysis reveals that DFP is rich in total dietary fiber (45.5%), residual sugars (35.3%), protein (10.6%), ash (4.1%), and fat …

Formate Dehydrogenase Immobilized On Metal Organic Framework For Enhanced Conversion Of Co2 To Formate, Shadeera Rouf

Dissertations

Enzymatic hydrogenation of carbon dioxide (CO₂) to formate by formate dehydrogenase (FDH) is a promising technology for reducing CO₂ concentrations in an environmentally friendly manner. For practical and economical implementation of the process, however, enhanced reusability of the enzyme is essential, which requires easy separation from the reaction mixture, coupled with sustained stability. One way to achieve this is by using the enzyme in an immobilized form. In this work, the effectiveness of using different metal organic frameworks (MOFs) as promising alternative supports for FDH immobilization has been assessed. A highly stable zirconium-based MOF, UiO66 and its …

Policymaker's Perspectives And Student Experiences Of Bullying At Schools In The Uae: A Mixed-Method Approach, Alfan Saghayir Alketbi

Dissertations

Bullying is a significant issue in schools worldwide, including Abu Dhabi, the UAE. This dissertation aims to explore the dynamics of school bullying and improve anti-bullying interventions through a mixed-method approach. The primary goal is to investigate current anti-bullying efforts in the UAE and develop a comprehensive framework for school-based bullying intervention, considering policymaker perspectives and student experiences.

The study includes a systematic scoping review, qualitative studies with policymakers, a SWOT analysis of Abu Dhabi's anti-bullying approaches, and a cross-sectional survey of students' perceptions, attitudes, and awareness in Al-Ain City. These methods provide an integrated view of bullying in the …

One Hand Doesn't Clap: The Moderation Of Individual Factors On The Relationship Between Leader Narcissism And Followers' Outcomes, Wafa Mohammed Al Wahedi

Dissertations

Drawing on leadership and social exchange theories, the purpose of this study is to examine the adverse impact of the toxic triangle; namely, Leader Narcissism (LN), Perceived Unethical Climate (PUC) and Followers' Submissiveness (FS) on followers' outcomes, particularly Psychological Capital (PsyCap) and Constructive Deviance (CD) through the mediation of Leader-Member Exchange (LMX). The study introduces Moral Identity (MI) as a moderator. The conceptual framework was put to the test with data collected from both leaders and their followers in UAE-based organizations. Hierarchical linear model analysis was run on leaders and followers' dyadic data for testing whether moral identity moderated the …

Satellite-Based Spatial And Temporal Characterization Of Rainfall Over The Uae, Yassin Ibrahim Ahmad

Theses

Studies on rainfall characteristics have been of particular interest in the arid and semi-arid regions, including the United Arab Emirates (UAE), due to its impact on the built and natural environment. Rainfall represents a vital water source and a potential threat to the built environment, particularly during flash rainfall. This thesis evaluates spatial and temporal variation of rainfall in the UAE. The UAE is classified into four ecosystems with a distinct distribution of precipitation (East Coast, Mountains, the Gravel Plains, and Desert Foreland). Satellite data on daily rainfall for the years starting from 2001 up to 2020 was obtained from …

Investigating The Magnetic Cycle Of The Planet-Hosting Star Hd189733, Mohammad Samih Raja

Theses

HD189733 is a planet-hosting star, harboring a hot Jupiter situated at approximately 8.8 stellar radii. This system is among the most studied systems for stellar magnetism, Star-Planet Interactions, and exoplanetary atmospheric studies. The largescale magnetic field of the star exhibits variability on relatively short timescales, however, no magnetic field cycle, including polarity flips, has been observed to date. This absence of cycle detection could be due to observational gaps between observing campaigns.

This thesis aims to explore the variability of the activity of HD189733 by conducting a homogeneous analysis of several publicly available datasets, obtained using several spectrographs/telescopes, spanning 18 …

Pea And Hemp Protein-Polyphenol Complexes For Improved Curcumin Delivery Via Emulsion System, Muhammed Suhail Ashraf

Theses

This study focuses on producing and characterizing protein-polyphenol complexes derived from pea and hemp proteins, paired with a variety of polyphenols including 2,4-Dihydroxybenzoic acid, Ferulic acid, and Rutin. The complexes were characterized using various techniques: UV-visible spectroscopy, fluorescence spectroscopy, total phenolic content analysis, and radical scavenging activities. Further, oil-in-water emulsions stabilised using these protein-polyphenol complexes and starch were explored as delivery systems for curcumin respectively. The formation of complexes was revealed by UV-visible spectroscopy, which indicated significant changes in the spectra of hemp and pea proteins upon complexation with polyphenols. The findings from UV-visible spectroscopy were supported by data from …

Leveraging Ai And Machine Learning To Develop And Evaluate A Contextualized User-Friendly Cough Audio Classifier For Detecting Respiratory Diseases: Protocol For A Diagnostic Study In Rural Tanzania, Kahabi Isangula, Rogers John Haule

School of Nursing & Midwifery, East Africa

Background:

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.

Objective:

This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.

Methods:

This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be …

Clinical Experiences Of Rn To Bscn Nursing Students In Kenyan Universities, Gladys Mbuthia, Gisela Rensburg Rensburg, Sheila Shaibu

School of Nursing & Midwifery, East Africa

Introduction and objective: Clinical learning environments play a great role in nursing training as they allow nursing students to develop their clinical skills by combining cognitive, psychomotor and affective skills. Consequently, clinical learning environments enable nursing students to bridge the theory-practice gap. Fewer studies have examined the clinical experiences of RN to BScN students in Kenya. This paper is part of analytical memo of a larger PhD study that sought to explore and describe the support needs of RN to BScN students in Kenyan universities. The paper focuses on clinical experiences of RN to BScN students.

Methods: Using a qualitative …

The Important Role Of System Dynamics Investigation On Business Model, Industry And Performance Management, Lina Gozali, Teuku Yuri M. Zagloel, Togar Mangihut Simatupang, Wahyudi Sutopo, Aldy Gunawan, Yun-Chia Liang, Bernardo Nugroho Yahya, Jose Arturo Garza-Reyes, Agustinus Purna Irawan, Yuliani Suseno

Research Collection School Of Computing and Information Systems

Purpose: This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in …

Continual Normalization: Rethinking Batch Normalization For Online Continual Learning, Quang Pham, Chenghao Liu, Steven Hoi

Research Collection School Of Computing and Information Systems

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training …

Development Of An Explainable Artificial Intelligence Model For Asian Vascular Wound Images, Zhiwen Joseph Lo, Malcolm Han Wen Mak, Shanying Liang, Yam Meng Chan, Cheng Cheng Goh, Tina Peiting Lai, Audrey Hui Min Tan, Patrick Thng, Patrick Thng, Tillman Weyde, Sylvia Smit

Research Collection School Of Computing and Information Systems

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into …

Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia

Research Collection School Of Computing and Information Systems

The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …

Marco: A Stochastic Asynchronous Concolic Explorer, Jie Hu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Concolic execution is a powerful program analysis technique for code path exploration. Despite recent advances that greatly improved the efficiency of concolic execution engines, path constraint solving remains a major bottleneck of concolic testing. An intelligent scheduler for inputs/branches becomes even more crucial. Our studies show that the previously under-studied branch-flipping policy adopted by state-of-the-art concolic execution engines has several limitations. We propose to assess each branch by its potential for new code coverage from a global view, concerning the path divergence probability at each branch. To validate this idea, we implemented a prototype Marco and evaluated it against the …

Redriver: Runtime Enforcement For Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Xiaodong Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous driving systems (ADSs) integrate sensing, perception, drive control, and several other critical tasks in autonomous vehicles, motivating research into techniques for assessing their safety. While there are several approaches for testing and analysing them in high-fidelity simulators, ADSs may still encounter additional critical scenarios beyond those covered once they are deployed on real roads. An additional level of confidence can be established by monitoring and enforcing critical properties when the ADS is running. Existing work, however, is only able to monitor simple safety properties (e.g., avoidance of collisions) and is limited to blunt enforcement mechanisms such as hitting the …

Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The rapid progress of autonomous vehicles (AVs) has brought the prospect of a driverless future closer than ever. Recent fatalities, however, have emphasized the importance of safety validation through large-scale testing. Multiple approaches achieve this fully automatically using high-fidelity simulators, i.e., by generating diverse driving scenarios and evaluating autonomous driving systems (ADSs) against different test oracles. While effective at finding violations, these approaches do not identify the decisions and actions that caused them -- information that is critical for improving the safety of ADSs. To address this challenge, we propose ACAV, an automated framework designed to conduct causality analysis for …

Towards Low-Resource Rumor Detection: Unified Contrastive Transfer With Propagation Structure, Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng

Research Collection School Of Computing and Information Systems

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a simple yet effective framework with unified contrastive transfer learning, to detect rumors by adapting the features learned from well-resourced rumor data to that …

Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …

Teaching Software Development For Real-World Problems Using A Microservice-Based Collaborative Problem-Solving Approach, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Experienced and skillful software developers are needed in organizations to develop software products effective for their business with shortened time-to-market. Such developers will not only need to code but also be able to work in teams and collaboratively solve real-world problems that organizations arefacing. It is challenging for educators to nurture students to become such developers with strong technical, social, and cognitive skills. Towards addressing the challenge, this study presents a Collaborative Software Development Project Framework for a course that focuses on learning microservices architectures anddeveloping a software application for a real-world business. Students get to work in teams to …

Experience Report: Identifying Common Misconceptions And Errors Of Novice Programmers With Chatgpt, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Identifying the misconceptions of novice programmers is pertinent for informing instructors of the challenges faced by their students in learning computer programming. In the current literature, custom tools, test scripts were developed and, in most cases, manual effort to go through the individual codes were required to identify and categorize the errors latent within the students' code submissions. This entails investment of substantial effort and time from the instructors. In this study, we thus propose the use of ChatGPT in identifying and categorizing the errors. Using prompts that were seeded only with the student's code and the model code solution …

Encoding Version History Context For Better Code Representation, Huy Nguyen, Christoph Treude, Patanamon Thongtanunam

Research Collection School Of Computing and Information Systems

With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit code representation for deep learning. Recent papers incorporate contextual data (e.g. call hierarchy) into vector representation to address program comprehension problems. This motivates further studies to explore additional contexts, such as version history, to enhance models' understanding of programs. That is, insights from version history enable recognition of patterns in …

Unleashing The Power Of Clippy In Real-World Rust Projects, Chunmiao Li, Yijun Yu, Haitao Wu, Luca Carlig, Shijie Nie, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The error messages generated by the Rust compiler (rustc) are useful for developers to identify and diagnose suspicious code segments. Complementing the compiler, linters can also play an important role in promoting the adherence to certain coding style conventions and best practices. Prominent linters utilized in the Rust ecosystem include Clippy [1] and Rustfmt [2]. Among them, the Rust community particularly emphasizes on the importance of heeding the warnings provided by Clippy to mitigate common errors and promote the adoption of idiomatic conventions. Clippy provides a set of more than 600 lints in addition to the built-in rustc lints. These …

Bidirectional Paper-Repository Tracing In Software Engineering, Daniel Garijo, Miguel Arroyo, Esteban González Guardia, Christoph Treude, Nicola Tarocco

Research Collection School Of Computing and Information Systems

While computer science papers frequently include their associated code repositories, establishing a clear link between papers and their corresponding implementations may be challenging due to the number of code repositories used in research publications. In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. We have used our approach to analyze more than 14000 PDF and Latex files in the Software Engineering category of Arxiv, generating a dataset of more than 1400 paper-code implementations and assessing current citation practices on it.

Dronlomaly: Runtime Log-Based Anomaly Detector For Dji Drones, Wei Minn, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We present an automated tool for realtime detection of anomalous behaviors while a DJI drone is executing a flight mission. The tool takes sensor data logged by drone at fixed time intervals and performs anomaly detection using a Bi-LSTM model. The model is trained on baseline flight logs from a successful mission physically or via a simulator. The tool has two modules --- the first module is responsible for sending the log data to the remote controller station, and the second module is run as a service in the remote controller station powered by a Bi-LSTM model, which receives the …