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How smart city growth principles can be adopted in the UAE to solve the local housing problems?
Full text linkShaikh Zayed Housing Program “SZHP” is the governmental program that is responsible for local housing in the UAE. The program’s main aim is to offer a variety of housing services such as loans, grants, and ready-to-move houses. However, both the continuously growing urban population and liveability challenges are impeding the program to meet its goal. As mentioned on SZHP’s official website “We should continue to explore new ways to establish informed dialogue with different groups of society and to ensure adequate housing for national families, in achieving stability and community development by meeting future needs” (SZHP, 2022 ). Accordingly, this research highlights some of the major challenges that face the local housing program in the UAE specially SZHP by analyzing the current housing practices and policies in the country and using the smart city growth principles to understand the new smart urbanization principles that can be adopted and applied in the UAE to reduce and minimize some of the housing challenges. Moreover, a study case for one of the recently developed local neighborhoods “Al Raqaib-2” will be done to address the area of development and study the possibilities to apply some of the smart urban planning principles in the area to enhance the neighborhood liveability and reduce the housing challenges
AI-Driven Prediction of Flight Cancellations: A Machine Learning Approach in Minimizing Airline Disruption
Full text linkThis thesis investigates the use of machine learning to predict flight cancellations, aiming to reduce operational disruption and improve airline decision-making. The research is motivated by the need for more proactive strategies in aviation, where flight cancellations often result in financial losses and customer dissatisfaction. The study is framed within the CRISP-DM methodology and demonstrates how historical flight data can be transformed into actionable insights using structured analytics. The research addresses three core questions: how effectively machine learning can predict cancellations, which evaluation metrics are most suitable in an imbalanced context, and how model outputs can support airline operations. A dataset of U.S. domestic flights from 2019 to 2023 was used, containing features such as delays, carriers, routes, and cancellation indicators. Through data preprocessing, irrelevant variables were removed, categorical features encoded, and outliers retained to preserve meaningful variation. Stratified sampling was applied to handle class imbalance and ensure fair evaluation. Several classification models were trained using stratified cross-validation and tested on a holdout set. Evaluation metrics such as precision, recall, F1-score, and AUC-ROC were used to compare models. Emphasis was placed on recall to reduce the chance of missing true cancellations. The results show that machine learning models, when carefully developed, can predict cancellations with strong reliability and practical relevance. The study concludes that predictive analytics has strong potential to enhance airline disruption management. The structured approach used in this research, which includes business understanding, data preparation, and performance evaluation, provides a replicable framework for practical implementation. Practical implications include integrating predictive tools into airline scheduling systems and training operational staff to interpret model outputs. Future research should consider incorporating real-time data and explainable AI to improve model responsiveness and transparency
Fake News Detection in Arabic Media Using Machine Learning and the AFND Dataset
Full text linkThe proliferation of fake news in digital media poses a significant challenge to information credibility, particularly in linguistically diverse regions such as the Arab world. This study addresses the critical problem of detecting fake news in Arabic media by leveraging advanced natural language processing (NLP) techniques and the AFND dataset, which contains over 600,000 news articles categorized into credible, not credible, and undecided classes. The research focuses on developing a robust system using AraBERT, a transformer-based model specifically pre-trained for Arabic text. Key contributions of this work include a comprehensive preprocessing pipeline tailored to Arabic linguistic complexities, incorporating stopword removal, stemming, normalization, and diacritic handling. The proposed model achieved an accuracy of 92.3% and a macro-averaged F1-score of 72%, outperforming traditional machine learning methods and demonstrating competitive results compared to state-of-the-art solutions in the field. The findings highlight the importance of leveraging deep learning models to capture contextual relationships in text, overcoming the limitations of traditional approaches. Despite computational constraints during training, the results suggest significant potential for further improvement with better hardware and additional fine-tuning. This research contributes to advancing Arabic fake news detection by providing a scalable and reliable framework that aligns with the growing need for accurate information in digital media. Future work aims to enhance performance through multimodal analysis, domain-specific pretraining, and real-time system deployment
Democratizing Community Discourse Analysis in Computational Social Science
No full textCommunity resource inequities and undetected infrastructure vulnerabilities cost municipalities billions annually, with disproportionate impacts on marginalized communities. Current computational approaches to community needs assessment suffer from two critical limitations: they rely on aggregate-level analysis that obscures granular community expressions, and they implement sophisticated computational tools that remain inaccessible to stakeholders without technical expertise. This creates what we term the Community-Computational Gap—a persistent divide where domain experts with vital contextual knowledge cannot access the analytical tools they need, while computational experts develop models without sufficient community context. This dissertation addresses these challenges through a novel methodological framework for fine-grained utterance-level classification of community discourse. We demonstrate that individual units of communication contain sufficient linguistic patterns to accurately detect both present and future community needs without relying on metadata or aggregated approaches. Unlike existing methods that operate at neighborhood or census-tract levels, our approach preserves the contextual integrity of individual expressions while enabling more precise signal extraction from social media noise. Our technical implementation achieves unprecedented accuracy—94\% for existing community needs and assets and 82\% for future infrastructural issues—through two specialized computational models validated on novel annotated datasets: 3,511 Reddit conversations for community needs assessment and 2,662 social web instances related to future infrastructure concerns. Our linguistic analysis reveals distinctive discourse patterns where needs-related conversations exhibit specific pain points and calls to action, while asset-related discussions emphasize local resources and community capabilities. To bridge the Community-Computational Gap, we develop two complementary platforms: Citizenly, a mobile application enabling community-driven data collection and visualization, and CommuniDI, an integrated framework leveraging large language models that enables non-technical stakeholders to create sophisticated classifiers without programming requirements. Through mixed-methods evaluation, we demonstrate that this democratization—the systematic transfer of analytical capabilities to local stakeholders—maintains technical rigor (with F1 scores consistently exceeding 85\% across diverse datasets) while significantly lowering barriers to computational social science research. This work represents a fundamental shift from computational tools \textit{for} communities to computational tools \textit{with} communities, enabling more equitable and effective approaches to addressing local challenges
A Robust Learning Framework for Resource-Constrained Domain Adaptation
No full textMost existing machine learning models assume that training and test data are independently and identically distributed (\iid) according to the same data distribution. Under this assumption, a model trained on labeled data is expected to generalize well to unseen test data. However, real-world applications often violate this assumption due to distributional shifts between the source and target domains (e.g., variations in lighting conditions in images or differences in writing styles in text). This research focuses on domain adaptation (DA)—a paradigm that aims to adapt models trained on one or more source domains to perform well on a distributionally different target domain, despite constraints on data availability and computing resources. Traditional DA methods assume access to labeled source domain data and unlabeled target domain data. However, in many real-world settings, these resources are often unavailable. For instance, privacy concerns restrict direct access to medical and financial data, while in autonomous driving, the vast diversity of environments—spanning different weather conditions, lighting scenarios, and geographic locations—makes it impractical to collect and label data for all possible object categories. In such cases, models must adapt to new conditions without additional labeling efforts, operating under resource-constrained settings where labeled source data or target supervision is limited or entirely absent. Under these constraints, robustness becomes a key challenge due to data corruption, unseen target categories, and model overconfidence. For instance, models trained on noisy source data risk error accumulation, failing to extract reliable domain-invariant features for generalization. The absence of prior knowledge about novel target classes makes it difficult to differentiate between known and unknown categories, leading to poor adaptation in open-set settings. And models trained on a different distribution tend to be overconfident in incorrect predictions, particularly for out-of-distribution samples, reducing reliability. To address these challenges naturally arising in many practical domain adaptation settings, this research aims to develop a robust domain adaptation framework that integrate noise-robust learning, open-set adaptation, and calibrated training to improve reliability under resource constrained conditions. It integrates the following key research components: (1) \textbf{Robust Domain adaptation with Noisy Labels}: Collection of clean labeled data is time-consuming and expensive. We study a robust domain adaptation framework under the sparsely labeled domains with corruptions in the context of few-shot learning and meta-learning. (2) \textbf{Multi-source domain adaptation with unlabeled data}: Extending domain adaptation to a multi-source setting, we incorporate an additional unlabeled source domain to mitigate reliance on labeled data. We introduce an unsupervised few-shot task and a noisy task filtration criterion to extract meaningful information from unlabeled and noisy samples. (3) \textbf{Source-free domain adaptation with pre-trained models}: When source data is inaccessible due to privacy or storage constraints, we enable adaptation using only pre-trained models. Our framework integrates knowledge from multiple pre-trained sources while mitigating error accumulation from noisy pseudo-labels. (4) \textbf{Open-set domain adaptation with a limited label space}: When the source domain has a restricted label set and the target domain includes unseen categories, we propose a domain-adaptive class-aware prompt to facilitate adaptation to both shared and novel categories, improving recognition in open-set settings. (5) \textbf{Calibrated fully test-time adaptation}: Due to the lack of supervision during adaptation, the source model’s predictions often become overconfident or under-confident. We design a calibration strategy to maintain reliable predictions during test-time adaptation. By systematically addressing these key challenges, this study provides a robust framework for domain adaptation in resource-constrained settings, enhancing model adaptability in scenarios where labeled data is scarce, noisy, or inaccessible. Our work contributes to the broader goal of making machine learning models more generalizable, efficient, and robust in real-world applications
Modernizing Military Education in Kosovo: From Theory to Practice through Innovation and Technology
No full textThis paper examines the need for Kosovo\u27s Military Academy (KMA) to modernize its education system, particularly its necessity to transition from a theory-based methodology to more modern and innovative teaching and training approaches that incorporate advanced technology. Despite ongoing institutional development, the Kosovo Military Academy continues to rely on traditional teaching methods, which limits its capacity to prepare cadets for the demands of modern military operations. Barriers such as limited funding, outdated infrastructure, and resistance to pedagogical change further complicate efforts to modernize.
To address these challenges, the paper advocates for the adoption of modern, learner-centered strategies, including Simulation and Scenario-Based Learning (SBL), Gamification, Wargaming, Virtual Reality (VR), and AI-driven tools, as well as ethical and cultural awareness training, with the goal of strengthening leadership, decision-making, and adaptability among cadets. Given Kosovo’s role in future multinational missions, training in intercultural communication and ethical decision-making is also important for preparing cadets to operate effectively in complex, diverse environments.
The paper employs a qualitative methodology, analyzing the current academic and professional training programs, curriculum documents, and selected syllabi at the Kosovo Military Academy. In addition, fifteen semi-structured interviews with officer cadets were conducted in order to understand firsthand perspectives on the effectiveness of existing educational practices and the perceived need for more technologically advanced and interactive learning tools. The paper concludes with recommendations to support the modernization of military education in Kosovo, emphasizing the importance of international collaboration, and the strategic integration of educational technologies to better align the Academy with NATO and EU standards
Into the Arms of Earth
No full textThis thesis investigates the construction of a visual language illuminating the Biblical theme of duality-union. Section 1 provides an overview of duality-union across Scripture, investigates the portrayal of duality-union in the Biblical theme of Heaven and Earth, and summarizes criteria for a successful visual language surrounding this concept. Section 2 outlines and justifies the building blocks of this visual language, which include clay, neon, a gestural building process, arboreal imagery, references to the mandorla shape, an optimistic aesthetic influenced by midcentury children\u27s book illustration, and found objects. Section 3 provides a description and assessment of the seven pieces that make up the body of thesis work, entitled Into the Arms of Earth. The completed body of work responds to themes of connection and reproduction across Biblical texts to present duality-union as the source of life
Illustrations for A Scientific Review Article Publication : Human Stem Cell-Derived Organoids
No full textThis thesis explores the role of scientific illustration in improving the comprehension of complex scientific concepts. It traces the historical development of illustrations in scientific literature and examines their functions in both research and review articles. The central contribution of this work is the development of twelve vector-based illustrations that elucidate the applications of human organoids within a review article context. These visuals transform intricate ideas into clear, engaging representations, supported by extensive research and verification to maintain scientific accuracy. The thesis concludes by addressing the future of scientific illustration, advocating for reader-oriented design approaches and the use of innovative visual techniques. Ultimately, this work aims to make the vast potential of human organoids more accessible to a broader audience
The Table
No full textThis thesis explores the relationship between identity formation, emotional rupture, and social performance through the symbolic framework of the dining table to examine how everyday rituals shape and destabilize the self. Beginning with a personal history of crafting prosthetic ears at the request of the artist’s otologist father, the project emerges from an initial desire to heal others and confronts the emotional weight of restoration. As the work evolves, this act of repair becomes a catalyst for the artist’s own confrontation with psychological fragmentation, shifting the focus from medical mending to the deconstruction of selfhood. Drawing on theories of performativity and material culture, the paper then analyzes how the conceptual and studio research comes together in the culminating artwork “title of installation,” an installation using masks and multi-sensory materials to create a space of quiet psychological tension. Rather than offering resolution, the paper presents an elaborate consideration of the term reflection and offers it as a conceptual tool to help navigate ideas about expectation, concealment, and vulnerability in both public and private spheres
Investigating the Intersection of Cultural Design Preferences and Web Accessibility Guidelines with Designers from the Global South
No full textCultural background influences aesthetic web design preferences, and aesthetic design impacts accessible design. However, limited research has focused on this intersection of cultural background and accessible web design. With the majority of HCI and design resources originating from the Global North, we investigated the conflicts experienced due to the cultural background of digital designers from the Global South and current web accessibility guidelines. We conducted a design activity and interview study with 10 designers from five countries in the Global South to identify how current web accessibility guidelines conflict with our participants’ cultural design preferences. We found there are specific cultural challenges encountered in accessible web design, both at the design level (e.g., typography and color scheme) and within broader societal contexts (e.g., designer-client interactions). Our paper also offers suggestions from our participants to make the accessible design process more culturally inclusive by improving the web accessibility resources to become culturally customized and engaging more cultural perspectives in accessibility research and education