http://hdl.handle.net/1893/35 This community contains the ePrints and eTheses produced by Computing Science and Mathematics staff and students. Mon, 05 Oct 2026 12:09:17 GMT 2026-10-05T12:09:17Z http://hdl.handle.net/1893/38355 Title: Network structures and the dynamics of takeover rumours: agent-based simulations of pre-announcement stock price drift Author(s): Cui, Zhaoxi Abstract: Takeover targets often see their stock prices rise sharply before merger or acquisition announcements, mainly because information leaks and related rumours circulate among investors. Although this pattern is well-documented, the underlying process driving such pre-bid increases is still not fully understood. This thesis rebuilds the mechanism from the bottom up: an agent-based market in which an Ignorant–Spreader–Stifler rumour process propagates over a range of networks while heterogeneous value investors and trend followers trade on perceived mispricing and recent returns. Through Monte Carlo simulations, we find a strong link between the magnitude of the run-up and the efficiency of the network topology. Sat, 01 Nov 2025 00:00:00 GMT http://hdl.handle.net/1893/38355 2025-11-01T00:00:00Z http://hdl.handle.net/1893/38347 Title: Bridging Learning Outcomes and Module Specifications: GenAI Framework for Curriculum Development in CS Education Author(s): Elawady, Mohamed; Ali, Hazrat Abstract: Recent developments in generative artificial intelligence (GenAI) have enabled the production of high-quality content in many domains , including education and computing. These advances offer opportunities to support tutors in curriculum development and to enhance the learning experience for students. This paper presents an end-to-end pipeline that automatically suggests a set of module specifications based on user input and structured guidelines. Thu, 03 Sep 2026 00:00:00 GMT http://hdl.handle.net/1893/38347 2026-09-03T00:00:00Z http://hdl.handle.net/1893/38346 Title: A Comprehensive Analysis of Adversarial Attacks against Spam Filters Author(s): Hotoğlu, Esra; Sen, Sevil; Can, Burcu Abstract: Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets. Six prominent deep learning models are evaluated on these datasets, analyzing attacks at the word, character sentence, and AI-generated paragraph-levels. Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness. A key contribution of this study is the analysis of spam-weight-and attention-weight-based scoring functions, highlighting their role in improving the effectiveness and efficiency of adversarial attacks. This comprehensive analysis sheds light on the vulnerabilities of spam filters and contributes to efforts to improve their security against evolving adversarial threats. Experimental results show that word-and sentence-level attacks markedly increase false negatives, while character-level perturbations disrupt token representations with minimal semantic change. AI-generated paragraph-level attacks remain challenging even for transformer-based models. In addition, spam-weight-based scoring consistently enables more effective adversarial attacks than alternative scoring strategies with lower computational cost. Tue, 01 Dec 2026 00:00:00 GMT http://hdl.handle.net/1893/38346 2026-12-01T00:00:00Z http://hdl.handle.net/1893/38322 Title: The Impact of Non-AKI eGFR Variability on CKD Progression in Individuals With Type 2 Diabetes and Preserved Kidney Function Author(s): Hapca, Simona; Yang, Qinbo; Li, Sheyu; McGurnaghan, Stuart J; Blackbourn, Luke A K; Pearson, Ewan R; Colhoun, Helen M; Bell, Samira Abstract: Introduction Variability in estimated glomerular filtration rate (eGFR) has been associated with increased risks of mortality and chronic kidney disease (CKD) progression in people with type 2 diabetes mellitus (T2DM) and impaired kidney function. However, its significance in individuals with preserved kidney function remains unclear. Methods In this nationwide retrospective population-based study of individuals with T2DM, eGFR variability was calculated by fitting a linear regression model to longitudinal data to estimate both the individual eGFR slope over the 5-year period as well as the variability in model residuals provided by the SD of the model residuals using longitudinal serum creatinine (SCr) measurements obtained during the first 5 years after diagnosis. Cox proportional hazards models were then applied to assess the association between eGFR variability and progression to stage G3b CKD among participants with preserved kidney function. Results This study included 98,322 participants who had an eGFR > 60 ml/min per 1.73 m2 at diagnosis, remained alive with an eGFR > 60 ml/min per 1.73 m2 5 years after diagnosis, and were subsequently followed for a mean of 5.1 years. Greater eGFR variability was associated with an increased risk of progression to stage G3b CKD- hazard ratios (HRs) for the second, third, and fourth quartiles of variability versus the first quartile were 1.56 (95% confidence interval [CI]: 1.38-1.75), 1.85 (95% CI: 1.65-2.08), and 2.56 (95% CI: 2.29-2.86), respectively. This association persisted after adjustment for multiple variables-HR: 1.57; 95% CI: 1.40-1.77 for the fourth quartiles of variability versus the first quartile. Conclusion eGFR variability in the absence of acute kidney injury (AKI) is associated with CKD progression in individuals with T2DM and preserved kidney function. Tue, 01 Sep 2026 00:00:00 GMT http://hdl.handle.net/1893/38322 2026-09-01T00:00:00Z