https://www.um.edu.mt/library/oar/handle/123456789/5626 2026-05-20T23:52:17Z 2026-05-20T23:52:17Z Doshi, Ruchi Hu, Yu-Chen Garg, Lalit Fagbola, Temitayo https://www.um.edu.mt/library/oar/handle/123456789/146322 2026-05-11T13:19:04Z 2024-01-01T00:00:00Z

Title: Special issue “Towards a higher education of the future : transformational roles of edge intelligence” Authors: Doshi, Ruchi; Hu, Yu-Chen; Garg, Lalit; Fagbola, Temitayo Abstract: Higher Education of the Future (HEF) is anticipated to be a scalable educational framework that is driven by new digital learning architectures and platforms, as well as collaborative learning systems, that are able to completely guarantee self-paced, customizable, personalized and flexible teaching/learning experiences. The HEF concept strongly points toward a “learning from everywhere” model. The need for HEF is motivated, among other things, by the fact that most state-of-the-art higher education system models currently being used for driving and transitioning higher education are structurally, socially, and technologically incapacitated to meet the key requirements towards delivering a foreseeable smart, real-time intelligence driven HEF. [excerpt]

2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146069 2026-04-30T09:37:45Z 2025-01-01T00:00:00Z

Title: Feedback loops and bias in machine learning algorithms for predictive policing Abstract: Predictive policing describes several emerging practices of implementing artificial intelligence and machine learning in police work, specifically in attempting to predict future crimes through algorithmic crime forecasting. These emerging practices have introduced many new opportunities for improved police work, but critics of predictive policing have raised both ethical and practical concerns. These concerns include the risk of feedback loops and bias. This thesis aims to contribute to this ongoing debate by examining how algorithmic crime forecasting tools produce bias and feedback loops and by exploring if it is possible to create algorithmic crime forecasting tools with reduced tendencies towards bias and feedback loops. Specifically, the focus is on the seminal and widely adopted PredPol system, which is based on an earthquake prediction system known as Epidemic Type Aftershock Sequence (ETAS). The methodology used in this studywas to replicate studies detailing the PredPol system, as well as studies criticising it. Based on previous findings by critics, a synthetic population and urn modelling was used to demonstrate the negative tendencies of the system. Based on this, an original framework was developed for evaluating modifications made to the algorithm by measuring the effectiveness in reducing feedback loop tendencies and improving fairness. This is done through metrics like the Predictive Accuracy Index (PAI), variations in the mean conditional intensity rates, λ, and the total fairness score, which evaluates the consistency of law enforcement attention across different demographic groups. To reduce the algorithm’s tendencies towards bias and feedback loops, a modified algorithm using rejection sampling and a fairness penalty was developed. While the proposed algorithmic adjustments lead to increased fairness and reduced feedback loop generation in predictive policing, they also introduce some trade-offs in predictive performance, particularly noted in the PAI values. However, the enhancements significantly mitigate biased policing practices and reduce the perpetuation of historical inequities, aligning more closely with ethical standards. Description: M.Sc. ICT(Melit.)

2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146067 2026-04-30T09:33:16Z 2025-01-01T00:00:00Z

Title: Monitoring incomplete traces navigating uncertainty Abstract: As software systems scale in size and complexity, manual code reviews become increasingly more resource intensive, underscoring the critical need for reliable Runtime Verification (RV) techniques. Traditional RV approaches assume complete access to error-free execution trace, an assumption that is untenable in real-world scenarios. This work address the challenges of monitoring such incomplete traces by proposing a novel methodology that synthesises sound and modular runtime monitors. Building upon detectEr, our monitors are able to handle non-consecutive missing events in observed traces. We demonstrate a scalable and sound framework that facilitates verdict generation through deterministic state inference, enabling monitors to achieve greater completeness on data restricted traces. The methodology combines a theoretically rigorous approach with practicality in mind, utilising an automaton-guided state regeneration technique for state inference. The findings from our evaluation demonstrate the efficacy of the proposed solution across several examples. The modular monitors are capable of navigating through data-restricted traces and producing sound, irrevocable verdicts. This thesis contributes to the field of RV by establishing a sound foundation for the deterministic inference of missing events in incomplete traces, and also provides a framework that increases the applicability of monitors by taking a compositional approach to monitor synthesis. By bridging a gap that is often overlooked in this field,such a solution is required, especially in recent years, as systems are becoming exponentially more complex. Description: M.Sc. ICT(Melit.)

2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146015 2026-04-29T09:56:23Z 2025-01-01T00:00:00Z

Title: Investment portfolio through evolutionary algorithm Abstract: This dissertation investigates the application of evolutionary algorithms—specifically the Genetic Algorithm (GA) and Simulated Annealing (SA)—for portfolio optimisation within the S&P 500, addressing the limitations of traditional models. The methodology uses Gower’s distance to handle mixed numerical and categorical data, allowing for the construction of factor-aligned portfolios based on Growth, Value, and Quality dimensions. The primary optimisation objective is to maximise the Sortino Ratio, focusing on downside-risk-adjusted returns. The methodology employs composite feature engineering to rank stocks across Growth, Value, and Quality dimensions, followed by distance-based clustering and anomaly detection to reveal market structures. GA and AGA are then applied with objective functions designed to maximise the Sortino Ratio, which emphasises downside-risk-adjusted returns. Hyperparameters such as population size, mutation rate, crossover probability, and annealing temperature schedules are tuned to balance exploration and exploitation. Empirical evaluation demonstrates that both GA and AGA generate highly diversified portfolios with competitive performance. The GA-optimised maximum Sortino portfolio achieved an annualised return of 18.95%, a Sortino Ratio of 1.0999, and a beta of 0.98, indicating strong returns with reduced downside risk relative to the market. Comparative analysis reveals that AGA converges faster and achieves marginally superior downside protection, validating its advantage in complex search landscapes. Complementary tools such as similarity maps, hierarchical clustering, and a diversification recommender further enhance interpretability and practical applicability. The results underscore the potential of evolutionary algorithms to construct robust, risk-aware investment portfolios that go beyond linear optimisation frameworks. By combining factor-based insights with evolutionary optimisation, this work contributes to the growing literature on computational finance and demonstrates actionable applications for institutional and retail portfolio managers. Description: M.Sc.(Melit.)

2025-01-01T00:00:00Z