School of Computer Science and Applied Mathematics (ETDs)

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Start date: 2023Subject: search.filters.subject.UCTD

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Now showing 1 - 10 of 29
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    Double-diffusive convection in rotating fluids under gravity modulation
    (University of the Witwatersrand, Johannesburg, 2024-09) Mathunyane, Alfred Ntobeng; Duba, C. Thama; Mason, D.P.
    This study employs the method of normal modes and linear stability analysis to investigate double-diffusive convection in a horizontally layered, rotating fluid, specifically focusing on its application to oceanic dynamics. Double diffusive convection arises when opposing gradients of salinity and temperature interact within a fluid, a phenomenon known as thermohaline convection, and it is crucial for the understanding of ocean circulation and its role in climate change. With the increasing mass of water due to glaciers melting, fluid pressure variations occur, leading to slight fluctuations in gravity. We conduct both stationary and oscillatory stability analyses to determine the onset of double-diffusive convection under gravity modulation. Our analysis reveals that time-dependent periodic modulation of gravitational fields can stabilize or destabilize thermohaline convection for both stationary and oscillatory convection, with amplitude stabilizing and frequency destabilizing. The wavenumber in the y- direction also affects convection in the equatorial regions. This wavenumber exhibits destabilizing effects for large values and stabilizing effects for small values for both stationary and oscillatory convection. Rotation along with gravity modulation tends to destabilize the system for both stationary and oscillatory convection. The key difference between stationary and oscillatory convection is that oscillatory convection exhibits large values of the Rayleigh number, thus susceptible to overstability while stationary convection tends to have relatively smaller Rayleigh numbers and thus more stable. This research provides insights into the complex interplay between gravity modulation and thermohaline convection, contributing to our understanding of ocean dynamics and their implications for climate change.
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    BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model
    (University of the Witwatersrand, Johannesburg, 2024-09) Muthivhi, Mufhumudzi; van Zyl, Terence; Bau, Hairong
    Sequential recommendation models aim to learn from users’ evolving preferences. However, current state-of-the-art models suffer from an inherent popularity bias. This study developed a novel framework, BiCoRec, that adaptively accommodates users’ changing preferences for popular and niche items. Our approach leverages a co-attention mechanism to obtain a popularity-weighted user sequence representation, facilitating more accurate predictions. We then present a new training scheme that learns from future preferences using a consistency loss function. The analysis of the experimental results shows that our approach is 7% more capable of uncovering the most relevant items.
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    Developing a Bayesian Network Model to Predict Students’ Performance Based on the Analysis of their Higher Education Trajectory
    (University of the Witwatersrand, Johannesburg, 2024-08) Ramaano, Thabo Victor; Jadhav, Ashwini; Ajoodha, Ritesh
    The Admission Point Score (APS) metric, utilised as a response to admit prospective students for an academic course, may appear effective in determining student success. In reality, almost 50% of students admitted to a science programme in a higher education institution failed to meet all the requirements necessary to complete the programme during the period of 2008 and 2015. This had a direct impact on the overall graduation throughput. Thus, the focus of this research was geared towards the adoption of a probabilistic graphical approach to advocate its mechanism as a viable alternative to the APS metric when determining student success trajectories at a higher education level. The purpose of this approach was to provide higher education institutions with a system to monitor students’ academic performance en-route to graduation from a probabilistic and graphical point of view. This research employed a probability distribution distance metric to ascertain how close the learned models were to the true model for varying sample sizes. The significance of these results addressed the need for knowledge discovery of dependencies that existed between the students’ module results in a higher education trajectory that spans three years.
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    3D Human pose estimation using geometric self-supervision with temporal methods
    (University of the Witwatersrand, Johannesburg, 2024-09) Bau, Nandi; Klein, Richard
    This dissertation explores the enhancement of 3D human pose estimation (HPE) through self-supervised learning methods that reduce reliance on heavily annotated datasets. Recognising the limitations of data acquired in controlled lab settings, the research investigates the potential of geometric self-supervision combined with temporal information to improve model performance in real-world scenarios. A Temporal Dilated Convolutional Network (TDCN) model, employing Kalman filter post-processing, is proposed and evaluated on both ground-truth and in-the-wild data from the Human3.6M dataset. The results demonstrate a competitive Mean Per Joint Position Error (MPJPE) of 62.09mm on unseen data, indicating a promising direction for self-supervised learning in 3D HPE and suggesting a viable pathway towards reducing the gap with fully supervised methods. This study underscores the value of self-supervised temporal dynamics in advancing pose estimation techniques, potentially making them more accessible and broadly applicable in real-world applications.
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    Symmetry reductions and approximate solutions for heat transfer in slabs and extended surfaces
    (University of the Witwatersrand, Johannesburg, 2023-06) Nkwanazana, Daniel Mpho; Moitsheki, Raseelo Joel
    In this study we analyse heat transfer models prescribed by reaction-diffusion equations. The focus and interest throughout the work is on models for heat transfer in solid slabs (hot bodies) and extended surface. Different phenomena of interest are heat transfer in slabs and through fins of different shapes and profiles. Furthermore, thermal conductivity and heat transfer coefficients are temperature dependent. As a result, the energy balance equations that are produced are nonlinear. Using the theory of Lie symmetry analysis of differential equations, we endeavor to construct exact solutions for these nonlinear models. We will employ a number of symmetry techniques such as the classical Lie point symmetry methods, the nonclassical symmetry, nonlocal and nonclassical potential symmetry approach to construct the group-invariant solutions. In order to identify the forms of the heat source term that appear in the considered equation for which the principal Lie algebra (PLA) is extended by one element, we first perform preliminary group classification of the transient state problem. Also, we consider the direct group classification method. Invariant solutions are constructed after some reductions have been performed. One-dimensional Differential Transform Method (1D DTM) will be used when it is impossible to determine an exact solution. The 1D DTM has been benchmarked using some exact solutions. To solve the transient/unsteady problem, we use the two-dimensional Differential Transform Method (2D DTM). Effects of parameters appearing in the equations on the temperature distribution will be studied.
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    Detecting and Understanding COVID-19 Misclassifications: A Deep Learning and Explainable AI Approach
    (University of the Witwatersrand, Johannesburg, 2023-08) Mandindi, Nkcubeko Umzubongile Siphamandla; Vadapalli, Hima Bindu
    Interstitial Lung Disease (IDL) is a catch-all term for over 200 chronic lung diseases. These diseases are distinguished by lung tissue inflammation (Pulmonary fibrosis). They are histologically heterogeneous dis eases with inconsistent microscopic appearances, but they have clinical manifestations similar to other lung disorders. The similarities in symptoms of these diseases make differential diagnosis difficult and may lead to COVID-19 misdiagnosis with various types of IDLs. Be cause the turnaround time is shorter and more sensitive for diagnosis, imaging technology has been mentioned as a critical detection method in combating the prevalence of COVID-19. The aim of this research is to investigate existing deep learning architectures for the aforementioned task, as well as incorporate evaluation modules to determine where and why misclassification occurred. In this study, three widely used deep learning architectures, ResNet-50, VGG-19, and CoroNet, were evaluated for detecting COVID-19 from other IDLs (bacterial pneumonia, nor mal (healthy), viral pneumonia, and tuberculosis). The baseline results demonstrate the effectivities of Coronet having a classification performance of 84.02% for accuracy, specificity of 89.87%, a sensitivity of 70.97%. Recall 84.12%, and F1 score of 0.84. The results further emphasize the effectiveness of transfer learning using pre-trained domain-specific architectures, resulting in fewer learnable parameters. The proposed work used Integrated Gradients (IG), an Explainable AI technique that uses saliency maps to observe pixel feature importances, to understand mis classifications. This refers to visually prominent features in input im ages that were used by the model to make predictions. As a result, the proposed work envisions future research directions for improved classi fication through misclassification understanding.
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    Creating an adaptive collaborative playstyle-aware companion agent
    (University of the Witwatersrand, Johannesburg, 2023-09) Arendse, Lindsay John; Rosman, Benjamin
    Companion characters in video games play a unique part in enriching player experience. Companion agents support the player as an ally or sidekick and would typically help the player by providing hints, resources, or even fight along-side the human player. Players often adopt a certain approach or strategy, referred to as a playstyle, whilst playing video games. Players do not only approach challenges in games differently, but also play games differently based on what they find rewarding. Companion agent characters thus have an important role to play by assisting the player in a way which aligns with their playstyle. Existing companion agent approaches fall short and adversely affect the collaborative experience when the companion agent is not able to assist the human player in a manner consistent with their playstyle. Furthermore, if the companion agent cannot assist in real time, player engagement levels are lowered since the player will need to wait for the agent to compute its action - leading to a frustrating player experience. We therefore present a framework for creating companion agents that are adaptive such that they respond in real time with actions that align with the player’s playstyle. Companion agents able to do so are what we refer to as playstyle-aware. Creating a playstyle-aware adaptive agent firstly requires a mechanism for correctly classifying or identifying the player style, before attempting to assist the player with a given task. We present a method which can enable the real time in-game playstyle classification of players. We contribute a hybrid probabilistic supervised learning framework, using Bayesian Inference informed by a K-Nearest Neighbours based likelihood, that is able to classify players in real time at every step within a given game level using only the latest player action or state observation. We empirically evaluate our hybrid classifier against existing work using MiniDungeons, a common benchmark game domain. We further evaluate our approach using real player data from the game Super Mario Bros. We out perform our comparative study and our results highlight the success of our framework in identifying playstyles in a complex human player setting. The second problem we explore is the problem of assisting the identified playstyle with a suitable action. We formally define this as the ‘Learning to Assist’ problem, where given a set of companion agent policies, we aim to determine the policy which best complements the observed playstyle. An action is complementary such that it aligns with the goal of the playstyle. We extend MiniDungeons into a two-player game called Collaborative MiniDungeons which we use to evaluate our companion agent against several comparative baselines. The results from this experiment highlights that companion agents which are able to adapt and assist different playstyles on average bring about a greater player experience when using a playstyle specific reward function as a proxy for what the players find rewarding. In this way we present an approach for creating adaptive companion agents which are playstyle-aware and able to collaborate with players in real time.
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    Procedural Content Generation for video game levels with human advice
    (University of the Witwatersrand, Johannesburg, 2023-07) Raal, Nicholas Oliver; James, Steven
    Video gaming is an extremely popular form of entertainment around the world and new video game releases are constantly being showcased. One issue with the video gaming industry is that game developers require a large amount of time to develop new content. A research field that can help with this is procedural content generation (PCG) which allows for an infinite number of video game levels to be generated based on the parameters provided. Many of the methods found in literature can generate content reliably that adhere to quantifiable characteristics such as playability, solvability and difficulty. These methods do not however, take into account the aesthetics of the level which is the parameter that makes them more reasonable levels for human players. In order to address this issue, we propose a method of incorporating high level human advice into the PCG loop. The method uses pairwise comparisons as a way in which a score can be assigned to a level based on its aesthetics. Using the score along with a feature vector describing each level, an SVR model is trained that will allow for a score to be assigned to unseen video game levels. This predicted score is used as an additional fitness function of a multi objective genetic algorithm (GA) and can be optimised as a standard fitness function would. We test the proposed method on two 2D platformer video games, Maze and Super Mario Bros (SMB), and our results show that the proposed method can successfully be used to generate levels with a bias towards the human preferred aesthetical features, whilst still adhering to standard video game characteristics such as solvability. We further investigate incorporating multiple inputs from a human at different stages of the PCG life cycle and find that it does improve the proposed method, but further testing is still required. The findings of this research is hopefully going to assist in using PCG in the video game space to create levels that are more aesthetically pleasing to a human player.
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    Play-style Identification and Player Modelling for Generating Tailored Advice in Video Games
    (University of the Witwatersrand, Johannesburg, 2023-09) Ingram, Branden Corwin; Rosman, Benjamin; Van Alten, Clint; Klein, Richard
    Recent advances in fields such as machine learning have enabled the development of systems that are able to achieve super-human performance on a number of domains, specifically in complex games such as Go and StarCraft. Based on these successes, it is reasonable to ask if these learned behaviours could be utilised to improve the performance of humans on the same tasks. However, the types of models used in these systems are typically not easily interpretable, and can not be directly used to improve the performance of a human. Additionally, humans tend to develop stylistic traits based on preference which aid in solving problems or competing at high levels. This thesis looks to address these difficulties by developing an end-to-end pipeline that can provide beneficial advice tailored to a player’s style in a video game setting. Towards this end, we demonstrate the ability to firstly cluster variable length multi-dimensional gameplay trajectories with respect to play-style in an unsupervised fashion. Secondly, we demonstrate the ability to learn to model an individual player’s actions during gameplay. Thirdly we demonstrate the ability to learn policies representative of all the play-styles identified with an environment. Finally, we demonstrate how the utilisation of these components can generate advice which is tailored to the individual’s style. This system would be particularly useful for improving tutorial systems that quickly become redundant lacking any personalisation. Additionally, this pipeline serves as a way for developers to garner insights on their player base which can be utilised for more informed decision-making on future feature releases and updates. For players, they gain a useful tool which can be utilised to learn how to play better as well identify as the characteristics of their gameplay as well as opponents. Furthermore, we contend that our approach has the potential to be employed in a broad range of learning domains.
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    Two-dimensional turbulent classical and momentumless thermal wakes
    (University of the Witwatersrand, Johannesburg, 2023-07) Mubai, Erick; Mason, David Paul
    The two-dimensional classical turbulent thermal wake and the two-dimensional momentumless turbulent thermal wake are studied. The governing partial differential equations result from Reynolds averaging the Navier-Stokes, the continuity and energy balance equations. The averaged Navier-Stokes and energy balance equations are closed using the Boussinesq hypothesis and an analogy of Fourier’s law of heat conduction. They are further simplified using the boundary layer approximation. This leads to one momentum equation with the continuity equation for an incompressible fluid and one thermal energy equation. The partial differential equations are written in terms of a stream function for the mean velocity deficit that identically satisfies the continuity equation and the mean temperature difference which vanishes on the boundary of the wake. The mixing length model and a model that assumes that the eddy viscosity and eddy thermal conductivity depend on spatial variables only are analysed. We extend the von Kármán similarity hypothesis to thermal wakes and derive a new thermal mixing length. It is shown that the kinematic viscosity and thermal conductivity play an important role in the mathematical analysis of turbulent thermal wakes. We obtain and use conservation laws and associated Lie point symmetries to reduce the governing partial differential equations to ordinary differential equations. As a result we find new analytical solutions for the two-dimensional turbulent thermal classical wake and momentumless wake. When the ordinary differential equations cannot be solved analytically we use a numerical shooting method that uses the two conserved quantities as the targets.