School of Computer Science and Applied Mathematics (ETDs)

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A comparative analysis of classic Geometrical methods and sparse regression methods for linearly unmixing hyperspectral image data
(2019) Nicolae, Aurel
This research report presents an across-the-board comparative analysis on algorithms for linearly unmixing hyperspectral image data cubes. Convex geometry based endmember extraction algorithms (EEAs) such as the pixel purity index (PPI) algorithm and N-FINDR have been commonly used to derive the material spectral signatures called endmembers from the hyperspectral images. The estimation of their corresponding fractional abundances is done by solving the related inverse problem in a least squares sense. Semi-supervised sparse regression algorithms such as orthogonal matching pursuit (OMP) and sparse unmixing algorithm via variable splitting and augmented Lagrangian (SUnSAL) bypass the endmember extraction process by employing widely available spectral libraries a priori, automatically returning the fractional abundances and sparsity estimates. The main contribution of this work is to serve as a rich resource on hyperspectral image unmixing, providing end-to-end evaluation of a wide variety of algorithms using di erent arti cial data sets.
Estimating skills in discrete pursuit-evasion games
(University of the Witwatersrand, Johannesburg, 2023) Gomes, Byron John; Rosman, Benjamin
Game Theory is a well-established field in mathematics, economics, and computer science, with a rich history of studying n-person, zero-sum games. Researchers have utilized the best computational power of their time to create computational players that are able to beat the best human players at complex two-player, zero-sum games such as Chess and Go. In the field of Reinforcement Learning and Robotics, these types of games are considered useful environments to conduct experiments about agent behavior and learning. In this research report we explore a subset of discrete skill-dependent pursuit-evasion games upon which we build a framework to estimate player skills. In this game environment a player’s skill determines the actions available to them in each state and the transition dynamics resulting from the chosen action. The game offers a simplified depresentation of more complex games which often have vast state and action spaces, making it difficult to model and analyze player behavior. In this game environment we find that players with incorrect assumptions about an opponent’s skill perform sub-optimally at winning games. Given that knowledge of an opponent’s skill impacts on player performance, we demonstrate that players can use Bayesian inference to estimate their opponent’s skill, based on the action outcomes of an opponent. We also demonstrate that skill estimation is a valuable exercise for players to undertake and show that the performance of players that estimate their opponent’s skill converges to the performance of players given perfect knowledge of their opponent’s skill. This research contributes to our understanding of Bayesian skill estimation in skill-dependent pursuit-evasion games which may be useful in the fields of Multi-agent Reinforcement Learning and Robotics.
Comparing the effectiveness of LSTM, ARIMA, and GRU algorithms for forecasting customer charging behavior in the electric mobility industry in Europe
(University of the Witwatersrand, Johannesburg, 2023) Pelwan, Robyne Chimere
Forecasting, a powerful technique for unveiling potential future events, relies on historical data and methodological approaches to provide valuable insights. This dissertation delves into the domain of electric mobility, investigating the effectiveness of three distinct algorithms—Long Short-term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Gated Recurrent Unit (GRU)—for predicting customer charging behavior. Specifically, it focuses on forecasting the number of charges over a 7-day period using time-series data from European electric mobility customers. In this study, we scrutinize the interplay between algorithmic performance and the intricacies of the dataset. Root mean squared error (RMSE) serves as a metric for gauging predictive accuracy. The findings highlight the supremacy of the ARIMA model in single-variable analysis, surpassing the predictive capabilities of both LSTM and GRU models. Even when additional features are introduced to enhance LSTM and GRU predictions, the superiority of ARIMA remains pronounced. Notably, this research underscores that ARIMA is particularly well-suited for time series data of this nature due to its tailored design. It contributes valuable insights for both researchers and practitioners in the electric mobility industry, aiding in the strategic selection of forecasting methodologies.
Learning to adapt: domain adaptation with cycle-consistent generative adversarial networks
(University of the Witwatersrand, Johannesburg, 2023) Burke, Pierce William; Klein, Richard
Domain adaptation is a critical part of modern-day machine learning as many practitioners do not have the means to collect and label all the data they require reliably. Instead, they often turn to large online datasets to meet their data needs. However, this can often lead to a mismatch between the online dataset and the data they will encounter in their own problem. This is known as domain shift and plagues many different avenues of machine learning. From differences in data sources, changes in the underlying processes generating the data, or new unseen environments the models have yet to encounter. All these issues can lead to performance degradation. From the success in using Cycle-consistent Generative Adversarial Networks(CycleGAN) to learn unpaired image-to-image mappings, we propose a new method to help alleviate the issues caused by domain shifts in images. The proposed model incorporates an adversarial loss to encourage realistic-looking images in the target domain, a cycle-consistency loss to learn an unpaired image-to-image mapping, and a semantic loss from a task network to improve the generator’s performance. The task network is con-currently trained with the generators on the generated images to improve downstream task performance on adapted images. By utilizing the power of CycleGAN, we can learn to classify images in the target domain without any target domain labels. In this research, we show that our model is successful on various unsupervised domain adaptation (UDA) datasets and can alleviate domain shifts for different adaptation tasks, like classification or semantic segmentation. In our experiments on standard classification, we were able to bring the models performance to near oracle level accuracy on a variety of different classification datasets. The semantic segmentation experiments showed that our model could improve the performance on the target domain, but there is still room for further improvements. We also further analyze where our model performs well and where improvements can be made.
Pipeline for the 3D Reconstruction of Rigid, Handheld Objects through the Use of Static Cameras
(University of the Witwatersrand, Johannesburg, 2023-04) Kambadkone, Saatwik Ramakrishna; Klein, Richard
In this paper, we develop a pipeline for the 3D reconstruction of handheld objects using a single, static RGB-D camera. We also create a general pipeline to describe the process of handheld object reconstruction. This general pipeline suggests the deconstruction of this task into three main constituents: input, where we decide our main method of data capture; segmentation and tracking, where we identify and track the relevant parts of our captured data; and reconstruction where we develop a method for reconstructing our previous information into 3D models. We successfully create a handheld object reconstruction method using a depth sensor as our input; hand tracking, depth segmentation and optical flow to retrieve relevant information; and reconstruction through the use of ICP and TSDF maps. During this process, we also evaluate other possible variations of this successful method. In one of these variations, we test the effect of using depth-estimation to generate data as- the input to our pipeline. While this experimentation helps us quantify our method’s robustness to noise in the input data, we do conclude that current depth estimation techniques do not provide adequate detail for the reconstruction of handheld objects.
Applying Machine Learning to Model South Africa’s Equity Market Index Price Performance
(University of the Witwatersrand, Johannesburg, 2023-07) Nokeri, Tshepo Chris; Mulaudzi, Rudzani; Ajoodha, Ritesh
Policymakers typically use statistical multivariate forecasting models to forecast the reaction of stock market returns to changing economic activities. However, these models frequently result in subpar performance due to inflexibility and incompetence in modeling non-linear relationships. Emerging research suggests that machine learning models can better handle data from non-linear dynamic systems and yield outstanding model performance. This research compared the performance of machine learning models to the performance of the benchmark model (the vector autoregressive model) when forecasting the reaction of stock market returns to changing economic activities in South Africa. The vector autoregressive model was used to forecast the reaction of stock market returns. It achieved a mean absolute percentage error (MAPE) value of 0.0084. Machine learning models were used to forecast the reaction of stock market returns. The lowest MAPE value was 0.0051. The machine learning model trained on low economic data dimensions performed 65% better than the benchmark model. Machine learning models also identified key economic activities when forecasting the reaction of stock market returns. Most research focused on whole features, few models for comparison, and barely focused on how different feature subsets and reduced dimensionality change model performance, a limitation this research addresses when considering the number of experiments. This research considered various experiments, i.e., different feature subsets and data dimensions, to determine whether machine learning models perform better than the benchmark model when forecasting the reaction of stock market returns to changing economic activities in South Africa.
Rationalization of Deep Neural Networks in Credit Scoring
(University of the Witwatersrand, Johannesburg, 2023-07) Dastile, Xolani Collen; Celik, Turgay
Machine learning and deep learning, which are subfields of artificial intelligence, are undoubtedly pervasive and ubiquitous technologies of the 21st century. This is attributed to the enhanced processing power of computers, the exponential growth of datasets, and the ability to store the increasing datasets. Many companies are now starting to view their data as an asset, whereas previously, they viewed it as a by-product of business processes. In particular, banks have started to harness the power of deep learning techniques in their day-to-day operations; for example, chatbots that handle questions and answers about different products can be found on banks’ websites. One area that is key in the banking sector is the credit risk department. Credit risk is the risk of lending money to applicants and is measured using credit scoring techniques that profile applicants according to their risk. Deep learning techniques have the potential to identify and separate applicants based on their lending risk profiles. Nevertheless, a limitation arises when employing deep learning techniques in credit risk, stemming from the fact that these techniques lack the ability to provide explanations for their decisions or predictions. Hence, deep learning techniques are coined as non-transparent models. This thesis focuses on tackling the lack of transparency inherent in deep learning and machine learning techniques to render them suitable for adoption within the banking sector. Different statistical, classic machine learning, and deep learning models’ performances were compared qualitatively and quantitatively. The results showed that deep learning techniques outperform traditional machine learning models and statistical models. The predictions from deep learning techniques were explained using state-of-the-art explanation techniques. A novel model-agnostic explanation technique was also devised, and credit-scoring experts assessed its validity. This thesis has shown that different explanation techniques can be relied upon to explain predictions from deep learning and machine learning techniques.
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.
Generative Model Based Adversarial Defenses for Deepfake Detectors
(University of the Witwatersrand, Johannesburg, 2023-08) Kavilan Dhavan, Nair; Klein, Richard
Deepfake videos present a serious threat to society as they can be used to spread mis-information through social media. Convolutional Neural Networks (CNNs) have been effective in detecting deepfake videos, but they are vulnerable to adversarial attacks that can compromise their accuracy. This vulnerability can be exploited by deepfake creators to evade detection. In this study, we evaluate the effectiveness of two genera- tive adversarial defense mechanisms, APE-GAN and MagNet, in the context of deepfake detection. We use the FaceForensics++ dataset and a CNN victim model based on the XceptionNet architecture, which we attack using the iterative fast gradient sign method at two different levels of ✏, ✏ = 0.0001 and ✏ = 0.01. We find that both APE-GAN and MagNet can purify the adversarial images and restore the performance of the vic- tim model to within 10% of the model’s accuracy on benign fake inputs. However, these methods were less effective at restoring accuracy for adversarial real examples and were not able to significantly restore accuracy when the adversarial attack was aggressive (✏ = 0.01). We recommend that an adversarial defense method be used in conjunction with a deepfake detector to improve the accuracy of predictions. APE-GAN and MagNet are effective methods in the deepfake context, but their effectiveness is limited when the adversarial attack is aggressive.
Improving Semi-Supervised Learning Generative Adversarial Networks
(University of the Witwatersrand, Johannesburg, 2023-08) Moolla, Faheem; Bau, Hairong; Van Zyl, Terence
Generative Adversarial Networks (GANs) have shown remarkable potential in generating high-quality images, with semi-supervised GANs providing a high classification accuracy. In this study, an enhanced semi supervised GAN model is proposed wherein the generator of the GAN is replaced by a pre-trained decoder from a Variational Autoencoder. The model presented outperforms regular GAN and semi-supervised GAN models during the early stages of training, as it produces higher quality images. Our model demonstrated significant improvements in image quality across three datasets - namely the MNIST, Fashion MNIST, and CIFAR-10 datasets - as evidenced by higher accuracies obtained from a Convolutional Neural Network (CNN) trained on generated images, as well as superior inception scores. Additionally, our model prevented mode collapse and exhibited smaller oscillations in the discriminator and generator loss graphs compared to baseline models. The presented model also provided remarkably high levels of classification accuracy, by obtaining 99.32% on the MNIST dataset, 92.78% on the Fashion MNIST dataset, and 83.22% on the CIFAR-10 dataset. These scores are notably robust as they improved some of the classification accuracies obtained by two state-of-the-art models, indicating that the presented model is a significantly improved semi-supervised GAN model. However, despite the high classification accuracy for the CIFAR-10 dataset, a considerable drop in accuracy was observed when comparing generated images to real images for this dataset. This suggests that the quality of those generated images can be bettered and the presented model performs better with less complex datasets. Future work could explore techniques to enhance our model’s performance with more intricate datasets, ultimately expanding its applicability across various domains.
Modelling Cohort Specific Metabolic Syndrome and Cardiovascular Disease Risk using Supervised Machine Learning
(University of the Witwatersrand, Johannesburg, 2023-08) Ngcayiya, Paulina Genet; Ranchod, Pravesh
Cardiovascular Disease (CVD) is the leading cause of death worldwide, with Coronary Heart Disease (CHD) being the most common type of CVD. The consequences of the presence of CVD risk factors often manifest as Metabolic Syndrome (MetS). In this study, a dataset from the Framingham Heart Study (FHS) was used to develop two different kinds of CHD risk prediction models. These models were developed using Random Forests (RF) and AutoPrognosis. Performance of the Framingham Risk Score model (AUC-ROC: 0.633) on the FHS dataset was used as the benchmark. The RF model with optimized hyperparameters (AUC-ROC: 0.728) produced the best results. This was by a very small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.714). The performance of RF against AutoPrognosis when predicting the existence of MetS was evaluated using a dataset from the National Health and Nutrition Examination Survey (NHANES). The RF model with optimized hyperparameters (AUC ROC: 0.851) produced the best results. This was by a small margin to the AutoPrognosis model with an ensemble pipeline (AUC-ROC: 0.851). Datasets, varying in size from 100 to 4900, were used to test the performance of RF against AutoPrognosis. The RF model with optimized hyperparameters had the best performance results.
A fully-decentralised general-sum approach for multi-agent reinforcement learning using minimal modelling
(University of the Witwatersrand, Johannesburg, 2023-08) Kruger, Marcel Matthew Anthony; Rosman, Benjamin; James, Steven; Shipton, Jarrod
Multi-agent reinforcement learning is a prominent area of research in machine learning, extending reinforcement learning to scenarios where multiple agents concurrently learn and interact within the same environment. Most existing methods rely on centralisation during training, while others employ agent modelling. In contrast, we propose a novel method that adapts the role of entropy to assist in fully-decentralised training without explicitly modelling other agents using additional information to which most centralised methods assume access. We augment entropy to encourage more deterministic agents, and instead, we let the non-stationarity inherent in MARL serve as a mode for exploration. We empirically evaluate the performance of our method across five distinct environments, each representing unique challenges. Our assessment encompasses both cooperative and competitive cases. Our findings indicate that the approach of penalising entropy, rather than rewarding it, enables agents to perform at least as well as the prevailing standard of entropy maximisation. Moreover, our alternative approach achieves several of the original objectives of entropy regularisation in reinforcement learning, such as increased sample efficiency and potentially better final rewards. Whilst entropy has a significant role, our results in the competitive case indicate that position bias is still a considerable challenge.
Using Machine Learning to Estimate the Photometric Redshift of Galaxies
(University of the Witwatersrand, Johannesburg, 2023-08) Salim, Shayaan; Bau, Hairong; Komin, Nukri
Machine learning has emerged as a crucial tool in the field of cosmology and astrophysics, leading to extensive research in this area. This research study aims to utilize machine learning models to estimate the redshift of galaxies, with a primary focus on utilizing photometric data to obtain accurate results. Five machine learning algorithms, including XGBoost, Random Forests, K-nearest neighbors, Artificial Neural Networks, and Polynomial Regression, are employed to estimate the redshifts, trained on photometric data derived from the Sloan Digital Sky Survey (SDSS) Data Release 17 database. Furthermore, various input parameters from the SDSS database are explored to achieve the most accurate redshift values. The research incorporates a comparative analysis, utilizing different evaluation metrics and statistical tests to determine the best-performing algorithm. The results indicate that the XGBoost algorithm achieves the highest accuracy, with an R2 value of 0.94, a Root Mean Square Error (RMSE) of 0.03, and a Mean Absolute Average Percentage (MAPE) of 12.04% when trained on the optimal feature subset. In comparison, the base model achieved an R2 of 0.84, a RMSE of 0.05, and a MAPE of 20.89%. The study contributes to the existing literature by utilizing photometric data during model training and comparing different high-performing algorithms from the literature.
Federated learning in the detection of Covid -19 in patient Ct-Scans: A practical evaluation of external generalisation
(University of the Witwatersrand, Johannesburg, 2023-08) Wapenaar, Korstiaan; Ranchod, Pravesh
This research explores the practical utility of using convolutional neural networks in a federated learning architecture for COVID-19 diagnostics using chest CT-scans, and whether federated learning models can generalise to data from healthcare facilities that did not participate in training. A model that can generalise to these healthcare facilities could provide lower-resourced or over-utilised facilities with access to supplementary diagnostic services. Eleven models are trained using a modified VGG-16. The models are trained using data from five ‘sites’: four sites are single healthcare facilities and the fifth site is a composite of data from a variety of healthcare facilities. Eleven models are trained, evaluated and compared: five ‘independent models’ are each trained with data from a single site; three ‘global models’ are trained using centrally pooled data from a variety of sites; three ‘federated models’ are trained using a federated averaging approach. The site with composite data is held-out and never included in training the federated and global models. With the exception of this composite site, all models achieve a test accuracy of at least 0.93 when evaluated using test data from the sites used in training these models. All models are then evaluated using data from the composite site. The global and federated models achieve a 0.5 to 0.6 accuracy for the composite site, indicating that the model and training regime is unable to achieve useful accuracies for sites non-participant in training. The federated models are therefore not accurate enough to motivate a healthcare facility decision maker to use the federated models as an alternative or supplementary diagnostic tool to radiographers, or to developing their own independent model. Evaluation of the results suggests that high-quality and consistent image pre-processing may be a necessary precondition for the task.
Generating Rich Image Descriptions from Localized Attention
(University of the Witwatersrand, Johannesburg, 2023-08) Poulton, David; Klein, Richard
The field of image captioning is constantly growing with swathes of new methodologies, performance leaps, datasets, and challenges. One new challenge is the task of long-text image description. While the vast majority of research has focused on short captions for images with only short phrases or sentences, new research and the recently released Localized Narratives dataset have pushed this to rich, paragraph length descriptions. In this work we perform additional research to grow the sub-field of long-text image descriptions and determine the viability of our new methods. We experiment with a variety of progressively more complex LSTM and Transformer-based approaches, utilising human-generated localised attention traces and image data to generate suitable captions, and evaluate these methods on a suite of common language evaluation metrics. We find that LSTM-based approaches are not well suited to the task, and under-perform Transformer-based implementations on our metric suite while also proving substantially more demanding to train. On the other hand, we find that our Transformer-based methods are well capable of generating captions with rich focus over all regions of the image and in a grammatically sound manner, with our most complex model outperforming existing approaches on our metric suite.
Self Supervised Salient Object Detection using Pseudo-labels
(University of the Witwatersrand, Johannesburg, 2023-08) Bachan, Kidhar; Wang, Hairong
Deep Convolutional Neural Networks have dominated salient object detection methods in recent history. A determining factor for salient object detection network performance is the quality and quantity of pixel-wise annotated labels. This annotation is performed manually, making it expensive (time-consuming, tedious), while limiting the training data to the available annotated datasets. Alternatively, unsupervised models are able to learn from unlabelled datasets or datasets in the wild. In this work, an existing algorithm [Li et al. 2020] is used to refine the generated pseudo labels before training. This research focuses on the changes made to the pseudo label refinement algorithm and its effect on performance for unsupervised saliency object detection tasks. We show that using this novel approach leads to statistically negligible performance improvements and discuss the reasons why this is the case.
A Continuous Reinforcement Learning Approach to Self-Adaptive Particle Swarm Optimisation
(University of the Witwatersrand, Johannesburg, 2023-08) Tilley, Duncan; Cleghorn, Christopher
Particle Swarm Optimisation (PSO) is a popular black-box optimisation technique due to its simple implementation and surprising ability to perform well on various problems. Unfortunately, PSO is fairly sensitive to the choice of hyper-parameters. For this reason, many self-adaptive techniques have been proposed that attempt to both simplify hyper-parameter selection and improve the performance of PSO. Surveys however show that many self-adaptive techniques are still outperformed by time-varying techniques where the value of coefficients are simply increased or decreased over time. More recent works have shown the successful application of Reinforcement Learning (RL) to learn self-adaptive control policies for optimisers such as differential evolution, genetic algorithms, and PSO. However, many of these applications were limited to only discrete state and action spaces, which severely limits the choices available to a control policy, given that the PSO coefficients are continuous variables. This dissertation therefore investigates the application of continuous RL techniques to learn a self-adaptive control policy that can make full use of the continuous nature of the PSO coefficients. The dissertation first introduces the RL framework used to learn a continuous control policy by defining the environment, action-space, state-space, and a number of possible reward functions. An effective learning environment that is able to overcome the difficulties of continuous RL is then derived through a series of experiments, culminating in a successfully learned continuous control policy. The policy is then shown to perform well on the benchmark problems used during training when compared to other self-adaptive PSO algorithms. Further testing on benchmark problems not seen during training suggest that the learned policy may however not generalise well to other functions, but this is shown to also be a problem in other PSO algorithms. Finally, the dissertation performs a number of experiments to provide insights into the behaviours learned by the continuous control policy.
Improving audio-driven visual dubbing solutions using self-supervised generative adversarial networks
(University of the Witwatersrand, Johannesburg, 2023-09) Ranchod, Mayur; Klein, Richard
Audio-driven visual dubbing (ADVD) is the process of accepting a talking-face video, along with a dubbing audio segment, as inputs and producing a dubbed video such that the speaker appears to be uttering the dubbing audio. ADVD aims to address the language barrier inherent in the consumption of video-based content caused by the various languages in which videos may be presented. Specifically, a video may only be consumed by the audience that is familiar with the spoken language. Traditional solutions, such as subtitles and audio-dubbing, hinder the viewer’s experience by either obstructing the on-screen content or introducing an unpleasant discrepancy between the speaker’s mouth movements and the input dubbing audio, respectively. In contrast, ADVD strives to achieve a natural viewing experience by synchronizing the speaker’s mouth movements with the dubbing audio. A comprehensive survey of several ADVD solutions revealed that most existing solutions achieve satisfactory visual quality and lip-sync accuracy but are limited to low-resolution videos with frontal or near frontal faces. Since this is in sharp contrast to real-world videos, which are high-resolution and contain arbitrary head poses, we present one of the first ADVD solutions trained with high-resolution data and also introduce the first pose-invariant ADVD solution. Our results show that the presented solution achieves superior visual quality while also achieving high measures of lip-sync accuracy, consequently enabling the solution to achieve significantly improved results when applied to real-world videos.
Regime Based Portfolio Optimization: A Look at the South African Asset Market
(University of the Witwatersrand, Johannesburg, 2023-09) Mdluli, Nkosenhle S.; Ajoodha, Ritesh; Mulaudzi, Rudzani
Financial markets change their properties (i.e mean, volatility, correlation, and distribution) with time. However, traditional portfolio optimization strategies seek to create static, all weather portfolios oblivious to this and current economic conditions. This produces portfolios that are unable to predict events with excessive skewness and kurtosis. This research investigated the difference in portfolio percentage return, of portfolios that incorporate regimes against one that does not. HMMs, binary segmentation, and PELT algorithms were used to identify regimes in 7 macro-economic features. These regimes, with regimes identified by the SARB, were incorporated into Markowitz’s mean-variance optimization technique to optimize portfolios. The base portfolio, which did not incorporate regimes, produced the least return of 761% during the period under consideration. Portfolios using HMMs identified regimes, produced, on average, the highest returns, averaging 3211% whilst the portfolio using SARB identified regimes returned 1878% during the same period. This research, therefore, shows that incorporating regimes into portfolio optimization increases the percentage return of a portfolio. Moreover, it shows that, although HMMs, on average, produced the most profitable portfolio, portfolios using regimes based on data-driven techniques do not always out-perform portfolios using the SARB identified regimes.
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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