Research Publications

Data analysis-based decision-making is performed daily by domain experts. As data grows, getting access to relevant data becomes a challenge. In an approach known as Ontology-based data access (OBDA), AQ1 ontologies are advocated as a suitable formal tool to address complex data access. This technique combines a domain ontology with a data source by using a declarative mapping specification to enable data access using a domain vocabulary.We investigate this approach by studying the theoretical background; conducting a literature review on the implementation of OBDA in production systems; implementing OBDA on a relational dataset using an OBDA tool and; providing results and analysis of query answering.We selected Ontop (https://ontop-vkg.org) to illustrate how this technique enhances the data usage of the GitHub community. AQ2 Ontop is an open-source OBDA tool applied in the domain of relational databases. The implementation consists of the GHTorrent dataset and an extended SemanGit ontology. We perform a set of queries to highlight a subset of the features of this data access approach. The results look positive and can assist various use cases related to GitHub data with a semantic approach. OBDA does provide benefits in practice, such as querying in domain vocabulary and making use of reasoning over the axioms in the ontology. However, the practical impediments we observe are in the “manual” development of a domain ontology and the creation of a mapping specification which requires deep knowledge of a domain and the data. Also, implementing OBDA within the practical context of an information system requires careful consideration for a suitable user interface to facilitate the query construction from ontology vocabulary. Finally, we conclude with a summary of the paper and direction for future research.

@inbook{493,
  author = {Yahlieel Jafta and Louise Leenen and Thomas Meyer},
  title = {Investigating Ontology-based Data Access with GitHub},
  abstract = {Data analysis-based decision-making is performed daily by
domain experts. As data grows, getting access to relevant data becomes a
challenge. In an approach known as Ontology-based data access (OBDA), AQ1
ontologies are advocated as a suitable formal tool to address complex
data access. This technique combines a domain ontology with a data
source by using a declarative mapping specification to enable data access
using a domain vocabulary.We investigate this approach by studying the
theoretical background; conducting a literature review on the implementation
of OBDA in production systems; implementing OBDA on a relational
dataset using an OBDA tool and; providing results and analysis of
query answering.We selected Ontop (https://ontop-vkg.org) to illustrate
how this technique enhances the data usage of the GitHub community. AQ2
Ontop is an open-source OBDA tool applied in the domain of relational
databases. The implementation consists of the GHTorrent dataset and
an extended SemanGit ontology. We perform a set of queries to highlight
a subset of the features of this data access approach. The results look
positive and can assist various use cases related to GitHub data with
a semantic approach. OBDA does provide benefits in practice, such as
querying in domain vocabulary and making use of reasoning over the
axioms in the ontology. However, the practical impediments we observe
are in the “manual” development of a domain ontology and the creation
of a mapping specification which requires deep knowledge of a domain
and the data. Also, implementing OBDA within the practical context
of an information system requires careful consideration for a suitable
user interface to facilitate the query construction from ontology vocabulary.
Finally, we conclude with a summary of the paper and direction
for future research.},
  year = {2023},
  journal = {Lecture Notes in Computer Science 13870 (Proceedings of the 20th Extended Semantic Web Conference)},
  volume = {13870},
  month = {2023},
  publisher = {Springer},
}

This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and compared with that of Double Deep Q-Networks. The two algorithms are implemented in a format similar to that of a zero-sum game. A two-tailed T-test analysis is done on the two game results containing the amount of turns taken for the defender to win. Another comparison is done on the game scores of the agents in the respective games. The analysis is done to determine which algorithm is the best in game performer and whether there is a significant difference between them, demonstrating if one would have greater preference over the other. It was found that there is no significant statistical difference between the two approaches.

@{556,
  author = {Luke Borchjes and Clement Nyirenda and Louise Leenen},
  title = {Model-Free Deep Reinforcement Learning in Software-Defined Networks},
  abstract = {This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and compared with that of Double Deep Q-Networks. The two algorithms are implemented in a format similar to that of a zero-sum game. A two-tailed T-test analysis is done on the two game results containing the amount of turns taken for the defender to win. Another comparison is done on the game scores of the agents in the respective games. The analysis is done to determine which algorithm is the best in game performer and whether there is a significant difference between them, demonstrating if one would have greater preference over the other. It was found that there is no significant statistical difference between the two approaches.},
  year = {2022},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC 2022)},
  month = {28-30 August 2022},
  address = {South Africa},
  url = {https://arxiv.org/abs/2209.01490},
}

Ransomware attacks have become the fastest growing and most serious type of cybercrime. A ransomware attack does not only capture victims' data, but also prevents victims from accessing their own data until a ransom has been paid. The prevention of and recovery from a ransomware attack have become a major concern for governments and organizations. This paper presents guidelines for institutions to secure their systems from ransomware attacks and to put steps in place for recovery if their systems have been attacked. The human is often the weak link in allowing an intrusion into a network. African countries are at even greater risk because their populations are often not sufficiently trained nor aware of cybersecurity risks.

@{554,
  author = {Joey van Vuuren and Louise Leenen and Anna-Marie van Vuuren},
  title = {Don't be Caught Unaware: A Ransomware Primer with a Specific Focus on Africa},
  abstract = {Ransomware attacks have become the fastest growing and most serious type of cybercrime. A ransomware attack does not only capture victims' data, but also prevents victims from accessing their own data until a ransom has been paid. The prevention of and recovery from a ransomware attack have become a major concern for governments and organizations. This paper presents guidelines for institutions to secure their systems from ransomware attacks and to put steps in place for recovery if their systems have been attacked. The human is often the weak link in allowing an intrusion into a network. African countries are at even greater risk because their populations are often not sufficiently trained nor aware of cybersecurity risks.},
  year = {2022},
  journal = {Human Choice and Digital by Default: Autonomy vs Digital Determination (HCC 2022). IFIP Advances in Information and Communication Technology},
  volume = {656},
  pages = {115-131},
  month = {2022},
  publisher = {Springer, Cham},
  doi = {10.1007/978-3-031-15688-5_11},
}

South Africa is increasingly targeted by cyber criminals and is often ranked under the top five countries suffering the most cyber-attacks. In an initiative to counter these attacks, the South African government has initiated various measures such as a National Cybersecurity Policy Framework Policy (NCPF) and a Cybercrimes Act. However, the structures and policies that follow from these measures have not been fully implemented yet. Although the government published the NCPF in 2015 and enacted the Cybercrimes Act in May 2021, there is still a gap in terms of interoperability and shared understanding within the environment. In addition, numerous new structures have been established and others are still being planned. One example of a new structure is the Cybersecurity Hub, the national CSIRT, which is mandated to co-ordinate attack information and provide support for cyber incidents. In addition, the Hub must also implement a national Cybersecurity Awareness program. This paper presents a model for the Cybersecurity Hub in the event of a cyber incident in South Africa. The model is based on different attack scenarios and depicts the complex interoperability problem of the various roles, responsibilities, and interactions of role players when there is a cyber incident. One of the scenarios is an attack on critical infrastructure. The model is a prototype of a semantic knowledge base (an ontology) that will help with planning and decision making. Core queries that should be answered concern the critical role players during and after a cyber event; the communication activities that have to take place; and the response actions and the skills required to handle the event.

@{553,
  author = {Aphile Kondlo and Louise Leenen and Joey van Vuuren},
  title = {An Ontological Model for a National Cyber-Attack Response in South Africa},
  abstract = {South Africa is increasingly targeted by cyber criminals and is often ranked under the top five countries suffering the most cyber-attacks. In an initiative to counter these attacks, the South African government has initiated various measures such as a National Cybersecurity Policy Framework Policy (NCPF) and a Cybercrimes Act. However, the structures and policies that follow from these measures have not been fully implemented yet. Although the government published the NCPF in 2015 and enacted the Cybercrimes Act in May 2021, there is still a gap in terms of interoperability and shared understanding within the environment. In addition, numerous new structures have been established and others are still being planned. One example of a new structure is the Cybersecurity Hub, the national CSIRT, which is mandated to co-ordinate attack information and provide support for cyber incidents. In addition, the Hub must also implement a national Cybersecurity Awareness program. This paper presents a model for the Cybersecurity Hub in the event of a cyber incident in South Africa. The model is based on different attack scenarios and depicts the complex interoperability problem of the various roles, responsibilities, and interactions of role players when there is a cyber incident. One of the scenarios is an attack on critical infrastructure. The model is a prototype of a semantic knowledge base (an ontology) that will help with planning and decision making. Core queries that should be answered concern the critical role players during and after a cyber event; the communication activities that have to take place; and the response actions and the skills required to handle the event.},
  year = {2022},
  journal = {Proceedings of the 21st European Conference on Cyber Warfare and Security (ECCWS 2022)},
  volume = {21},
  pages = {130-139},
  month = {24-25 June 2022},
  issue = {1},
  publisher = {Academic Conferences International},
  address = {Chester, United Kingdom},
  doi = {10.34190/eccws.21.1.213},
}

Text generation is one of the important sub-tasks of natural language generation (NLG), and aims to produce humanly readable text given some input text. Deep learning approaches based on neural networks have been proposed to solve text generation tasks. Although these models can generate text, they do not necessarily capture long-term dependencies accurately, making it difficult to coherently generate longer sentences. Transformer-based models have shown significant improvement in text generation. However, these models are computationally expensive and data hungry. In this study, we develop a Sepedi text generation model using a Transformerbased approach and explore its performance. The developed model has one Transformer block with causal masking on the attention layers and two separate embedding layers. To train the model, we use the National Centre for Human Language Technology (NCHLT) Sepedi text corpus. Our experimental setup varied the model embedding size, batch size and the sequence length. The final model was able to reconstruct unseen test data with 75% accuracy: the highest accuracy achieved to date, using a Sepedi corpus.

@{511,
  author = {Simon Ramalepe and Thipe Modipa and Marelie Davel},
  title = {The development of a Sepedi text generation model using transformers},
  abstract = {Text generation is one of the important sub-tasks of natural language generation (NLG), and aims to produce humanly readable text given some input text. Deep learning approaches based on neural networks have been proposed to solve text generation tasks. Although these models can generate text, they do not necessarily capture long-term dependencies accurately, making it difficult to coherently generate longer sentences. Transformer-based models have shown significant improvement in text generation. However, these models are computationally expensive and data hungry. In this study, we develop a Sepedi text generation model using a Transformerbased approach and explore its performance. The developed model has one Transformer block with causal masking on the attention layers and two separate embedding layers. To train the model, we use the National Centre for Human Language Technology (NCHLT) Sepedi text corpus. Our experimental setup varied the model embedding size, batch size and the sequence length. The final model was able to reconstruct unseen test data with 75% accuracy: the highest accuracy achieved to date, using a Sepedi corpus.},
  year = {2022},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC)},
  pages = {51 - 56},
  month = {August 2022},
}

Traffic speed prediction using deep learning has been the topic of many studies. In this paper, we analyse the performance of Graph Neural Network-based techniques during periods of traffic congestion. We first compare a selection of recently proposed techniques that claim to achieve good results using the METR-LA and PeMS-BAY data sets. We then investigate the performance of three of these approaches – Graph WaveNet, Spacetime Neural Network (STNN) and Spatio-Temporal Attention Wavenet (STAWnet) – during congested periods, using recurrent congestion patterns to set a threshold for general congestion through the entire traffic network. Our results show that performance deteriorates significantly during congested time periods, which is concerning, as traffic speed prediction is usually of most value during times of congestion. We also found that, while the above approaches perform almost equally in the absence of congestion, there are much bigger differences in performance during peri ods of congestion.

@{510,
  author = {Marko Oosthuizen and Marko Oosthuizen and Marelie Davel},
  title = {A Comparative Study of Graph Neural Network Speed Prediction during Periods of Congestion},
  abstract = {Traffic speed prediction using deep learning has been the topic of many studies. In this paper, we analyse the performance of Graph Neural Network-based techniques during periods of traffic congestion. We first compare a selection of recently proposed techniques that claim to achieve good results using the METR-LA and PeMS-BAY data sets. We then investigate the performance of three of these approaches – Graph WaveNet, Spacetime Neural Network (STNN) and Spatio-Temporal Attention Wavenet (STAWnet) – during congested periods, using recurrent congestion patterns to set a threshold for general congestion through the entire traffic network. Our results show that performance deteriorates significantly during congested time periods, which is concerning, as traffic speed prediction is usually of most value during times of congestion. We also found that, while the above approaches perform almost equally in the absence of congestion, there are much bigger differences in performance during peri ods of congestion.},
  year = {2022},
  journal = {In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
  volume = {1},
  pages = {331 - 338},
  month = {October 2022},
  isbn = {978-989-758-611-8},
  doi = {10.5220/0011374100003332},
}

Deep neural networks can be utilised for channel state information (CSI) estimation in wireless communications. We aim to decrease the bit error rate of such networks without increasing their complexity, since the wireless environment requires solutions with high performance while constraining implementation cost. For this reason, we investigate the use of adversarial training, which has been successfully applied to image super-resolution tasks that share similarities with CSI estimation tasks. CSI estimators are usually trained in a Single-In Single-Out (SISO) configuration to estimate the channel between two specific antennas and then applied to multi-antenna configurations. We show that the performance of neural networks in the SISO training environment is not necessarily indicative of their performance in multi-antenna systems. The analysis shows that adversarial training does not provide advantages in the SISO environment, however, adversarially trained models can outperform non-adversarially trained models when applying antenna diversity to Long-Term Evolution systems. The use of a feature extractor network is also investigated in this study and is found to have the potential to enhance the performance of Multiple-In Multiple-Out antenna configurations at higher SNRs. This study emphasises the importance of testing neural networks in the context of use while also showing possible advantages of adversarial training in multi-antenna systems without necessarily increasing network complexity.

@{509,
  author = {Andrew Oosthuizen and Albert Helberg and Marelie Davel},
  title = {Adversarial training for channel state information estimation in LTE multi-antenna systems},
  abstract = {Deep neural networks can be utilised for channel state information (CSI) estimation in wireless communications. We aim to decrease the bit error rate of such networks without increasing their complexity, since the wireless environment requires solutions with high performance while constraining implementation cost. For this reason, we investigate the use of adversarial training, which has been successfully applied to image super-resolution tasks that share similarities with CSI estimation tasks. CSI estimators are usually trained in a Single-In Single-Out (SISO) configuration to estimate the channel between two specific antennas and then applied to multi-antenna configurations. We show that the performance of neural networks in the SISO training environment is not necessarily indicative of their performance in multi-antenna systems. The analysis shows that adversarial training does not provide advantages in the SISO environment, however, adversarially trained models can outperform non-adversarially trained models when applying antenna diversity to Long-Term Evolution systems. The use of a feature extractor network is also investigated in this study and is found to have the potential to enhance the performance of Multiple-In Multiple-Out antenna configurations at higher SNRs. This study emphasises the importance of testing neural networks in the context of use while also showing possible advantages of adversarial training in multi-antenna systems without necessarily increasing network complexity.},
  year = {2022},
  journal = {Southern African Conference for Artificial Intelligence Research},
  volume = {1734},
  pages = {3 - 17},
  month = {November 2022},
  publisher = {Springer, Cham},
  isbn = {978-3-031-22320-4},
  doi = {https://doi.org/10.1007/978-3-031-22321-1_1},
}

Passive acoustic monitoring with hydrophones makes it possible to detect the presence of marine animals over large areas. For monitoring to be cost-effective, this process should be fully automated. We explore a new approach to detecting whale calls, using an end-to-end neural architecture and traditional speech features. We compare the results of the new approach with a convolutional neural network (CNN) applied to spectrograms, currently the standard approach to whale call detection. Experiments are conducted using the “Acoustic trends for the blue and fin whale library” from the Australian Antarctic Data Centre (AADC). We experiment with different types of speech features (mel frequency cepstral coefficients and filter banks) and different ways of framing the task. We demonstrate that a time delay neural network is a viable solution for whale call detection, with the additional benefit that spectrogram tuning – required to obtain high-quality spectrograms in challenging acoustic conditions – is no longer necessary. While the initial speech feature-based system (accuracy 96%) did not outperform the CNN (accuracy 98%) when trained on exactly the same dataset, it presents a viable approach to explore further.

@{508,
  author = {Edrich Fourie and Marelie Davel and Jaco Versfeld},
  title = {Neural speech processing for whale call detection},
  abstract = {Passive acoustic monitoring with hydrophones makes it possible to detect the presence of marine animals over large areas. For monitoring to be cost-effective, this process should be fully automated. We explore a new approach to detecting whale calls, using an end-to-end neural architecture and traditional speech features. We compare the results of the new approach with a convolutional neural network (CNN) applied to spectrograms, currently the standard approach to whale call detection. Experiments are conducted using the “Acoustic trends for the blue and fin whale library” from the Australian Antarctic Data Centre (AADC). We experiment with different types of speech features (mel frequency cepstral coefficients and filter banks) and different ways of framing the task. We demonstrate that a time delay neural network is a viable solution for whale call detection, with the additional benefit that spectrogram tuning – required to obtain high-quality spectrograms in challenging acoustic conditions – is no longer necessary. While the initial speech feature-based system (accuracy 96%) did not outperform the CNN (accuracy 98%) when trained on exactly the same dataset, it presents a viable approach to explore further.},
  year = {2022},
  journal = {Southern African Conference for AI Research (SACAIR)},
  volume = {1734},
  pages = {276 - 290},
  month = {November 2022},
  publisher = {Springer, Cham},
  doi = {https://doi.org/10.1007/978-3-031-22321-1_19},
}

Classification margins are commonly used to estimate the generalization ability of machine learning models. We present an empirical study of these margins in artificial neural networks. A global estimate of margin size is usually used in the literature. In this work, we point out seldom considered nuances regarding classification margins. Notably, we demonstrate that some types of training samples are modelled with consistently small margins while affecting generalization in different ways. By showing a link with the minimum distance to a different-target sample and the remoteness of samples from one another, we provide a plausible explanation for this observation. We support our findings with an analysis of fully-connected networks trained on noise-corrupted MNIST data, as well as convolutional networks trained on noise-corrupted CIFAR10 data.

@inbook{505,
  author = {Marthinus Theunissen and Coenraad Mouton and Marelie Davel},
  title = {The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs},
  abstract = {Classification margins are commonly used to estimate the generalization ability of machine learning models. We present an empirical study of these margins in artificial neural networks. A global estimate of margin size is usually used in the literature. In this work, we point out seldom considered nuances regarding classification margins. Notably, we demonstrate that some types of training samples are modelled with consistently small margins while affecting generalization in different ways. By showing a link with the minimum distance to a different-target sample and the remoteness of samples from one another, we provide a plausible explanation for this observation. We support our findings with an analysis of fully-connected networks trained on noise-corrupted MNIST data, as well as convolutional networks trained on noise-corrupted CIFAR10 data.},
  year = {2022},
  journal = {Artificial Intelligence Research (SACAIR 2022), Communications in Computer and Information Science},
  volume = {1734},
  pages = {78 - 92},
  month = {November 2022},
  publisher = {Springer, Cham},
  doi = {https://doi.org/10.48550/arXiv.2302.06925},
}

We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.

@article{492,
  author = {Walter Heymans and Marelie Davel and Charl Van Heerden},
  title = {Efficient acoustic feature transformation in mismatched environments using a Guided-GAN},
  abstract = {We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.},
  year = {2022},
  journal = {Speech Communication},
  volume = {143},
  pages = {10 - 20},
  month = {09/2022},
  doi = {https://doi.org/10.1016/j.specom.2022.07.002},
}

The accurate estimation of channel state information (CSI) is an important aspect of wireless communications. In this paper, a multi-layer perceptron (MLP) is developed as a CSI estimator in long-term evolution (LTE) transmission conditions. The representation of the CSI data is investigated in conjunction with batch normalisation and the representational ability of MLPs. It is found that discontinuities in the representational feature space can cripple an MLP’s ability to accurately predict CSI when noise is present. Different ways in which to mitigate this effect are analysed and a solution developed, initially in the context of channels that are only affected by additive white Guassian noise. The developed architecture is then applied to more complex channels with various delay profiles and Doppler spread. The performance of the proposed MLP is shown to be comparable with LTE minimum mean squared error (MMSE), and to outperform least square (LS) estimation over a range of channel conditions.

@{491,
  author = {Andrew Oosthuizen and Marelie Davel and Albert Helberg},
  title = {Multi-Layer Perceptron for Channel State Information Estimation: Design Considerations},
  abstract = {The accurate estimation of channel state information (CSI) is an important aspect of wireless communications. In this paper, a multi-layer perceptron (MLP) is developed as a CSI estimator in long-term evolution (LTE) transmission conditions. The representation of the CSI data is investigated in conjunction with batch normalisation and the representational ability of MLPs. It is found that discontinuities in the representational feature space can cripple an MLP’s ability to accurately predict CSI when noise is present. Different ways in which to mitigate this effect are analysed and a solution developed, initially in the context of channels that are only affected by additive white
Guassian noise. The developed architecture is then applied to more complex channels with various delay profiles and Doppler spread. The performance of the proposed MLP is shown to be comparable with LTE minimum mean squared error (MMSE), and to outperform least square (LS) estimation over a range of channel conditions.},
  year = {2022},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC)},
  pages = {94 - 99},
  month = {08/2022},
  address = {Fancourt, George},
}

Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.

@article{488,
  author = {C. Sue Price and Deshen Moodley and Anban Pillay and Gavin Rens},
  title = {An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making},
  abstract = {Building computational models of agents in dynamic, partially observable and stochastic environments is challenging.  We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities.  Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane.  The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture.  However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for.  Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions.  Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals.  In addition, we provide a mapping for using a DDN in a BDI architecture.  This architecture can be used for modelling sugarcane grower agents in an agent-based simulation.  The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.},
  year = {2022},
  journal = {South African Computer Journal},
  volume = {34},
  pages = {152-191},
  issue = {1},
  url = {https://sacj.cs.uct.ac.za/index.php/sacj/article/view/857},
  doi = {https://doi.org/10.18489/sacj.v34i1.857},
}

Artifcial Intelligence (AI) systems are ubiquitous. From social media timelines, video recommendations on YouTube, and the kinds of adverts we see online, AI, in a very real sense, flters the world we see. More than that, AI is being embedded in agent-like systems, which might prompt certain reactions from users. Specifcally, we might fnd ourselves feeling frustrated if these systems do not meet our expectations. In normal situations, this might be fne, but with the ever increasing sophistication of AI-systems, this might become a problem. While it seems unproblematic to realize that being angry at your car for breaking down is unfitting, can the same be said for AI-systems? In this paper, therefore, I will investigate the so-called “reactive attitudes”, and their important link to our responsibility practices. I then show how within this framework there exist exemption and excuse conditions, and test whether our adopting the “objective attitude” toward agential AI is justifed. I argue that such an attitude is appropriate in the context of three distinct senses of responsibility (answerability, attributability, and accountability), and that, therefore, AI-systems do not undermine our responsibility ascriptions.

@article{487,
  author = {Fabio Tollon},
  title = {Responsibility gaps and the reactive attitudes},
  abstract = {Artifcial Intelligence (AI) systems are ubiquitous. From social media timelines, video recommendations on YouTube, and the kinds of adverts we see online, AI, in a very real sense, flters the world we see. More than that, AI is being embedded in agent-like systems, which might prompt certain reactions from users. Specifcally, we might fnd ourselves feeling frustrated if these systems do not meet our expectations. In normal situations, this might be fne, but with the ever increasing sophistication of AI-systems, this might become a problem. While it seems unproblematic to realize that being angry at your car for breaking down is unfitting, can the same be said for AI-systems? In this paper, therefore, I will investigate the so-called “reactive attitudes”, and their important link to our responsibility practices. I then show how within this framework there exist exemption and excuse conditions, and test whether our adopting the “objective attitude” toward agential AI is justifed. I argue that such an attitude is appropriate in the context of three distinct senses of responsibility (answerability, attributability, and accountability), and that, therefore, AI-systems do not undermine our responsibility ascriptions.},
  year = {2022},
  journal = {AI and Ethics},
  publisher = {Springer},
  url = {https://link.springer.com/article/10.1007/s43681-022-00172-6},
  doi = {https://doi.org/10.1007/s43681-022-00172-6},
}

We report on the development of two reference corpora for the analysis of SepediEnglish code-switched speech in the context of automatic speech recognition. For the first corpus, possible English events were obtained from an existing corpus of transcribed Sepedi-English speech. The second corpus is based on the analysis of radio broadcasts: actual instances of code switching were transcribed and reproduced by a number of native Sepedi speakers. We describe the process to develop and verify both corpora and perform an initial analysis of the newly produced data sets. We find that, in naturally occurring speech, the frequency of code switching is unexpectedly high for this language pair, and that the continuum of code switching (from unmodified embedded words to loanwords absorbed into the matrix language) makes this a particularly challenging task for speech recognition systems.

@article{483,
  author = {Thipe Modipa and Marelie Davel},
  title = {Two Sepedi‑English code‑switched speech corpora},
  abstract = {We report on the development of two reference corpora for the analysis of SepediEnglish code-switched speech in the context of automatic speech recognition. For the first corpus, possible English events were obtained from an existing corpus of transcribed Sepedi-English speech. The second corpus is based on the analysis of radio broadcasts: actual instances of code switching were transcribed and reproduced by a number of native Sepedi speakers. We describe the process to develop and verify both corpora and perform an initial analysis of the newly produced data sets. We find that, in naturally occurring speech, the frequency of code switching is unexpectedly high for this language pair, and that the continuum of code switching (from unmodified embedded words to loanwords absorbed into the matrix language) makes this a particularly challenging task for speech recognition systems.},
  year = {2022},
  journal = {Language Resources and Evaluation},
  volume = {56},
  pages = {https://rdcu.be/cO6lD)},
  publisher = {Springer},
  address = {South Africa},
  url = {https://rdcu.be/cO6lD},
  doi = {https://doi.org/10.1007/s10579-022-09592-6 (Read here: https://rdcu.be/cO6lD)},
}

Mismatched data is a challenging problem for automatic speech recognition (ASR) systems. One of the most common techniques used to address mismatched data is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We explore the impact of different MTR styles on system performance when testing conditions are different from training conditions in the context of deep neural network hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created using the LibriSpeech corpus, where we isolate the effect of different MTR styles on final system performance. We evaluate our findings on a South African call centre dataset that contains noisy, WAV49-encoded audio.

@article{480,
  author = {Walter Heymans and Marelie Davel and Charl Van Heerden},
  title = {Multi-style Training for South African Call Centre Audio},
  abstract = {Mismatched data is a challenging problem for automatic speech recognition (ASR) systems. One of the most common techniques used to address mismatched data is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We explore the impact of different MTR styles on system performance when testing conditions are different from training conditions in the context of deep neural network hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created using the LibriSpeech corpus, where we isolate the effect of different MTR styles on final system performance. We evaluate our findings on a South African call centre dataset that contains noisy, WAV49-encoded audio.},
  year = {2022},
  journal = {Communications in Computer and Information Science},
  volume = {1551},
  pages = {111 - 124},
  publisher = {Southern African Conference for Artificial Intelligence Research},
  address = {South Africa},
  doi = {https://doi.org/10.1007/978-3-030-95070-5_8},
}

While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still poorly understood. Conversely, common decision trees are easily interpretable and theoretically well understood. We show that by encoding the discrete sample activation values of nodes as a binary representation, we are able to extract a decision tree explaining the classification procedure of each layer in a ReLU-activated multilayer perceptron (MLP). We then combine these decision trees with existing feature attribution techniques in order to produce an interpretation of each layer of a model. Finally, we provide an analysis of the generated interpretations, the behaviour of the binary encodings and how these relate to sample groupings created during the training process of the neural network.

@article{479,
  author = {Coenraad Mouton and Marelie Davel},
  title = {Exploring layerwise decision making in DNNs},
  abstract = {While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still poorly understood. Conversely, common decision trees are easily interpretable and theoretically well understood. We show that by encoding the discrete sample activation values of nodes as a binary representation, we are able to extract a decision tree explaining the classification procedure of each layer in a ReLU-activated multilayer perceptron (MLP). We then combine these decision trees with existing feature attribution techniques in order to produce an interpretation of each layer of a model. Finally, we provide an analysis of the generated interpretations, the behaviour of the binary encodings and how these relate to sample groupings created during the training process of the neural network.},
  year = {2022},
  journal = {Communications in Computer and Information Science},
  volume = {1551},
  pages = {140 - 155},
  publisher = {Artificial Intelligence Research (SACAIR 2021)},
  doi = {https://doi.org/10.1007/978-3-030-95070-5_10},
}

We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual

@inbook{478,
  author = {Tezira Wanyana and Mbithe Nzomo and C. Sue Price and Deshen Moodley},
  title = {Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation},
  abstract = {We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual},
  year = {2022},
  journal = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
  pages = {81-92},
  publisher = {SciTePress},
  address = {INSTICC},
  isbn = {978-989-758-566-1},
  doi = {https://doi.org/10.5220/0011046100003188},
}

Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic systems with complex spatial and temporal dependencies such as city traffic networks. In this research, we apply three state-of-the-art ST-GNN architectures, i.e. Graph WaveNet, MTGNN and StemGNN, to predict the closing price of shares listed on the Johannesburg Stock Exchange (JSE) and attempt to capture complex inter-share dependencies. The results show that ST-GNN architectures, specifically Graph WaveNet, produce superior performance relative to an LSTM and are potentially capable of capturing complex intra-share and inter-share temporal dependencies in the JSE. We found that Graph WaveNet outperforms the other approaches over short-term and medium-term horizons. This work is one of the first studies to apply these ST-GNNs to share price prediction.

@article{443,
  author = {Kialan Pillay and Deshen Moodley},
  title = {Exploring Graph Neural Networks for Stock Market Prediction on the JSE},
  abstract = {Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic systems with complex spatial and temporal dependencies such as city traffic networks. In this research, we apply three state-of-the-art ST-GNN architectures, i.e. Graph WaveNet, MTGNN and StemGNN, to predict the closing price of shares listed on the Johannesburg Stock Exchange (JSE) and attempt to capture complex inter-share dependencies. The results show that ST-GNN architectures, specifically Graph WaveNet, produce superior performance relative to an LSTM and are potentially capable of capturing complex intra-share and inter-share temporal dependencies in the JSE. We found that Graph WaveNet outperforms the other approaches over short-term and medium-term horizons. This work is one of the first studies to apply these ST-GNNs to share price prediction.},
  year = {2022},
  journal = {Communications in Computer and Information Science},
  volume = {1551},
  pages = {95-110},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-030-95070-5},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-95070-5_7},
  doi = {10.1007/978-3-030-95070-5_7},
}

This research proposes an architecture and prototype implementation of a knowledge-based system for automating share evaluation and investment decision making on the Johannesburg Stock Exchange (JSE). The knowledge acquired from an analysis of the investment domain for a value investing approach is represented in an ontology. A Bayesian network, developed using the ontology, is used to capture the complex causal relations between different factors that influence the quality and value of individual shares. The system was found to adequately represent the decision-making process of investment professionals and provided superior returns to selected benchmark JSE indices from 2012 to 2018.

@{442,
  author = {Rachel Drake and Deshen Moodley},
  title = {INVEST: Ontology Driven Bayesian Networks for Investment Decision Making on the JSE},
  abstract = {This research proposes an architecture and prototype implementation of a knowledge-based system for automating share evaluation and investment decision making on the Johannesburg Stock Exchange (JSE). The knowledge acquired from an analysis of the investment domain for a value investing approach is represented in an ontology. A Bayesian network, developed using the ontology, is used to capture the complex causal relations between different factors that influence the quality and value of individual shares. The system was found to adequately represent the decision-making process of investment professionals and provided superior returns to selected benchmark JSE indices from 2012 to 2018.},
  year = {2022},
  journal = {Second Southern African Conference for AI Research (SACAIR 2022)},
  pages = {252-273},
  month = {06/12/2021-10/12/2021},
  address = {Online},
  isbn = {978-0-620-94410-6},
  url = {https://protect-za.mimecast.com/s/OFYSCpgo02fL1l9gtDHUkY},
}

Within complex societies, social communities are distinguishable based on social interactions. The interactions can be between members or communities and can range from simple conversations between family members and friends to complex interactions that represent the flow of money, information, or power. In our modern digital society, social media platforms present unique opportunities to study social networks through social network analysis (SNA). Social media platforms are usually representative of a specific user group, and Twitter, a microblogging platform, is characterised by the fast distribution of news and often provocative opinions, as well as social mobilizing, which makes it popular for political interactions. The nature of Twitter generates a valuable SNA data source for investigating political conversations and communities, and in related research, specific archetypal conversation patterns between communities were identified that allow for unique interpretations of conversations about a topic. This paper reports on a study where social network analysis (SNA) was performed on Twitter data about political events in 2021 in South Africa. The purpose was to determine which distinct conversation patterns could be detected in datasets collected, as well as what could be derived from these patterns given the South African political landscape and perceptions. The results indicate that conversations in the South African political landscape are less polarized than expected. Conversations often manifest broadcast patterns from key influencers in addition to tight crowds or community clusters. Tight crowds or community clusters indicate intense conversation across communities that exhibits diverse opinions and perspectives on a topic. The results may be of value for researchers that aim to understand social media conversations within the South African society.

@article{434,
  author = {Aurona Gerber},
  title = {The Detection of Conversation Patterns in South African Political Tweets through Social Network Analysis},
  abstract = {Within complex societies, social communities are distinguishable based on social interactions. The interactions can be between members or communities and can range from simple conversations between family members and friends to complex interactions that represent the flow of money, information, or power. In our modern digital society, social media platforms present unique opportunities to study social networks through social network analysis (SNA). Social media platforms are usually representative of a specific user group, and Twitter, a microblogging platform, is characterised by the fast distribution of news and often provocative opinions, as well as social mobilizing, which makes it popular for political interactions. The nature of Twitter generates a valuable SNA data source for investigating political conversations and communities, and in related research, specific archetypal conversation patterns between communities were identified that allow for unique interpretations of conversations about a topic. This paper reports on a study where social network analysis (SNA) was performed on Twitter data about political events in 2021 in South Africa. The purpose was to determine which distinct conversation patterns could be detected in datasets collected, as well as what could be derived from these patterns given the South African political landscape and perceptions. The results indicate that conversations in the South African political landscape are less polarized than expected. Conversations often manifest broadcast patterns from key influencers in addition to tight crowds or community clusters. Tight crowds or community clusters indicate intense conversation across communities that exhibits diverse opinions and perspectives on a topic. The results may be of value for researchers that aim to understand social media conversations within the South African society.},
  year = {2022},
  journal = {Communications in Computer and Information Science},
  volume = {1551},
  pages = {15-31},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-030-95070-5},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-95070-5_2},
  doi = {10.1007/978-3-030-95070-5_2},
}

Knowledge representation and reasoning (KRR) is an approach to artificial intelligence (AI) in which a system has some information about the world represented formally (a knowledge base), and is able to reason about this information. Defeasible reasoning is a non-classical form of reasoning that enables systems to reason about knowledge bases which contain seemingly contradictory information, thus allowing for exceptions to assertions. Currently, systems which support defeasible entailment for propositional logic are ad hoc, and few and far between, and little to no work has been done on improving the scalability of defeasible reasoning algorithms. We investigate the scalability of defeasible entailment algorithms, and propose optimised versions thereof, as well as present a tool to perform defeasible entailment checks using these algorithms. We also present a knowledge base generation tool which can be used for testing implementations of these algorithms.

@{428,
  author = {Joel Hamilton and Joonsoo Park and Aidan Bailey and Thomas Meyer},
  title = {An Investigation into the Scalability of Defeasible Reasoning Algorithms},
  abstract = {Knowledge representation and reasoning (KRR) is an approach to artificial intelligence (AI) in which a system has some information about the world represented formally (a knowledge base), and is able to reason about this information. Defeasible reasoning is a non-classical form of reasoning that enables systems to reason about knowledge bases which contain seemingly contradictory information, thus allowing for exceptions to assertions. Currently, systems which support defeasible entailment for propositional logic are ad hoc, and few and far between, and little to no work has been done on improving the scalability of defeasible reasoning algorithms. We investigate the scalability of defeasible entailment algorithms, and propose optimised versions thereof, as well as present a tool to perform defeasible entailment checks using these algorithms. We also present a knowledge base generation tool which can be used for testing implementations of these algorithms.},
  year = {2022},
  journal = {Second Southern African Conference for Artificial Intelligence},
  pages = {235-251},
  month = {06/12-10/12},
  publisher = {SACAIR 2021 Organising Committee},
  address = {Online},
  isbn = {978-0-620-94410-6},
  url = {https://2021.sacair.org.za/proceedings/},
}

Belief revision and belief update are approaches to represent and reason with knowledge in artificial intelligence. Previous empirical studies have shown that human reasoning is consistent with non-monotonic logic and postulates of defeasible reasoning, belief revision and belief update. We extended previous work, which tested natural language translations of the postulates of defeasible reasoning, belief revision and belief update with human reasoners via surveys, in three respects. Firstly, we only tested postulates of belief revision and belief update, taking the position that belief change aligns more with human reasoning than non-monotonic defeasible reasoning. Secondly, we decomposed the postulates of revision and update into material implication statements of the form “If x is the case, then y is the case”, each containing a premise and a conclusion, and then translated the premises and conclusions into natural language. Thirdly, we asked human participants to judge each component of the postulate for plausibility. In our analysis, we measured the strength of the association between the premises and the conclusion of each postulate. We used Possibility theory to determine whether the postulates hold with our participants in general. Our results showed that our participants’ reasoning is consistent with postulates of belief revision and belief update when judging the premises and conclusion of the postulate separately.

@{427,
  author = {Clayton Baker and Tommie Meyer},
  title = {Belief Change in Human Reasoning: An Empirical Investigation on MTurk},
  abstract = {Belief revision and belief update are approaches to represent and reason with knowledge in artificial intelligence. Previous empirical studies have shown that human reasoning is consistent with non-monotonic logic and postulates of defeasible reasoning, belief revision and belief update. We extended previous work, which tested natural language translations of the postulates of defeasible reasoning, belief revision and belief update with human reasoners via surveys, in three respects.
Firstly, we only tested postulates of belief revision and belief update, taking the position that belief change aligns more with human reasoning than non-monotonic defeasible reasoning. Secondly, we decomposed the postulates of revision and update into material implication statements of the form “If x is the case, then y is the case”, each containing a premise
and a conclusion, and then translated the premises and conclusions into natural language. Thirdly, we asked human participants to judge each component of the postulate for plausibility. In our analysis, we measured the strength of the association between the premises and the conclusion of each postulate. We used Possibility theory to determine whether the postulates hold with our participants in general. Our results showed that our participants’ reasoning is consistent with postulates of belief
revision and belief update when judging the premises and conclusion of the postulate separately.},
  year = {2022},
  journal = {Second Southern African Conference for AI Research (SACAIR 2022)},
  pages = {218-234},
  month = {06/12/2021-10/12/2021},
  publisher = {SACAIR 2021 Organising Committee},
  address = {Online},
  isbn = {978-0-620-94410-6},
  url = {https://2021.sacair.org.za/proceedings/},
}

Explanation services are a crucial aspect of symbolic reasoning systems but they have not been explored in detail for defeasible formalisms such as KLM. We evaluate prior work on the topic with a focus on KLM propositional logic and find that a form of defeasible explanation initially described for Rational Closure which we term weak justification can be adapted to Relevant and Lexicographic Closure as well as described in terms of intuitive properties derived from the KLM postulates. We also consider how a more general definition of defeasible explanation known as strong explanation applies to KLM and propose an algorithm that enumerates these justifications for Rational Closure.

@inbook{426,
  author = {Lloyd Everett and Emily Morris and Tommie Meyer},
  title = {Explanation for KLM-Style Defeasible Reasoning},
  abstract = {Explanation services are a crucial aspect of symbolic reasoning systems but they have not been explored in detail for defeasible formalisms such as KLM. We evaluate prior work on the topic with a focus on KLM propositional logic and find that a form of defeasible explanation initially described for Rational Closure which we term weak justification can be adapted to Relevant and Lexicographic Closure as well as described in terms of intuitive properties derived from the KLM postulates. We also consider how a more general definition of defeasible explanation known as strong explanation applies to KLM and propose an algorithm that enumerates these justifications for Rational Closure.},
  year = {2022},
  journal = {Artificial Intelligence Research. SACAIR 2021.},
  edition = {1551},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-030-95069-9},
  url = {https://link.springer.com/book/10.1007/978-3-030-95070-5},
  doi = {10.1007/978-3-030-95070-5_13},
}

@{558,
  author = {Louise Leenen and Trishana Ramluckan and Brett van Niekerk},
  title = {Impact of AI Regulations on Cybersecurity Practitioners},
  abstract = {},
  year = {2021},
  journal = {Proceedings of the 20th European Conference on Cyber Warfare and Security (ECCWS 2021)},
  volume = {20},
  month = {24-25 June 2021},
  publisher = {Academic Conferences International},
  address = {Chester, United Kingdom},
}

Ethical clearances are mandatory for most research work involving or impacting humans, animals or the environment. The approvals ensure that research is conducted in an acceptable manner, do not affect other people, animals or the environment negatively and is done within expected boundaries. Though important, it takes a long time to obtain Ethical Clearances approvals; inevitably slowing down research work(s), demoralizing researcher(s) and rendering the research obsolete before commencement. Major causes of delay in Ethical Clearance approval processes, especially in Universities, have been attributed to the manual paper-based approach in use and the lack of effective tracking and feedback processes. In a bid to address these issues and speed up Ethical Clearance approvals, this work proposes an Ethical Clearance workflow system. The main functions of the system were developed, tested by stakeholders and was found acceptable and useful.

@{555,
  author = {Wanga Mbabe and Olasupo Ajayi and Antoine Bagula and Louise Leenen and Natasha Schoeman},
  title = {A Workflow System for Managing Ethical Clearance in Research Work},
  abstract = {Ethical clearances are mandatory for most research work involving or impacting humans, animals or the environment. The approvals ensure that research is conducted in an acceptable manner, do not affect other people, animals or the environment negatively and is done within expected boundaries. Though important, it takes a long time to obtain Ethical Clearances approvals; inevitably slowing down research work(s), demoralizing researcher(s) and rendering the research obsolete before commencement. Major causes of delay in Ethical Clearance approval processes, especially in Universities, have been attributed to the manual paper-based approach in use and the lack of effective tracking and feedback processes. In a bid to address these issues and speed up Ethical Clearance approvals, this work proposes an Ethical Clearance workflow system. The main functions of the system were developed, tested by stakeholders and was found acceptable and useful.},
  year = {2021},
  journal = {2021 IST-Africa Conference (IST-Africa)},
  pages = {1-9},
  month = {10-14 May 2021},
  publisher = {IEEE},
}