A comparison of implementation platforms for the visualisation of animal family trees
- Authors: Kanotangudza, Priviledge
- Date: 2024-04
- Subjects: Business intelligence -- Computer programs , Human-computer interaction , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64105 , vital:73653
- Description: Genealogy is the study of family history. Family trees are used to show ancestry and visualise family history. Animal family trees are different from human family trees as animals have more offspring to represent in a family tree visualisation. Auctioneering organisations, such as Boere Korporasie Beperk (BKB), provide livestock auction catalogues containing pictures of the animal on sale, the animal’s family tree and its breeding and selection data. Modern-day farming has become data-driven and livestock farmers use various online devices and platforms to obtain information, such as real-time milk production, animal health monitoring and to manage farming operations. This study investigated and compared two Business Intelligence (BI) platforms namely Microsoft Power BI and Tableau (Salesforce) and the Python programming language used in the implementation of cattle family tree charts. Animal family tree visualisation requirements were identified from analysing data collected from 23 agriculture users and auction attendees who responded to an online questionnaire. The results of an online survey showed that agriculture users preferred an animal family tree that resembled a human one, which is not currently used in livestock auction catalogues. A conference paper was published based on the survey results. The Design Science Research Methodology (DSRM) was used to aid in creating animal family tree charts using Power BI, Tableau and Python. The author compared the visualisation tools against selected criteria, such as learnability, portability interoperability and security. Usability evaluations using eye tracking were conducted with agriculture users in a usability lab to compare the artefacts developed using Power BI and Python. Tableau was discarded during the implementation process as it did not produce the required family tree visualisation The Technology Acceptance Model (TAM) theory, which seeks to predict the acceptance and use of technology based on users' perception of its usefulness and ease of use, was used to guide the research study in evaluating the artefacts. According to TAM, the adoption of the proposed technology to solve the problem of a static animal family tree in livestock auction catalogues was dependent on the agriculture user’s beliefs. This was based upon that the technology would help them make better buying decisions at livestock auctions effortlessly. The other theory used in this study was the Task Technology Fit (TTF). This theory was used mainly to create the task list to be used in the usability test. The results showed that the author of this work and the agriculture users preferred the artefact produced by Power BI. The learnability and development time was shorter and the User Interface (UI) created was more intuitive. The findings of this study indicated that the present auction catalogue could be supplemented using interactive online animal family tree visualisations created using Power BI. This study recommended that livestock auctioneering organisations should, in addition to providing paper catalogues, provide farmers with an online platform to view the family trees of cattle on auction to enhance purchasing decisions. , Thesis (MCom) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Kanotangudza, Priviledge
- Date: 2024-04
- Subjects: Business intelligence -- Computer programs , Human-computer interaction , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64105 , vital:73653
- Description: Genealogy is the study of family history. Family trees are used to show ancestry and visualise family history. Animal family trees are different from human family trees as animals have more offspring to represent in a family tree visualisation. Auctioneering organisations, such as Boere Korporasie Beperk (BKB), provide livestock auction catalogues containing pictures of the animal on sale, the animal’s family tree and its breeding and selection data. Modern-day farming has become data-driven and livestock farmers use various online devices and platforms to obtain information, such as real-time milk production, animal health monitoring and to manage farming operations. This study investigated and compared two Business Intelligence (BI) platforms namely Microsoft Power BI and Tableau (Salesforce) and the Python programming language used in the implementation of cattle family tree charts. Animal family tree visualisation requirements were identified from analysing data collected from 23 agriculture users and auction attendees who responded to an online questionnaire. The results of an online survey showed that agriculture users preferred an animal family tree that resembled a human one, which is not currently used in livestock auction catalogues. A conference paper was published based on the survey results. The Design Science Research Methodology (DSRM) was used to aid in creating animal family tree charts using Power BI, Tableau and Python. The author compared the visualisation tools against selected criteria, such as learnability, portability interoperability and security. Usability evaluations using eye tracking were conducted with agriculture users in a usability lab to compare the artefacts developed using Power BI and Python. Tableau was discarded during the implementation process as it did not produce the required family tree visualisation The Technology Acceptance Model (TAM) theory, which seeks to predict the acceptance and use of technology based on users' perception of its usefulness and ease of use, was used to guide the research study in evaluating the artefacts. According to TAM, the adoption of the proposed technology to solve the problem of a static animal family tree in livestock auction catalogues was dependent on the agriculture user’s beliefs. This was based upon that the technology would help them make better buying decisions at livestock auctions effortlessly. The other theory used in this study was the Task Technology Fit (TTF). This theory was used mainly to create the task list to be used in the usability test. The results showed that the author of this work and the agriculture users preferred the artefact produced by Power BI. The learnability and development time was shorter and the User Interface (UI) created was more intuitive. The findings of this study indicated that the present auction catalogue could be supplemented using interactive online animal family tree visualisations created using Power BI. This study recommended that livestock auctioneering organisations should, in addition to providing paper catalogues, provide farmers with an online platform to view the family trees of cattle on auction to enhance purchasing decisions. , Thesis (MCom) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
A cyber security strategy to mitigate cloud computing risks within the investment management sector in Cape Town
- Authors: Monareng, Glacier Jamela
- Date: 2024-04
- Subjects: Cloud computing , Computer security , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64717 , vital:73866
- Description: Adoption of cloud computing has become a transformative force in modern information technology, revolutionizing how organisations procure, manage, and deliver IT resources as well as IT services. This treatise explores the implementation of cloud computing in the investment management sector. It focuses on potential cloud security risks, broader implications for businesses and IT ecosystems, and subsequently the treatise conceptualises a strategy that may help in responding to these security risks. The study began by surveying the motivations behind cloud adoption in the investment sector, emphasising the potential cost savings, scalability, and flexibility cloud services offer. It then delves into the challenges faced during implementation, including security concerns, data privacy, compliance issues, and the intricacies of transitioning legacy systems to cloud environments. In response to these challenges, the treatise outlines strategies for successful cloud implementation within the investment sector, in Cape Town, South Africa. It highlights the significance of selecting appropriate cloud service models (for example, IaaS, PaaS, or SaaS) and deployment options (for example, public, private, hybrid, or multi-cloud) to align with organisational needs and objectives. The study followed a qualitative research study. In collecting data an open-ended online survey was sent to participants. The participants were from an investment company in Cape Town. The study employed the design science research paradigm with the aim of developing an artefact. The methodology used was the Nelson Mandela University-Design Science Strategy Methodology (NMU-DSSM) In conclusion, this treatise conceptualises a strategy that may help companies investing in cloud computing technologies to mitigate cyber security and cloud risks. It recommends practices that underscore cloud computing's transformative potential while acknowledging its complexity and challenges. The strategy may serve as a valuable resource for IT professionals, decision-makers, and organisations embarking on the cloud journey, offering guidance and perspectives to navigate the complexities and to realise the potential benefits of cloud technology. , Thesis (MPhil) -- Faculty of Engineering, the Built Environment and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Monareng, Glacier Jamela
- Date: 2024-04
- Subjects: Cloud computing , Computer security , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64717 , vital:73866
- Description: Adoption of cloud computing has become a transformative force in modern information technology, revolutionizing how organisations procure, manage, and deliver IT resources as well as IT services. This treatise explores the implementation of cloud computing in the investment management sector. It focuses on potential cloud security risks, broader implications for businesses and IT ecosystems, and subsequently the treatise conceptualises a strategy that may help in responding to these security risks. The study began by surveying the motivations behind cloud adoption in the investment sector, emphasising the potential cost savings, scalability, and flexibility cloud services offer. It then delves into the challenges faced during implementation, including security concerns, data privacy, compliance issues, and the intricacies of transitioning legacy systems to cloud environments. In response to these challenges, the treatise outlines strategies for successful cloud implementation within the investment sector, in Cape Town, South Africa. It highlights the significance of selecting appropriate cloud service models (for example, IaaS, PaaS, or SaaS) and deployment options (for example, public, private, hybrid, or multi-cloud) to align with organisational needs and objectives. The study followed a qualitative research study. In collecting data an open-ended online survey was sent to participants. The participants were from an investment company in Cape Town. The study employed the design science research paradigm with the aim of developing an artefact. The methodology used was the Nelson Mandela University-Design Science Strategy Methodology (NMU-DSSM) In conclusion, this treatise conceptualises a strategy that may help companies investing in cloud computing technologies to mitigate cyber security and cloud risks. It recommends practices that underscore cloud computing's transformative potential while acknowledging its complexity and challenges. The strategy may serve as a valuable resource for IT professionals, decision-makers, and organisations embarking on the cloud journey, offering guidance and perspectives to navigate the complexities and to realise the potential benefits of cloud technology. , Thesis (MPhil) -- Faculty of Engineering, the Built Environment and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
Augmenting encoder-decoder networks for first-order logic formula parsing using attention pointer mechanisms
- Authors: Tissink, Kade
- Date: 2024-04
- Subjects: Translators (Computer programs) , Computational linguistics , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64390 , vital:73692
- Description: Semantic parsing is the task of extracting a structured machine-interpretable representation from natural language utterance. This representation can be used for various applications such as question answering, information extraction, and dialogue systems. However, semantic parsing is a challenging problem that requires dealing with the ambiguity, variability, and complexity of natural language. This dissertation investigates neural parsing of natural language (NL) sentences to first-order logic (FOL) formulas. FOL is a widely used formal language for expressing logical statements and reasoning. FOL formulas can capture the meaning and structure of natural language sentences in a precise and unambiguous way. The problem is initially approached as a sequence-to-sequence mapping task using both LSTM-based and transformer encoder-decoder architectures for character-, subword-, and wordlevel text tokenisation. These models are trained on NL-FOL datasets using supervised learning and evaluated on various metrics such as exact match accuracy, syntactic validity, formula structure accuracy, and predicate/constant similarity. A novel augmented model is then introduced that decomposes the task of neural FOL parsing into four inter-dependent subtasks: template decoding, predicate and constant recognition, predicate set pointing, and object set pointing. The components for the four subtasks are jointly trained using multi-task learning and evaluated using the same metrics from the sequence-tosequence models. The results indicate improved performance over the sequence-to-sequence models and the modular design allows for more interpretability and flexibility. Additionally, to compensate for the scarcity of open-source, labelled NL-FOL datasets, a new benchmark is constructed from publicly accessible data. The data consists of NL sentences paired with corresponding FOL formulas in a standardised notation. The data is split into training, validation, and test sets. The main contributions of this dissertation are: an in-depth literature review covering decades of research presented with a consistent notation, the formation of a complex NL-FOL benchmark that includes algorithmically generated and human-annotated FOL formulas, proposal of a novel transformer encoder-decoder architecture that is shown to successfully train at significant depths, evaluation of twenty sequence-to-sequence models on the task of neural FOL parsing for different text representations and encoder-decoder architectures, the proposal of a novel augmented FOL parsing architecture, and an in-depth analysis of the strengths and weaknesses of these models. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics , 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Tissink, Kade
- Date: 2024-04
- Subjects: Translators (Computer programs) , Computational linguistics , Computer science
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64390 , vital:73692
- Description: Semantic parsing is the task of extracting a structured machine-interpretable representation from natural language utterance. This representation can be used for various applications such as question answering, information extraction, and dialogue systems. However, semantic parsing is a challenging problem that requires dealing with the ambiguity, variability, and complexity of natural language. This dissertation investigates neural parsing of natural language (NL) sentences to first-order logic (FOL) formulas. FOL is a widely used formal language for expressing logical statements and reasoning. FOL formulas can capture the meaning and structure of natural language sentences in a precise and unambiguous way. The problem is initially approached as a sequence-to-sequence mapping task using both LSTM-based and transformer encoder-decoder architectures for character-, subword-, and wordlevel text tokenisation. These models are trained on NL-FOL datasets using supervised learning and evaluated on various metrics such as exact match accuracy, syntactic validity, formula structure accuracy, and predicate/constant similarity. A novel augmented model is then introduced that decomposes the task of neural FOL parsing into four inter-dependent subtasks: template decoding, predicate and constant recognition, predicate set pointing, and object set pointing. The components for the four subtasks are jointly trained using multi-task learning and evaluated using the same metrics from the sequence-tosequence models. The results indicate improved performance over the sequence-to-sequence models and the modular design allows for more interpretability and flexibility. Additionally, to compensate for the scarcity of open-source, labelled NL-FOL datasets, a new benchmark is constructed from publicly accessible data. The data consists of NL sentences paired with corresponding FOL formulas in a standardised notation. The data is split into training, validation, and test sets. The main contributions of this dissertation are: an in-depth literature review covering decades of research presented with a consistent notation, the formation of a complex NL-FOL benchmark that includes algorithmically generated and human-annotated FOL formulas, proposal of a novel transformer encoder-decoder architecture that is shown to successfully train at significant depths, evaluation of twenty sequence-to-sequence models on the task of neural FOL parsing for different text representations and encoder-decoder architectures, the proposal of a novel augmented FOL parsing architecture, and an in-depth analysis of the strengths and weaknesses of these models. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics , 2024
- Full Text:
- Date Issued: 2024-04
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