Research Publications

The entertainment industry world-wide provides lucrative business opportunities, and within South Africa, this market is still underdeveloped, especially within rural communities. Within this context, the FP7 MOSAIC 2B project aimed to empower micro-entrepreneurs by providing them with a Cinema-in-a-Backpack, which is a set of equipment allowing them to screen movies in rural areas within South Africa. However, to ensure compliance, these micro-entrepreneurs had to acquire the necessary licenses, which proved to be challenging given the regulatory environment as well as the different stakeholders involved. Using a systematic literature review as well as the MOSAIC 2B project as a case study, this paper reports on an investigation on the process and procedure necessary for a micro-entrepreneur to acquire licenses in order to screen multimedia content within rural South Africa. The paper provides an overview of the regulatory landscape as well as the nature of the film industry in South Africa. The main contribution of the investigation is a process model that could be used by a microbusiness to understand the requirements and process to follow when acquiring a legal licence for the screening of multi-media content within South Africa.

@{457,
  author = {Khumbo Masilela and Aurona Gerber and Alta van der Merwe},
  title = {Challenges and Opportunities Faced by Micro-Entrepreneurs to Legally Screen Movies in Rural South Africa},
  abstract = {The entertainment industry world-wide provides lucrative business opportunities, and within South Africa, this market is still underdeveloped, especially within rural communities. Within this context, the FP7 MOSAIC 2B project aimed to empower micro-entrepreneurs by providing them with a Cinema-in-a-Backpack, which is a set of equipment allowing them to screen movies in rural areas within South Africa. However, to ensure compliance, these micro-entrepreneurs had to acquire the necessary licenses, which proved to be challenging given the regulatory environment as well as the different stakeholders involved. Using a systematic literature review as well as the MOSAIC 2B project as a case study, this paper reports on an investigation on the process and procedure necessary for a micro-entrepreneur to acquire licenses in order to screen multimedia content within rural South Africa. The paper provides an overview of the regulatory landscape as well as the nature of the film industry in South Africa. The main contribution of the investigation is a process model that could be used by a microbusiness to understand the requirements and process to follow when acquiring a legal licence for the screening of multi-media content within South Africa.},
  year = {2016},
  journal = {IST Africa},
  month = {11/05-13/05},
  address = {Durban, South Africa},
  isbn = {978-1-905824-55-7},
}

The concept of psychological ownership where someone can identify something as their own is part of every person's life. Psychological ownership is important because someone that own something take responsibility for its wellbeing. Currently there is no mechanism to measure psychological ownership of equipment within the context of small entrepreneurial business in South Africa. In this MOSAIC-2B project case study cinema-in-a-backpack equipment was given to entrepreneurs to empower them to start their own successful businesses screening multi-media content in rural South Africa. This research aimed to identify whether or not individuals developed psychological ownership towards the cinema equipment and what the possible effects of having psychological ownership could be. This study resulted in the development of a measuring tool for psychological ownership in the context of small entrepreneurial businesses in South Africa. Psychological ownership can give valuable insight into how entrepreneurs run their businesses in South Africa and this study also established that individuals that perceive themselves as successful has a higher indication of psychological ownership.

@{456,
  author = {Toinette Barnard and Alta van der Merwe and Aurona Gerber},
  title = {Psychological Ownership: A Human Factor to Consider for the Success of Technology Entrepreneurial Activities},
  abstract = {The concept of psychological ownership where someone can identify something as their own is part of every person's life. Psychological ownership is important because someone that own something take responsibility for its wellbeing. Currently there is no mechanism to measure psychological ownership of equipment within the context of small entrepreneurial business in South Africa. In this MOSAIC-2B project case study cinema-in-a-backpack equipment was given to entrepreneurs to empower them to start their own successful businesses screening multi-media content in rural South Africa. This research aimed to identify whether or not individuals developed psychological ownership towards the cinema equipment and what the possible effects of having psychological ownership could be. This study resulted in the development of a measuring tool for psychological ownership in the context of small entrepreneurial businesses in South Africa. Psychological ownership can give valuable insight into how entrepreneurs run their businesses in South Africa and this study also established that individuals that perceive themselves as successful has a higher indication of psychological ownership.},
  year = {2016},
  journal = {IEEE International Conference on Systems, Man and Cybernetics (SMC)},
  pages = {4646-4651},
  month = {09/10-12/10},
  publisher = {IEEE},
  address = {Budapest, Hungary},
  isbn = {978-1-5090-1898-7},
  doi = {10.1109/SMC.2016.7844964},
}

This paper investigates how Data as a disruptive technology could be integrated into TOGAF. Given the recent attention of Big Data and Data Science as disruptors, this paper investigates what the impact on the enterprise could be and how Enterprise Architecture (EA) should accommodate data to enable data-driven EA. There is no model currently available that investigates how Big Data can be incorporated into data-driven EA solutions. This study specifically focuses on how the TOGAF ADM could support a data-driven enterprise. Through document analysis and a systematic literature review, a specific adaption of the TOGAF ADM is proposed that indicates the influence that Data and Big Data has on each phase within the ADM.

@{455,
  author = {},
  title = {Data-driven Enterprise Architecture and the TOGAF ADM Phases},
  abstract = {This paper investigates how Data as a disruptive technology could be integrated into TOGAF. Given the recent attention of Big Data and Data Science as disruptors, this paper investigates what the impact on the enterprise could be and how Enterprise Architecture (EA) should accommodate data to enable data-driven EA. There is no model currently available that investigates how Big Data can be incorporated into data-driven EA solutions. This study specifically focuses on how the TOGAF ADM could support a data-driven enterprise. Through document analysis and a systematic literature review, a specific adaption of the TOGAF ADM is proposed that indicates the influence that Data and Big Data has on each phase within the ADM.},
  year = {2016},
  journal = {IEEE International Conference on Systems, Man and Cybernetics (SMC)},
  month = {09/10-12/10},
  address = {Budapest, Hungary},
  isbn = {978-1-5090-1897-0},
}

Fruit fly control and management in Africa has been the topic of several scientific investigations resulting in diverse sources of knowledge on the topic. Despite the existence of this knowledge, frequently it is not readily accessible to all targeted beneficiaries; this can be due to, for example, the remote locations of farms and the complexity of the knowledge. However, recent technological developments such as web technologies and networking allow for the engagement and participation of stakeholder groups in the acquisition and dissemination of knowledge and these technologies can also be applied to fruit fly knowledge. In order to facilitate this stakeholder participation in fruit fly knowledge sharing, the relevant domain knowledge needs to be available in a format that can support stakeholder engagement, preferably through the Web. Fruit fly knowledge has not been modelled in this manner and this paper reports on an investigation to model and capture the relevant domain knowledge using ontologies. The objective of this work is thus the development of the domain ontology and its evaluation using a prototype stakeholder participation system for fruit fly control and management that was capable of utilising the ontology. We describe our findings on the use of ontology technologies for representation of fruit fly knowledge, the fruit fly ontology developed, as well as a prototype Web-based system that uses the ontology as a source of knowledge.

@inbook{451,
  author = {Caroline Kiptoo and Aurona Gerber and Alta van der Merwe},
  title = {The Ontological Modelling of Fruit Fly Control and Management Knowledge},
  abstract = {Fruit fly control and management in Africa has been the topic of several scientific investigations resulting in diverse sources of knowledge on the topic. Despite the existence of this knowledge, frequently it is not readily accessible to all targeted beneficiaries; this can be due to, for example, the remote locations of farms and the complexity of the knowledge. However, recent technological developments such as web technologies and networking allow for the engagement and participation of stakeholder groups in the acquisition and dissemination of knowledge and these technologies can also be applied to fruit fly knowledge. In order to facilitate this stakeholder participation in fruit fly knowledge sharing, the relevant domain knowledge needs to be available in a format that can support stakeholder engagement, preferably through the Web. Fruit fly knowledge has not been modelled in this manner and this paper reports on an investigation to model and capture the relevant domain knowledge using ontologies. The objective of this work is thus the development of the domain ontology and its evaluation using a prototype stakeholder participation system for fruit fly control and management that was capable of utilising the ontology. We describe our findings on the use of ontology technologies for representation of fruit fly knowledge, the fruit fly ontology developed, as well as a prototype Web-based system that uses the ontology as a source of knowledge.},
  year = {2016},
  journal = {Fruit Fly Research and Development in Africa - Towards a Sustainable Management Strategy to Improve Horticulture},
  pages = {235-249},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-319-43226-7},
  url = {https://link.springer.com/book/10.1007/978-3-319-43226-7},
  doi = {10.1007/978-3-319-43226-7_11},
}

The Web Ontology Language OWL is a prominent ontology language for specifying ontologies. Although OWL ontologies are well-used for representing and reasoning about knowledge in various domains, they are sparsely studied for visual language specification. The work in this paper, therefore, explores OWL for visual language specification by specifying the concrete syntax of selected UML class diagram notations in an ontology. The selected diagram notations are specified as spatial configurations of primitive elements and qualitative base spatial relationships of Region Connection Calculus-8 (RCC-8). Furthermore, the automated reasoning features of ontology reasoners are investigated to verify the completeness and the correctness of the specification. The verification results indicate that the given specification needs to be revised to support applications to draw the selected notations. The value of such a specification in supporting a semantic diagram interpretation application is demonstrated using the automated instance classification feature of ontology reasoners.

@article{448,
  author = {Anitta Thomas and Aurona Gerber and Alta van der Merwe},
  title = {An Investigation into OWL for Concrete Syntax Specification using UML Notations},
  abstract = {The Web Ontology Language OWL is a prominent ontology language for specifying ontologies. Although OWL ontologies are well-used for representing and reasoning about knowledge in various domains, they are sparsely studied for visual language specification. The work in this paper, therefore, explores OWL for visual language specification by specifying the concrete syntax of selected UML class diagram notations in an ontology. The selected diagram notations are specified as spatial configurations of primitive elements and qualitative base spatial relationships of Region Connection Calculus-8 (RCC-8). Furthermore, the automated reasoning features of ontology reasoners are investigated to verify the completeness and the correctness of the specification. The verification results indicate that the given specification needs to be revised to support applications to draw the selected notations. The value of such a specification in supporting a semantic diagram interpretation application is demonstrated using the automated instance classification feature of ontology reasoners.},
  year = {2016},
  journal = {Lecture Notes in Computer Science},
  volume = {9781},
  pages = {197-211},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-319-42333-3},
  url = {https://link.springer.com/chapter/10.1007/978-3-319-42333-3_15},
  doi = {10.1007/978-3-319-42333-3_15},
}

This article proposes a conceptual architecture for citizen-expert knowledge exchange in biodiversity management. Expert services, such as taxonomic identification, are required in many biodiversity management activities, yet these services remain inaccessible to poor communities, such as small-scale farmers. The aim of this research was to combine ontology and crowdsourcing technologies to provide taxonomic services to such communities. The study used a design science research (DSR) approach to develop the conceptual architecture. The DSR approach generates knowledge through building and evaluation of novel artefacts. The research instantiated the architecture through the development of a platform for experts and farmers to share knowledge on fruit flies. The platform is intended to support rural fruit farmers in Kenya with control and management of fruit flies. Expert knowledge about fruit flies is captured in an ontology that is integrated into the platform. The non-expert citizen participation includes harnessing crowdsourcing technologies to assist with organism identification. An evaluation of the architecture was done through an experiment of fruit fly identification using the platform. The results showed that the crowds, supported by an ontology of expert knowledge, could identify most samples to species level and in some cases to sub-family level. The conceptual architecture may guide and enable creation of citizen-expert knowledge exchange applications, which may alleviate the taxonomic impediment, as well as allow poor citizens access to expert knowledge. Such a conceptual architecture may also enable the implementation of systems that allow non-experts to participate in sharing of knowledge, thus providing opportunity for the evolution of comprehensive biodiversity knowledge systems.

@article{447,
  author = {Caroline Kiptoo and Aurona Gerber and Alta van der Merwe},
  title = {Towards citizen-expert knowledge exchange for biodiversity informatics: A conceptual architecture},
  abstract = {This article proposes a conceptual architecture for citizen-expert knowledge exchange in biodiversity management. Expert services, such as taxonomic identification, are required in many biodiversity management activities, yet these services remain inaccessible to poor communities, such as small-scale farmers. The aim of this research was to combine ontology and crowdsourcing technologies to provide taxonomic services to such communities. The study used a design science research (DSR) approach to develop the conceptual architecture. The DSR approach generates knowledge through building and evaluation of novel artefacts. The research instantiated the architecture through the development of a platform for experts and farmers to share knowledge on fruit flies. The platform is intended to support rural fruit farmers in Kenya with control and management of fruit flies. Expert knowledge about fruit flies is captured in an ontology that is integrated into the platform. The non-expert citizen participation includes harnessing crowdsourcing technologies to assist with organism identification. An evaluation of the architecture was done through an experiment of fruit fly identification using the platform. The results showed that the crowds, supported by an ontology of expert knowledge, could identify most samples to species level and in some cases to sub-family level. The conceptual architecture may guide and enable creation of citizen-expert knowledge exchange applications, which may alleviate the taxonomic impediment, as well as allow poor citizens access to expert knowledge. Such a conceptual architecture may also enable the implementation of systems that allow non-experts to participate in sharing of knowledge, thus providing opportunity for the evolution of comprehensive biodiversity knowledge systems.},
  year = {2016},
  journal = {The African Journal of Information and Communication},
  volume = {2016},
  pages = {33-54},
  issue = {18},
  url = {https://journals.co.za/doi/abs/10.10520/EJC-7e06bad44},
  doi = {10539/21758},
}

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioural ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.

@article{446,
  author = {Willem Coetzer and Deshen Moodley and Aurona Gerber},
  title = {Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data.},
  abstract = {Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioural ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.},
  year = {2016},
  journal = {PLOS ONE},
  volume = {11},
  issue = {11},
  isbn = {e0166559},
  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166559},
  doi = {10.1371/journal.pone.0166559},
}

Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.

@{361,
  author = {Blessing Ojeme and Audrey Mbogho and Tommie Meyer},
  title = {Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders},
  abstract = {Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.},
  year = {2016},
  journal = {15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  month = {18/12 - 20/12},
  publisher = {IEEE},
  doi = {10.1109/ICMLA.2016.0105},
}

We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the field of formal ontologies where coherence is interpreted to mean that a concept name has to be satisfiable. In the propositional case we consider here, this translates to a propositional formula being satisfiable. We define belief change operators in a framework of nonmonotonic preferential reasoning. We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.

@{360,
  author = {Giovanni Casini and Tommie Meyer},
  title = {Using Defeasible Information to Obtain Coherence},
  abstract = {We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the field of formal ontologies where coherence is interpreted to mean that a concept name has to be satisfiable. In the propositional case we consider here, this translates to a propositional formula being satisfiable. We define belief change operators in a framework of nonmonotonic preferential reasoning. We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.},
  year = {2016},
  journal = {Fifteenth International Conference on  Principles of Knowledge Representation and Reasoning (KR)},
  pages = {537-540},
  month = {25/04 - 29/04},
  publisher = {AAAI Press},
  doi = {https://dl.acm.org/doi/10.5555/3032027.3032097},
}

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy methods are reasonable, yet yield different results.

@{359,
  author = {Gavin Rens and Giovanni Casini and Tommie Meyer},
  title = {On Revision of Partially Specified Convex Probabilistic Belief Bases},
  abstract = {We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy methods are reasonable, yet yield different results.},
  year = {2016},
  journal = {European Conference on Artificial Intelligence (ECAI)},
  pages = {921-929},
  month = {29/08 - 2/09},
  publisher = {IO Press},
  url = {https://www.researchgate.net/publication/307577667_On_Revision_of_Partially_Specified_Convex_Probabilistic_Belief_Bases},
}

This paper describes the continuing development of a pronunciation resource for speech synthesis of Afrikaans by augmenting an existing pronunciation dictionary to include syllable boundaries and stress. Furthermore, different approaches for grapheme to phoneme conversion and syllabification derived from the dictionary are evaluated. Cross-validation experiments suggest that joint sequence models are effective at directly modelling pronunciations including syllable boundaries. Finally, some informal observations and demonstrations are presented regarding the integration of this work into a typical text-to-speech system.

@{285,
  author = {Daniel Van Niekerk},
  title = {Syllabification for Afrikaans speech synthesis},
  abstract = {This paper describes the continuing development of a pronunciation resource for speech synthesis of Afrikaans by augmenting an existing pronunciation dictionary to include syllable boundaries and stress. Furthermore, different approaches for grapheme to phoneme conversion and syllabification derived from the dictionary are evaluated. Cross-validation experiments suggest that joint sequence models are effective at directly modelling pronunciations including syllable boundaries. Finally, some informal observations and demonstrations are presented regarding the integration of this work into a typical text-to-speech system.},
  year = {2016},
  journal = {Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)},
  pages = {31-36},
  address = {Stellenbosch, South Africa},
  isbn = {978-1-5090-3335-5},
  doi = {10.1109/RoboMech.2016.7813143},
}

We investigate modeling strategies for English code-switched words as found in a Swahili spoken term detection system. Code switching, where speakers switch language in a conversation, occurs frequently in multilingual environments, and typically deteriorates STD performance. Analysis is performed in the context of the IARPA Babel program which focuses on rapid STD system development for under-resourced languages. Our results show that approaches that specifically target the modeling of code-switched words, significantly improve the detection performance of these words.

@article{271,
  author = {Neil Kleynhans and William Hartman and Daniel Van Niekerk and Charl Van Heerden and Richard Schwartz and Stavros Tsakalidis and Marelie Davel},
  title = {Code-switched English Pronunciation Modeling for Swahili Spoken Term Detection},
  abstract = {We investigate modeling strategies for English code-switched words as found in a Swahili spoken term detection system. Code
switching, where speakers switch language in a conversation, occurs frequently in multilingual environments, and typically deteriorates STD performance. Analysis is performed in the context of the IARPA Babel program which focuses on rapid STD
system development for under-resourced languages. Our results show that approaches that specifically target the modeling of
code-switched words, significantly improve the detection performance of these words.},
  year = {2016},
  journal = {Procedia Computer Science},
  volume = {81},
  pages = {128-135},
  publisher = {Elsevier B.V.},
  address = {Yogyakarta, Indonesia},
  isbn = {1877-0509},
  doi = {10.1016/j.procs.2016.04.040},
}

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

@article{229,
  author = {Louise Leenen and Tommie Meyer},
  title = {Semantic Technologies and Big Data: Analytics for Cyber Defence},
  abstract = {The Governments, military forces and other organisations responsible for cybersecurity deal with
vast amounts of data that has to be understood in order to lead to intelligent decision making. Due
to the vast amounts of information pertinent to cybersecurity, automation is required for processing
and decision making, specifically to present advance warning of possible threats. The ability to detect
patterns in vast data sets, and being able to understanding the significance of detected patterns are
essential in the cyber defence domain. Big data technologies supported by semantic technologies
can improve cybersecurity, and thus cyber defence by providing support for the processing and
understanding of the huge amounts of information in the cyber environment. The term big data
analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and
other intelligent processing techniques applied to large data sets that contain different data types. The
purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies
is a knowledge representation paradigm where the meaning of data is encoded separately from the
data itself. The use of semantic technologies such as logic-based systems to support decision making
is becoming increasingly popular. However, most automated systems are currently based on syntactic
rules. These rules are generally not sophisticated enough to deal with the complexity of decisions
required to be made. The incorporation of semantic information allows for increased understanding and
sophistication in cyber defence systems. This paper argues that both big data analytics and semantic
technologies are necessary to provide counter measures against cyber threats. An overview of the
use of semantic technologies and big data technologies in cyber defence is provided, and important
areas for future research in the combined domains are discussed.},
  year = {2016},
  journal = {International Journal of Cyber Warfare and Terrorism},
  volume = {6},
  issue = {3},
}

No Abstract

@phdthesis{207,
  author = {L. van Niekerk and Bruce Watson},
  title = {The Development and Evaluation of an Electronic Serious Game Aimed at the Education of Core Programming Skills},
  abstract = {No Abstract},
  year = {2016},
  volume = {MA},
  url = {http://hdl.handle.net/10019.1/100119},
}

Research has shown that shape features can be used in the process of object recognition with promising results. However, due to a wide variety of shape descriptors, selecting the right one remains a difficult task. This paper presents a new shape recognition feature: Convexity Moment of Polygons. The Convexity Moments of Polygons is derived from the Convexity measure of polygons. A series of experimentations based on FLAVIA images dataset was performed to demonstrate the accuracy of the proposed feature compared to the Convexity measure of polygons in the field of leaf classification. A classification rate of 92% was obtained with the Convexity Moment of Polygons, 80% with the convexity Measure of Polygons using the Radial Basis function neural networks classifier (RBF).

@{161,
  author = {J.R. Kala and S. Viriri and Deshen Moodley},
  title = {Leaf Classification Using Convexity Moments of Polygons},
  abstract = {Research has shown that shape features can be used in the process of object recognition with promising results. However, due to a wide variety of shape descriptors, selecting the right one remains a difficult task. This paper presents a new shape recognition feature: Convexity Moment of Polygons. The Convexity Moments of Polygons is derived from the Convexity measure of polygons. A series of experimentations based on FLAVIA images dataset was performed to demonstrate the accuracy of the proposed feature compared to the Convexity measure of polygons in the field of leaf classification. A classification rate of 92% was obtained with the Convexity Moment of Polygons, 80% with the convexity Measure of Polygons using the Radial Basis function neural networks classifier (RBF).},
  year = {2016},
  journal = {International Symposium on Visual Computing},
  pages = {300-339},
  month = {14/12-16/12},
  isbn = {978-3-319-50832-0},
}

The 3D computer gaming industry is constantly exploring new avenues for creating immersive and engaging environments. One avenue being explored is autonomous control of the behaviour of non-player characters (NPC). This paper reviews and compares existing artificial intelligence (AI) techniques for controlling the behaviour of non-human characters in Multiplayer Online Battle Arena (MOBA) game environments. Two techniques, the fuzzy state machine (FuSM) and the emotional behaviour tree (EBT), were reviewed and compared. In addition, an alternate and simple mechanism to incorporate emotion in a behaviour tree is proposed and tested. Initial tests of the mechanism show that it is a viable and promising mechanism for effectively tracking the emotional state of an NPC and for incorporating emotion in NPC decision making.

@{157,
  author = {Michael Waltham and Deshen Moodley},
  title = {An Analysis of Artificial Intelligence Techniques in Multiplayer Online Battle Arena Game Environments},
  abstract = {The 3D computer gaming industry is constantly exploring new avenues for creating immersive and engaging environments. One avenue being explored is autonomous control of the behaviour of non-player characters (NPC). This paper reviews and compares existing artificial intelligence (AI) techniques for controlling the behaviour of non-human characters in Multiplayer Online Battle Arena (MOBA) game environments. Two techniques, the fuzzy state machine (FuSM) and the emotional behaviour tree (EBT), were reviewed and compared. In addition, an alternate and simple mechanism to incorporate emotion in a behaviour tree is proposed and tested. Initial tests of the mechanism show that it is a viable and promising mechanism for effectively tracking the emotional state of an NPC and for incorporating emotion in NPC decision making.},
  year = {2016},
  journal = {Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT 2016)},
  pages = {45},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {Johannesburg},
  isbn = {978-1-4503-4805-8},
  doi = {http://dx.doi.org/10.1145/2987491.2987513},
}

Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.

@{156,
  author = {A. Clark and Deshen Moodley},
  title = {A System for a Hand Gesture-Manipulated Virtual Reality Environment},
  abstract = {Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.},
  year = {2016},
  journal = {Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT 2016)},
  pages = {10},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {Johannesburg},
  isbn = {978-1-4503-4805-8},
  doi = {http://dx.doi.org/10.1145/2987491.2987511},
}

We investigate the impact of recent advances in speech recognition techniques for under-resourced languages. Specifically, we review earlier results published on the Lwazi ASR corpus of South African languages, and experiment with additional acoustic modeling approaches. We demonstrate large gains by applying current state-of-the-art techniques, even if the data itself is neither extended nor improved. We analyze the various performance improvements observed, report on comparative performance per technique – across all eleven languages in the corpus – and discuss the implications of our findings for under-resourced languages in general.

@{153,
  author = {Charl Van Heerden and Neil Kleynhans and Marelie Davel},
  title = {Improving the Lwazi ASR baseline},
  abstract = {We investigate the impact of recent advances in speech recognition techniques for under-resourced languages. Specifically, we review earlier results published on the Lwazi ASR corpus of South African languages, and experiment with additional acoustic modeling approaches. We demonstrate large gains by applying current state-of-the-art techniques, even if the data itself is neither extended nor improved. We analyze the various performance improvements observed, report on comparative performance per technique – across all eleven languages in the corpus – and discuss the implications of our findings for under-resourced languages in general.},
  year = {2016},
  journal = {Interspeech},
  pages = {3529-3538},
  month = {08/09-12/09},
  address = {San Francisco, USA},
  doi = {http://dx.doi.org/10.21437/Interspeech.2016-1412},
}

No Abstract

@misc{150,
  author = {James Lapalme and Aurona Gerber and Alta van der Merwe and John Zachman and Marne de Vries and Knut Hinkelmann},
  title = {Exploring the future of enterprise architecture: A Zachman perspective.},
  abstract = {No Abstract},
  year = {2016},
  url = {http://www.sciencedirect.com/science/journal/01663615/79},
}

No Abstract

@misc{149,
  author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
  title = {Armstrong Relations for Ontology Design and Evaluation},
  abstract = {No Abstract},
  year = {2016},
  url = {http://ceur-ws.org/Vol-1577/},
}