Research Publications

In this paper we present what we believe to be the first systematic approach for extending the framework for defeasible entailment first presented by Kraus, Lehmann, and Magidor—the so-called KLM approach. Drawing on the properties for KLM, we first propose a class of basic defeasible entailment relations. We characterise this basic framework in three ways: (i) semantically, (ii) in terms of a class of properties, and (iii) in terms of ranks on statements in a knowlege base. We also provide an algorithm for computing the basic framework. These results are proved through various representation results. We then refine this framework by defining the class of rational defeasible entailment relations. This refined framework is also characterised in thee ways: semantically, in terms of a class of properties, and in terms of ranks on statements. We also provide an algorithm for computing the refined framework. Again, these results are proved through various representation results. We argue that the class of rational defeasible entailment relations—a strengthening of basic defeasible entailment which is itself a strengthening of the original KLM proposal—is worthy of the term rational in the sense that all of them can be viewed as appropriate forms of defeasible entailment. We show that the two well-known forms of defeasible entailment, rational closure and lexicographic closure, fall within our rational defeasible framework. We show that rational closure is the most conservative of the defeasible entailment relations within the framework (with respect to subset inclusion), but that there are forms of defeasible entailment within our framework that are more “adventurous” than lexicographic closure.

@{200,
  author = {Giovanni Casini and Tommie Meyer and Ivan Varzinczak},
  title = {Defeasible Entailment: from Rational Closure to Lexicographic Closure and Beyond},
  abstract = {In this paper we present what we believe to be the first systematic approach for extending the framework for defeasible entailment first presented by Kraus, Lehmann, and Magidor—the so-called KLM approach. Drawing on the properties for KLM, we first propose a class of basic defeasible entailment relations. We characterise this basic framework in three ways: (i) semantically, (ii) in terms of a class of properties, and (iii) in terms of ranks on statements in a knowlege base. We also provide an algorithm for computing the basic framework. These results are proved through various representation results. We then refine this framework by defining the class of rational defeasible entailment relations. This refined framework is also characterised in thee ways: semantically, in terms of a class of properties, and in terms of ranks on statements. We also provide an algorithm for computing the refined framework. Again, these results are proved through various representation results.

We argue that the class of rational defeasible entailment relations—a strengthening of basic defeasible entailment which is itself a strengthening of the original KLM proposal—is worthy of the term rational in the sense that all of them can be viewed as appropriate forms of defeasible entailment. We show that the two well-known forms of defeasible entailment, rational closure and lexicographic closure, fall within our rational defeasible framework. We show that rational closure is the most conservative of the defeasible entailment relations within the framework (with respect to subset inclusion), but that there are forms of defeasible entailment within our framework that are more “adventurous” than lexicographic closure.},
  year = {2018},
  journal = {7th International Workshop on Non-Monotonic Reasoning (NMR 2018)},
  pages = {109-118},
  month = {27/10-29/10},
  url = {http://orbilu.uni.lu/bitstream/10993/37393/1/NMR2018Paper.pdf},
}

Modern regular expression matching software features many extensions, some general while some are very narrowly specied. Here we consider the generalization of adding a class of operators which can be described by, e.g. nite-state transducers. Combined with backreferences they enable new classes of languages to be matched. The addition of nite-state transducers is shown to make membership testing undecidable. Following this result, we study the complexity of membership testing for various restricted cases of the model.

@{199,
  author = {Martin Berglund and F. Drewes and Brink van der Merwe},
  title = {On Regular Expressions with Backreferences and Transducers},
  abstract = {Modern regular expression matching software features many extensions, some general while some are very narrowly specied. Here we consider the generalization of adding a class of operators which can be described by, e.g. nite-state transducers. Combined with backreferences they enable new classes of languages to be matched. The addition of nite-state transducers is shown to make membership testing undecidable. Following this result, we study the complexity of membership testing for various restricted cases of the model.},
  year = {2018},
  journal = {10th Workshop on Non-Classical Models of Automata and Applications (NCMA 2018)},
  pages = {1-19},
  month = {21/08-22/08},
}

We propose a new framework for reasoning about the reputation of multiple agents, based on the partially observable Markov decision process (POMDP). It is general enough for the specification of a variety of stochastic multi-agent system (MAS) domains involving the impact of agents on each other’s reputations. Assuming that an agent must maintain a good enough reputation to survive in the system, a method for an agent to select optimal actions is developed.

@{198,
  author = {Gavin Rens and Tommie Meyer and A. Nayak},
  title = {Maximizing Expected Impact in an Agent Reputation Network},
  abstract = {We propose a new framework for reasoning about the reputation of multiple agents, based on the partially observable Markov decision process (POMDP). It is general enough for the specification of a variety of stochastic multi-agent system (MAS) domains involving the impact of agents on each other’s reputations. Assuming that an agent must maintain a good enough reputation to survive in the system, a method for an agent to select optimal actions is developed.},
  year = {2018},
  journal = {41st German Conference on AI, Berlin, Germany, September 24–28, 2018},
  pages = {99-106},
  month = {24/09-28/09},
  publisher = {Springer},
  isbn = {978-3-030-00110-0},
  url = {https://www.springer.com/us/book/9783030001100},
}

Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we investigate the integration of the two formalisms by studying belief change for a (preferential) non-monotonic framework. We show that the standard AGM approach to belief change can be transferred to a preferential non-monotonic framework in the sense that change operations can be defined on conditional knowledge bases. We take as a point of departure the results presented by Casini and Meyer (2017), and we develop and extend such results with characterisations based on semantics and entrenchment relations, showing how some of the constructions defined for propositional logic can be lifted to our preferential non-monotonic framework.

@{197,
  author = {Giovanni Casini and F. Eduardo and Tommie Meyer and Ivan Varzinczak},
  title = {A Semantic Perspective on Belief Change in a Preferential Non-Monotonic Framework},
  abstract = {Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we investigate the integration of the two formalisms by studying belief change for a (preferential) non-monotonic framework. We show that the standard AGM approach to belief change can be transferred to a preferential non-monotonic framework in the sense that change operations can be defined on conditional knowledge bases. We take as a point of departure the results presented by Casini and Meyer (2017), and we develop and extend such results with characterisations based on semantics and entrenchment relations, showing how some of the constructions defined for propositional logic can be lifted to our preferential non-monotonic framework.},
  year = {2018},
  journal = {16th International Conference on Principles of Knowledge Representation and Reasoning},
  pages = {220-229},
  month = {27/10-02/11},
  publisher = {AAAI Press},
  address = {United States of America},
  isbn = {978-1-57735-803-9},
  url = {https://dblp.org/db/conf/kr/kr2018.html},
}

Whereas Perl-compatible regular expression matchers typically exhibit some variation of leftmost-greedy semantics, those conforming to the posix standard are prescribed leftmost-longest semantics. However, the posix standard leaves some room for interpretation, and Fowler and Kuklewicz have done experimental work to confirm differences between various posix matchers. The Boost library has an interesting take on the posix standard, where it maximises the leftmost match not with respect to subexpressions of the regular expression pattern, but rather, with respect to capturing groups. In our work, we provide the first formalisation of Boost semantics, and we analyse the complexity of regular expression matching when using Boost semantics.

@{196,
  author = {Brink van der Merwe and Martin Berglund and Willem Bester},
  title = {Formalising Boost POSIX Regular Expression Matching},
  abstract = {Whereas Perl-compatible regular expression matchers typically exhibit some variation of leftmost-greedy semantics, those conforming to the posix standard are prescribed leftmost-longest semantics. However, the posix standard leaves some room for interpretation, and Fowler and Kuklewicz have done experimental work to confirm differences between various posix matchers. The Boost library has an interesting take on the posix standard, where it maximises the leftmost match not with respect to subexpressions of the regular expression pattern, but rather, with respect to capturing groups. In our work, we provide the first formalisation of Boost semantics, and we analyse the complexity of regular expression matching when using Boost semantics.},
  year = {2018},
  journal = {International Colloquium on Theoretical Aspects of Computing},
  pages = {99-115},
  month = {17/02},
  publisher = {Springer},
  isbn = {978-3-030-02508-3},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-02508-3_6},
}

This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Dia- betes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outper- forms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained.

@inbook{195,
  author = {Moeketsi Ndaba and Anban Pillay and Absalom Ezugwu},
  title = {An Improved Generalized Regression Neural Network for Type II Diabetes Classification},
  abstract = {This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Dia- betes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outper- forms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained.},
  year = {2018},
  journal = {ICCSA 2018, LNCS 10963},
  edition = {10963},
  pages = {659-671},
  publisher = {Springer International Publishing AG},
  isbn = {3319951718},
}

Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.

@{194,
  author = {Edgar Jembere and Randhir Rawatlal and Anban Pillay},
  title = {Matrix Factorisation for Predicting Student Performance},
  abstract = {Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.},
  year = {2018},
  journal = {2017 7th World Engineering Education Forum (WEEF)},
  pages = {513-518},
  month = {13/11-16/11},
  publisher = {IEEE},
  isbn = {978-1-5386-1523-2},
}

Diabetes is a metabolic disorder that develops when the body does not make enough insulin or is not able to use insulin effectively. Accurate and early detection of diabetes can aid in effective management of the disease. Several machine learning techniques have shown promise as cost ef- fective ways for early diagnosis of the disease to reduce the occurrence of health complications arising due to delayed diagnosis. This study compares the efficacy of three broad machine learning approaches; viz. Artificial Neural Networks (ANNs), Instance-based classification technique, and Statistical Regression to diagnose type II diabetes. For each approach, this study proposes novel techniques that extend the state of the art. The new techniques include Artificial Neural Networks hybridized with an improved K-Means clustering and a boosting technique; improved variants of Logistic Regression (LR), K-Nearest Neighbours algorithm (KNN), and K-Means clustering. The techniques were evaluated on the Pima Indian diabetes dataset and the results were compared to recent results reported in the literature. The highest classification accuracy of 100% with 100% sensitivity and 100% specificity were achieved using an ensemble of the Boosting technique, the enhanced K-Means clustering algorithm (CVE-K-Means) and the Generalized Regression Neu- ral Network (GRNN): B-KGRNN. A hybrid of CVE-K-Means algorithm and GRNN (KGRNN) achieved the best accuracy of 86% with 83% sensitivity. The improved LR model (LR-n) achieved the highest classification accuracy of 84% with 72% sensitivity. The new multi-layer percep- tron (MLP-BPX) achieved the best accuracy of 82% and 72% sensitivity. A hybrid of KNN and CVE-K-Means (CKNN) technique achieved the best accuracy of 81% and 89% sensitivity. CVE- K-Means technique achieved the best accuracy of 80% and 61% sensitivity. The B-KGRNN, KGRNN, LR-n, and CVE-K-Means technique outperformed similar techniques in literature in terms of classification accuracy by 15%, 1%, 2%, and 3% respectively. CKNN and KGRNN tech- nique proved to have less computational complexity compared to the standard KNN and GRNN algorithm. Employing data pre-processing techniques such as feature extraction and missing value removal improved the classification accuracy of machine learning techniques by more than 11% in most instances.

@phdthesis{192,
  author = {Moeketsi Ndaba and Anban Pillay and Absalom Ezugwu},
  title = {A Comparative Study of Machine Learning Techniques for Classifying Type II Diabetes Mellitus},
  abstract = {Diabetes is a metabolic disorder that develops when the body does not make enough insulin or is not able to use insulin effectively. Accurate and early detection of diabetes can aid in effective management of the disease. Several machine learning techniques have shown promise as cost ef- fective ways for early diagnosis of the disease to reduce the occurrence of health complications arising due to delayed diagnosis. This study compares the efficacy of three broad machine learning approaches; viz. Artificial Neural Networks (ANNs), Instance-based classification technique, and Statistical Regression to diagnose type II diabetes. For each approach, this study proposes novel techniques that extend the state of the art. The new techniques include Artificial Neural Networks hybridized with an improved K-Means clustering and a boosting technique; improved variants of Logistic Regression (LR), K-Nearest Neighbours algorithm (KNN), and K-Means clustering. The techniques were evaluated on the Pima Indian diabetes dataset and the results were compared to recent results reported in the literature. The highest classification accuracy of 100% with 100% sensitivity and 100% specificity were achieved using an ensemble of the Boosting technique, the enhanced K-Means clustering algorithm (CVE-K-Means) and the Generalized Regression Neu- ral Network (GRNN): B-KGRNN. A hybrid of CVE-K-Means algorithm and GRNN (KGRNN) achieved the best accuracy of 86% with 83% sensitivity. The improved LR model (LR-n) achieved the highest classification accuracy of 84% with 72% sensitivity. The new multi-layer percep- tron (MLP-BPX) achieved the best accuracy of 82% and 72% sensitivity. A hybrid of KNN and CVE-K-Means (CKNN) technique achieved the best accuracy of 81% and 89% sensitivity. CVE- K-Means technique achieved the best accuracy of 80% and 61% sensitivity. The B-KGRNN, KGRNN, LR-n, and CVE-K-Means technique outperformed similar techniques in literature in terms of classification accuracy by 15%, 1%, 2%, and 3% respectively. CKNN and KGRNN tech- nique proved to have less computational complexity compared to the standard KNN and GRNN algorithm. Employing data pre-processing techniques such as feature extraction and missing value removal improved the classification accuracy of machine learning techniques by more than 11% in most instances.},
  year = {2018},
  volume = {MSc},
}

Intelligent cognitive agents requiring a high level of adaptability should contain min- imal initial data and be able to autonomously gather new knowledge from their own experiences. 3D virtual worlds provide complex environments in which autonomous software agents may learn and interact. In many applications within this domain, such as video games and virtual reality, the environment is partially observable and agents must make decisions and react in real-time. Due to the dynamic nature of virtual worlds, adaptability is of great importance for virtual agents. The Reinforce- ment Learning paradigm provides a mechanism for unsupervised learning that allows agents to learn from their own experiences in the environment. In particular, the Q- Learning algorithm allows agents to develop an optimal action-selection policy based on their environment experiences. This research explores the potential of cognitive architectures utilizing Reinforcement Learning whereby agents may contain a library of action-selection policies within virtual environments. The proposed cognitive archi- tecture, Q-Cog, utilizes a policy selection mechanism to develop adaptable 3D virtual agents. Results from experimentation indicates that Q-Cog provides an effective basis for developing adaptive self-learning agents for 3D virtual worlds.

@phdthesis{190,
  author = {Michael Waltham and Deshen Moodley and Anban Pillay},
  title = {Q-Cog: A Q-Learning Based Cognitive Agent  Architecture for Complex 3D Virtual Worlds},
  abstract = {Intelligent cognitive agents requiring a high level of adaptability should contain min- imal initial data and be able to autonomously gather new knowledge from their own experiences. 3D virtual worlds provide complex environments in which autonomous software agents may learn and interact. In many applications within this domain, such as video games and virtual reality, the environment is partially observable and agents must make decisions and react in real-time. Due to the dynamic nature of virtual worlds, adaptability is of great importance for virtual agents. The Reinforce- ment Learning paradigm provides a mechanism for unsupervised learning that allows agents to learn from their own experiences in the environment. In particular, the Q- Learning algorithm allows agents to develop an optimal action-selection policy based on their environment experiences. This research explores the potential of cognitive architectures utilizing Reinforcement Learning whereby agents may contain a library of action-selection policies within virtual environments. The proposed cognitive archi- tecture, Q-Cog, utilizes a policy selection mechanism to develop adaptable 3D virtual agents. Results from experimentation indicates that Q-Cog provides an effective basis for developing adaptive self-learning agents for 3D virtual worlds.},
  year = {2018},
  volume = {MSc},
  publisher = {Durban University},
}

Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high confidence recommendations performs better than a classical content-based filtering solution. The results obtained using the proposed methodology confirmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high confidence recommendations.

@{189,
  author = {Joshua Dzitiro and Edgar Jembere and Anban Pillay},
  title = {A DeepQA Based Real-Time Document Recommender System},
  abstract = {Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high confidence recommendations performs better than a classical content-based filtering solution. The results obtained using the proposed methodology confirmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high confidence recommendations.},
  year = {2018},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018},
  pages = {304-309},
  month = {02/09-05/09},
  publisher = {SATNAC},
  address = {South Africa},
}

The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: the structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines can be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.

@{188,
  author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
  title = {Informative Armstrong RDF datasets for n-ary relations},
  abstract = {The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: the structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines can be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.},
  year = {2018},
  journal = {Formal Ontology in   Information Systems: 10th International Conference, Cape Town, South Africa},
  pages = {187-198},
  month = {17/09-21/09},
  publisher = {IOS Press},
}

Description logics have been extended in a number of ways to support defeasible reasoning in the KLM tradition. Such features include preferential or rational defeasible concept subsumption, and defeasible roles in complex concept descriptions. Semantically, defeasible subsumption is obtained by means of a preference order on objects, while defeasible roles are obtained by adding a preference order to role interpretations. In this paper, we address an important limitation in defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing a modular preference order on objects from each preference order on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised rational defeasible subsumption, with contexts described by roles. We also provide a semantic construction for and a method for the computation of contextual rational closure, and present a correspondence result between the two.

@{187,
  author = {Katarina Britz and Ivan Varzinczak},
  title = {Context and rationality in defeasible subsumption},
  abstract = {Description logics have been extended in a number of ways to support defeasible reasoning in the KLM tradition. Such features include preferential or rational defeasible concept subsumption, and defeasible roles in complex concept descriptions. Semantically, defeasible subsumption is obtained by means of a preference order on objects, while defeasible roles are obtained by adding a preference order to role interpretations. In this paper, we address an important limitation in defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing a modular preference order on objects from each preference order on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised rational defeasible subsumption, with contexts described by roles. We also provide a semantic construction for and a method for the computation of contextual rational closure, and present a correspondence result between the two.},
  year = {2018},
  journal = {Foundations of Information and Knowledge Systems: 10th International Symposium FoIKS 2018, Budapest, Hungary},
  pages = {114-132},
  month = {14/05-18/05},
  publisher = {Springer},
}

A challenge in ontology engineering is the mismatch in ex- pertise between the ontology engineer and domain expert, which often leads to important constraints not being specified. Domain experts often only focus on specifying constraints that should hold and not on specify- ing constraints that could possibly be violated. In an attempt to bridge this gap we propose the use of “perfect test data”. The generated test data is perfect in that it satisfies all the constraints of an application domain that are required, including ensuring that the test data violates constraints that can be violated. In the context of Description Logic on- tologies we call this test data an “Armstrong ABox”, a notion derived from Armstrong relations in relational database theory. In this paper we detail the theoretical development of Armstrong ABoxes for ALC TBoxes as well as an algorithm for generating such Armstrong ABoxes. The proposed algorithm is based, via the ontology completion algorithm of Baader et al., on attribute exploration in formal concept analysis.

@{186,
  author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
  title = {Generating Armstrong ABoxes for ALC TBoxes},
  abstract = {A challenge in ontology engineering is the mismatch in ex- pertise between the ontology engineer and domain expert, which often leads to important constraints not being specified. Domain experts often only focus on specifying constraints that should hold and not on specify- ing constraints that could possibly be violated. In an attempt to bridge this gap we propose the use of “perfect test data”. The generated test data is perfect in that it satisfies all the constraints of an application domain that are required, including ensuring that the test data violates constraints that can be violated. In the context of Description Logic on- tologies we call this test data an “Armstrong ABox”, a notion derived from Armstrong relations in relational database theory. In this paper we detail the theoretical development of Armstrong ABoxes for ALC TBoxes as well as an algorithm for generating such Armstrong ABoxes. The proposed algorithm is based, via the ontology completion algorithm of Baader et al., on attribute exploration in formal concept analysis.},
  year = {2018},
  journal = {Theoretical Aspects of   Computing: 15th International Colloquium, Stellenbosch, South Africa},
  pages = {211-230},
  month = {16/10-19/10},
  publisher = {Springer},
}

Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency.

@{185,
  author = {Joshua Berndt and Bernd Fischer and Katarina Britz},
  title = {Scaling the ConceptCloud browser to large semi-structured data sets},
  abstract = {Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency.},
  year = {2018},
  journal = {14th African Conference on Research in   Computer Science and Applied Mathematics, Stellenbosch, South Africa, Proceedings},
  pages = {276- 283},
  month = {14/10-16/10},
  publisher = {HAL archives-ouvertes},
  url = {https://hal.inria.fr/hal-01881376},
}

Modal accounts of normality in non-monotonic reasoning traditionally have an underlying semantics based on a notion of preference amongst worlds. In this paper, we motivate and investigate an alternative semantics, based on ordered accessibility relations in Kripke frames. The underlying intuition is that some world tuples may be seen as more normal, while others may be seen as more exceptional. We show that this delivers an elegant and intuitive semantic construction, which gives a new perspective on defeasible necessity. Technically, the revisited logic does not change the expressive power of our previously defined preferential modalities. This conclusion follows from an analysis of both semantic constructions via a generalisation of bisimulations to the preferential case. Reasoners based on the previous semantics therefore also suffice for reasoning over the new semantics. We complete the picture by investigating different notions of defeasible conditionals in modal logic that can also be captured within our framework.\footnote{A preliminary version of the work reported in this paper was presented at the Workshop on Nonmonotonic Reasoning.

@article{183,
  author = {Katarina Britz and Ivan Varzinczak},
  title = {Preferential accessibility and preferred worlds},
  abstract = {Modal accounts of normality in non-monotonic reasoning traditionally have an underlying semantics based on a notion of preference amongst worlds. In this paper, we motivate and investigate an alternative semantics, based on ordered accessibility relations in Kripke frames. The underlying intuition is that some world tuples may be seen as more normal, while others may be seen as more exceptional. We show that this delivers an elegant and intuitive semantic construction, which gives a new perspective on defeasible necessity. Technically, the revisited logic does not change the expressive power of our previously defined preferential modalities. This conclusion follows from an analysis of both semantic constructions via a generalisation of bisimulations to the preferential case. Reasoners based on the previous semantics therefore also suffice for reasoning over the new semantics. We complete the picture by investigating different notions of defeasible conditionals in modal logic that can also be captured within our framework.\footnote{A preliminary version of the work reported in this paper was presented at the Workshop on Nonmonotonic Reasoning.},
  year = {2018},
  journal = {Journal of Logic, Language and Information},
  volume = {27},
  pages = {133-155},
  issue = {2},
  publisher = {Springer},
  url = {https://doi.org/10.1007/s10849-017-9264-0},
}

We investigate an aspect of defeasibility that has somewhat been overlooked by the non-monotonic reasoning community, namely that of defeasible modes of reasoning. These aim to formalise defeasibility of the traditional notion of necessity in modal logic, in particular of its different readings as action, knowledge and others in specific contexts, rather than defeasibility of conditional forms. Building on an extension of the preferential approach to modal logics, we introduce new modal operators with which to formalise the notion of defeasible necessity and distinct possibility, and that can be used to represent expected effects, refutable knowledge, and so on. We show how KLM-style conditionals can smoothly be integrated with our richer language. We also propose a tableau calculus which is sound and complete with respect to our modal preferential semantics, and of which the computational complexity remains in the same class as that of the underlying classical modal logic.

@article{182,
  author = {Katarina Britz and Ivan Varzinczak},
  title = {From KLM-Style Conditionals to Defeasible Modalities, and Back},
  abstract = {We investigate an aspect of defeasibility that has somewhat been overlooked by the non-monotonic reasoning community, namely that of defeasible modes of reasoning. These aim to formalise defeasibility of the traditional notion of necessity in modal logic, in particular of its different readings as action, knowledge and others in specific contexts, rather than defeasibility of conditional forms. Building on an extension of the preferential approach to modal logics, we introduce new modal operators with which to formalise the notion of defeasible necessity and distinct possibility, and that can be used to represent expected effects, refutable knowledge, and so on. We show how KLM-style conditionals can smoothly be integrated with our richer language. We also propose a tableau calculus which is sound and complete with respect to our modal preferential semantics, and of which the computational complexity remains in the same class as that of the underlying classical modal logic.},
  year = {2018},
  journal = {Journal of Applied Non-Classical Logics},
  volume = {28},
  pages = {92-121},
  issue = {1},
  publisher = {Taylor & Francis},
  url = {https://doi.org/10.1080/11663081.2017.1397325},
}

Sugarcane growers usually burn their cane to facilitate its harvesting and transportation. Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing. This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn. A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers’ adaptive pre-harvest burning decisions. It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions. The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses. The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts. The DBDN is a promising way of modelling such dynamic, adaptive operational decisions.

@{181,
  author = {C. Sue Price and Deshen Moodley and Anban Pillay},
  title = {Dynamic Bayesian decision network to represent growers’ adaptive pre-harvest burning decisions in a sugarcane supply chain},
  abstract = {Sugarcane growers usually burn their cane to facilitate its harvesting and transportation.  Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing.  This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn.  A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers’ adaptive pre-harvest burning decisions.  It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions.  The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses.  The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts.   The DBDN is a promising way of modelling such dynamic, adaptive operational decisions.},
  year = {2018},
  journal = {Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT '18)},
  pages = {89-98},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {New York NY},
  isbn = {978-1-4503-6647-2},
  url = {https://dl.acm.org/citation.cfm?id=3278681},
}

@{573,
  author = {Frans Botes and Louise Leenen and Retha De La Harpe},
  title = {Ant Colony Induced Decision Trees for Intrusion Detection},
  abstract = {},
  year = {2017},
  journal = {Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017)},
  month = {June 2017},
  publisher = {Academic Conferences International},
  address = {Dublin, Ireland},
}

@{574,
  author = {Michael Aschmann and Louise Leenen and Joey van Vuuren},
  title = {The Utilisation of the Deep Web for Military Counter Terrorist Operations},
  abstract = {},
  year = {2017},
  journal = {Proceedings of the 12th International Conference on Cyber Warfare and Security (ICCWS 2017)},
  month = {March 2017},
  publisher = {Academic Conferences International},
  address = {Dayton, United States of America},
}

@article{572,
  author = {Joey van Vuuren and Louise Leenen and Graeme Plint and Jannie Zaaiman and Jackie Phahlamohlaka},
  title = {Formulating the Building Blocks for National Cyberpower},
  abstract = {},
  year = {2017},
  journal = {International Journal of Cyber Warfare and Terrorism},
  volume = {7},
  pages = {616-628},
  issue = {3},
  publisher = {IGI Global},
}

Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. In general, human operators are often highly susceptible to manipulation, and tend to be one of the weakest links in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy with respect to formal definitions, attack frameworks, examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been proven to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the interconnections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. In addition, it facilitates additional analysis on state transitions that are difficult to extract from the original flowchart based model.

@{571,
  author = {Francois Mouton and Alastair Nottingham and Louise Leenen and Hein Venter},
  title = {Underlying Finite State Machine for the Social Engineering Attack Detection Model},
  abstract = {Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. In general, human operators are often highly susceptible to manipulation, and tend to be one of the weakest links in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy with respect to formal definitions, attack frameworks, examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been proven to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the interconnections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. In addition, it facilitates additional analysis on state transitions that are difficult to extract from the original flowchart based model.},
  year = {2017},
  journal = {2017 Information Security for South Africa (ISSA)},
  pages = {98-105},
  month = {August 2017},
  publisher = {IEEE},
  address = {Johannesburg, South Africa},
  doi = {10.1109/ISSA.2017.8251781},
}

Intrusion Detection Systems (IDSs) analyse network traffic to identify suspicious patterns which indicate the intention to compromise the system. Traditional detection methods are still the norm for commercial products promoting a rigid, manual, and static detection platform. This paper focuses on recent advances in machine learning by implementing the Ant Tree Miner Amyntas (ATMa) classifier within intrusion detection. The proposed ATMa use Ant Colony Optimisation and a cost-based evaluation function to automatically select features from a data set before inducing Decision Trees (DTs) that classify network data.

@article{569,
  author = {Frans Botes and Louise Leenen and Retha De La Harpe},
  title = {Ant Tree Miner Amyntas: Automatic, Cost-Based Feature Selection for Intrusion Detection},
  abstract = {Intrusion Detection Systems (IDSs) analyse network traffic to identify suspicious patterns which indicate the intention to compromise the system. Traditional detection methods are still the norm for commercial products promoting a rigid, manual, and static detection platform. This paper focuses on recent advances in machine learning by implementing the Ant Tree Miner Amyntas (ATMa) classifier within intrusion detection. The proposed ATMa use Ant Colony Optimisation and a cost-based evaluation function to automatically select features from a data set before inducing Decision Trees (DTs) that classify network data.},
  year = {2017},
  journal = {Journal of Information Warfare},
  volume = {16},
  pages = {73-92},
  issue = {4},
  publisher = {ArmisteadTEC},
}