Abstract Rule-governed behavior is a unique form of human behavior that comes with many advantages. Rule-following can however become problematic when it makes us insensitive to the consequences of our behavior and undermines our ability to adapt to changes in contingencies. This phenomenon has been hypothesized to play a central role in different psychological problems. In this thesis project, we investigated whether a mindfulness exercise from Acceptance and Commitment Therapy (ACT) could improve people's ability to adapt to changes in contingencies, as measured with the Wisconsin Card Sorting Test (WCST). We also explored whether recently developed self-report questionnaires of rule-governed behavior, the Generalized Pliance Questionnaire (GPQ-9) and the Generalized Tracking Questionnaire (GTQ) could predict the participants' performance on the WCST. In addition, we looked at the association between intolerance of uncertainty (IUS-12) and generalized pliance (GPQ-9). The sample consisted of 45 university students at Örebro university in Sweden. The results revealed that the brief mindfulness exercise did not improve the participants ability to adapt to contingency changes. The questionnaires of rule-governed behavior also did not predict this performance on the WCST. However, we found a novel association between generalized pliance and intolerance of uncertainty, which could be a future research path. Further, generalized pliance and generalized tracking displayed a moderate negative correlation, in line with previous research. Despite limited significant findings in this study, it was an effort to investigate central claims from the ACTand behavioral literature, centered around psychological flexibility and rule-governed behavior.

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

Doctor of Philosophy / Department of Computing and Information Sciences / William H. Hsu / Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied to rank candidate rules in domain-dependent tasks such as link existence prediction. In addition, I use interestingness measures themselves as numerical features to improve link existence prediction. The focus of this dissertation is on developing and testing an analytical framework for association rule interestingness measures, to make them sensitive to the relative size of itemsets. I survey existing interestingness measures and then introduce adaptive parametric models for normalizing and optimizing these measures, based on the size of itemsets containing a candidate pair of co-occurring entities. The central thesis of this work is that in certain domains, the link strength between entities is related to the rarity of their shared memberships (i.e., the size of itemsets in which they co-occur), and that a data-driven approach can capture such properties by normalizing the quantitative measures used to rank associations. To test this hypothesis under different levels of variability in itemset size, I develop several test bed domains, each containing an association rule mining task and a link existence prediction task. The definitions of itemset membership and link existence in each domain depend on its local semantics. My primary goals are: to capture quantitative aspects of these local semantics in normalization factors for association rule interestingness measures; to represent these factors as quantitative features for link existence prediction, to apply them to significantly improve precision and recall in several real-world domains; and to build an experimental framework for measuring this improvement, using information theory and classification-based validation.

The study makes a descriptive and analytical study of the development of the dynamic concept of the rule of law with special reference to the African contribution. First, the study shows that the Diceyan concept of the rule of law was narrow and peculiar to the Western liberal legal culture, and that more specifically, the substantive content of the concept of the rule of law was limited to the first generation of human rights. In its international and African context the concept was expanded to include all three generations of human rights and also identified with the concepts of democracy and the right of peoples and nations to self-determination. The expanded concept came to be known as the Dynamic Concept of the rule of law. Secondly, the study traces the origins and development of the principle of equal rights and self-determination and their extension to all peoples and nations and shows that these rights are universal, not relative, as they derive from the inherent worth and dignity of the individual. Also, the study shows that in the African context the three generations of human rights have been interlinked, made inter-dependent, and then identified with the rule of law, human rights and the right of self-determination (perceived as a right to democratic self-governance). Hence, the worth and dignity of the human personality has been made the fountainhead of human rights and have been elevated to the substantive elements of the Dynamic Concept of the rule of law and the basis of the modern African Constitutional State. Under the Colonial Rule both the Diceyan and the dynamic concept of the rule of law were not recognised. Instead, Colonial and racist regimes tried to create alternative institutions of government which denied the oppressed peoples the right to democratic self-governance and independence. However, Colonial and oppressed peoples relied on the dynamic concept of the rule of law in their freedom struggles and in the elaboration of their policies. Hence, the constitutions of all the former colonies in southern Africa under discussion were to different degrees informed by the Dynamic Concept of the rule of law. / Constitutional, International & Indigenous Law / LL.D

Submitted in fulfilment of the academic requirements for the Masters Degree in Technology: Chemistry, Durban University of Technology, 2012. / In this work, the thermodynamics of ternary liquid mixtures involving carboxylic acids with sulfolane, hydrocarbons including cycloalkane, and alcohols are presented. In South Africa, Sasol is one of the leading companies that produce synthesis gas from low grade coal. Carboxylic acids together with many other oxygenate and hydrocarbons are produced by Sasol using the Fischer-Tropsch process. Carboxylic acids class is one of the important classes of compounds with great number of industrial uses and applications. The efficient separation of carboxylic acids from hydrocarbons and alcohols from hydrocarbons is of economic importance in the chemical industry, and many solvents have been tried and tested to improve such recovery. This work focussed on the use of the polar solvent sulfolane in the effective separation by solvent extraction and not by more common energy intensive method of distillation. The first part of the experimental work focussed on ternary liquid-liquid equilibria of mixtures of [sulfolane (1) + carboxylic acid (2) + heptane (3) or cyclohexane or dodecane] at T = 303.15 K, [sulfolane (1) + alcohol (2) + heptane (3)] at T = 303.15 K. Carboxylic acid refers to acetic acid, propanoic acid, butanoic acid, 2-methylpropanoic acid, pentanoic acid and 3-methylbutanoic acid. Alcohol refers to methanol, ethanol, 1- propanol, 2-propanol, 1-butanol, 2-butanol, 2-methyl-1-propanol and 2-methyl-2-propanol. Ternary liquid- liquid equilibrium data are essential for the design and selection of solvents used from liquid- liquid extraction process. Abstract vi The separation of carboxylic acids from hydrocarbons and the alcohols from hydrocarbons is commercially lucrative consideration and is an important reason of this study. The separation of carboxylic acids or alcohols from hydrocarbons by extraction with sulfolane was found to be feasible as all selectivity values obtained are greater than 1. The modified Hlavatý, beta (β) and log equations were fitted to the experimental binodal data measured in this work. Hlavatý gave the best overall fit as compared to beta ( ) and log function. The NRTL (Non-Random, Two Liquid) and UNIQUAC Universal Quasichemical) model were used to correlate the experimental tie-lines and calculate the phase compositions of the ternary systems. The correlation work served three purposes:  to summarise experimental data  to test theories of liquid mixtures  prediction of related thermodynamics properties. The final part of the study was devoted to the determination of the excess molar volumes of mixtures of [sulfolane (1) + alcohol (2)] at T = 298.15 K, T = 303.15 K and T = 309.15 K. Density was used to determine the excess molar volumes of the mixtures of [sulfolane (1) + alcohols (2)]. Alcohol refers to methanol, ethanol, 1- propanol, 2-propanol, 1-butanol, 2-butanol, 2-methyl-1-propanol, 2-methyl-2-propanol. The work was done to investigate the effect of temperature on excess molar volumes of binary mixtures of alcohols and sulfolane, as well as to get some idea of interactions involved between an alcohol and sulfolane. The excess molar volume data for each binary mixture was fitted in the Redlich–Kister equation to correlate the composition dependence of the excess property. / National Research Foundation

Submitted in fulfilment of the academic requirements for the Masters Degree in Technology: Chemistry, Durban University of Technology, 2012. / National Research Foundation

Motivation: In the Sub-Saharan Africa countries there are several factors contributing to the collapse of firms. Most firms have failed due to poor corporate governance practices. The recent collapse of some firms in the financial and non-financial sectors in the Sub-region shows that there are challenges hindering effective corporate governance of firms in the Subregion. Consequently, this study uses empirical evidence to identify views about the important components of good corporate governance practice for listed firms: institutional characteristics; the board of directors; and the effects of external factors. Research question: The pertinent research question that this study addresses is the identification of the components that are essential for good corporate governance of firms in the Sub-region. This study tries to prioritise the components. Methodology: Data were collected by questionnaire administered to stakeholders of corporate governance of listed firms in Ghana, Nigeria and South Africa. Regression is used to estimate the relationship between institutional characteristic, responsibilities of the board of directors and external factors on corporate governance system. Main findings: 1. Enforcement, disclosure, transparency and regulatory frameworks may be necessary to improve corporate governance practice in all the countries in the Sub-region (SSAA). 2. There is evidence that commitment of board members to disclosure and communication may provide effective corporate governance practice. 3. Board duality (separation of role between chairman and CEO) is likely to hinder corporate governance practices. 4. We found that in all the countries in the Sub-region accounting system plays a major role to promote sound corporate governance practice. However, the political environment, societal and cultural factor, corruption, and economic factors such as macro-economic policies may hinder corporate governance practices.Policy recommendations: This study recommends that corporate governance stakeholders should adopt a whistle blowing method and also that institutional bodies should be more prudent in monitoring of rules and laws with stringent penalties. In addition, there should be adequate information and disclosure on the rights and obligation of the shareholder of firms in the sub-region region. There is need to increase the number and role of independent directors, increase the use of advisory vote by shareholders on executive compensation and facilitation of shareholders activism. Furthermore, there is a need to have autonomous regulatory bodies and supervisory agencies free from any political/ government interference in the implementation of the Code and Guideline of corporate governance. The regulatory bodies and the supervisory agencies should be manned or be under the leadership of people of goodwill, good character and trust. The Code or Guideline of corporate governance of Sub-Saharan Africa Anglophone countries should take cognisance of and be aligned with socio-cultural environment of the countries in the Sub-region.

Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called 'website phishing detection' was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.

<p>Avhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.</p>