ETD Collection

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    Policy for managing access to intelligence information in post-apartheid South Africa
    (2008-03-10T12:27:41Z) Africa, Sandra Elizabeth
    ABSTRACT Under apartheid, the South African intelligence services operated in secrecy and without the framework of a Constitution upholding basic human rights. The situation changed drastically with the introduction of a democratic political dispensation in 1994, and with the adoption of the Republic of South Africa Constitution Act, 1996. One of the fundamental rights contained in the Bill of Rights (Chapter 2 of the Constitution) was the right of access to information. The subsequent passage of legislation to give effect to this right, required all state structures - including the civilian intelligence services, the National Intelligence Agency and the South African Secret Service - to actively disclose information about themselves, and to receive and respond to requests for access to records that were made in terms of the enabling legislation. The main issue with which the study is concerned - the balance between secrecy and transparency in a democracy - is one of a wider set of concerns related to democratic control and accountability of the intelligence and security services. The study explores policy options for reconciling the public’s right to information with the intelligence services’ need for a degree of secrecy with which to conduct their work. Inter alia, it compares the policy choices of three countries about how their intelligence services should function in relation to access to information legislation. The research reveals that there was uneven and erratic compliance by the intelligence services with key provisions of the Promotion of Access to Information Act, 2000, up to and including August 2005. The weaknesses arose because of the absence of clear policy on how to implement the Act in relation to the intelligence services, and in relation to information held by the intelligence and security services. The study therefore argues the need for a comprehensive policy package, which sets criteria for the conditions under which information should be protected from disclosure, and the criteria for determining when information no longer requires such protection. Finally, it argues for strict oversight of the intelligence services’ choices around secrecy and transparency.
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    Image Shape Clasification Using Computational Intelligence and Object Orientation
    (2006-03-13) Machowski, Lukasz Antoni
    With the increase in complexity of modern software systems, there is a great demand for software engineering techniques. Calculation processes are becoming more and more complex, especially in the field of machine vision and computational intelligence. A suitable object oriented calculation process framework is developed in order to address this problem. To demonstrate the effectiveness of the framework, a simple shape classification system is implemented in C#. A suitable method for representing shapes of images is developed and it is used for classification by a neural network. Sets of real-world images of hands and automobiles are used to test the system. The performance of the object oriented system in C# is compared to a functional paradigm system in Matlab and it is found that object orientation is well suited to the later stages of machine vision while the functional approach is well suited to low level image processing tasks.
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    Intelligence and the ‘War against Terrorism’: Multilateral Counter-Terrorism Policies Implemented post-September 11
    (2006-02-14) Fulton, Wayne
    The events of September 11 (9/11) have proved to be the catalyst for the evolution of ‘traditional’ terrorism methodologies into those of a transnational dimension. As a consequence, 9/11 has reshaped the international security community’s perceptions regarding the transnational threat of terrorism. Security analysts have called for a ‘networked’ response as the most effective strategy of defence against global terrorist networks. Hence, efforts to contain the threat of transnational terrorism will be more effective if implemented in conjunction with policies and mechanisms designed to facilitate international counter-terrorism co-operation. Therefore, taking into account the ‘perceived’ intelligence failure of 9/11, intelligence and anti-terrorism law enforcement agencies of governments committed to the ‘war against terrorism’ will need to integrate their intelligence capabilities and establish operational co-ordination on a multilateral level as an effective counter-terrorism mechanism. This research will focus on the multilateral intelligence sharing and counter-terrorism co-ordination mechanisms implemented post-9/11 by governments and International Organisations, such as the UN’s Counter Terrorism Committee and NATO’s invoking of Article 5, to contain and confront transnational terrorism. It is not within the scope of this study to analyse the reasons and ideologies behind 9/11 and modern-day terrorism.
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    Optimal selection of stocks using computational intelligence methods
    (2006-02-08) Betechuoh, Brain Leke
    Various methods, mostly statistical in nature have been introduced for stock market modelling and prediction. These methods are, however, complex and difficult to manipulate. Computational intelligence facilitates this approach of predicting stocks due to its ability to accurately and intuitively learn complex patterns and characterise these patterns as simple equations. In this research, a methodology that uses neural networks and Bayesian framework to model stocks is developed. The NASDAQ all-share index was used as test data. A methodology to optimise the input time-window for stock prediction using neural networks was also devised. Polynomial approximation and reformulated Bayesian frameworks methodologies were investigated and implemented. A neural network based algorithm was then designed. The performance of this final algorithm was measured based on accuracy. The effect of simultaneous use of diverse neural network engines is also investigated. The test result and accuracy measurements are presented in the final part of this thesis. Key words: Neural Networks, Bayesian framework and Markov Chain Monte Carlo