Computer Science Conference Papers
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- ItemCyber Security Awareness and Education Framework for Zimbabwe Universities: A Case of National University of Science and Technology(2022-04-05) Mutunhu, B.; Dube, S.; Ncube N.; Sibanda, S.Internet-related attacks have become prevalent and are expected to increase as the reliance on the internet also increases. Consequently, cyber security has become an essential concept in everyday life and cyber security awareness is key in the protection of people and systems against cyber threats. The study is sought to establish the current levels of cyber security awareness among students and staff in universities and propose a framework for conducting cyber security awareness and education programs. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) and represented in descriptive and frequency ways as well as in percentage form. Based on research findings in this study, it has been established that students and staff at universities do not have the requisite knowledge and understanding of the importance of cyber security principles, their practical application in their day-to-day activities, and are not aware of how to protect their data. It is therefore recommended that universities should implement comprehensive awareness and education programs for the adoption of necessary safety measures and a framework for conducting such programs are proposed.
- ItemBio-metric Implementation of Bayesian Networks for Face Recognitionon Android Mobile Devices(2013-02-01) Masundire, Daniel; Chilumani, Khesani R.; Nyathi, ThamboFace recognition is a field that receives attention and inquiry by Artificial Intelligence, Computer Security and Intelligent Data Processing researchers. Its application as a biometric has also grown due to the growth of the camera technologies. The digital camera’s resolution and noise reduction in images makes the camera view the world in more or less the same as a human being. However, this technology has been difficult to implement on mobile devices, phones in particular. This has been chiefly because of the computational complexity of the algorithms used. The memory size and processing speed of the mobile phones has been a constraint as well. There has been a huge growth experienced in the mobile phones’ industry with respect to the gadgets’ processing power. Face detection has been implemented using other statistical methods but this research uses the Bayesian network. This method is graphical and uses probabilistic inference. This helps in reasoning with incomplete and unknown information. The Bayesian Network is applied on data that would have acquired from a face image using Principal Component Analysis (PCA). PCA is used to reduce the dimensions of the image. The co-variances matrix from the principal components is used in the Bayesian network. The Bayesian network uses prior knowledge and the weights on the nodes are modified using weights formulated by a probability distribution function. This means, unlike other neural networks, the weights of a Bayesian network are not constants. We formulate our problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function (PDF). A Bayesian network is used to represent the PDF as well as the domain knowledge needed for interpretation (Kumar and Desai, 1996). The development is done for Android-based mobile phones. The Android is an open source mobile phone operating system that runs on a Linux kernel. In this dissertation, we seek to improve face recognition for mobile devices by using dedicated statistical generative models. Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), which are classical generative models, have been successfully proven in face recognition. The GMM and HMM based models treat the facial features under investigation as independent. This is not consistent for a face because facial features are related to each other. Bayesian networks can be used to analyse and process the face as a graphical representation of dependent features. It only detects the frontal view of the face, that is, the face to be detected has to be showing the frontal features of the face. An appearance-based recognition and detection method is used. The prototype was built for Androidbased mobile phones. Mobile phones which run on Symbian, Windows and Apple (iOS) operating systems are not going to be covered.
- ItemA Semantic Web Solution for Circumventing Disparities between Electronic-Learning Systems and Mobile-Learning Systems(2016) Ngwenya, Sibangiso; Mangena, Sikhulumani Bayeza; Chilumani, Khesani R.