Development of high performance computing cluster for evaluation of sequence alignment algorithms
- Authors: Ngxande, Mkhuseli
- Date: 2015
- Language: English
- Type: Thesis , Masters , MSc (Computer Science)
- Identifier: vital:11399 , http://hdl.handle.net/10353/d1020163
- Description: As the biological databases are increasing rapidly, there is a challenge for both Biologists and Computer Scientists to develop algorithms and databases to manage the increasing data. There are many algorithms developed to align the sequences stored in biological databases - some take time to process the data while others are inefficient to produce reasonable results. As more data is generated, and time consuming algorithms are developed to handle them, there is a need for specialized computers to handle the computations. Researchers are typically limited by the computational power of their computers. High Performance Computing (HPC) field addresses this challenge and can be used in a cost-effective manner where there is no need for expensive equipment, instead old computers can be used together to form a powerful system. This is the premise of this research, wherein the setup of a low-cost Beowulf cluster is explored, with the subsequent evaluation of its performance for processing sequent alignment algorithms. A mixed method methodology is used in this dissertation, which consists of literature study, theoretical and practise based system. This mixed method methodology also have a proof and concept where the Beowulf cluster is designed and implemented to perform the sequence alignment algorithms and also the performance test. This dissertation firstly gives an overview of sequence alignment algorithms that are already developed and also highlights their timeline. A presentation of the design and implementation of the Beowulf Cluster is highlighted and this is followed by the experiments on the baseline performance of the cluster. A detailed timeline of the sequence alignment algorithms is given and also the comparison between ClustalW-MPI and T-Coffee (Tree-based Consistency Objective Function For alignment Evaluation) algorithm is presented as part of the findings in the research study. The efficiency of the cluster was observed to be 19.8%, this percentage is unexpected because the predicted efficiency is 83.3%, which is found in the theoretical cluster calculator. The theoretical performance of the cluster showed a high performance as compared with the experimental performance, this is attributable to the slow network, which was 100Mbps, low processor speed of 2.50 GHz, and low memory of 2 Gigabytes.
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Exploring user experience (UX) factors For ICTD services
- Authors: Nyambi, Pride Bongiwe
- Date: 2015
- Language: English
- Type: Thesis , Masters , MSc (Computer Science)
- Identifier: vital:11400 , http://hdl.handle.net/10353/d1020164
- Description: Consistent with global entities such as the United Nations- through the World Summit of the Information Society (WSIS), introduction of Information and Communication Technology (ICT) for human development has seen the introduction of ICT-based services aimed at facilitating socio-economic development of marginalized communities. The use of ICTs has always solicited the concept of Human Computer Interaction (HCI), which involves the methods which humans interact with technology. The types of User Interfaces (UIs) and interaction techniques that people use to interact with ICTs affects the way they perceive technology and eventually, their acceptance of the technology. Current ICT systems still haven‟t adopted the concept of placing the user at the core of the interaction. Users are still required to adapt themselves to the interface‟s characteristics; which limits the number of people who can use the system due to inabilities to adapt to the interface. As a result, the information embedded in these technologies is still inaccessible and useless to Marginalized Rural Area (MRA) users. Such usability challenges can be mitigated against and avoided by matching UI components with the users‟ mental models, language, preferences, needs and other socio-cultural artefacts. In this research, literature in Human-Computer Interaction (HCI) is reviewed with emphasis on the usability and User Experience (UX) during user interaction with ICTs using various modes of interactions. HCI emphasizes the need for systems to take account of user‟s characteristics such as their abilities, needs, socio-cultural experiences, behaviours and interests. In efforts to meet the requirement of UX, the user, system and the context of use, need to be evaluated, taking into consideration that changing one entity modifies the UX. This will be achieved by persona profiling to determine the key characteristics of the user communities, clustered according to the key UX attributes. Subsequently, through detailed usability evaluations, including the use of System Usability Scale (SUS) to determine user satisfaction with various UI components/techniques per identified persona- thus providing and persona mapping for usability of Information and Communication Technology for Development (ICTD) services. The results from this research are reflective of the importance of creating personas for usability testing. Some of the personas do not have a problem with interacting with most of the interfaces but their choice of interface comes from a preference point of view. For some personas, their skills and level of experience with ICTs motivates their choice of interface. The common UI component that users from across the spectrum appreciate is UI consistency which makes interaction easier and more natural. Common obstacles with current User Interfaces (UIs) that inhibit users from MRAs include the hefty use of text in interfaces, unintuitive navigation structures and the use of a foreign language. Differences in UIs from different application developers present an inconsistency which challenges the users from rural areas. These differences include the layout, the text entry methods and the form of output produced. A solution to this has been identified from the usability test as the use of speech-enabled interfaces in a language that can be understood by the target audience. In addition, through literature study it has been found that UX of interfaces can be improved by the use of less textual or text-free interfaces. Based on literature, users from MRAs can benefit from using hand-writing based UIs for text-based entry which mimics pen and paper environment for literate users who have experience with writing. Finally, the use of numbered options can assist illiterate users in tasks that requires users to choose options and for navigation. Therefore, consistency in UIs designed to be used by MRA users can improve usability of these interfaces and thus, improving the overall UX.
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The classification performance of Bayesian Networks Classifiers: a case study of detecting Denial of Service (DoS) attacks in cloud computing environments
- Authors: Moyo, Lindani
- Date: 2015
- Language: English
- Type: Thesis , Masters , MSc (Computer Science)
- Identifier: vital:11405 , http://hdl.handle.net/10353/d1021327
- Description: In this research we propose a Bayesian networks approach as a promissory classification technique for detecting malicious traffic due to Denial of Service (DoS) attacks. Bayesian networks have been applied in numerous fields fraught with uncertainty and they have been proved to be successful. They have excelled tremendously in classification tasks i.e. text analysis, medical diagnoses and environmental modeling and management. The detection of DoS attacks has received tremendous attention in the field of network security. DoS attacks have proved to be detrimental and are the bane of cloud computing environments. Large business enterprises have been/or are still unwilling to outsource their businesses to the cloud due to the intrusive tendencies that the cloud platforms are prone too. To make use of Bayesian networks it is imperative to understand the ―ecosystem‖ of factors that are external to modeling the Bayesian algorithm itself. Understanding these factors have proven to result in comparable improvement in classification performance beyond the augmentation of the existing algorithms. Literature provides discussions pertaining to the factors that impact the classification capability, however it was noticed that the effects of the factors are not universal, they tend to be unique for each domain problem. This study investigates the effects of modeling parameters on the classification performance of Bayesian network classifiers in detecting DoS attacks in cloud platforms. We analyzed how structural complexity, training sample size, the choice of discretization method and lastly the score function both individually and collectively impact the performance of classifying between normal and DoS attacks on the cloud. To study the aforementioned factors, we conducted a series of experiments in detecting live DoS attacks launched against a deployed cloud and thereafter examined the classification performance in terms of accuracy of different classes of Bayesian networks. NSL-KDD dataset was used as our training set. We used ownCloud software to deploy our cloud platform. To launch DoS attacks, we used hping3 hacker friendly utility. A live packet capture was used as our test set. WEKA version 3.7.12 was used for our experiments. Our results show that the progression in model complexity improves the classification performance. This is attributed to the increase in the number of attribute correlations. Also the size of the training sample size proved to improve classification ability. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. Furthermore, our results indicate that the choice of scoring function does not affect the classification performance of Bayesian networks. Conclusions drawn from this research are prescriptive particularly for a novice machine learning researcher with valuable recommendations that ensure optimal classification performance of Bayesian networks classifiers.
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