Computing and Information Systems - Theses
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ItemConcept-based Decision Tree Explanations( 2021)This thesis evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neu- ral Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in crit- ical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based expla- nations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation al- gorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to origi- nal model’s labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject ex- periments (for interpretability). We find that TreeICE outperforms the baseline in interpretability and generates more human-readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.
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ItemLearned Hashmap for Spatial Queries( 2020)Spatial indexes such as R-Tree are widely used for managing spatial objects data efficiently, which is influenced by the popular one-dimensional range index B-Tree. Research has suggested that applying machine learning techniques such as linear regression or a neural network can improve the performance of traditional data structures. However, most studies are focused on tuning recursive learned models or learning a different ordering of data items. Many of them cannot guarantee query accuracy as in the traditional methods. This study investigates a different approach to the learned spatial index, namely Learned Spatial Hashmap (LSPH), which combines the learned model and hashmap. It only requires values from one of the data dimensions to build. Results from experiments on both synthetic and real-world datasets show that our approach significantly reduces the query processing time and maintains 100% accuracy, which is more efficient than traditional spatial indexes and more robust than recently proposed learned spatial indexes.
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ItemSafe acceptance of zero-confirmation transactions in Bitcoin( 2016)Acceptance of zero confirmation transactions in Bitcoin is inherently unsafe due to the lack of consistency in states between nodes in the network. As a consequence of this, Bitcoin users must endure a mean wait time of 10 minutes to accept confirmed transactions. Even so, due to the possibility of forks in the Blockchain, users who may want to avoid invalidation risks completely may have to wait up to 6 confirmations, which in turn results in a 60 minute mean wait time. This is untenable and remains a deterrent to the utility of Bitcoin as a payment method for merchants. Our work seeks to address this problem by introducing a novel insurance scheme to guarantee a deterministic outcome for transaction recipients. The proposed insurance scheme utilizes standard Bitcoin scripts and transactions to produce inter-dependent transactions which will be triggered or invalidated based on the occurance of potential doublespend attacks. A library to setup the insurance scheme and a test suite was implemented for anyone who may be interested in using this scheme to setup a fully anonymous and trustless insurance scheme. Based on our test in Testnet, our insurance scheme was successful at defending against 10 out of 10 doublespend attacks.
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ItemPredicting Students' Intention to Use Gamified Mobile Learning in Higher Education( 2020)Background and Objectives: While gamified mobile learning holds the potential to offer an interactive learning environment that can improve students’ engagement, the predictors of its adoption remain underexplored, especially in a higher education context. The aim of this study, therefore, is threefold: (i) to identify predictors of higher education students’ intention to use gamified mobile learning; (ii) to examine the correlations among these predictors; (iii) to explore students’ attitudes towards different game elements. Methods: For the first and second objectives, the study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) with cognitive gratification and perceived enjoyment. For the third objective, the study explored students’ attitudes towards five popular game elements in gamifying learning systems; these are Points, Levels, Leaderboard, Teams, and Gifting. A total of 440 responses were collected from higher education students from different regions of Saudi Arabia, using Qualtrics survey tool. After conducting data screening, 271 valid responses were considered in the analysis of Structural Equation Modeling (SEM), particularly in the items of the hypothesised model, using AMOS 27. For the third research objective, 399 valid responses were obtained and analysed, using SPSS 27. Results: Our findings reveal that perceived enjoyment (β= .507, p < .001) and social influence (β= .261, p < .001) had the strongest positive effects on intention to use gamified mobile learning, followed by performance expectancy (β= .179, p= .008) and effort expectancy (β= .138, p= .034), while cognitive gratification had no influence (β= -.020, p= .770). The proposed model was able to explain 71% of the data variance in usage intentions. For the third objective, the results showed that game elements that allow students to quantify their achievements as individuals, i.e., Points and Levels, are the most favourable, while there seemed to be high variation with the one that encourages competition, Leaderboard, especially among female groups. Lastly, the elements that encourage collaboration, Teams and Gifting, received the lowest positive perceptions. Originality: The original contribution of this study is the empirically backed impact of the extended UTAUT on students’ intention to use gamified mobile learning in higher education. It also contributes in shedding light on which game elements are most promising in this context. The study offers a set of practical outcomes to contribute to realising successful adoption of gamified mobile learning in higher education.
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ItemAutomatic caloric expenditure estimation with smartphone's built-in sensors( 2016)Fitness-tracking systems are technologies commonly used to enhance peoples' lifestyles. Feedback, usability, and ease of acquisition are fundamental to achieving the good physical condition goal. Users need constant motivation as a way to keep their interest in the fitness system and consequently, continue on a healthy lifestyle track. However, although feedback is increasingly being incorporated in many fitness-tracking systems, usability and ease of acquisition are remaining shortcomings that need to be enhanced. Features such as automatic activity identification, low-energy consumption, simplicity and goals-achieved notifications provide a good user experience. Nevertheless, most of these functions require the acquisition of a relatively expensive fitness-tracking device. Smartphones provide a partial solution by allowing users an easy access to multiple fitness applications, which reduce the need for purchasing another gadget. Nonetheless, improvements in the user experience are still necessary. In the other hand, wearables devices satisfy the usability, however, the cost of their acquisition represents an impediment to some users. The system proposed in this research aims to handle these issues and offers a solution by combining the benefits from mobile applications such as feedback and ease of acquisition, with the usability that wearable devices provide, into a smartphone Android application. Data collected from a single user while performing a series of common daily activities namely walking, jogging, cycling, climbing stairs, and walking downstairs, was used to classify and provide an automatic identification of these activities with an overall accuracy of 91%, and identifying the stairs activities with an accuracy of 81%. Finally, the caloric expenditure, which we considered the most important metric for motivating a user to perform a physical activity, was estimated by following the oxygen consumption equations from the American College of Sports Medicine (ACSM).
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ItemExploring knowledge leakage risk in knowledge-intensive organisations: behavioural aspects and key controls( 2019)Knowledge leakage poses a critical risk to the competitiveness advantages of knowledge-intensive organisations. Although knowledge leakage is a human-centric security issue, little is known in relation to the key factors of individual-level leaking behaviour. Therefore, the aim of this thesis was to explore security practitioners’ perspectives on the key enablers and inhibitors of behavioural knowledge leakage risk in the context of knowledge-intensive organisations. An exploratory, qualitative design was used to carry out the study. Moreover, seven security practitioners working in Australian organisations were recruited to participate in this research. The data were collected using semi-structured questions via two focus group discussions. The discussion sessions lasted between 90 and 120 minutes, including a 10-minute break. The sessions were audio recorded, transcribed, and thematically analysed following Braun and Clarke’s (2006) strategy. Furthermore, two main trends emerged from the analysed data. First, ‘interpersonal enabling factors’ included leaking behaviours and employees’ personality’ traits. Second, contributing ‘organisational practices around knowledge leakage mitigation’ included poor knowledge sensitivity classification systems and poor knowledge security management practices. In conclusion, it is essential that security practitioners address the key identified factors of behavioural leakage risk to mitigate the leaking incidents effectively. Three key security practices that were found to have a superior impact in mitigating leaking enablers included human resource management practices, knowledge security training and awareness practices, and compartmentalisation.
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ItemProtecting organizational knowledge: a strategic perspective framework( 2014)Organizational knowledge is considered a valuable resource for providing competitive advantage. Extensive research has been done on strategies to encourage knowledge creation and sharing. However, limited research has been done on strategies for protecting this valuable resource from the risk of leakage. This research aims to contribute in bridging this gap by two contributions: developing a model that describes knowledge leakage, and providing a framework of strategies for protecting competitive organisational knowledge. The research is grounded on two bodies of literature: Knowledge management and information security. The research aims for identifying security strategies in literature and adapting them to address knowledge protection needs.
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ItemThe dark web as a phenomenon: a review and research agenda( 2018)The internet can broadly be divided into three parts: surface, deep and dark among which the latter offers anonymity to its users and hosts. The dark web has become notorious in the media for being a hidden part of the web where all manner of illegal activities take place. The more restrictions placed upon the free exchange of information, goods and services between people the more likely there exist hidden spaces for it to take place. The ‘black market’ of the internet – the dark web - represents such a hidden space. This review looks at the purposes it is widely used for with an emphasis on cybercrime, and how the law enforcement plays the role of its adversary. The review describes these hidden spaces, sheds light on their history, the activities that they harbour – including cybercrime, the nature of attention they receive, and methodologies employed by law enforcement in an attempt to defeat their purpose. More importantly, it is argued that these spaces should be considered a phenomenon and not an isolated occurrence to be taken as merely a natural consequence of technology. The review is conducted by looking at existing literature in academic journal databases. It contributes to the area of the dark web by serving as a reference document and by proposing a research agenda.
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ItemA secure innovation process for start-ups: Minimising knowledge leakage and protecting IP( 2016)Failing to profit from innovations as a result of knowledge leakage is a key business risk for high-tech start-ups. Innovation is central to the success of a start-up and their competitive advantage in the market place therefore methods to protect intellectual property (IP) and minimise knowledge leakage is crucial. However, high-tech start-ups have limited resources rendering them more vulnerable to knowledge leakage risks compared to mature enterprises. Unfortunately, research on knowledge leakage and innovation processes falls short of addressing the needs of high-tech start-ups. Since knowledge leakage can occur in a number of ways involving many scenarios, organisations typically employ a variety of IP protection and knowledge leakage mitigation methods to minimise the risks. This minor thesis fills the research gaps on innovation processes and knowledge leakage for start-ups. A literature review was conducted into the bodies of research on knowledge leakage and innovation. Following the literature review, a secure innovation process (SIP) model was developed from the research. SIP includes the concept of the risk window which allows a start-up to identify, assess and manage knowledge leakage risks at various stages in the innovation process.
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ItemAn exploratory study of information security auditing( 2016)Management of Information security in organizations is a form of risk management where threats to information assets are managed by implementing various controls. An important task in this cycle of Information Security risk management is Audit, whose function is to provide assurance to organizations that their security controls are indeed working as intended. Numerous frameworks and guidelines are available for auditing Information security. However, there is scant empirical evidence for the process followed in practice. This research explores how security audits are conducted in practice. In order to do so, a qualitative study is conducted where 11 auditors are interviewed. The findings indicate a gap between what is expected of audit and what actually happens in practice. On exploring the Accounting roots of audit, we postulate that this gap is due to the differences in conceptualization of risk between the Accounting and Information Security discipline.