Computing and Information Systems - Theses
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ItemA Person-Centred Information Needs Framework (PcINF) for Hospital Discharge: Exploring personal information needs from hospital to home( 2022)Despite advances in the design and development of healthcare information systems, there is still a lack of adequate process and system support for person-centred discharge. A large body of research highlights the need for tailored and valuable person-centred discharge information that patients can enact and draw on for recovery when they leave the hospital environment. Former studies highlight the need to analyse the information needed for person-centred discharge. More specifically, there is a need to investigate to what extent discharge information needs for a person can be fulfilled from admission through the entire patient journey. Even though several studies focus on the notion of person-centred discharge, there is a lack of theoretical insight on person-centred information needs and the unique attributes of this information. The latter refers to the format, presentation style and time intervals of releasing discharge information to support adaptation during a patient’s recovery process when home. Hence, this study focuses on the critical research question: How can a person-centred information needs framework (PcINF) combined with process and systems thinking improve discharge information? and sub-research questions: i) What are current problems in hospital discharge information? ii) What obstacles prevent a person from utilising hospital discharge information at home? and iii) How can process and systems thinking improve person-centred communication at discharge? In response to these questions and to better understand the problems and challenges associated with person-centred discharge information, this study first developed an integrated person-centred information framework (PcINFv0) by synthesising Roy's (Roy, 1970) Nursing Theory and Adaptive Structuration Theory (AST) (Giddens, 1984). Using a single in-depth case study, this framework was then evaluated through an exploratory multi-method research approach. The approach combined qualitative data collection and analysis methods (medical records and healthcare systems analyses, individual interviews, and a focus group) with business process analysis and process mining techniques. Findings indicate the PcINFv0 is valuable for classifying information relating to lifestyle activities and a person’s social capacity, which impacts adaptation at home. The framework identifies two key aspects: standard information about patient stressors and rules and resources that a person needs to understand during their recovery period at home. In addition, the findings call for attention to the format, presentation style and time-driven release of person-centred discharge information to prevent further readmissions to the hospital. The study is both timely and relevant, particularly in filling the gaps in understanding the problems, challenges and information needs of person-centred discharge information systems. The study highlights the need for a deeper understanding of patient needs in a new era of digitalisation. The findings highlight the need to apply process and systems thinking to the discharge planning and fulfilment process from the perspective of the patient/person’s experience. This calls for more emphasis on the discharge process and the quality and value of the information delivered to the person. This research also paves the way to develop a new generation of innovative and intelligent person-centred discharge systems that empower personal recovery at home.
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ItemAppropriation of formal and informal learning technology in higher education: the case of Saudi students in home country and in Australia( 2022)Currently, students in higher education utilise a wide range of technologies, some of which have been formally provided and mandated by universities (e.g., learning management system (LMS)) whilst others are more informal and used voluntarily (e.g., social networking sites (SNSs)). Both types of technologies provide many advantages to the learning process, including increased communication with peers and academics and better engagement with learning content. Despite the extensive research on the adoption and use of various formal and informal learning technologies, little is known about how these different platforms are appropriated simultaneously within higher education systems. This qualitative interpretive research is composed of three studies that explore the appropriation of formal and informal learning technologies by Saudi students in higher education in their home country and in Australia. The findings of this research show how technology appropriation patterns of the same technology differ in different educational contexts. Specifically, the thesis explores the transition of students’ attitudes and behaviours towards the appropriation of formal and informal learning technologies based on their physical relocation from Saudi Arabia to Australia. The research provides a new context-oriented model as an extension of Adaptive Structuration Theory (AST) with concepts from networked learning to explain students' appropriation of technologies in their learning interactions. The model describes the appropriation process undertaken when students deal with formal and informal learning technologies while addressing the structures of the technology, the environment and the individual characteristics that influence this process. Moreover, the model shows the emergent social and technological structures in the appropriation process and the intended and unintended learning outcomes. Recommendations are provided based on the research findings for different stakeholders involved in students’ learning experiences, including academics, administrators and policymakers in higher education in both Saudi Arabia and Australia.
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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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ItemThe Intersection of Planning and Learning through Cost-to-go Approximations, Imitation and Symbolic Regression( 2022)This thesis explores the intersection between planning and learning methods for autonomous sequential decision-making. Planning is a model-based approach to autonomous sequential decision-making where action policies are derived automatically through a model of an environment. Alternatively, learning methods learn action policies through interaction with an environment. The planning and learning approaches can be likened to current theories of human cognition which propose a fast and associative system works in conjunction with a slow and deliberative one. From this observation previous work has conjectured that in order to create intelligent systems that are more general and robust than existing ones, a combination of planning and learning methods may be required. Two common high-level approaches for combining planning and learning are to use learning to help guide the search effort of planners and to use planners to teach learning algorithms. This thesis examines these two high-level approaches through the topics of cost-to-go approximations, symbolic regression and imitation. We propose and study a number of new algorithms which provide new insights into methods that combine planning and learning, namely, we introduce methods for learning value and policy functions from lookeaheads; learning from single demonstrations produced by planners; and learning heuristics for planning algorithms.
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ItemA Process Model to Improve Information Security Governance in Organisations( 2022)Information security is an increasingly important topic among senior organisational stakeholders (i.e. the board and executive management) as organisations acknowledge the potential for operational disruption, reputational loss, impact to share value and financial penalties. As information resources are a strategic asset to organisations, there is an expectation that these stakeholders will demonstrate their fiduciary duty of care by implementing information security governance (ISG). Compared to corporate governance, ISG is a relatively new and under-researched area. A review of the literature shows the lack of an ISG framework or model that: (1) incorporates the broad areas of ISG; (2) explains how to implement ISG; (3) is empirically grounded; and (4) identifies the processes required to be undertaken by various stakeholder groups involved in ISG. The practical requirement for an ISG framework or model to help organisations improve their implementation of ISG and the research gaps have led to the following research question: “How can ISG be implemented in organisations?” To address the research question, this research has adopted an exploratory research approach. First, a conceptual ISG process model was proposed based on synthesis of extant literature and detailed review of relevant frameworks and models. The conceptual ISG process model was subsequently refined based on empirical data gathered from 3 case study organisations comprising one financial institution in Singapore and two financial institutions in Malaysia. The refined ISG process model was finally validated in 6 expert interviews. This research addresses the aforementioned practice requirements and research gaps by introducing an empirically grounded ISG process model as a practical reference to facilitate the implementation of ISG in organisations. Specifically, the research contributes by: (1) developing ISG process theory, as ISG is a series of events occurring within an organisational context; and (2) developing an information-processing perspective on ISG, as the process model identifies the information and communication flows, and the relationships among stakeholder groups. In addition, the research has: (3) empirically examined and validated the ISG process model based on how ISG is practised in real-world organisations; (4) examined corporate governance theories to provide additional perspectives to ensure that the ISG process model is aligned with corporate governance objectives; (5) identified additional factors that influence the implementation of ISG requiring further research; and finally (6) expanded existing seminal research by introducing an empirically grounded ISG process model that has been developed based on synthesis of cumulative knowledge from previous research and validated with empirical data. This research is the most comprehensive study to date that has developed an empirically grounded ISG process model identifying stakeholder groups and explaining how core ISG processes and sub-processes interact. An ISG process model is easier to visualise for practitioners and easier to implement as it allows practitioners to structure their thinking according to the stages of the process model and change activities in their organisations.
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ItemCredibility Assessment of Online Consumer Reviews( 2022)In the digital transformation era, online reviews have become an important source of information for decisions about purchasing products and services. Research shows that online reviews influence users’ purchasing behaviours and product sales. However, the overwhelming number of online reviews with unknown reviewers has made it difficult for users to find credible information. Thus, this thesis focuses on the credibility evaluation of online consumer reviews and develops a comprehensive credibility model to help differentiate reviews based on credibility. We, first, outline a baseline model for credibility assessment of online consumer reviews by building upon related literature and using the Heuristic Systematic Model (HSM) of information processing as the theoretical lens. We extend the baseline model by conducting several in-depth semi-structured interviews as a way of understanding how online shoppers assess the credibility of online reviews. Next, we identify important attributes that impact the credibility of online reviews and explore the moderating role of the reader’s perspective in this process through performing a user study on the Amazon Mechanical Turk platform. Finally, we develop a machine learning model to predict the credibility of online reviews and conduct a series of ANOVA analyses (analysis of variance) to differentiate the characteristics of fake and credible reviews. This thesis advances the state-of-the-art studies regarding the credibility of online consumer reviews by making the following key contributions: (1) synthesizing the related literature and providing a taxonomy of the key attributes that impact the credibility of online consumer reviews, (2) extending the HSM model in the area of the credibility of online reviews and explaining how users assess online reviews, (3) confirming important elements in the HSM including concurrency and complex effects of attributes in information processing, and exploring the moderating role of individuals in the credibility evaluation of online consumer reviews, (4) developing a predictive model for the credibility of online consumer reviews, and; (5) identifying different characteristics of fake and credible reviews.
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ItemMultimodal Emotion Recognition using Mobile Devices( 2022)Emotion recognition is an emerging interdisciplinary research topic that has received increasing attention over the past few years. It has the potential to play a vital role in mental health and well-being management and can aid in early diagnosis, ongoing monitoring, and timely interventions. With the rapid development of mobile and wearable devices, it has become easier to access users’ affective data passively, continuously, and remotely. This creates new opportunities to help identify and interpret spontaneous human emotions in a less intrusive manner. This thesis first investigates the recent advances in mobile emotion recognition research, especially the state-of-the-art methodologies. Subsequently, we turn our attention to off-the-shelf commercial facial expression-based mobile emotion recognition systems. We identify and classify different distortions in real-world mobile device usage scenarios that can lead to poor image quality. On this basis, we present a replicable assessment protocol to analyse the performance of commercial emotion recognition systems that are based on facial features. Next, we present an emotion recognition system with a hybrid multimodal architecture. It extracts high-level affective features from facial expression, speech, keystroke, and three types of physiological signals (blood volume pulse, electrodermal activity, and skin temperature) using attention-based deep learning architectures, and learns cross-modal correlations for fusion through several deep neural network layers. We demonstrate its robust performance by conducting a user study with 45 participants and explore different real-world usage scenarios where signals are partially available. Finally, we present an efficient hierarchical multimodal network that relies on self-attention and convolution mechanisms to model both local and global dependencies of multiple physiological signals. We evaluate the proposed system on two publicly available mobile multi-sensor datasets and achieve state-of-the-art performance. This thesis demonstrates the feasibility and effectiveness of using mobile devices to automatically and passively detect human emotions and provides novel approaches for designing robust multimodal mobile emotion recognition systems.
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ItemTowards Robust Medical Machine Learning( 2022)Machine learning systems have been developed to address a number of problems in numerous domains, among which medical solutions have been facilitated by machine learning approaches for decades. These approaches play important roles in automated disease diagnosis, medical image processing, and auxiliary surgical operation, etc. Despite the highly efficient diagnosis benefited from machine learning approaches, these methods may not be robust to common challenges in practical scenarios, such as special while crucial characteristics of medical data, annotation variations from multiple experts, noisy annotations, and multi-source datasets. Such problems impede machine learning methods from being applied accurately and safely to medical tasks. In the thesis, we introduce special while important medical problems that were not brought into the spotlight before. We then provide corresponding robust machine learning solutions for each problem when existing machine learning methods degrade significantly in these tasks. Specifically, the first problem is the similarity analysis for time series with large discontinuities, which is common in surgical time series. We thus propose a robust distance measurement for time series with large discontinuities when they disable the accurate measurement of local characteristics using existing algorithms. Second, surgical policies provided by different surgeons for the same patient/surgery may not be exactly the same. We then propose the reward-penalty Dice loss (RPDL) to learn non-unique surgical segmentation regions for deep vision networks. RPDL is robust to varying annotations for the same input, which enables the comprehensive learning of models from multiple experts. Third, medical datasets might be composed of limited examples and noisy annotations, making it challenging to train deep learning models. To address this challenge, we propose alpha-IoU, a family of power Intersection over Union (IoU) losses for bounding box (bbox) regression. We show that alpha-IoU losses are more robust to small datasets and noisy bboxes in lesion detection. Fourth, large-scale medical datasets are often collected from different institutions cooperated by a number of experts. In this case, we build a one-stage framework SpineOne for detecting degenerative discs and vertebrae from spinal MRIs, which implements both the keypoint localization and classification tasks simultaneously. SpineOne is a robust detector to multi-source MRI slices with various scales, numbers and quality. All four proposed machine learning approaches outperform existing baselines by a noticeable margin in specific medical tasks. In summary, four medical issues are thoroughly investigated in the thesis, i.e., the distance measurement for surgical time series with large discontinuities, the surgical region segmentation with a variety of clinician annotations, the lesion detection with limited examples and noisy bboxes, and the anatomical keypoint detection with multi-source medical data. Towards more robust medical machine learning, we then propose one robust machine learning approach for each corresponding problem.
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ItemThe Ethics of Multiplayer Gameplay and Design( 2022)Online multiplayer games present players with vast opportunities to interact with one another in rich and interesting ways. However, this freedom also allows players to interact in harmful or ‘toxic’ ways, from trolling, griefing and disruptive play to abusive chat and player harassment. This toxicity is problematic because it can cause significant harm to players and because it can drive players away and deter new players from joining games. This has led to calls for more ethical play and design. However, ethics in the context of multiplayer gaming is a nebulous concept. Literature suggests that the context of an in-game act can heavily dictate how it is interpreted in a specific game and community, and that different players can hold very different moral views about the same in-game act. This presents a problem: If there are multiple ways of understanding in-game ethics, what does it mean to ethically play and design multiplayer games? This thesis addresses this question through a comprehensive, contextualist, empirical examination of the ethics of multiplayer gameplay and design. It investigates the ethical understandings of gaming communities from the bottom up, examines how these understandings are tied to ethical disagreements within these communities, and explores what ethical design might look like in these varied, shifting and morally complex contexts. In doing so, this thesis adopts a qualitative, constructionist and reflexive methodological approach. It presents three studies encompassing analysis of hundreds of online comments, 40 in-depth interviews, and 3 focus groups with both players and game industry professionals as two key groups making up gaming communities. The first study examines a morally important case study of groping in a virtual reality game. Thematic analysis of over 300 online responses to this particular incident demonstrates the ways that commenters attempt to make sense of this act by comparing and contrasting it to other physical and in-game acts amidst a tense sociomoral context. The second study analyses 20 individual interviews and 3 focus groups with players to formulate the ‘apathetic villager theory’ of player amorality, which suggests that players’ apparently morally disengaged stances can disguise a variety of attitudes towards in-game acts, including a sense of helplessness in the face of disruptive behaviours, as well as an active valuing of disruption. This study also presents a novel framework of ludomorality that captures the main concepts that players draw on in their ethical deliberations—in particular, it highlights the importance of the ludic and digital context in shaping their ethical judgments. The third study presents a reflexive thematic analysis of 21 interviews with game industry professionals. It highlights the wicked problem that this group faces in the design of multiplayer games, and puts forward a set of design considerations to address these issues. Through these three studies, this thesis contributes a nuanced theoretical perspective on multiplayer ethics. Rather than understanding players and industry professionals as amoral, this thesis emphasises the multiple layers in which the ludic and digital context influence ethical deliberation. It finds that rapid developments in games and gaming communities, as well as wider discourses around ethical practice tied to technology, produce conflicting understandings of what it means to play and to play ‘right’. This complex social context, along with current industry structures that emphasise a game’s functionality and profits over ethical governance, produce a number of difficulties in designing multiplayer games in ethical ways. Based on these findings, this thesis introduces the ISA principles (Information, Specificity, and Agility), which aim to assist designers and researchers in navigating multiplayer design in ethically divided contexts. In doing so, this thesis advocates for a contextualist approach to multiplayer ethics and design that both responds to and challenges the ambiguous ludomoral contexts at play.
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ItemThe Recency Problem and its Applications( 2022)Our thesis is centered on the question of how to calculate the recency of items over a stream of data. Informally, the recency query asks, when was the last time I saw item x. This measure is a key component in the identification of hot data for workloads that exhibit high temporal localities. The objective is pertinent in the fields of data caching and flash storage, applications where available memory is limited. In large memory, existing structures, such as hash tables, can support a recency query by augmenting item occurrences with timestamps. Therefore, we are interested in small memory solutions for resource constrained environments. The thesis is divided into two halves. In the first half, we exhibit two novel data structures that support recency queries in small memory. In the second, we expand the scope of the recency query and demonstrate a meaningful application in the field of oblivious storage. Our first result, Historical Membership, builds on sliding-window dictionaries, which provide dynamic membership queries over a window of the most recent occurrences. By combining sliding-window dictionaries in a hierarchical structure, and with careful design of the underlying hash tables, Historical Membership supports recency queries with bounded relative error on top of a succinct representation of the window. The second result, the Princess List, supports stack distance queries in small memory. The stack distance is a variation of the recency query. The Princess List is the first succinct representation of a list that supports updates and index and search queries in optimal time. To consolidate our work, we provide an application of Historical Membership in the field of oblivious storage. An oblivious RAM is a remote storage protocol that provides a client with access pattern privacy. We construct a new ORAM scheme, Rank ORAM, that leverages the support of Historical Membership to achieve better bandwidth and latency performance against existing approaches. In addition, we present a new solution to oblivious permutation, which is a key primitive in many ORAM schemes. The proven asymptotic behavior of the algorithm, WaksmanOP, and our experimental results, show that under several realistic scenarios, compared to baseline solutions, WaksmanOP is the best trade-off.