Computing and Information Systems - Theses

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Practical declarative debugging of mercury programs

MacLarty, Ian Douglas. (University of Melbourne, 2006)
Practical declarative debugging of mercury programs

MacLarty, Ian Douglas. (University of Melbourne, 2006)
A multistage computer model of picture scanning, image understanding, and environment analysis, guided by research into human and primate visual systems

Rogers, T. J. (University of Melbourne, Faculty of Engineering,, 1983)

This paper describes the design and some testing of a computational model of picture scanning and image understanding (TRIPS), which outputs a description of the scene in a subset of English. This model can be extended to control the analysis of a three dimensional environment and changes of the viewing system's position within that environment. The model design is guided by a summary of neurophysiological, psychological, and psychophysical observations and theories concerning visual perception in humans and other primates, with an emphasis on eye movements. These results indicate that lower level visual information is processed in parallel in a spatial representation while higher level processing is mostly sequential, using a symbolic, post iconic, representation. The emphasis in this paper is on simulating the cognitive aspects of eye movement control and the higher level post iconic representation of images. The design incorporates several subsystems. The highest level control module is described in detail, since computer models Of eye movement which use cognitively guided saccade selection are not common. For other modules, the interfaces with the whole system and the internal computations required are out lined, as existing image processing techniques can be applied to perform these computations. Control is based on a production . system, which uses an "hypothesising" system - a simplified probabilistic associative production system - to determine which production to apply. A framework for an image analysis language (TRIAL), based on "THINGS". and "RELATIONS" is presented, with algorithms described in detail for the matching procedure and the transformations of size, orientation, position, and so On. TRIAL expressions in the productions are used to generate "cognitive expectations" concerning future eye movements and their effects which can influence the control of the system. Models of low level feature extraction, with parallel processing of iconic representations have been common in computer vision literature, as are techniques for image manipulation and syntactic and statistical analysis� Parallel and serial systems have also been extensively investigated. This model proposes an integration Of these approaches using each technique in the domain to which it is suited. The model proposed for the inferotemporal cortex could be also suitable as a model of the posterior parietal cortex. A restricted version of the picture scanning model (TRIPS) has been implemented, which demonstrates the consistency of the model and also exhibits some behavioural characteristics qualitatively similar to primate visual systems. A TRIAL language is shown to be a useful representation for the analysis and description of scenes. key words: simulation, eye movements, computer vision systems, inferotemporal, parietal, image representation, TRIPS, TRIAL.
Safe acceptance of zero-confirmation transactions in Bitcoin

Yang, Renlord ( 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.
Do personality traits drive online commitment to vote in social networks?

Wood, Miguel - Angel ( 2019)

This study examines social network effects in a community election, and actor attributes effects in fostering commitment to vote behaviour. Across Western societies political participation is in decline posing major challenges for democracy. Since 2000,, political advocacy has undergone a rapid transformation led by a disruptive wave of IT-led innovation in infrastructure, predictive analytics, and online social networks. Political campaigns have harnessed these advances to target, influence, and mobilise partisan voters. Yet is political participation uniform across voters? Network interventions using online social networks and behavioural science are found to increase voter turnout, and reveal individual differences in political behaviour (Bakshy, Messing, & Adamic, 2015; Bond et al., 2012). In particular, extroverted individuals relative to other users may play an enhanced role (Messing, 2015). Our current picture of personality traits and political behaviour is largely offline. Few studies relate to online behaviour (Jordan, Pope, Wallis, & Iyer, 2015). Studies examine individual responses to general/targeted information against two-step flow communication models (Lazarsfeld, Berelson, & Gaudet, 1948). Yet opinion leaders and social networks still shape individual attitudes and behaviours (Contractor & DeChurch, 2014). How personality traits guide social interactions and social influence online during an election is unclear. In this research we examine the interaction of personality traits, internal political efficacy and human social motives to enact social influence during a community election. Our research model investigates whether personality traits drive commitment to vote behaviour by eliciting implementation intentions using an online vote plan. The attribute of gender, often ignored, is incorporated to determine influence processes operating within the network. Prior online political network analysis has relied on data collected from third-party platforms intended for other purposes. We overcome this limitation using a novel, mobile-first, social media app operating as a social network to better connect community members to political information, increase engagement, and improve transparency. The app enables unobtrusive data collection based on voluntary user interaction with online information. The app is supported by scalable graph-database architecture for behaviour tracking, real-time analytics, and increased granularity. All attribute-validated measurement instruments for personality traits, internal political efficacy, and commitment to vote are integrated into the app with only single responses recorded along with demographic information for each user. To understand how an individual’s attributes and their relationships with others affect commitment to vote, we adopt an Autologistic actor attribute model (ALAAM), a social influence model for statistical analysis of observed network data. With political disengagement endemic across Western democracies, new interventions that mitigate, or turn around current trends, are highly valued. Beyond political network analysis, social network research opportunities in national and international economic, institutional, and community settings exist. By deepening our understanding of small world interactions we hope to respond to the collective challenge of restoring the link between the meaning and purpose of voting, and help reinvigorate democracy.
Automatic caloric expenditure estimation with smartphone's built-in sensors

Cabello Wilson, Nestor Stiven ( 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).
Scalable clustering of high dimensional data in non-disjoint axis-parallel subspaces

Doan, Minh Tuan ( 2019)

Clustering is the task of grouping similar objects together, where each group formed is called a cluster. Clustering is used to discover hidden patterns or underlying structures from the data, and has a wide range of applications in areas such as the Internet of Things (IoT), biology, medicine, marketing, business, and computing. Recent developments in sensor and storage technology have led to a rapid growth of data, both in terms of volume and dimensionality. This raises challenges for existing clustering algorithms and led to the development of subspace clustering algorithms that cope with the characteristics, volumes, and dimensionality of the datasets that are now available. In this thesis, we address the challenges of finding subspace clusters in high dimensional data to achieve subspace clustering with high quality and scalability. We provide a comprehensive literature review of existing algorithms, and identify the open challenges in subspace clustering of high dimensional data that we address in this thesis, namely: devising appropriate similarity measures, finding non-disjoint subspace clusters, and achieving scalability to high dimensional data. We further illustrate these challenges in a real-life application. We show that clustering can be used to construct a meaningful model of the pedestrian distribution in the City of Melbourne, Australia, in low dimensional space. However, we demonstrate that the clustering quality deteriorates rapidly as the number of dimensions (pedestrian observation points) increase. This also serves as a motivating example on why subspace clustering is needed and what challenges need to be addressed. We first address the challenge of measuring similarity between data points, which is a key challenge in analyzing high dimensional data that directly impacts the clustering results. We propose a novel method that generates meaningful similarity measures for subspace clustering. Our proposed method considers the similarity between any two points as the union of base similarities that are frequently observed in lower dimensional subspaces. This allows our method to first search for similarity in lower dimensional subspaces and aggregate these similarity values to determine the overall similarity. We show that this method can be applied for measuring similarity based on distance, density, and grids, which enables our similarity measurements to be used with different types of clustering algorithms, i.e., distance-based, density-based, and grid-based clustering algorithms. We then use our similarity measurement to build a subspace clustering algorithm that can find clusters in non-disjoint subspaces. Our proposed algorithm follows a bottom-up strategy. The first phase of our algorithm searches for base clusters in low dimensional subspaces. Subsequently, the second phase forms clusters in higher dimensional subspaces by aggregating these base clusters. The novelty of our proposed method is reflected in both phases. First, we show that our similarity measurement can be integrated in a subspace clustering algorithm. This not only helps prevent the false formation of clusters, but also significantly reduces the time and space complexity of the algorithm by pruning irrelevant subspaces at an early stage. Second, our algorithm transforms the common sequential aggregation of base clusters into a problem of frequent pattern mining. This enables efficient formation of clusters in high dimensional subspaces using FP-Trees. We then demonstrate that our proposed algorithm can outperform traditional subspace clustering algorithms using bottom-up strategies, as well as state-of-the-art algorithms with other clustering strategies, in terms of clustering quality and scalability to large volumes and high dimensionality of data. Subsequently, we evaluate the ability of our proposed subspace clustering algorithm to find clusters in datasets from different real-life applications. We conduct experiments on datasets from three different applications. First, we apply our proposed clustering algorithm to pedestrian measurements in the City of Melbourne, and construct a meaningful model that describes the profiles of the distributions of pedestrians that correspond to pedestrian activities at different times of the day. We then use our clustering algorithm to analyze the impacts of a major change in the public transport of Melbourne on the activities of pedestrians. In the second application, we evaluate the ability of the proposed algorithm to work with very high dimensional data. Specifically, we apply our algorithm on ten gene expression datasets, which comprise up to 10,000 dimensions. Next, we explore the ability of our algorithm to produce clustering results that can be used as an intermediate step that assists the construction of a more complicated model. Specifically, we use our clustering result to build an ensemble classification model, and show that this model improves the accuracy of predicting the car parking occupancy in the central business district (CBD) of the City of Melbourne. By applying our proposed methods in a wide range of applications on datasets with different sizes and dimensionalities, we demonstrate the ability of our algorithm to cluster high dimensional datasets that possess complex structures with high levels of noise and outliers to produce meaningful clustering results that can have practical impact.
Ontologies in neuroscience and their application in processing questions

Eshghishargh, Aref ( 2019)

Neuroscience is a vast, multi-dimensional and complex field of study based on both its medical importance and unresolved issues regarding how brain and the nervous system work. This is because of the huge amount of brain disorders and their burden on people and society. Furthermore, scientist have been excited about the function and structure of brain, ever since it was discovered to be responsible for all our emotions, thoughts and behaviour. Ontologies are concepts whose origins go back to philosophy and the concern with the nature and relation of being. They have emerged as promising tools for assistance with neuroscience research recently and provide additional data on a field of study. They connect each entity or element to other ones through descriptive relationships. Ontologies seem to suit the complex, multi-dimensional and still incomplete nature of neuroscience very well because of their characteristics. The first study shines light on applications of ontologies in neuroscience. It incorporated a systematic literature review and methodically reviewed over 1000 research papers from eight databases and three journals. After scanning all documents, 208 of them were selected. Then, a full text analysis was performed on the selected documents. This study found eight major applications for ontologies in neuroscience, most of them consisted of several subcategories. The analysis not only demonstrated the current applications of ontologies in neuroscience, but also their potential future in this field. The second study was set to represent neuroscience questions and then, classify them using ontologies. For this purpose, a questions set was gathered from two research teams and analysed. This, results in a set of dimensions which represents questions. Then, a question hierarchy was formed based on dimensions and questions were classified according to that hierarchy. Two different approaches were used for the classification including an ontology-based approach and a statistical approach. The ontology-based approach exceeded the statistical approach by 15.73% better classification results. The last study was designed to tackle and resolve questions with the assistance of ontologies. It first proposed a set of templates that acted as a translation mechanism for changing questions into machine readable code. Templates were based on the question hierarchy presented in the previous study. Second, this study created an integrated collection of resources including two domain ontologies (NIFSTD and NeuroFMA) and a neuroimaging annotation application (Freesurfer). Subsequently, the code created using templates was executed upon the integrated resource (knowledge base) to find the appropriate answer. While processing the questions, ontologies were used for disambiguation purposes too. At the end, all parts created in this study along with the question classification method created in the previous study were merged as different modules of a question processing model. In conclusion, this thesis reviewed all current ontology applications in neuroscience in detail and demonstrated the extent to which they can assist scientists in classifying and resolving questions. The results of this thesis show that applications of ontologies in neuroscience are diverse and cover a wide range; they are steadily becoming more used in this field; and they can be powerful semantic tools in performing different tasks in neuroscience.
Towards Robust Representation of Natural Language Processing

Li, Yitong ( 2019)

There are many challenges in building robust natural language applications. Machine learning based methods require large volumes of annotated text data, and variations over text can lead to problems, namely: (1) language can be highly variable and expressed with different variations, such as lexical and syntactic. Robust models should be able to handle these variations. (2) A text corpus is heterogeneous, often making language systems domain-brittle. Solutions for domain adaptation and training with corpora comprised of multiple domains are required for language applications in the real world. (3) Many language applications tend to be biased to the demographic of the authors of documents the system is trained on, and lack model fairness. Demographic bias also causes privacy issues when a model is made available to others. In this thesis, I aim to build robust natural language models to tackle these problems, focusing on deep learning approaches which have shown great success in language processing via representation learning. I pose three basic research questions: how to learn representations that are robust to language variation, robust to domain variation, and robust to demographic variables. Each of these research questions is tackled using different approaches, including data augmentation, adversarial learning, and variational inference. For learning robust representations to language variation, I study lexical variation and syntactic variation. To be specific, a regularisation method is proposed to tackle lexical variation, and a data augmentation method is proposed to build robust models, using a range of language generation methods from both linguistic and machine learning perspectives. For domain robustness, I focus on multi-domain learning and investigate domain supervised and unsupervised learning, where domain labels may or may not be available. Two types of models are proposed, via adversarial learning and latent domain gating, to build robust models for heterogeneous text. For robustness to demographics, I show that demographic bias in the training corpus leads to model fairness problems with respect to the demographic of the authors, as well as privacy issues under inference attacks. Adversarial learning is adopted to mitigate bias in representation learning, to improve model fairness and privacy-preservation. To demonstrate the proposed approaches, a range of tasks are considered, including text classification and POS tagging. To evaluate the generalisation and robustness, both in-domain and out-of-domain experiments are conducted with two classes of language tasks: text classification and part-of-speech tagging. For multi-domain learning, multi-domain language identification and multi-domain sentiment classification are conducted, and I simulate domain supervised learning and domain unsupervised learning to evaluate domain robustness. I evaluate model fairness with different demographic attributes and apply inference attacks to test model privacy. The experiments show the advantages and the robustness of the proposed methods. Finally, I discuss the relations between the different forms of robustness, including their commonalities and differences. The limitations of this thesis are discussed in detail, including potential methods to address these shortcomings in future work, and potential opportunities to generalise the proposed methods to other language tasks. Above all, these methods of learning robust representations can contribute towards progress in natural language processing.
Health Information Systems Enabled Transformation of Service Ecosystems: The Case of Indonesian Healthcare

Ramadani, Luthfi ( 2019)

Information and Communication Technology (ICT) has contributed significantly to the socio-economic development of societies. In particular, developing countries are now beginning to undertake ICT-enabled transformations that previously took place in the western world. However, while the proliferation of ICT is considered a crucial enabler of this transformation, ICT for Development (ICT4D) projects continue to fail as they do not achieve the anticipated societal impacts. Therefore, a holistic and systemic perspective of ICT4D research is needed to enhance the current understanding of these phenomena. This study addresses this knowledge gap through an in-depth investigation on how the structure of public health ecosystem in Indonesia is changed and transformed following Health Information Systems (HIS) introduction. A qualitative multiple case study was conducted across three district-level government. The analysis reveals the distinctive impacts of HIS introduction on the structural properties of the ecosystem, which include institutional rules, resources configuration, actors’ institutional logics, and practices. This study also identifies three mechanisms (adoption-incorporation, breaking-making, and self-reinforcing) of HIS enabled transformation which constitute two pathways (enslaving and emergence) of the ecosystem's transformation. The findings of this study offer theoretical contributions to ICT4D and service literature and practical contributions to HIS implementation in Indonesia. The transformation process of the ecosystem’s structure offers a systemic perspective of ICT4D, which overcomes the tendency to overemphasise the significance role of technology and agency in developing countries. The pathways of transformation complement those earlier studies investigating the reasons for numerous failures of the top-down technological transfer and the importance of inclusion, engagement, and empowerment of the societal groups in ICT4D. To service literature, this study offers insights into the origins and lifecycle of practices and how they emerge in the ecosystem, which shed light on the dynamic and evolving nature of ecosystem’s structure that currently has not been adequately understood. Finally, the results of this study advocate the autonomy of the district’s health providers, the inclusion and engagement of local actors, and the use of the incremental approach to HIS implementation in public health ecosystem.

Computing and Information Systems - Theses

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