Computing and Information Systems - Research Publications
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ItemExamining the effects of the COVID-19 pandemic on technology-enabled social interactions(ACM, 2023)This study investigated the impact of the COVID-19 pandemic on technology-enabled social interactions using the Social Presence Theory (SPT). Cross-sectional data were collected through an online survey, with participation from 515 Australian adults aged 18 years and above. Bivariate and multinomial logistic regression analysis showed that age group differences significantly predicted satisfaction levels with online interactions, with the 18-40 age group being less satisfied than participants aged 41 and over. In addition, face-to-face interaction was highly preferred over online interactions. Socio-demographic factors such as age, gender, and marital status were significantly associated with the type of interaction individuals reported missing the most when they could not interact face-to-face. Most participants reported missing physical and emotional connection when they could not interact face-to-face. The study adds to the existing knowledge about the effects of the COVID-19 pandemic on social interactions, providing insights into both practical paraphernalia and implications for further research.
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ItemStrategy Extraction in Single-Agent Games(AAMAS, 2023)The ability to continuously learn and adapt to new situations is one where humans are far superior compared to AI agents. We propose an approach to knowledge transfer using behavioural strategies as a form of transferable knowledge influenced by the human cognitive ability to develop strategies. A strategy is defined as a partial sequence of events – where an event is both the result of an agent’s action and changes in state – to reach some predefined event of interest. This information acts as guidance or a partial solution that an agent can generalise and use to make predictions about how to handle unknown observed phenomena. As a first step toward this goal, we develop a method for extracting strategies from an agent’s existing knowledge that can be applied in multiple contexts. Our method combines observed event frequency information with local sequence alignment techniques to find patterns of significance that form a strategy. We show that our method can identify plausible strategies in three environments: Pacman, Bank Heist and a dungeon-crawling video game. Our evaluation serves as a promising first step toward extracting knowledge for generalisation and, ultimately, transfer learning.
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ItemIOQP: A simple Impact-Ordered Query Processor written in Rust(RWTH, Aachen University, 2023-01-01)Impact-ordered index organizations are suited to score-at-a-time query evaluation strategies. A key advantage of score-at-a-time processing is that query latency can be tightly controlled, leading to lower tail latency and less latency variance overall. While score-at-a-time evaluation strategies have been explored in the literature, there is currently only one notable system that promotes impact-ordered indexing and efficient score-at-a-time query processing. In this paper, we propose an alternative implementation of score-at-a-time retrieval over impact-ordered indexes in the Rust programming language. We detail the efficiency-effectiveness characteristics of our implementation through a range of experiments on two test collections. Our results demonstrate the efficiency of our proposed model in terms of both single-threaded latency, and multi-threaded throughput capability. We make our system publicly available to benefit the community and to promote further research in efficient impact-ordered query processing.
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ItemNo Preview AvailableAlgorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableKey Considerations for The Design of Technology for Enrichment in Residential Aged Care: An Ethnographic Study(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableLossy Compression Options for Dense Index Retention(Association for Computing Machinery, 2023)Dense indexes derived from whole-of-document neural models are now more effective at locating likely-relevant documents than are conventional term-based inverted indexes. That effectiveness comes at a cost, however: inverted indexes require less than a byte per posting to store, whereas dense indexes store a fixed-length vector of floating point coefficients (typically 768) for each document, making them potentially an order of magnitude larger. In this paper we consider compression of indexes employing dense vectors. Only limited space savings can be achieved via lossless compression techniques, but we demonstrate that dense indexes are responsive to lossy techniques that sacrifice controlled amounts of numeric resolution in order to gain compressibility. We describe suitable schemes, and, via experiments on three different collections, show that substantial space savings can be achieved with minimal loss of ranking fidelity. These techniques further boost the attractiveness of dense indexes for practical use.
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ItemNo Preview Available
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly Detection(IEEE, 2023-01-01)
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ItemNo Preview AvailableRepeated Builds During Code Review: An Empirical Study of the OpenStack Community(Institute of Electrical and Electronics Engineers, 2023)Code review is a popular practice where developers critique each others' changes. Since automated builds can identify low-level issues (e.g., syntactic errors, regression bugs), it is not uncommon for software organizations to incorporate automated builds in the code review process. In such code review deployment scenarios, submitted change sets must be approved for integration by both peer code reviewers and automated build bots. Since automated builds may produce an unreliable signal of the status of a change set (e.g., due to 'flaky' or non-deterministic execution behaviour), code review tools, such as Gerrit, allow developers to request a 'recheck', which repeats the build process without updating the change set. We conjecture that an unconstrained recheck command will waste time and resources if it is not applied judiciously. To explore how the recheck command is applied in a practical setting, in this paper, we conduct an empirical study of 66,932 code reviews from the OpenStack community. We quantitatively analyze (i) how often build failures are rechecked; (ii) the extent to which invoking recheck changes build failure outcomes; and (iii) how much waste is generated by invoking recheck. We observe that (i) 55% of code reviews invoke the recheck command after a failing build is reported; (ii) invoking the recheck command only changes the outcome of a failing build in 42% of the cases; and (iii) invoking the recheck command increases review waiting time by an average of 2,200% and equates to 187.4 compute years of waste-enough compute resources to compete with the oldest land living animal on earth. Our observations indicate that the recheck command is frequently used after the builds fail, but does not achieve a high likelihood of build success. Based on a developer survey and our history-based quantitative findings, we encourage reviewer teams to think twice before rechecking and be considerate of waste. While recheck currently generates plenty of wasted computational resources and bloats waiting times, it also presents exciting future opportunities for researchers and tool builders to propose solutions that can reduce waste.
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ItemIdentification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning(SCITEPRESS - Science and Technology Publications, 2024)Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.