ORCID Profile
0000-0003-4805-1467
Current Organisation
Deakin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Software Engineering | Adaptive Agents and Intelligent Robotics | Health Information Systems (incl. Surveillance) | Medical Devices | Distributed and Grid Systems | Computer Software | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Behaviour and Health | Integrated Circuits and Devices | Computer Software and Services not elsewhere classified | Application Software Packages (excl. Computer Games) |
Related Links
Publications
New Genetic Variant in the MYH7 Gene Associated With Hypoplastic Right Heart Syndrome and Hypertrophic Cardiomyopathy in the Same Family
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 11-10-2023
Measuring traffic congestion: An approach based on learning weighted inequality, spread and aggregation indices from comparison data
Publisher: Elsevier BV
Date: 06-2018
Designing an evaluation tool to measure emotional goals
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-3923-0.CH038
Abstract: Human emotions have been widely researched in many disciplines such as psychology, philosophy, neuroscience and medicine. Their importance cannot be underestimated. Unfortunately, so far in software engineering, requirements engineers focus mostly on gathering functional and quality requirements and rarely consider how stakeholders feel or would like to feel when using a software product. Incorporating the user's emotional goals in software engineering can be very challenging considering that emotions are very complex and subjective. Moreover, when it comes to incorporating user emotions in software engineering, existing methodologies or frameworks provide very little guidance to software professionals. In this paper, the authors present work on evaluating emotional goals in a software engineering context. The authors describe the development of a questionnaire as an evaluation tool and evaluate the questionnaire in the context of a digital photo frame placed in the homes of nine older persons living on their own. Further improvements to the tool are proposed based on the findings from the study.
ICT-enabled time-critical clinical practices: Examining the affordances of an information processing solution
Publisher: Australian Journal of Information Systems
Date: 22-11-2015
Abstract: In this paper, we present a case study of a decision-support system deployment at The Alfred Hospital, in Melbourne, Australia. This work outlines Information and Communications Technology (ICT) affordances and their actualisations in time-critical clinical practices to enable better information processing. From our study findings, we present a stage-wise model describing the role played by ICT in the context of the Trauma Centre practices. This addresses a knowledge gap surrounding the role and impact of ICT in the delivery of quality improvements to processes and culture in time-critical environments, amid increasing expenditure on ICT globally. Our model has implications for research and practice, such that we observe for the first time how information standards, synergy and renewal are developed between the system and its users in order to reduce error rates in the healthcare context. Through the study findings, we demonstrate that healthcare quality can be further refined as ICT allows for knowledge dissemination and informs existing practices.
Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services
Publisher: IEEE
Date: 09-2019
A revised open source usability defect classification taxonomy
Publisher: Elsevier BV
Date: 12-2020
Case study of designing and evaluating an independent open learner model tool
Publisher: Informa UK Limited
Date: 20-07-2023
Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture
Publisher: IEEE
Date: 08-01-2023
The inevitable stability of software change
Publisher: IEEE
Date: 10-2007
Agile practices in software development - experiences from student projects
Publisher: IEEE
Date: 2006
DOI: 10.1109/ASWEC.2006.9
A Conceptual Model for Architecting Mobile Applications
Publisher: IEEE
Date: 05-2015
Supporting multi-view development for mobile applications
Publisher: Elsevier BV
Date: 04-2019
Analysis of the Textual Content of Mined Open Source Usability Defect Reports
Publisher: IEEE
Date: 12-2018
QoS-Aware Service Selection for Customisable Multi-tenant Service-Based Systems: Maturity and Approaches
Publisher: IEEE
Date: 06-2015
A preliminary analysis of mobile app user reviews
Publisher: ACM
Date: 26-11-2012
Subspace based Anomaly Detection Framework for Point Clouds
Publisher: IEEE
Date: 10-2022
A preliminary analysis of vocabulary in mobile app user reviews
Publisher: ACM
Date: 26-11-2012
Bootstrapping Mobile App Development
Publisher: IEEE
Date: 05-2015
EEG datasets for seizure detection and prediction— A review
Publisher: Wiley
Date: 16-02-2023
DOI: 10.1002/EPI4.12704
Abstract: Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.
A multi-view framework for generating mobile apps
Publisher: IEEE
Date: 10-2015
Merging Intelligent API Responses Using a Proportional Representation Approach
Publisher: Springer International Publishing
Date: 2019
User perceptions of using an open learner model visualisation tool for facilitating self-regulated learning
Publisher: ACM
Date: 31-01-2017
Learning Better Inspection Optimization Policies
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2012
DOI: 10.1142/S0218194012500179
Abstract: Recent research has shown the value of social metrics for defect prediction. Yet many repositories lack the information required for a social analysis. So, what other means exist to infer how developers interact around their code? One option is static code metrics that have already demonstrated their usefulness in analyzing change in evolving software systems. But do they also help in defect prediction? To address this question we selected a set of static code metrics to determine what classes are most "active" (i.e., the classes where the developers spend much time interacting with each other's design and implementation decisions) in 33 open-source Java systems that lack details about in idual developers. In particular, we assessed the merit of these activity-centric measures in the context of "inspection optimization" — a technique that allows for reading the fewest lines of code in order to find the most defects. For the task of inspection optimization these activity measures perform as well as (usually, within 4%) a theoretical upper bound on the performance of any set of measures. As a result, we argue that activity-centric static code metrics are an excellent predictor for defects.
Reporting usability defects-do reporters report what software developers need?
Publisher: ACM
Date: 06-2016
On the use of properties in Java applications
Publisher: IEEE
Date: 2010
State-Based Markers of Disordered Eating Symptom Severity
Publisher: MDPI AG
Date: 22-06-2020
DOI: 10.3390/JCM9061948
Abstract: Recent work using naturalistic, repeated, ambulatory assessment approaches have uncovered a range of within-person mood- and body image-related dynamics (such as fluctuation of mood and body dissatisfaction) that can prospectively predict eating disorder behaviors (e.g., a binge episode following an increase in negative mood). The prognostic significance of these state-based dynamics for predicting trait-level eating disorder severity, however, remains largely unexplored. The present study uses within-person relationships among state levels of negative mood, body image, and dieting as predictors of baseline, trait-level eating pathology, captured prior to a period of state-based data capture. Two-hundred and sixty women from the general population completed baseline measures of trait eating pathology and demographics, followed by a 7 to 10-day ecological momentary assessment phase comprising items measuring state body dissatisfaction, negative mood, upward appearance comparisons, and dietary restraint administered 6 times daily. Regression-based analyses showed that, in combination, state-based dynamics accounted for 34–43% variance explained in trait eating pathology, contingent on eating disorder symptom severity. Present findings highlight the viability of within-person, state-based dynamics as predictors of baseline trait-level disordered eating severity. Longitudinal testing is needed to determine whether these dynamics account for changes in disordered eating over time.
Comparative analysis of evolving software systems using the Gini coefficient
Publisher: IEEE
Date: 09-2009
An empirical study of user perceived usefulness and preference of open learner model visualisations
Publisher: IEEE
Date: 09-2016
Beware the evolving ‘intelligent’ web service! an integration architecture tactic to guard AI-first components
Publisher: ACM
Date: 08-11-2020
Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up stu
Publisher: BMJ
Date: 04-2023
DOI: 10.1136/BMJOPEN-2022-066249
Abstract: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). ACTRN12621001223820.
App reviews: Breaking the user and developer language barrier
Publisher: Springer International Publishing
Date: 22-10-2016
Requirements of API Documentation: A Case Study into Computer Vision Services
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Patterns of component evolution
Publisher: Springer Berlin Heidelberg
Date: 2007
A Preliminary Study of Open Learner Model Representation Formats to Support Formative Assessment
Publisher: IEEE
Date: 07-2015
Impact of viral propagation on user interface design
Publisher: ACM
Date: 26-11-2012
What influences usability defect reporting? - A survey of software development practitioners
Publisher: IEEE
Date: 2017
Factors affecting interpretation of dental radiographs
Publisher: British Institute of Radiology
Date: 2023
Abstract: To identify the factors influencing errors in the interpretation of dental radiographs. A protocol was registered on Prospero. All studies published until May 2022 were included in this review. The search of the electronic databases spanned Ovid Medline, PubMed, EMBASE, Web of Science and Scopus. The quality of the studies was assessed using the MMAT tool. Due to the heterogeneity of the included studies, a meta-analysis was not conducted. The search yielded 858 articles, of which eight papers met the inclusion and exclusion criteria and were included in the systematic review. These studies assessed the factors influencing the accuracy of the interpretation of dental radiographs. Six factors were identified as being significant that affected the occurrence of interpretation errors. These include clinical experience, clinical knowledge, and technical ability, case complexity, time pressure, location and duration of dental education and training and cognitive load. The occurrence of interpretation errors has not been widely investigated in dentistry. The factors identified in this review are interlinked. Further studies are needed to better understand the extent of the occurrence of interpretive errors and their impact on the practice of dentistry.
Object-Oriented Reengineering
Publisher: Springer Berlin Heidelberg
Date: 2004
Do metrics help to identify refactoring?
Publisher: ACM
Date: 20-09-2010
Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: The Vibe Up Stu
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2022
DOI: 10.1101/2022.12.05.22283129
Abstract: Meta-analytic evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI) driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multi-arm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically-oriented, decentralised AI-adaptive group sequential randomised controlled trial (RCT) comparing the effectiveness of one of three brief, two week digital self-guided interventions (mindfulness, physical activity, or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (DASS-21 total score) from pre-intervention to post-intervention. Secondary outcomes include change in depression, anxiety, and stress (measured by DASS-21 subscales) from pre-intervention to post-intervention. Planned contrasts will compare the four groups (i.e., the three intervention and control) using self-reported psychological distress at pre-specified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice [1] was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). The trial is registered with the Australian New Zealand Clinical Trials Registry (AC-TRN12621001223820). The study addresses an important clinical question using novel, advanced methods The trial uses short-duration interventions designed to improve coping responses to transient stressors, which addresses the most common needs of university students A value of information analysis is included to compare the value of the new trial methods with traditionalapproaches Digital phenotyping is used to explore smartphone sensor information with clinical outcomes More than 12 mini-trials might be required to determine the ranking for the interventions The interventions may prove to be of the same level of effectiveness for each level of severity Interventions other than those examined in this study, such as CBT, may be more effective and remain untested The methodology assumes that the three digital interventions are configured to deliver similar doses and/or have approximate fidelity with standard methods
Multi-node multi-agent cloud simulation
Publisher: IEEE
Date: 08-2015
Detecting structural changes in object oriented software systems
Publisher: IEEE
Date: 2005
Towards a taxonomy for annotation of data science experiment repositories
Publisher: IEEE
Date: 09-2021
Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications
Publisher: JMIR Publications Inc.
Date: 06-11-2019
DOI: 10.2196/16399
Abstract: In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.
Reporting Usability Defects: A Systematic Literature Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Technical debt interest assessment: From issues to project
Publisher: ACM
Date: 22-05-2017
On the resilience of classes to change
Publisher: European Association of Software Science and Technology
Date: 2008
Evaluating an open learner model visualisation prototype tool with user experience metrics
Publisher: IEEE
Date: 11-2018
Spatio-Temporal Reference Frames as Geographic Objects
Publisher: ACM
Date: 07-11-2017
Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications (Preprint)
Publisher: JMIR Publications Inc.
Date: 25-09-2019
Abstract: n this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.
Spectrum-Based Runtime Anomaly Localisation in Service-Based Systems
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.22
Agile development spikes applied to computer science education
Publisher: IEEE
Date: 08-2013
What should i document? A preliminary systematic mapping study into API documentation knowledge
Publisher: IEEE
Date: 09-2019
How usability defects defer from non-usability defects?: A case study on open source projects
Publisher: Insight Society
Date: 19-02-2020
Threshy: supporting safe usage of intelligent web services
Publisher: ACM
Date: 08-11-2020
Reporting usability defects: Limitations of open source defect repositories and suggestions for improvement
Publisher: ACM
Date: 28-09-2015
Spreading Word: Author Frequency of App User Reviews
Publisher: ACM Press
Date: 2016
PiMS: A Pre-ML Labelling Tool
Publisher: IEEE
Date: 10-2022
UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
Publisher: MDPI AG
Date: 27-04-2023
DOI: 10.3390/A16050227
Abstract: Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource intensive and require prior knowledge of the environment, making them difficult to use in real-world applications. In this paper, we propose a Lightweight Deep Vision Reinforcement Learning (LDVRL) framework for dynamic object tracking that uses the camera as the only input source. Our framework employs several techniques such as stacks of frames, segmentation maps from the simulation, and depth images to reduce the overall computational cost. We conducted the experiment with a non-sparse Deep Q-Network (DQN) (value-based) and a Deep Deterministic Policy Gradient (DDPG) (actor-critic) to test the adaptability of our framework with different methods and identify which DRL method is the most suitable for this task. In the end, a DQN is chosen for several reasons. Firstly, a DQN has fewer networks than a DDPG, hence reducing the computational resources on physical UAVs. Secondly, it is surprising that although a DQN is smaller in model size than a DDPG, it still performs better in this specific task. Finally, a DQN is very practical for this task due to the ability to operate in continuous state space. Using a high-fidelity simulation environment, our proposed approach is verified to be effective.
A Framework for Evaluating MRC Approaches with Unanswerable Questions
Publisher: IEEE
Date: 10-2022
Proof of concept of removal of carbon and nitrogen from wastewater through a novel process of biofilm SND
Publisher: Springer International Publishing
Date: 2017
Software architecture design reasoning: A case for improved methodology support
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
DOI: 10.1109/MS.2009.46
Preliminary evaluation of a guided usability defect report form
Publisher: IEEE
Date: 11-2018
Related Organisations
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End Date: 2024
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View Funded ActivityLinkage Projects - Grant ID: LP130100324
Start Date: 07-2013
End Date: 10-2017
Amount: $360,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH170100013
Start Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
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View Funded Activity