ORCID Profile
0000-0003-2011-9618
Current Organisations
Monash University Monash eResearch Centre
,
Monash University
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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.
Software engineering | Software testing verification and validation
Publisher: ACM
Date: 27-06-2020
Publisher: Elsevier BV
Date: 06-2014
DOI: 10.1016/J.ARTMED.2014.05.002
Abstract: This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises. The supervisory level of the developed hierarchical system is implemented on a portable monitoring device (iPhone/iPad) as a client application, while the real-time control of treadmill exercises is accomplished by using an on-line adaptive neural network control scheme in a local computer system. During training or rehabilitation exercises, the intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient. In order to achieve adaptive tracking performance, a neural network controller has been designed and implemented. Six real-time experiments have been conducted to test the performance of the developed monitoring system. Experimental results obtained in real-time with heart-rate set-point varying from 145 bpm to 180 bmp, demonstrate that the proposed system can quickly and accurately regulate exercise intensity of treadmill running exercises with desired performance (no overshoot, settling time Ts ≤ 100 s). Subjects aged from 29 to 38 years old participated in different set-point experiments to confirm the system's adaptability to inter- and intra-model uncertainty. The desired system performance under external disturbances has also been confirmed in a final real-time experiment demonstrating a user carrying the 10 kg bag then removing it during the exercise. In contrast with conventional control approaches, the proposed adaptive controller achieves better heart rate tracking performance under inter- and intra-model uncertainty and external disturbances. The developed system can automatically adapt to various in idual exercisers and a range of exercise intensity.
Publisher: Springer Science and Business Media LLC
Date: 18-09-2018
Publisher: Walter de Gruyter GmbH
Date: 2016
Abstract: This article outlines a decision support system that seeks to help community nurses monitor the well-being of their chronically ill patients. It is designed for nurses to stay in contact with their patients without spending unnecessary time on less productive aspects of community nursing, such as avoidable driving to and from patients’ houses and taking measurements of vital signs to assess their health condition. It therefore allows the nurse to spend more time on managing the factors that could lead to a healthier patient. The decision support system is developed for two levels of mathematical capability. Nurses with a statistical background are provided with in-depth information allowing them to detect changes in mean, mean square error (and hence variation), and correlations using a variation on dynamic principle components. Less mathematically inclined nurses are offered information about trends, change points, and a simpler multivariate view of a patient’s well-being involving parallel coordinate plots.
Publisher: Association for Computing Machinery (ACM)
Date: 14-10-2020
DOI: 10.1145/3415194
Abstract: Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others' designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded ex les which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI ex les so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, ersity and satisfaction.
Publisher: IEEE
Date: 12-2011
Publisher: Association for Computing Machinery (ACM)
Date: 16-06-2020
DOI: 10.1145/3391613
Abstract: UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.
Publisher: ACM
Date: 03-09-2018
Publisher: IEEE
Date: 05-2019
Publisher: Association for Computing Machinery (ACM)
Date: 07-11-2019
DOI: 10.1145/3359282
Abstract: Online communities like Dribbble and GraphicBurger allow GUI designers to share their design artwork and learn from each other. These design sharing platforms are important sources for design inspiration, but our survey with GUI designers suggests additional information needs unmet by existing design sharing platforms. First, designers need to see the practical use of certain GUI designs in real applications, rather than just artworks. Second, designers want to see not only the overall designs but also the detailed design of the GUI components. Third, designers need advanced GUI design search abilities (e.g., multi-facets search) and knowledge discovery support (e.g., demographic investigation, cross-company design comparison). This paper presents Gallery D.C. mui-collection.herokuapp.com/, a gallery of GUI design components that harness GUI designs crawled from millions of real-world applications using reverse-engineering and computer vision techniques. Through a process of invisible crowdsourcing, Gallery D.C. supports novel ways for designers to collect, analyze, search, summarize and compare GUI designs on a massive scale. We quantitatively evaluate the quality of Gallery D.C. and demonstrate that Gallery D.C. offers additional support for design sharing and knowledge discovery beyond existing platforms.
Publisher: ACM
Date: 03-09-2018
Publisher: IEEE
Date: 08-2010
Publisher: IEEE
Date: 08-2015
Publisher: Springer Science and Business Media LLC
Date: 06-02-2014
Publisher: ACTAPRESS
Date: 2012
Publisher: Springer Science and Business Media LLC
Date: 2014
Publisher: ACM
Date: 18-08-2021
Publisher: IEEE
Date: 05-2021
Publisher: Informa UK Limited
Date: 18-10-2013
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 08-2014
Publisher: ACM
Date: 25-08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 04-11-2022
DOI: 10.1145/3531065
Abstract: Approximately 50% of development resources are devoted to user interface (UI) development tasks [ 9 ]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this article, we present Auto-Icon+ , an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real-world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.
Publisher: ICST
Date: 2013
Publisher: Bentham Science Publishers Ltd.
Date: 28-05-2010
DOI: 10.2174/1874431101004020081
Abstract: Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise.
Publisher: ACM
Date: 27-05-2018
Publisher: IEEE
Date: 05-2021
Publisher: Springer Science and Business Media LLC
Date: 25-09-2016
Publisher: Inderscience Publishers
Date: 2012
Publisher: IEEE
Date: 08-2016
Publisher: Inderscience Publishers
Date: 2012
Publisher: IEEE
Date: 08-2016
Publisher: IEEE
Date: 08-2016
Publisher: IEEE
Date: 08-2010
Publisher: Springer Science and Business Media LLC
Date: 30-04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 08-2008
Publisher: American Chemical Society (ACS)
Date: 05-07-2017
Publisher: ACTAPRESS
Date: 2012
Publisher: Association for Computing Machinery (ACM)
Date: 06-12-2017
DOI: 10.1145/3134667
Abstract: Community edits to questions and answers (called post edits) plays an important role in improving content quality in Stack Overflow. Our study of post edits in Stack Overflow shows that a large number of edits are about formatting, grammar and spelling. These post edits usually involve small-scale sentence edits and our survey of trusted contributors suggests that most of them care much or very much about such small sentence edits. To assist users in making small sentence edits, we develop an edit-assistance tool for identifying minor textual issues in posts and recommending sentence edits for correction. We formulate the sentence editing task as a machine translation problem, in which an original sentence is "translated" into an edited sentence. Our tool implements a character-level Recurrent Neural Network (RNN) encoder-decoder model, trained with about 6.8 millions original-edited sentence pairs from Stack Overflow post edits. We evaluate our edit assistance tool using a large-scale archival post edits, a field study of assisting a novice post editor, and a survey of trusted contributors. Our evaluation demonstrates the feasibility of training a deep learning model with post edits by the community and then using the trained model to assist post editing for the community.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 02-2008
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: ACM
Date: 15-02-2014
Publisher: IEEE
Date: 08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 22-02-2011
DOI: 10.1007/S11517-011-0749-8
Abstract: Low frequency variability in the fingertip photoplethysmogram (PPG) waveform has been utilized for inferring sympathetic vascular control, but its relationship with a quantitative measure of vascular tone has not been established. In this study, we examined the association between fingertip PPG waveform variability (PPGV) and systemic vascular resistance (SVR) obtained from thermodilution cardiac output (CO) and intra-arterial pressure measurements in 48 post cardiac surgery intensive care unit patients. Among the hemodynamic measurements, both CO (P < 0.05) and SVR (P < 0.0001) had statistically significant relationships with the normalized low frequency power (LF(nu)) of PPGV. The LF(nu) of baseline PPGV had moderate but significant positive correlation with SVR (r = 0.54, P < 0.0001), and a value below 52.5 nu was able to identify SVR < 900 dyn s cm⁻⁵ with sensitivity of 59% and specificity of 95%. The results have provided quantitative evidence to confirm the link between fingertip PPGV and sympathetic vascular control. Suppression of LF vasomotor waves leading to dominance of respiration-related HF fluctuations in the fingertip circulation was a specific (though not sensitive) marker of systemic vasodilatation, which could be potentially utilized for the assessment of critical care patients.
Publisher: IEEE
Date: 07-2017
Publisher: ACM
Date: 29-10-2023
Publisher: IEEE
Date: 08-2007
Publisher: Elsevier BV
Date: 04-2022
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 06-2016
Publisher: Association for Computing Machinery (ACM)
Date: 27-03-2022
DOI: 10.1145/3581998
Abstract: Nowadays, voice assistants help users complete tasks on the smartphone with voice commands, replacing traditional touchscreen interactions when such interactions are inhibited. However, the usability of those tools remains moderate due to the problems in understanding rich language variations in human commands, along with efficiency and comprehensibility issues. Therefore, we introduce Voicify, an Android virtual assistant that allows users to interact with on-screen elements in mobile apps through voice commands. Using a novel deep learning command parser, Voicify interprets human verbal input and performs matching with UI elements. In addition, the tool can directly open a specific feature from installed applications by fetching application code information to explore the set of in-app components. Our command parser achieved 90% accuracy on the human command dataset. Furthermore, the direct feature invocation module achieves better feature coverage in comparison to Google Assistant. The user study demonstrates the usefulness of Voicify in real-world scenarios.
Publisher: IEEE
Date: 05-2021
Publisher: ACM
Date: 30-04-2023
Publisher: Association for Computing Machinery (ACM)
Date: 13-10-2021
DOI: 10.1145/3479547
Abstract: Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q& A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.
Publisher: ACM
Date: 30-04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 05-2021
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 05-2021
Publisher: IEEE
Date: 08-2011
Publisher: Springer Science and Business Media LLC
Date: 16-02-2012
Abstract: Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Preliminary testing with 8 healthy in iduals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.
Publisher: IEEE
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: IEEE
Date: 02-2019
Publisher: Elsevier BV
Date: 2014
Publisher: ACM
Date: 27-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: ACM
Date: 08-09-2016
Publisher: ACM
Date: 10-10-2022
Publisher: IOP Publishing
Date: 27-06-2011
DOI: 10.1088/0967-3334/32/8/012
Abstract: There is a need for robust techniques for early and accurate diagnosis of acute coronary syndromes (ACSs), to avoid inappropriate discharge of patients. This study examined the use of frequency spectrum analysis of heart rate variability (HRV) and photoplethysmogram (PPG) waveform variability for the identification of high-risk ACS patients defined by an elevated cardiac troponin level. The study cohort comprised a convenience s le of adult patients presenting to the emergency department of the Prince of Wales Hospital over a 4 month period complaining of non-traumatic chest pain. Valid electrocardiogram (ECG) and earlobe PPG waveforms together with troponin I test results were obtained from 52 patients at presentation, 4 of which were troponin I positive (Trop 0+). Frequency spectrum analysis was performed on the beat-to-beat HRV and PPG waveform variability (PPGV). The Trop 0+ were found to have significantly higher normalized mid-frequency power (MF(nu)) in HRV (P = 0.017), PPG litude variability (P = 0.009) and the cross-spectrum of HRV and PPGV (P = 0.001), which were attributed to reflex sympathetic response to myocardial ischemia. MF(nu) of PPG litude had the best overall performance in detecting Trop 0+, with ROC area under the curve of 0.93. The results demonstrate the potential use of ear PPG waveform to identify high-risk heart disease patients, and further highlight the utility of frequency spectrum analysis of PPGV in critical care.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/696813
Abstract: Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%–100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions.
Publisher: IOP Publishing
Date: 06-02-2009
DOI: 10.1088/0967-3334/30/3/001
Abstract: This study aims to quantitatively describe the steady-state relationships among percentage changes in key central cardiovascular variables (i.e. stroke volume, heart rate (HR), total peripheral resistance and cardiac output), measured using non-invasive means, in response to moderate exercise, and the oxygen uptake rate, using a new nonlinear regression approach-support vector regression. Ten untrained normal males exercised in an upright position on an electronically braked cycle ergometer with constant workloads ranging from 25 W to 125 W. Throughout the experiment, VO(2) was determined breath by breath and the HR was monitored beat by beat. During the last minute of each exercise session, the cardiac output was measured beat by beat using a novel non-invasive ultrasound-based device and blood pressure was measured using a tonometric measurement device. Based on the analysis of experimental data, nonlinear steady-state relationships between key central cardiovascular variables and VO(2) were qualitatively observed except for the HR which increased linearly as a function of increasing VO(2). Quantitative descriptions of these complex nonlinear behaviour were provided by nonparametric models which were obtained by using support vector regression.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 11-2018
DOI: 10.1145/3274302
Abstract: To ensure the post quality, Q& A sites usually develop a list of quality assurance guidelines for "dos and don'ts", and adopt collaborative editing mechanism to fix quality violations. Quality guidelines are mostly high-level principles, and many tacit and context-sensitive aspects of the expected quality cannot be easily enforced by a set of explicit rules. Collaborative editing is a reactive mechanism after low-quality posts have been posted. Our study of collaborative editing data on Stack Overflow suggests that tacit and context-sensitive quality-assurance knowledge is manifested in the editing patterns of large numbers of collaborative edits. Inspired by this observation, we develop and evaluate a Convolutional Neural Network based approach to learn editing patterns from historical post edits for predicting the need of editing a post. Our approach provides a proactive policy assurance mechanism that warns users potential quality issues in a post before it is posted.
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 29-10-2023
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 05-2017
DOI: 10.1109/ICSE.2017.48
Publisher: Elsevier BV
Date: 2022
Publisher: IMPERIAL COLLEGE PRESS
Date: 04-07-2012
Publisher: IEEE
Date: 12-2021
Publisher: IMPERIAL COLLEGE PRESS
Date: 04-07-2012
Publisher: IEEE
Date: 10-2016
Publisher: Association for Computing Machinery (ACM)
Date: 23-04-2021
DOI: 10.1145/3447808
Abstract: UI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues of mobile apps. In particular, we analyze more than 3M UI-related reviews from 22,199 top free-to-download apps and 9,380 top non-free apps in the Google Play Store. By comparing the rating of UI-related reviews and other reviews of an app, we observe that UI-related reviews have lower ratings than other reviews. By manually analyzing a random s le of 1,447 UI-related reviews with a 95% confidence level and a 5% interval, we identify 17 UI-related issues types that belong to four categories (i.e., “Appearance,” “Interaction,” “Experience,” and “Others” ). In these issue types, we find “Generic Review” is the most occurring one. “Comparative Review” and “Advertisement” are the most negative two UI issue types. Faced with these UI issues, we explore the patterns of interaction between app owners and users. We identify eight patterns of how app owners dialogue with users about UI issues by the review-response mechanism. We find “Apology or Appreciation” and “Information Request” are the most two frequent patterns. We find updating UI timely according to feedback is essential to satisfy users. Besides, app owners could also fix UI issues without updating UI, especially for issue types belonging to “Interaction” category. Our findings show that there exists a positive impact if app owners could actively interact with users to improve UI quality and boost users’ satisfactoriness about the UIs.
Publisher: ACM
Date: 30-04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Start Date: 2024
End Date: 12-2026
Amount: $442,302.00
Funder: Australian Research Council
View Funded Activity