Computing and Information Systems - Research Publications
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ItemA Transferable Technique for Detecting and Localising Segments of Repeating Patterns in Time series(IEEE, 2021)In time series data, consecutively repeated patterns occur in many applications, including activity recognition from wearable sensors. Repeating patterns may vary over time and present in various shapes and sizes, which makes their detection a challenging problem. We develop a novel technique, RP-Mask, that can detect and localise segments of consecutively repeated patterns, without prior knowledge about the shape and length of the repeats. Our technique represents time series using recurrence plots (RP), a method for visualising repetition in time series. We identify two key features of recurrence plots-checkerboard patterns and vertical/horizontal lines marking the start and end of checkerboard patterns. We use object recognition on RP images to detect and localise the checkerboard patterns, which are mapped to the segments of consecutively repeating patterns on the underlying time series. Since the collection and labeling of a real world dataset that exhibits all possible variations of a repetition is prohibitive, we demonstrate that our model is able to effectively learn from synthetically curated data and perform equally effective on a real world dataset, while it is noise tolerant. We compare our method to a number of state-of-the-art techniques and show that our method outperforms the state of the art both when trained using real activity recognition and synthetic data.
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ItemPrivacy- and context-aware release of trajectory data(ACM, 2020-03)The availability of large-scale spatio-temporal datasets along with the advancements in analytical models and tools have created a unique opportunity to create valuable insights into managing key areas of society from transportation and urban planning to epidemiology and natural disasters management. This has encouraged the practice of releasing/publishing trajectory datasets among data owners. However, an ill-informed publication of such rich datasets may have serious privacy implications for individuals. Balancing privacy and utility, as a major goal in the data exchange process, is challenging due to the richness of spatio-temporal datasets. In this article, we focus on an individual's stops as the most sensitive part of the trajectory and aim to preserve them through spatio-temporal perturbation. We model a trajectory as a sequence of stops and moves and propose an efficient algorithm that either substitutes sensitive stop points of a trajectory with moves from the same trajectory or introduces a minimal detour if no safe Point of Interest (POI) can be found on the same route. This hinders the amount of unnecessary distortion, since the footprint of the original trajectory is preserved as much as possible. Our experiments shows that our method balances user privacy and data utility: It protects privacy through preventing an adversary from making inferences about sensitive stops while maintaining a high level of similarity to the original dataset.
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ItemExploiting patterns to explain individual predictions(Springer London, 2020-03)Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation (PALEX) to provide instance-level explanations for any classifier. PALEX takes a classifier, a test instance and a frequent pattern set summarizing the training data of the classifier as inputs, and then outputs the supporting evidence that the classifier considers important for the prediction of the instance. To study the local behavior of a classifier in the vicinity of the test instance, PALEX uses the frequent pattern set from the training data as an extra input to guide generation of new synthetic samples in the vicinity of the test instance. Contrast patterns are also used in PALEX to identify locally discriminative features in the vicinity of a test instance. PALEX is particularly effective for scenarios where there exist multiple explanations. In our experiments, we compare PALEX to several state-of-the-art explanation methods over a range of benchmark datasets and find that it can identify explanations with both high precision and high recall.
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ItemRelationships Between Local Intrinsic Dimensionality and Tail Entropy(SPRINGER INTERNATIONAL PUBLISHING AG, 2021)The local intrinsic dimensionality (LID) model assesses the complexity of data within the vicinity of a query point, through the growth rate of the probability measure within an expanding neighborhood. In this paper, we show how LID is asymptotically related to the entropy of the lower tail of the distribution of distances from the query. We establish tight relationships for cumulative Shannon entropy, entropy power, and their generalized Tsallis entropy variants, all with the potential for serving as the basis for new estimators of LID, or as substitutes for LID-based characterization and feature representations in classification and other learning contexts.
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ItemPRESS: A personalised approach for mining top-k groups of objects with subspace similarity(Elsevier, 2020-07)Personalised analytics is a powerful technology that can be used to improve the career, lifestyle, and health of individuals by providing them with an in-depth analysis of their characteristics as compared to other people. Existing research has often focused on mining general patterns or clusters, but without the facility for customisation to an individual's needs. It is challenging to adapt such approaches to the personalised case, due to the high computational overhead they require for discovering patterns that are good across an entire dataset, rather than with respect to an individual. In this paper, we tackle the challenge of personalised pattern mining and propose a query-driven approach to mine objects with subspace similarity. Given a query object in a categorical dataset, our proposed algorithm, PRESS (Personalised Subspace Similarity), determines the top-k groups of objects, where each group has high similarity to the query for some particular subspace. We evaluate the efficiency and effectiveness of our approach on both synthetic and real datasets.
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ItemSTOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management(WILEY, 2022-12)Abstract Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.
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ItemResearch profiles of Australian computing education authors: A scientometric analysis(IEEE, 2022)Computing education (CE) is a growing, but well-established field of research. However, relatively little is known about the research profiles of CE researchers: whether they tend to publish more educational or non-educational papers and when during their career they tend to commence CE research. Using a scientometric approach and data from Scopus, 189 CE authors from Australia were identified who had published in the field between 2018 and 2021. Their research publication history was then retrieved, and each publication was classified as educational or non-educational using a computer aided approach. It was found that CE researchers have diverse research profiles; well established researchers tended to have fewer educational papers, new researchers tend to have more educational papers, and that it is becoming more common to start a research career doing CE research. This has implications for how the research field may be viewed by university computing departments.
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ItemRedesigning Software Architecture and Design Curriculum to Promote Professional Skills Among Software Engineering Students: An Experience Report(AAEE, 2022)Context: Professional skills have become increasingly important in software engineering education; however, this is not always reflected in today’s teaching curricula (Petkovic et al., 2017). The Australian Computer Society report states software engineering and ICT students are lacking essential lifelong non-technical skills necessary to create successful software systems and that higher education institutions do not sufficiently assess professional skills as learning outcomes (ACS, 2019). Besides combining and promoting professional skills in software engineering and transmitting information, teachers in software engineering must also associate theory and practice (Matthews et al., 2012). Purpose or Goal: We redesigned the Software Design and Architecture final year compulsory subject curriculum, which is part of the Master of Software Engineering, to promote professional skills and self-regulated learning within the subject. This paper describes and evaluates our teaching initiatives. Approach or Methodoly/Methods: New teaching initiatives and subject redesign were aimed at better promoting technical and professional skills, and at supporting self-regulated learning among students. The initiatives were organised in 5 main stages (Kennedy, 2020): (i) assessment, (ii) getting the basics right, (iii) establishing our presence regularly, (iv) tutorials, (v) teaching and learning resources. We gathered information from Student Evaluation Survey (SES), and evidence from discussion board usage in 2020 and 2021 (updated subject version) to evaluate our methods. Actual or Anticipated Outcomes: Student Evaluation Survey results showed an increase in general satisfaction with the subject’s score (contents and delivery) increasing from 3.47 and 3.7 in 2018 and 2019, respectively, to 4.13 and 4 in 2020 and 2021 (scale goes from 1 to 5. The higher the score, the better the evaluation of the subject). These were the first two times this subject has received a score equal or above 4. Conclusions/Recommendations/Summary: Among the lessons learned, we can highlight that planning the subject in advance and working within an inclusive space that promotes continuous communication and collaboration between the teaching team and students are the primary activities for its success. In addition, we observed that clearer assessment guidelines and continuous feedback were able to promote professional and technical skills among students.
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ItemSame graph, different data: A usability study of a student-facing dashboard based on self-regulated learning theory(Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), 2022)Student-facing learning analytics dashboards have the potential to reconnect students with their purpose for learning, reminding them of their goals and promoting reflection about their learning journey. However, far less is known about the specifics of the relationship between different types of visualisations and data presented in dashboards and their impact on students’ motivation. In this study, we used a Human-Centred Design method across three iterations to (1) understand how students prioritise similar visualisations when presenting different data (2) examine how they interact with these, and (3) propose a dashboard design that would accommodate students’ different motivational needs. In the first iteration, 26 participants ranked their preferred visualisations using paper prototypes; in the second iteration, a digital wireframe was created based on the results from the first iteration to conduct user tests with two participants; and in the third iteration, a high-fidelity prototype was created to reflect findings from the previous iterations. Overall, findings showed that students mostly valued setting goals and monitoring their progress from a multiple goals approach, and were reluctant about comparing their performance with peers due to concerns related to promoting unproductive competition amongst peers and data privacy. Implications for educators and learning designers are discussed.
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ItemThe Impact of Cognitive Load on Students’ Academic Writing: An Authorship Verification Investigation(Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), 2022)Automatic authorship verification is known to be a challenging machine learning task. In this paper, we examine the efficacy of an enhanced common n-gram profile-based approach to assist educational institutions to validate students' essays and assignments through their writing styles. We investigated the impact that essays with different cognitive load requirements have in students' writing styles, which may or may not impact authorship verification methods. A total of 46 undergraduate students completed six essays in a laboratory study. Although results showed small and mixed effects of the tasks differing in cognitive load on the different writing product metrics, students' essays and assignments texts contained features that remained stable across essays requiring different levels of cognitive load. These results suggest that our approach could be successfully used in authorship verification, potentially helping to address issues related to academic integrity in higher education settings.