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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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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ItemSurvival text regression for time-to-event prediction in conversations(Association for Computational Linguistics, 2021)Time-to-event prediction tasks are common in conversation modelling, for applications such as predicting the length of a conversation or when a user will stop contributing to a platform. Despite the fact that it is natural to frame such predictions as regression tasks, recent work has modelled them as classification tasks, determining whether the time-to-event is greater than a pre-determined cut-off point. While this allows for the application of classification models which are well studied in NLP, it imposes a formulation that is contrived, as well as less informative. In this paper, we explore how to handle time-to-event forecasting in conversations as regression tasks. We focus on a family of regression techniques known as survival regression, which are commonly used in the context of healthcare and reliability engineering. We adapt these models to time-to-event prediction in conversations, using linguistic markers as features. On three datasets, we demonstrate that they outperform commonly considered text regression methods and comparable classification models.
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ItemI Beg to Differ: A study of constructive disagreement in online conversations(Association for Computational Linguistics, 2021)Disagreements are pervasive in human communication. In this paper we investigate what makes disagreement constructive. To this end, we construct WikiDisputes, a corpus of 7 425 Wikipedia Talk page conversations that contain content disputes, and define the task of predicting whether disagreements will be escalated to mediation by a moderator. We evaluate feature-based models with linguistic markers from previous work, and demonstrate that their performance is improved by using features that capture changes in linguistic markers throughout the conversations, as opposed to averaged values. We develop a variety of neural models and show that taking into account the structure of the conversation improves predictive accuracy, exceeding that of feature-based models. We assess our best neural model in terms of both predictive accuracy and uncertainty by evaluating its behaviour when it is only exposed to the beginning of the conversation, finding that model accuracy improves and uncertainty reduces as models are exposed to more information.
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ItemReview of Tools for Early Detection and Screening of Diabetes(AIS Library, 2020-12-14)This paper reviews features of numerous tools, techniques and technologies that help to identify and detect early risk of diabetes. The paper uses systematic literature review (SLR) guidelines and searched most of the popular journals limiting the results tied to studies that discussed the screening and detection of the risk of diabetes. We reviewed the architecture, features and limitations of the various tools and technologies using the following classification: Continuous Glucose Monitoring Systems (CGMS), Flash Glucose Monitoring Systems (FGMS) and the Unobtrusive Systems. Under the unobtrusive system, we studied the Child Health Improvement through Computer Automation (CHICA) system and while there are pieces of evidence that proves its benefits and usefulness, we found some required enhancements in areas of decision support system, data entry automation and flexible integration with other systems. Future work will examine the usage of intelligent automation to detect early risk of diabetes during a patient-physician visit.
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ItemCybernetic Funeral Systems(IEEE, 2021)Using Postphenomenology (one of many methods informed by Wiener's cybernetics) as an analytical approach, this paper examines three examples of robot participation in, and mediation of, funerals. The analysis of robot mediation of funerals challenges the idea that death rituals are exclusively human performances and experiences, and instead repositions them as cybernetic systems of entanglement and impact. The paper begins with an introduction to the relevance of postphenomenological theory, then moves to the case of CARL, a robot that enables remote participation in funeral ceremonies. We argue that the [Human-Robot-Funeral] relation and its variants are both engaging and alienating, through revealing-concealing, magnification-reduction and a more generalised enabling-constraining. Technological mediation is also evident in the case of Pepper, a robot that has officiated at funerals as a Buddhist monk. We describe similarities and differences in the way CARL and Pepper manifest the [Human-Robot-Funeral] relation. The final example is AIBO, a companion robot that becomes the locus of a funeral ritual. This offers a radical case that directly challenges humans' self-proclaimed exceptional ontology.
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ItemClinical decision support for increased-risk organ transplants: Participatory Design(Association for Computer Machinery: Digital Library, 2021)Currently there are over 1,600 Australians awaiting a life-saving organ transplant. Approximately 20% of potential donors have a history of behaviors before their death that increased their risk of acquiring and transmitting HIV, Hepatitis B or Hepatitis C to potential recipients. Donation and transplant professionals need to weigh the risks of disease transmission against the benefits of timely transplantation. Using participatory design methodology, we explored the design needs for a mobile and web-based disease transmission risk calculator to support transplant decisions. We held five design activities involving different occupation groups. Participants included donation and transplantation clinicians, coordinators, administrators, and specialist consultants. Methods included surveys, workshops, interviews, and usability studies. This paper describes our design process, presents the findings, and describes our design decisions and the resulting app. The application will soon be trialed within multiple hospitals in Australia.
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ItemObserving multiplayer boardgame play at a distance(Association for Computing Machinery, 2021-10-15)More than 18 months after it was first identified, the COVID-19 pandemic continues to restrict researchers' opportunities to conduct research in face-to-face settings. This affects studies requiring participants to be co-located, such as those that examine the play of multiplayer boardgames. We present two methods for observing the play of boardgames at a distance, supported by two case studies. We report on the value and use of both methods, and reflect on five core concepts that we observed during the studies: data collection and analysis, recruitment and participation, the temporality of play, the sociality of play and material engagement, and the researcher's role in the study. This work highlights the different considerations that online studies generate when compared to in-person play and other study methods. Future work will present an in-depth discussion of the findings of these studies and present recommendations for the adoption of these distinct methods.
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ItemEmbracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data(AAAI Press, 2021)With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.
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ItemMining Rare Recurring Events in Network Traffic using Second Order Contrast Patterns(IEEE, 2021)Data mining techniques such as contrast pattern mining provide a promising approach to detecting and characterizing changes in network traffic. However, a major challenge for network managers is how to prioritize their analysis of these changes, without being overwhelmed by uninformative patterns. In particular, some changes in traffic occur on a regular basis, such as system backups, and it is important to filter out these rare recurring events, so that network managers can focus on new events. In this paper we address the problem of identifying rare recurring events in network traffic, and we propose a novel solution to detecting new events based on the approach of mining second order contrast patterns. Based on an empirical evaluation using a variety of real traffic sources, we show that our method can achieve high accuracy and F1-Score in detecting new events. Our work demonstrates the importance of higher order contrast pattern mining in practice, and provides an effective method for finding such higher order patterns in large datasets.