Computing and Information Systems - Research Publications
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ItemNye tray vs sieve tray: A comparison based on computational fluid dynamics and tray efficiency(Wiley, 2021-10)Nye and sieve trays were hydrodynamically simulated and compared. The simulations were performed in a Eulerian‐Eulerian framework under unsteady (transient) conditions at industrial scale. Conducted on an air‐water system, the simulations included three dimensions and two phases. The velocity distribution across the tray, the height of clear liquid, the froth height, and the pressure drop were investigated and compared with experimental data. Péclet number was calculated using hydrodynamic and geometric parameters. The tray efficiencies were also predicted to further compare the two trays. The results showed that the liquid flow was steadier on the Nye tray rather than the sieve tray, possibly because of the special structure of the liquid and gas inlets for the Nye tray.
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ItemComputational Fluid Dynamics versus Experiment: An Investigation on Liquid Weeping of Nye Trays(Wiley, 2021-01)The weeping phenomenon was investigated using some experimental tests and a numerical model. The tests were performed within a 1.22‐m‐diameter pilot‐scale column including two chimney trays and two Nye test trays with an air‐water system. The rates of weeping were measured in the Nye trays with two heights of the weir and a hole area of 5 %. Moreover, the weeping rates in the outlet and inlet halves of the Nye tray and the total weeping rate were calculated. In the next step, an Eulerian‐Eulerian computational fluid dynamics (CFD) technique was used. The results show good agreement between the attained CFD findings and the experimental data.
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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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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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ItemUniversal Architectural Concepts Underlying Protein Folding Patterns(FRONTIERS MEDIA SA, 2021-04-30)What is the architectural "basis set" of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures-called concepts-typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence-structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.