Computing and Information Systems - Research Publications
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ItemNo Preview AvailableModeling of microalgal shear-induced flocculation and sedimentation using a coupled CFD-population balance approach.(Wiley, 2018)In this study, shear-induced flocculation modeling of Chlorella sp. microalgae was conducted by combination of population balance modeling and CFD. The inhomogeneous Multiple Size Group (MUSIG) and the Euler-Euler two fluid models were coupled via Ansys-CFX-15 software package to achieve both fluid and particle dynamics during the flocculation. For the first time, a detailed model was proposed to calculate the collision frequency and breakage rate during the microalgae flocculation by means of the response surface methodology as a tool for optimization. The particle size distribution resulted from the model was in good agreement with that of the jar test experiment. Furthermore, the subsequent sedimentation step was also examined by removing the shear rate in both simulations and experiments. Consequently, variation in the shear rate and its effects on the flocculation behavior, sedimentation rate and recovery efficiency were evaluated. Results indicate that flocculation of Chlorella sp. microalgae under shear rates of 37, 182, and 387 s-1 is a promising method of pre-concentration which guarantees the cost efficiency of the subsequent harvesting process by recovering more than 90% of the biomass.
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ItemAn effective and versatile distance measure for spatiotemporal trajectories(SPRINGER, 2019-05)The analysis of large-scale trajectory data has tremendous benefits for applications ranging from transportation planning to traffic management. A fundamental building block for the analysis of such data is the computation of similarity between trajectories. Existing work for similarity computation focuses mainly on the spatial aspects of trajectories, but more rarely takes into account time in conjunction with space. A key challenge when considering time is how to handle trajectories that are sampled asynchronously or at variable rates, which can lead to uncertainty. To tackle this problem, we quantify trajectory similarity as an interval, rather than a single value, to capture the uncertainty that can result from different sampling rates and asynchronous sampling. Based on this perspective, we develop a new trajectory similarity measure, Trajectory Interval Distance Estimation, which models similarity computation as a convex optimisation problem. Using two real datasets, we demonstrate that our proposed measure is extremely effective for assessing similarity in comparison to existing state of the art measures.
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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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ItemAn automated matrix profile for mining consecutive repeats in time series(Springer Nature, 2018-01-01)A key application of wearable sensors is remote patient monitoring, which facilitates clinicians to observe patients non-invasively, by examining the time series of sensor readings. For analysis of such time series, a recently proposed technique is Matrix Profile (MP). While being effective for certain time series mining tasks, MP depends on a key input parameter, the length of subsequences for which to search. We demonstrate that MP’s dependency on this input parameter impacts its effectiveness for finding patterns of interest. We focus on finding consecutive repeating patterns (CRPs), which represent human activities and exercises whilst tracked using wearable sensors. We demonstrate that MP cannot detect CRPs effectively and extend it by adding a locality preserving index. Our method automates the use of MP, and reduces the need for data labeling by experts. We demonstrate our algorithm’s effectiveness in detecting regions of CRPs through a number of real and synthetic datasets.
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ItemCharacteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis(Springer, 2019-01-01)The local intrinsic dimensionality (LID) model enables assessment of the complexity of the local neighbourhood around a specific query object of interest. In this paper, we study variations in the LID of a query, with respect to different subspaces and local neighbourhoods. We illustrate the surprising phenomenon of how the LID of a query can substantially decrease as further features are included in a dataset. We identify the role of two key feature properties in influencing the LID for feature combinations: correlation and dominance. Our investigation provides new insights into the impact of different feature combinations on local regions of the data.
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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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ItemImproving the quality of explanations with local embedding perturbations(ACM, 2019-07-25)Classifier explanations have been identified as a crucial component of knowledge discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given instance. A key step in this approach is to generate synthetic neighbors of the given instance. This neighbor generation process is challenging and it has considerable impact on the quality of explanations. To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. Based on this, we then propose a new neighborhood generation method. Our method first fits a local embedding/subspace around a given instance using the LID of the test instance as the target dimensionality, then generates neighbors in the local embedding and projects them back to the original space. Experimental results show that our method generates more realistic neighborhoods and consequently better explanations. It can be used in combination with existing local explanation algorithms.
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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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ItemNo Preview AvailableWidespread remodeling of proteome solubility in response to different protein homeostasis stresses(National Academy of Sciences, 2020-02-04)The accumulation of protein deposits in neurodegenerative diseases has been hypothesized to depend on a metastable subproteome vulnerable to aggregation. To investigate this phenomenon and the mechanisms that regulate it, we measured the solubility of the proteome in the mouse Neuro2a cell line under six different protein homeostasis stresses: 1) Huntington’s disease proteotoxicity, 2) Hsp70, 3) Hsp90, 4) proteasome, 5) endoplasmic reticulum (ER)-mediated folding inhibition, and 6) oxidative stress. Overall, we found that about one-fifth of the proteome changed solubility with almost all of the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodeling of protein complexes involved in adaptation to perturbation, most notably, stress granule (SG) proteins, which responded differently to different stresses. These results indicate that the protein homeostasis system is organized in a modular manner and aggregation patterns were not correlated with protein folding stability (ΔG). Instead, distinct cellular mechanisms regulate assembly patterns of multiple classes of protein complexes under different stress conditions.
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ItemConceptual Model for the Use of Smart Glasses in Ubiquitous Teaching (u-teaching)(AISEL, 2019-12-09)Smart glasses, a wearable headset technology, currently trending provide hands-free and augmented reality features. This paper looks at the research around mediated-reality tools to improve the delivery of education. Despite its potential, it has not seen widespread use in education. A suitable implementation framework and pedagogy have been proposed so that smart glasses can be used towards creating digitally-mediated learning (DML) environments. Aurasma is recommended as the implementation framework after a comparison with other frameworks based on factors such as cost, ease-of-use, and accessibility among others. For a suitable pedagogy, new assessment strategies, content personalization, and the use of 3-D learning spaces are recommended. It is argued, that the recommended framework and the pedagogy approach have the potential to improve learning environments for teachers and students. However, there are privacy concerns due to the pervasive nature of Augmented Reality (AR). In the current research, the overall learning environment is considered.