Computing and Information Systems - Research Publications
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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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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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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.
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ItemNo Preview AvailableDesigning an App for Pregnancy Care for a Culturally and Linguistically Diverse Community(The Association for Computing Machinery, 2017)We report a study to design and evaluate an app to support pregnancy information provided to women through an Australian health service. As part of a larger project to provide prenatal resources for culturally and linguistically diverse groups, this study focused on the design and reception of an app with the local Vietnamese community and health professionals of a particular hospital. Our study had three stages: an initial design workshop with the hospital; prototype design and development; prototype-based interviews with health professionals and focus groups with Vietnamese women. We explore how an app of this sort must be designed for a range of different use scenarios, considering its use by consumers with a multiplicity of differing viewpoints about its nature and purpose in relation to pregnancy care.
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ItemCooperating to compete: The mutuality of cooperation and competition in boardgame play(Association for Computing Machinery (ACM), 2018-04-20)This paper examines the complex relationship between competition and cooperation in boardgame play. We understand boardgaming as distributed cognition, where people work together in a shared activity to accomplish the game. Although players typically compete against each other, this competition is only possible through ongoing cooperation to negotiate, enact and maintain the rules of play. In this paper, we report on a study of people playing modern boardgames. We analyse how knowledge of the game's state is distributed amongst the players and the game components, and examine the different forms of cooperation and collaboration that occur during play. Further, we show how players use the material elements of the game to support articulation work and to improve their awareness and understanding of the game's state. Our goal is to examine the coordinative practices that the players use during play and explicate the ways in which these enable competition.
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ItemNo Preview Available"I love all the bits": The Materiality of Boardgames(ASSOC COMPUTING MACHINERY, 2016-01-01)This paper presents findings from a study of boardgamers which stress the importance of the materiality of modern boardgames. It demonstrates that materiality is one of four significant factors in the player experience of tabletop gaming and describes four domains of materiality in boardgaming settings. Further, building on understanding of non-use in HCI, it presents boardgames as a unique situation of parallel use, in which users simultaneously engage with a single game in both digital and material, non-digital environments.
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ItemSummarizing Significant Changes in Network Traffic Using Contrast Pattern Mining(Association for Computing Machinery, 2017)Extracting knowledge from the massive volumes of network traffic is an important challenge in network and security management. In particular, network managers require concise reports about significant changes in their network traffic. While most existing techniques focus on summarizing a single traffic dataset, the problem of finding significant differences between multiple datasets is an open challenge. In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers. We propose the use of contrast pattern mining, which finds patterns whose support differs significantly from one dataset to another. We show that contrast patterns are highly effective at extracting meaningful changes in traffic data. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. Our experimental results show that with the proposed unsupervised approach, the vast majority of extracted patterns are pure, i.e., most changes are either attack traffic or normal traffic, but not a mixture of both.
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ItemJob Insecurity in Academic Research Employment: An Agent-Based Model(MIT Press, 2016-01-01)This paper presents an agent-based model of fixed-term academic employment in a competitive research funding environment based on UK academia. The goal of the model is to investigate the effects of job insecurity on research productivity. Agents may be either established academics who may apply for grants, or postdoctoral researchers who are unable to apply for grants and experience hardship when reaching the end of their fixed-term contracts. Model results show that in general adding fixed-term postdocs to the system produces less total research output than adding half as many permanent academics. An in-depth sensitivity analysis is performed across postdoc scenarios, and indicates that promoting more postdocs into permanent positions produces significant increases in research output.
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ItemAnomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features(Springer, 2016)important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.