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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ItemNavigating Online Down Under: International Students’ Digital Journeys in Australia(Rowman & Littlefield, 2018)Research focusing on the experiences of international students tends to centre directly on their educational experiences rather than their everyday lives outside study. Moreover, much of this research has concentrated almost exclusively on the various impacts of the physical, geographic mobility of international students as they move from one country to another, with very little exploration of their digital experiences. There also exists extensive research on the social media and information seeking experiences of young people in different regions of the world. Some of this research provides a comparison between different sources of information and uses of social media. However, there has been little research on what happens when young people move between regions or countries. Borrowing Chang and Gomes’ (2017a) concept of the digital journey, where in crossing transnational borders, migrants might also cross digital borders, this chapter provides some concrete examples of the digital experiences of international students as they transition––wholly or partly––to the Australian digital environment. How do international students transition from certain online environments into others that may be completely different, even alien, to what they have previously experienced? Referring to qualitative and quantitative data collected from three separate projects conducted between 2012 and 2017, this chapter shows that in making the digital journey, international students in Australia do not so much quit their original digital comfort zones as widen their digital horizons. Understanding international students’ digital journeys is particularly significant since it has implications for future research in international student well-being and the provision of support services for students.
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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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ItemFinding Time for Tabletop: Board Game Play and Parenting(SAGE PUBLICATIONS INC, 2018)Hobby board gaming is a serious leisure pastime that entails large commitments of time and energy. When serious hobby board gamers become parents, their opportunities for engaging in the pastime are constrained by their new family responsibilities. Based on an ethnographic study of serious hobby board gamers, we investigate how play is constrained by parenting and how serious board gamers with these responsibilities create opportunities to continue to play board games by negotiating the context, time, location, and medium of play. We also examine how these changes influence the enjoyment players derive from board games across the key dimensions of sociality, intellectual challenge, variety, and materiality.
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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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ItemCrowd Activity Change Point Detection in Videos via Graph Stream Mining(IEEE, 2018)In recent years, there has been a growing interest in detecting anomalous behavioral patterns in video. In this work, we address this task by proposing a novel activity change point detection method to identify crowd movement anomalies for video surveillance. In our proposed novel framework, a hyperspherical clustering algorithm is utilized for the automatic identification of interesting regions, then the density of pedestrian flows between every pair of interesting regions over consecutive time intervals is monitored and represented as a sequence of adjacency matrices where the direction and density of flows are captured through a directed graph. Finally, we use graph edit distance as well as a cumulative sum test to detect change points in the graph sequence. We conduct experiments on four real-world video datasets: Dublin, New Orleans, Abbey Road and MCG Datasets. We observe that our proposed approach achieves a high F-measure, i.e., in the range [0.7, 1], for these datasets. The evaluation reveals that our proposed method can successfully detect the change points in all datasets at both global and local levels. Our results also demonstrate the efficiency and effectiveness of our proposed algorithm for change point detection and segmentation tasks.
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ItemLearning Free-Form Deformations for 3D Object Reconstruction(Springer, 2018)Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing voxel-based representations. While these approaches benefit greatly from advances in computer vision by generalizing 2D convolutions to the 3D setting, they also have several considerable drawbacks. The computational complexity of voxel-encodings grows cubically with the resolution thus limiting such representations to low-resolution 3D reconstruction. In an attempt to solve this problem, point cloud representations have been proposed. Although point clouds are more efficient than voxel representations as they only cover surfaces rather than volumes, they do not encode detailed geometric information about relationships between points. In this paper we propose a method to learn free-form deformations (FFD) for the task of 3D reconstruction from a single image. By learning to deform points sampled from a high-quality mesh, our trained model can be used to produce arbitrarily dense point clouds or meshes with fine-grained geometry. We evaluate our proposed framework on synthetic data and achieve state-of-the-art results on surface and volumetric metrics. We make our implementation publicly available (Tensorflow implementation available at github.com/jackd/template_ffd.).
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ItemAdversarially Parameterized Optimization for 3D Human Pose Estimation(IEEE, 2017)We propose Adversarially Parameterized Optimization, a framework for learning low-dimensional feasible parameterizations of human poses and inferring 3D poses from 2D input. We train a Generative Adversarial Network to `imagine' feasible poses, and search this imagination space for a solution that is consistent with observations. The framework requires no scene/observation correspondences and enforces known geometric invariances without dataset augmentation. The algorithm can be configured at run time to take advantage of known values such as intrinsic/extrinsic camera parameters or target height when available without additional training. We demonstrate the framework by inferring 3D human poses from projected joint positions for both single frames and sequences. We show competitive results with extremely simple shallow network architectures and make the code publicly available.
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ItemNo Preview AvailableReal-Time UAV Maneuvering via Automated Planning in Simulations(International Joint Conferences on Artificial Intelligence, 2017-08-19)The automatic generation of realistic behaviour such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose hand–crafted algorithms and heuristics whose performance depends heavily on the initial conditions and specific aerodynamic characteristics of the UAVs involved. This demo shows the ability of domain–independent planners, embedded into simulators, to generate on–line, feed–forward, control signals that steer simulated aircraft as best suits the situation