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 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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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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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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ItemNo Preview AvailableAction Selection for Transparent Planning(IFAAMAS International Foundation for Autonomous Agents and Multiagent Systems, 2018)We introduce a novel framework to formalize and solve transparent planning tasks by executing actions selected in a suitable and timely fashion. A transparent planning task is defined as a task where the objective of the agent is to communicate its true goal to observers, thereby making its intentions and its action selection transparent. We formally define and model these tasks as Goal Pomdps where the state space is the Cartesian product of the states of the world and a given set of hypothetical goals. Action effects are deterministic in the world states of the problem but probabilistic in the observer's beliefs. Transition probabilities are obtained from making a call to a model-based plan recognition algorithm, which we refer to as an observer stereotype. We propose an action selection strategy via online planning that seeks actions to quickly convey the goal being pursued to an observer assumed to fit a given stereotype. In order to keep run-times feasible, we propose a novel model-based plan recognition algorithm that approximates well-known probabilistic plan recognition methods. The resulting on-line planner, after being evaluated over a diverse set of domains and three different observer stereotypes, is found to convey goal information faster than purely goal-directed planners.
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ItemIntegrated Hybrid Planning and Programmed Control for Real–Time UAV Maneuvering(International Foundation for Autonomous Agents and Multiagent Systems, 2018)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 handcrafted algorithms and heuristics whose performance depends heavily on the initial conditions and aerodynamic properties of the UAVs involved. This paper shows how to employ domain-independent planners, embedded into professional multi-agent simulations, to implement two-level Model Predictive Control (MPC) hybrid control systems for simulated UAVs. We compare the performance of controllers using planners with others based on behaviour trees that implement real world tactics. Our results indicate that hybrid planners derive novel and effective tactics from first principles inherent to the dynamical constraints UAVs are subject to.
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ItemSocial Planning for Trusted Autonomy(Springer International Publishing, 2018)In this chapter, we describe social planning mechanisms for constructing and representing explainable plans in human-agent interactions, addressing one aspect of what it will take to meet the requirements of a trusted autonomous system. Social planning is automated planning in which the planning agent maintains and reasons with an explicit model of the other agents, human or artificial, with which it interacts, including the humans’ goals, intentions, and beliefs, as well as their potential behaviours. The chapter includes a brief overview of the challenge of planning in human-agent teams, and an introduction to a recent body of technical work in multi-agent epistemic planning. The benefits of planning in the presence of nested belief reasoning and first-person multi-agent planning are illustrated in two scenarios, hence indicating how social planning could be used for planning human-agent interaction explicitly as part of an agent’s deliberation.