Computing and Information Systems - Research Publications
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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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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.
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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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ItemNot For Me: Older Adults Choosing Not to Participate in a Social Isolation Intervention(Association for Computing Machinery, 2016-05)This paper considers what we can learn from the experiences of people who choose not to participate in technology-based social interventions. We conducted ethnographically-informed field studies with socially isolated older adults, who used and evaluated a new iPad application designed to help build new social connections. In this paper we reflect on how the values and assumptions guiding the technological intervention were not always shared by those participating in the evaluation. Drawing on our field notes and interviews with the older adults who chose to discontinue participation, we use personas to illustrate the complexities and tensions involved in individual decisions to not participate. This analysis contributes to HCI research calling for a more critical perspective on technological interventions. We provide detailed examples highlighting the complex circumstances of our non-participants' lives, present a framework that outlines the socio-technical context of non-participation, and use our findings to promote reflective practice in HCI research that aims to address complex social issues.
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ItemNo Preview Available'Knowing Whether' in Proper Epistemic Knowledge Bases(AAAI Press, 2016)Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: 'knowing whether.' An agent i knows whether Q iff agent i knows Q or knows not Q; that is, []Q or []not(Q). In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, and present sound polynomial-time entailment algorithms on PEKBs with the knowing whether operator in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.
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ItemNo Preview AvailablePlanning for a Single Agent in a Multi-Agent Environment Using FOND(AAAI Press, 2016)Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multiagent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multiagent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.
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ItemNo Preview AvailableSequencing operator counts(AAAI Press, 2016-01-01)Copyright 2016 AAAI, all rights reserved.). Operator-counting is a recently developed framework for analysing and integrating many state-ofthe- art heuristics for planning using Linear Programming. In cost-optimal planning only the objective value of these heuristics is traditionally used to guide the search. However the primal solution, i.e. the operator counts, contains useful information. We exploit this information using a SATbased approach which given an operator-count, either finds a valid plan; or generates a generalized landmark constraint violated by that count. We show that these generalized landmarks can be used to encode the perfect heuristic, h∗, as a Mixed Integer Program. Our most interesting experimental result is that finding or refuting a sequence for an operator-count is most often empirically efficient, enabling a novel and promising approach to planning based on Logic-Based Benders Decomposition (LBBD). This paper originally appeared at ICAPS 2015 and is reproduced with the permission of the Association for Artificial Intelligence ([Davies et al., 2015]