Computing and Information Systems - Research Publications

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    Designing an App for Pregnancy Care for a Culturally and Linguistically Diverse Community
    Smith, W ; Wadley, G ; Daly, JO ; Webb, M ; Hughson, J ; Hajek, J ; Parker, A ; Woodward-Kron, R ; Story, DA (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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    Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining
    Chavary, EA ; Erfani, SM ; Leckie, C (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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    Adversarially Parameterized Optimization for 3D Human Pose Estimation
    Jack, D ; Maire, F ; Eriksson, A ; Shirazi, S (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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    Real-Time UAV Maneuvering via Automated Planning in Simulations
    Ramirez Javega, M ; Papasimeon, M ; Benke, L ; Lipovetzky, N ; Miller, T ; Pearce, A ; Sierra, C (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
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    Session details: Sense Making for Creativity
    Waycott, J (ACM, 2017-06-22)
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    Extracting Audio Summaries to Support Effective Spoken Document Search
    Spina, D ; Trippas, JR ; Cavedon, L ; Sanderson, M (WILEY, 2017-09)
    We address the challenge of extracting query biased audio summaries from podcasts to support users in making relevance decisions in spoken document search via an audio‐only communication channel. We performed a crowdsourced experiment that demonstrates that transcripts of spoken documents created using Automated Speech Recognition (ASR), even with significant errors, are effective sources of document summaries or “snippets” for supporting users in making relevance judgments against a query. In particular, the results show that summaries generated from ASR transcripts are comparable, in utility and user‐judged preference, to spoken summaries generated from error‐free manual transcripts of the same collection. We also observed that content‐based audio summaries are at least as preferred as synthesized summaries obtained from manually curated metadata, such as title and description. We describe a methodology for constructing a new test collection, which we have made publicly available.
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    iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
    Gupta, H ; Dastjerdi, AV ; Ghosh, SK ; Buyya, R (WILEY, 2017-09)
    Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.
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    On the effectiveness of isolation-based anomaly detection in cloud data centers
    Calheiros, RN ; Ramamohanarao, K ; Buyya, R ; Leckie, C ; Versteeg, S (WILEY, 2017-09-25)
    Summary The high volume of monitoring information generated by large‐scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource‐intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large‐scale environment such as clouds. Isolation‐based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation‐based anomaly detection method, to detect anomalies in large‐scale cloud data centers. We propose a method to code time‐series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time‐series and to be applied online and in real‐time.
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    Online virtual machine migration for renewable energy usage maximization in geographically distributed cloud datacenters
    Khosravi, A ; Toosi, AN ; Buyya, R (WILEY, 2017-09-25)
    Summary Energy consumption and its associated costs represent a huge part of cloud providers' operational costs. In this study, we explore how much energy cost savings can be made knowing the future level of renewable energy (solar/wind) available in data centers. Since renewable energy sources have intermittent nature, we take advantage of migrating virtual machines to the nearby data centers with excess renewable energy. In particular, we first devise an optimal offline algorithm with full future knowledge of renewable level in the system. Since in practice, accessing long‐term and exact future knowledge of renewable energy level is not feasible, we propose two online deterministic algorithms, one with no future knowledge called deterministic and one with limited knowledge of the future renewable availability called future‐aware. We show that the deterministic and future‐aware algorithms are 1+1/s and 1+1/s−ω/s.Tm competitive in comparison to the optimal offline algorithm, respectively, where s is the network to the brown energy cost, ω is the look‐ahead window‐size, and Tm is the migration time. The effectiveness of the proposed algorithms is analyzed through extensive simulation studies using real‐world traces of meteorological data and Google cluster workload.
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    A model linking video gaming, sleep quality, sweet drinks consumption and obesity among children and youth
    Turel, O ; Romashkin, A ; Morrison, KM (WILEY, 2017-08)
    There is a growing need to curb paediatric obesity. The aim of this study is to untangle associations between video-game-use attributes and obesity as a first step towards identifying and examining possible interventions. Cross-sectional time-lagged cohort study was employed using parent-child surveys (t1) and objective physical activity and physiological measures (t2) from 125 children/adolescents (mean age = 13.06, 9-17-year-olds) who play video games, recruited from two clinics at a Canadian academic children's hospital. Structural equation modelling and analysis of covariance were employed for inference. The results of the study are as follows: (i) self-reported video-game play duration in the 4-h window before bedtime is related to greater abdominal adiposity (waist-to-height ratio) and this association may be mediated through reduced sleep quality (measured with the Pittsburgh Sleep Quality Index); and (ii) self-reported average video-game session duration is associated with greater abdominal adiposity and this association may be mediated through higher self-reported sweet drinks consumption while playing video games and reduced sleep quality. Video-game play duration in the 4-h window before bedtime, typical video-game session duration, sweet drinks consumption while playing video games and poor sleep quality have aversive associations with abdominal adiposity. Paediatricians and researchers should further explore how these factors can be altered through behavioural or pharmacological interventions as a means to reduce paediatric obesity.