Computing and Information Systems - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 1272
  • Item
    No Preview Available
    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.
  • Item
    No Preview Available
    "I love all the bits": The Materiality of Boardgames
    Rogerson, MJ ; Gibbs, M ; Smith, W (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.
  • Item
    Thumbnail Image
    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.
  • Item
    No Preview Available
    TREE-BASED STATISTICAL MACHINE TRANSLATION: EXPERIMENTS WITH THE ENGLISH AND BRAZILIAN PORTUGUESE PAIR
    Beck, D ; Caseli, H (SBIC, 2013)
    Machine Learning paradigms have dominated recent research in Machine Translation. Current state-of-the-art approaches rely only on statistical methods that gather all necessary knowledge from parallel corpora. However, this lack on explicit linguistic knowledge makes them unable to model some linguistic phenomena. In this work, we focus on models that take into account the syntactic information from the languages involved on the translation process. We follow a novel approach that preprocess parallel corpora using syntactic parsers and uses translation models composed by Tree Transducers. We perform experiments with English and Brazilian Portuguese, providing the first known results in syntax-based Statistical Machine Translation for this language pair. These results show that this approach is able to better model phenomena like long-distance reordering and give directions to future improvements in building syntax-based translation models for this pair.
  • Item
    Thumbnail Image
    Job Insecurity in Academic Research Employment: An Agent-Based Model
    Silverman, E ; Geard, N ; Wood, I ; Gershenson, C ; Froese, T ; Siqueiros, JM ; Aguilar, W ; Izquierdo, E ; Sayama, H (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.
  • Item
    Thumbnail Image
    Stratification bias in low signal microarray studies
    Parker, BJ ; Guenter, S ; Bedo, J (BMC, 2007-09-02)
    BACKGROUND: When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated. RESULTS: We show that when estimating the performance of classifiers on low signal datasets (i.e. those which are difficult to classify), which are typical of many prognostic microarray studies, commonly used performance measures can suffer from a substantial negative bias. For error rate this bias is only severe in quite restricted situations, but can be much larger and more frequent when using ranking measures such as the receiver operating characteristic (ROC) curve and area under the ROC (AUC). Substantial biases are shown in simulations and on the van 't Veer breast cancer dataset. The classification error rate can have large negative biases for balanced datasets, whereas the AUC shows substantial pessimistic biases even for imbalanced datasets. In simulation studies using 10-fold cross-validation, AUC values of less than 0.3 can be observed on random datasets rather than the expected 0.5. Further experiments on the van 't Veer breast cancer dataset show these biases exist in practice. CONCLUSION: Stratification bias can substantially affect several performance measures. In computing the AUC, the strategy of pooling the test samples from the various folds of cross-validation can lead to large biases; computing it as the average of per-fold estimates avoids this bias and is thus the recommended approach. As a more general solution applicable to other performance measures, we show that stratified repeated holdout and a modified version of k-fold cross-validation, balanced, stratified cross-validation and balanced leave-one-out cross-validation, avoids the bias. Therefore for model selection and evaluation of microarray and other small biological datasets, these methods should be used and unstratified versions avoided. In particular, the commonly used (unbalanced) leave-one-out cross-validation should not be used to estimate AUC for small datasets.
  • Item
    Thumbnail Image
    RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements
    Chatagnon, A ; Veber, P ; Morin, V ; Bedo, J ; Triqueneaux, G ; Semon, M ; Laudet, V ; d'Alche-Buc, F ; Benoit, G (OXFORD UNIV PRESS, 2015-05-26)
    In mouse embryonic cells, ligand-activated retinoic acid receptors (RARs) play a key role in inhibiting pluripotency-maintaining genes and activating some major actors of cell differentiation. To investigate the mechanism underlying this dual regulation, we performed joint RAR/RXR ChIP-seq and mRNA-seq time series during the first 48 h of the RA-induced Primitive Endoderm (PrE) differentiation process in F9 embryonal carcinoma (EC) cells. We show here that this dual regulation is associated with RAR/RXR genomic redistribution during the differentiation process. In-depth analysis of RAR/RXR binding sites occupancy dynamics and composition show that in undifferentiated cells, RAR/RXR interact with genomic regions characterized by binding of pluripotency-associated factors and high prevalence of the non-canonical DR0-containing RA response element. By contrast, in differentiated cells, RAR/RXR bound regions are enriched in functional Sox17 binding sites and are characterized with a higher frequency of the canonical DR5 motif. Our data offer an unprecedentedly detailed view on the action of RA in triggering pluripotent cell differentiation and demonstrate that RAR/RXR action is mediated via two different sets of regulatory regions tightly associated with cell differentiation status.
  • Item
    Thumbnail Image
    Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features
    Yang, M ; Rajasegarar, S ; Rao, AS ; Leckie, C ; Palaniswami, M ; Shi, Z ; Vadera, S ; Li, G (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.
  • Item
    Thumbnail Image
    Plasma lipid profiling in a large population-based cohort
    Weir, JM ; Wong, G ; Barlow, CK ; Greeve, MA ; Kowalczyk, A ; Almasy, L ; Comuzzie, AG ; Mahaney, MC ; Jowett, JBM ; Shaw, J ; Curran, JE ; Blangero, J ; Meikle, PJ (ELSEVIER, 2013-10)
    We have performed plasma lipid profiling using liquid chromatography electrospray ionization tandem mass spectrometry on a population cohort of more than 1,000 individuals. From 10 μl of plasma we were able to acquire comparative measures of 312 lipids across 23 lipid classes and subclasses including sphingolipids, phospholipids, glycerolipids, and cholesterol esters (CEs) in 20 min. Using linear and logistic regression, we identified statistically significant associations of lipid classes, subclasses, and individual lipid species with anthropometric and physiological measures. In addition to the expected associations of CEs and triacylglycerol with age, sex, and body mass index (BMI), ceramide was significantly higher in males and was independently associated with age and BMI. Associations were also observed for sphingomyelin with age but this lipid subclass was lower in males. Lysophospholipids were associated with age and higher in males, but showed a strong negative association with BMI. Many of these lipids have previously been associated with chronic diseases including cardiovascular disease and may mediate the interactions of age, sex, and obesity with disease risk.
  • Item
    Thumbnail Image
    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.