The nature of caregiving in children of a parent with multiple sclerosis from multiple sources and the associations between caregiving activities and youth adjustment overtime

This study explored youth caregiving for a parent with multiple sclerosis (MS) from multiple perspectives, and examined associations between caregiving and child negative (behavioural emotional difficulties, somatisation) and positive (life satisfaction, positive affect, prosocial behaviour) adjustment outcomes overtime. A total of 88 families participated; 85 parents with MS, 55 partners and 130 children completed questionnaires at Time 1. Child caregiving was assessed by the Youth Activities of Caregiving Scale (YACS). Child and parent questionnaire data were collected at Time 1 and child data were collected 12 months later (Time 2). Factor analysis of the child and parent YACS data replicated the four factors (instrumental, social-emotional, personal-intimate, domestic-household care), all of which were psychometrically sound. The YACS factors were related to parental illness and caregiving context variables that reflected increased caregiving demands. The Time 1 instrumental and social-emotional care domains were associated with poorer Time 2 adjustment, whereas personal-intimate was related to better adjustment and domestic-household care was unrelated to adjustment. Children and their parents exhibited highest agreement on personal-intimate, instrumental and total caregiving, and least on domestic-household and social-emotional care. Findings delineate the key dimensions of young caregiving in MS and the differential links between caregiving activities and youth adjustment.

Distinguishing forced versus discretionary replacements in new product diffusion models

In many product categories of durable goods such as TV, PC, and DVD player, the largest component of sales is generated by consumers replacing existing units. Aggregate sales models proposed by diffusion of innovation researchers for the replacement component of sales have incorporated several different replacement distributions such as Rayleigh, Weibull, Truncated Normal and Gamma. Although these alternative replacement distributions have been tested using both time series sales data and individual-level actuarial “life-tables” of replacement ages, there is no census on which distributions are more appropriate to model replacement behaviour. In the current study we are motivated to develop a new “modified gamma” distribution by two reasons. First we recognise that replacements have two fundamentally different drivers – those forced by failure and early, discretionary replacements. The replacement distribution for each of these drivers is expected to be quite different. Second, we observed a poor fit of other distributions to out empirical data. We conducted a survey of 8,077 households to empirically examine models of replacement sales for six electronic consumer durables – TVs, VCRs, DVD players, digital cameras, personal and notebook computers. This data allows us to construct individual-level “life-tables” for replacement ages. We demonstrate the new modified gamma model fits the empirical data better than existing models for all six products using both a primary and a hold-out sample.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Design of a model-generated repository as a service for USDL

SAP and its research partners have been developing a lan- guage for describing details of Services from various view- points called the Unified Service Description Language (USDL). At the time of writing, version 3.0 describes technical implementation aspects of services, as well as stakeholders, pricing, lifecycle, and availability. Work is also underway to address other business and legal aspects of services. This language is designed to be used in service portfolio management, with a repository of service descriptions being available to various stakeholders in an organisation to allow for service prioritisation, development, deployment and lifecycle management. The structure of the USDL metadata is specified using an object-oriented metamodel that conforms to UML, MOF and EMF Ecore. As such it is amenable to code gener-ation for implementations of repositories that store service description instances. Although Web services toolkits can be used to make these programming language objects available as a set of Web services, the practicalities of writing dis- tributed clients against over one hundred class definitions, containing several hundred attributes, will make for very large WSDL interfaces and highly inefficient “chatty” implementations. This paper gives the high-level design for a completely model-generated repository for any version of USDL (or any other data-only metamodel), which uses the Eclipse Modelling Framework’s Java code generation, along with several open source plugins to create a robust, transactional repository running in a Java application with a relational datastore. However, the repository exposes a generated WSDL interface at a coarse granularity, suitable for distributed client code and user-interface creation. It uses heuristics to drive code generation to bridge between the Web service and EMF granularities.

Narrative on a doctoral narrative : reflections on postgraduate study and pedagogy

This paper explores the stages of one student’s intellectual journey through a Doctor of Philosophy program of study in an Australian university. It outlines the theoretical and methodological insights made as she came to understand that data was discourse, entailing a politics and position of power that ran contrary to the aims of the study that the student was undertaking in and on her own community. The article is a reflective narrative produced from the experience of having to come to terms with some of the personal and professional tensions and contradictions that postgraduate study can, and maybe should, engender if it is to be any of real value.

Review of regulatory framework for environmental information

If Australian scientists are to fully and actively participate in international scientific collaborations utilising online technologies, policies and laws must support the data access and reuse objectives of these projects. To date Australia lacks a comprehensive policy and regulatory framework for environmental information and data generally. Instead there exists a series of unconnected Acts that adopt historically-based, sector-specific approaches to the collection, use and reuse of environmental information. This paper sets out the findings of an analysis of a representative sample of Australian statutes relating to environmental management and protection to determine the extent to which they meet best practice criteria for access to and reuse of environmental information established in international initiatives. It identifies issues that need to be addressed in the legislation governing environmental information to ensure that Australian scientists are able to fully engage in international research collaborations.

Motivating domestic energy conservation through comparative, community-based feedback in mobile and social media

The progress of technology has led to the increased adoption of energy monitors among household energy consumers. While the monitors available on the market deliver real-time energy usage feedback to the consumer, the format of this data is usually unengaging and mundane. Moreover, it fails to address consumers with different motivations and needs to save and compare energy. This paper presents a study that seeks to provide initial indications for motivation-specific design of energy-related feedback. We focus on comparative feedback supported by a community of energy consumers. In particular, we examine eco-visualisations, temporal self-comparison, norm comparison, one-on-one comparison and ranking, whereby the last three allow us to explore the potential of socialising energy-related feedback. These feedback types were integrated in EnergyWiz – a mobile application that enables users to compare with their past performance, neighbours, contacts from social networking sites and other EnergyWiz users. The application was evaluated in personal, semi-structured interviews, which provided first insights on how to design motivation-related comparative feedback.

Shifting the technology context : career-change entrants’ transition into teaching

Becoming a teacher in technology-rich classrooms is a complex and challenging transition for career-change entrants. Those with generic or specialist Information and Communication Technology (ICT) expertise bring a mindset about purposeful uses of ICT that enrich student learning and school communities. The transition process from a non-education environment is both enhanced and constrained by shifting the technology context of generic or specialist ICT expertise, developed through a former career as well as general life experience. In developing an understanding of the complexity of classrooms and creating a learner centred way of working, perceptions about learners and learning evolve and shift. Shifts in thinking about how ICT expertise supports learners and enhances learning preceded shifts in perceptions about being a teacher, working with colleagues, and functioning in schools that have varying degrees of intensity and impact on evolving professional identities. Current teacher education and school induction programs are seen to be falling short of meeting the needs of career-change entrants and, as a flow on, the students they nurture. Research (see, for example, Tigchelaar, Brouwer, & Korthagen, 2008; Williams & Forgasz, 2009) highlights the value of generic and specialist expertise career-change teachers bring to the profession and draws attention to the challenges such expertise begets (Anthony & Ord, 2008; Priyadharshini & Robinson-Pant, 2003). As such, the study described in this thesis investigated perceptions of career-change entrants, who have generic (Mishra & Koehler, 2006) or specialist expertise, that is, ICT qualifications and work experience in the use of ICT. The career-change entrants‘ perceptions were sought as they shifted the technology context and transitioned into teaching in technology-rich classrooms. The research involved an interpretive analysis of qualitative data and quantitative data. The study used the explanatory case study (Yin, 1994) methodology enriched through grounded theory processes (Strauss & Corbin, 1998), to develop a theory about professional identity transition from the perceptions of the participants in the study. The study provided insights into the expertise and experiences of career change entrants, particularly in relation to how professional identities that include generic and specialist ICT knowledge and expertise were reconfigured while transitioning into the teaching profession. This thesis presents the Professional Identity Transition Theory that encapsulates perceptions about teaching in technology-rich classrooms amongst a selection of the increasing number of career-change entrants. The theory, grounded in the data, (Strauss & Corbin, 1998) proposes that career-change entrants experience transition phases of varying intensity that impact on professional identity, retention and development as a teacher. These phases are linked to a shift in perceptions rather than time as a teacher. Generic and specialist expertise in the use of ICT is a weight of the past and an asset that makes the transition process more challenging for career-change entrants. The study showed that career-change entrants used their experiences and perceptions to develop a way of working in a school community. Their way of working initially had an adaptive orientation focussed on immediate needs as their teaching practice developed. Following a shift of thinking, more generative ways of working focussed on the future emerged to enable continual enhancement and development of practice. Sustaining such learning is a personal, school and systemic challenge for the teaching profession.

Discriminative optimisation of the figure of merit for phonetic spoken term detection

This work proposes to improve spoken term detection (STD) accuracy by optimising the Figure of Merit (FOM). In this article, the index takes the form of phonetic posterior-feature matrix. Accuracy is improved by formulating STD as a discriminative training problem and directly optimising the FOM, through its use as an objective function to train a transformation of the index. The outcome of indexing is then a matrix of enhanced posterior-features that are directly tailored for the STD task. The technique is shown to improve the FOM by up to 13% on held-out data. Additional analysis explores the effect of the technique on phone recognition accuracy, examines the actual values of the learned transform, and demonstrates that using an extended training data set results in further improvement in the FOM.

Assessing the impact of transit and personal characteristics on mode choice of TOD users

Comparison are required to understand transport benefits of Transit Oriented Developments (TODs). Mode shares of TOD users need to be understood. Accurate travel demand models for TODs are needed.

Developing research data management services at QUT

QUT Library and the High Performance Computing and Research Support (HPC) Team have been collaborating on developing and delivering a range of research support services, including those designed to assist researchers to manage their data. QUT’s Management of Research Data policy has been available since 2010 and is complemented by the Data Management Guidelines and Checklist. QUT has partnered with the Australian Research Data Service (ANDS) on a number of projects including Seeding the Commons, Metadata Hub (with Griffith University) and the Data Capture program. The HPC Team has also been developing the QUT Research Data Repository based on the Architecta Mediaflux system and have run several pilots with faculties. Library and HPC staff have been trained in the principles of research data management and are providing a range of research data management seminars and workshops for researchers and HDR students.

Automatically detecting pain in video through facial action units

In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.

An analysis of the variability in Queensland WiM data

The Queensland Department of Main Roads uses Weigh-in-Motion (WiM) devices to covertly monitor (at highway speed) axle mass, axle configurations and speed of heavy vehicles on the road network. Such data is critical for the planning and design of the road network. Some of the data appears excessively variable. The current work considers the nature, magnitude and possible causes of WiM data variability. Over fifty possible causes of variation in WiM data have been identified in the literature. Data exploration has highlighted five basic types of variability specifically: ----- • cycling, both diurnal and annual;----- • consistent but unreasonable data;----- • data jumps;----- • variations between data from opposite sides of the one road; and ----- • non-systematic variations.----- This work is part of wider research into procedures to eliminate or mitigate the influence of WiM data variability.

School-based experiences : developing primary science preservice teachers' practice

Reviews into teacher education emphasise the need for preservice teachers to have more school-based experiences. In this study, a school-based experience was organised within a nine-week science curriculum university unit that allowed preservice teachers’ repeated experiences in teaching primary science. This research uses a survey, questionnaire with extended written responses, and researcher observations to investigate preservice teachers’ (n=38) learning experiences in two school settings. Survey results indicated that the majority of these preservice teachers either agreed or strongly agreed that school-based experiences developed their: personal-professional skill development (100%); system requirements (range: 81-100%); teaching practices (81-100%); student behaviour management (range: 94-100%); providing student feedback (89-94%); and reflection on practice (92-100%). Qualitative data provided insights into their development particularly for science content knowledge and receiving positive reinforcement on effective teaching behaviours. According to these preservice teachers, the school-based experiences facilitated “teachable moments – having the knowledge or skills to run with students’ questions or ideas” and allowed preservice teachers to “critically reflect between groups to make the task flow better”. Embedding school-based experiences needs to be part of each and every preservice teacher education unit so preservice teachers can develop confidence, knowledge and skills within authentic school contexts.