Role of 3D visual conceptualisation of groundwater system models as a management support tool

After the recent prolonged drought conditions in many parts of Australia it is increasingly recognised that many groundwater systems are under stress. Although this is obvious for systems that are utilised for intensive irrigation many other groundwater systems are also impacted.Management strategies are highly variable to non-existent. Policy and regulation are also often inadequate, and are reactive or politically driven. In addition, there is a wide range of opinion by water users and other stakeholders as to what is “reasonable”management practice. These differences are often related to the “value”that is put on the groundwater resource. Opinions vary from “our right to free water”to an awareness that without effective management the resource will be degraded. There is also often misunderstanding of surface water-groundwater linkages, recharge processes, and baseflow to drainage systems.

Open standards-based system integration for asset management decision support

Over the last decade, system integration has grown in popularity as it allows organisations to streamline business processes. Traditionally, system integration has been conducted through point-to-point solutions – as a new integration scenario requirement arises, a custom solution is built between the relevant systems. Bus-based solutions are now preferred, whereby all systems communicate via an intermediary system such as an enterprise service bus, using a common data exchange model. This research investigates the use of a common data exchange model based on open standards, specifically MIMOSA OSA-EAI, for asset management system integration. A case study is conducted that involves the integration of processes between a SCADA, maintenance decision support and work management system. A diverse number of software platforms are employed in developing the final solution, all tied together through MIMOSA OSA-EAI-based XML web services. The lessons learned from the exercise are presented throughout the paper.

Trends in National Party support

The National Party of Australia is under challenge. Will it be able to adapt and survive or will it become increasingly irrelevant in Australian politics? With population growth in some coastal and hinterland areas and decline in inland agricultural areas, the face of rural and regional Australia is changing. As a result, the National Party's traditional support is being eroded. Within the long-standing Coalition, the influence of the Nationals appears to be in decline, yet they continue to resist amalgamation with the Liberal Party.

Social support and fires in the workplace : a preliminary investigation

Workers who experience fire in the workplace are faced with disruption to their work routine, as well as the emotional strain of the fire. In the broader occupational stress literature, researchers have suggested that social support will be most effective at reducing the negative effects of stressors on strain when the type of support matches the type of stressor being experienced (either instrumental or emotional). This study was a preliminary investigation into employee responses to less routine stressors, such as workplace fires, and the role of different sources of social support in predicting coping effectiveness. This study also was a first attempt at considering the influence of the social context (in terms of group identification) on the effectiveness of social support as a predictor of coping effectiveness. Specifically, it was predicted that social support would be more effective when it came from multiple sources within the organization, that it would be especially effective when provided from a group that workers identified more strongly with, and that simply feeling part of a group would improve adjustment. Both quantitative and qualitative data were collected from 33 employees who had recently experienced a significant fire in their workplace. Results suggested that the type of stressors experienced and the type of support were mismatched, but despite this, coping effectiveness was generally moderate to high. There was mixed support for predictions about the effects of social support–no moderating effect of group identification on coping effectiveness was observed for measures of workplace support, although it did moderate the effects of family support on this adjustment indicator.

Future-proofing : the academic library’s role in e-research support

Purpose – The paper describes a project created to enhance e-research support activities within an Australian university, based on environmental scanning of e-research activities and funding both nationally and internationally. Participation by the university library is also described.----- Design/methodology/approach – The paper uses a case study that describes the stages of a project undertaken to develop an academic library’s capacity to offer e-research support to its institution’s research community.----- Findings – While the outcomes of the project have been successfully achieved, the work needs to be continued and eventually mainstreamed as core business in order to keep pace with developments in e-research. The continual skilling up of the university’s researchers and research support staff in e-research activities is imperative in reaching the goal of becoming a highly competitive research institution.----- Research limitations/implications – Although a single case study, the work has been contextualised within the national research agenda.----- Practical implications – The paper provides a project model that can adapted within an academic library without external or specialist skills. It is also scalable and can be applied at a divisional or broader level.----- Originality/value – The paper highlights the drivers for research investment in Australia and provides a model of how building e-research support activities can leverage this investment and contribute towards successful research activity.

Parental physical activity : exploring the role of social support

Objectives: To explore the influence of social support on parental physical activity (PA). Methods: Forty parents (21 mothers, 19 fathers) participated in semistructured individual or group interviews. Data were analyzed using thematic content analysis.---------- Results: Instrumental (eg, providing child care, taking over chores), emotional (eg, encouragement, companionship), and informational support (eg, ideas and advice) as well as reciprocal support (eg, giving as well as receiving support) and autonomy support (eg, respecting one’s choices) are important for parents’ PA behavior. However, having support for being active is not straightforward in that many parents discussed issues that inhibited the facilitative nature of social support for PA performance (eg, guilt in getting help). Conclusions: Results highlight the complex nature of social support in facilitating parental PA.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Automated centralised separation management with onboard decision support

This paper proposes a novel automated separation management concept in which onboard decision support is integrated within a centralised air traffic separation management system. The onboard decision support system involves a decentralised separation manager that can overrule air traffic management instructions under certain circumstances. This approach allows the advantages of both centralised and decentralised concepts to be combined (and disadvantages of each separation management approach to be mitigated). Simulation studies are used to illustrate the potential benefits of the combined separation management concept.

Coordinated research support services at Queensland University of Technology, Australia

Queensland University of Technology (QUT) is a large multidisciplinary university located in Brisbane, Queensland, Australia. QUT is increasing its research focus and is developing its research support services. It has adopted a model of collaboration between the Library, High Performance Computing and Research Support (HPC) and more broadly with Information Technology Services (ITS). Research support services provided by the Library include the provision of information resources and discovery services, bibliographic management software, assistance with publishing (publishing strategies, identifying high impact journals, dealing with publishers and the peer review process), citation analysis and calculating authors’ H Index. Research data management services are being developed by the Library and HPC working in collaboration. The HPC group within ITS supports research computing infrastructure, research development and engagement activities, researcher consultation, high speed computation and data storage systems , 2D/ 3D (immersive) visualisation tools, parallelisation and optimization of research codes, statistics/ data modeling training and support (both qualitative and quantitative) and support for the university’s central Access Grid collaboration facility. Development and engagement activities include participation in research grants and papers, student supervision and internships and the sponsorship, incubation and adoption of new computing technologies for research. ITS also provides other services that support research including ICT training, research infrastructure (networking, data storage, federated access and authorization, virtualization) and corporate systems for research administration. Seminars and workshops are offered to increase awareness and uptake of new and existing services. A series of online surveys on eResearch practices and skills and a number of focus groups was conducted to better inform the development of research support services. Progress towards the provision of research support is described within the context organizational frameworks; resourcing; infrastructure; integration; collaboration; change management; engagement; awareness and skills; new services; and leadership. Challenges to be addressed include the need to redeploy existing operational resources toward new research support services, supporting a rapidly growing research profile across the university, the growing need for the use and support of IT in research programs, finding capacity to address the diverse research support needs across the disciplines, operationalising new research support services following their implementation in project mode, embedding new specialist staff roles, cross-skilling Liaison Librarians, and ensuring continued collaboration between stakeholders.