Effect of surface conditions on flow of a micropolar fluid driven by a porous stretching sheet

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A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Listening, sharing understanding and facilitating consumer, family and community empowerment through a priority driven partnership in Far North Queensland

Objectives: This paper provides an example of a mental health research partnership underpinned by empowerment principles that seeks to foster strength among community organizations to support better outcomes for consumers, families and communities. It aims to raise awareness among researchers and service providers that empowerment approaches to assist communities to address mental health problems are not too difficult to be practical but require long-term commitment and appropriate support. Methods: A collaborative research strategy that has become known as the Priority Driven Research (PDR) Partnership emerged through literature review,consultations, Family Wellbeing Program delivery with community groups and activities in two discrete Indigenous communities. Progress to date on three of the four components of the strategy is described. Results: The following key needs were identified in a pilot study and are now being addressed in a research-based implementation phase: (i) gaining two-way understanding of perspectives on mental health and promoting universal awareness; (ii) supporting the empowerment of carers, families, consumers and at-risk groups through existing community organizations to gain greater understanding and control of their situation; (iii) developing pathways of care at the primary health centre level to enable support of social and emotional wellbeing as well as more integrated mental health care; (iv) accessing data to enable an ongoing process of analysis/sharing/planning and monitoring to inform future activity. Conclusion: One of the key learnings to emerge in this project so far is that empowerment through partnership becomes possible when there is a concerted effort to strengthen grassroots community organizations. These include social health teams and men’s and women’s groups that can engage local people in an action orientation. Key words: Aboriginal, empowerment, Indigenous, mental health.

In Pursuit of Excellence : A Policy Driven Systemic Initiative to Embed Gifted Education

Embedding gifted education practices requires major professional development strategies supported by transparent, credible and enforceable policy. This paper describes an analysis of a state-wide initiative involving the establishment of a series of schools tasked to develop and disseminate gifted education principles. The authors have been involved with this initiative at a number of levels over a ten-year period. Their involvement culminated in a commissioned review of the program. Extensive qualitative data were purposively collected from all stakeholders and the effectiveness of the initiative is examined from a theoretical framework of policy development and excellence. The findings summarised in this proposal, indicate the achievement of excellence at a systemic level was constrained by lack of vision, leadership and commitment to long term achievements of excellence. At a local level evidence exists that excellence can be manifested when there is synchronicity of vision, purpose, decisions, and actions.

Improved GMM-based speaker verification using SVM-driven impostor dataset selection

The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

Reliability driven reconfiguration of rural power distribution systems

This paper presents a reliability-based reconfiguration methodology for power distribution systems. Probabilistic reliability models of the system components are considered and Monte Carlo method is used while evaluating the reliability of the distribution system. The reconfiguration is aimed at maximizing the reliability of the power supplied to the customers. A binary particle swarm optimization (BPSO) algorithm is used as a tool to determine the optimal configuration of the sectionalizing and tie switches in the system. The proposed methodology is applied on a modified IEEE 13-bus distribution system.