Contracting for community development : a case study based perspective of a public sector client initiative in Hong Kong

The construction industry is known to be an important contributor towards the gross domestic product of many countries. Moreover, the health of the construction industry is positively correlated to the economic growth of a country and in many economies public sector clients account for a major share of construction works. Given this strength, it is important for public sector clients to initiate innovations aimed at the betterment of the industry. In this context, concern about sustainable development has been a major driver of some innovative initiatives in construction industries worldwide. Furthermore, the Government of Hong Kong regards both sustainability and community development as important criteria when planning and procuring construction projects. This paper is based on a case study of a public sector development project in Hong Kong, and presents the salient features of the procurement and contractual systems adopted in the project, which foster sustainability and community development. The reported interim findings are based on a preliminary document analysis that is part of an ongoing longitudinal case study into the project. The document analysis takes a three-pronged approach in terms of how the procurement and contractual systems foster economic, environmental and social sustainability, and sums up their impact on the community as a whole.

Prediction of adjacent vertebral fracture risk associated with vertebroplasty : a computer-based simulation study

Vertebrplasty involved injecting cement into a fractured vertebra to provide stabilisation. There is clinical evidence to suggest however that vertebroplasty may be assocated with a higher risk of adjacent vertebral fracture; which may be due to the change in material properties of the post-procedure vertebra modifying the transmission of mechanical stresses to adjacent vertebrae.

A Vision Based Target Detection System for Docking of an Autonomous Underwater Vehicle

This paper describes the development and preliminary experimental evaluation of a visionbased docking system to allow an Autonomous Underwater Vehicle (AUV) to identify and attach itself to a set of uniquely identifiable targets. These targets, docking poles, are detected using Haar rectangular features and rotation of integral images. A non-holonomic controller allows the Starbug AUV to orient itself with respect to the target whilst maintaining visual contact during the manoeuvre. Experimental results show the proposed vision system is capable of robustly identifying a pair of docking poles simultaneously in a variety of orientations and lighting conditions. Experiments in an outdoor pool show that this vision system enables the AUV to dock autonomously from a distance of up to 4m with relatively low visibility.

Effective employment-based training models for childcare workers

Childcare workers play a significant role in the learning and development of children in their care. This has major implications for the training of workers. Under new reforms of the childcare industry the Australian government now requires all workers to obtain qualifications from a vocational education and training provider (eg. Technical and Further Education) or university. Effective models of employment-based training are critical to provide training to highly competent workers. This paper presents findings from a study that examined current and emerging models of employment-based training in the childcare sector, particularly at the Diploma level. Semi-structured interviews were conducted with a sample of 16 participants who represented childcare directors, employers, and workers located in childcare services in urban, regional and remote locations in the State of Queensland. The study proposes a ‘best-fit’ employment-based training approach that is characterised by a compendium of five models instead of a ‘one size fits all’. Issues with successful implementation of the EBT models are also discussed

Evidence-based decision making to strengthen local governance : nutritional health interventions in Bantul and Gunungkidul

Since 2001, district governments have had the main responsibility for providing public health care in Indonesia. One of the main public health challenges facing many district governments is improving nutritional standards, particularly among poorer segments of the population. Developing effective policies and strategies for improving nutrition requires a multi-sectoral approach encompassing agricultural development policy, access to markets, food security (storage) programs, provision of public health facilities, and promotion of public awareness of nutritional health. This implies a strong need for a coordinated approach involving multiple government agencies at the district level. Due to diverse economic, agricultural, and infrastructure conditions across the country, district governments’ ought to be better placed than central government both to identify areas of greatest need for public nutrition interventions, and devise policies that reflect local characteristics. However, in the two districts observed in this study—Bantul and Gunungkidul—it was clear that local government capacity to generate, obtain and integrate evidence about local conditions into the policy-making process was still limited. In both districts, decision-makers tended to rely more on intuition,anecdote, and precedent in formulating policy. The potential for evidence-based decision making was also severely constrained by a lack of coordination and communication between agencies, and current arrangements related to central government fiscal transfers, which compel local governments to allocate funding to centrally determined programs and priorities.

Evidence based decision making to strenghten local governance : nutritional health interventions

Since 2001, district governments have had the main responsibility for providing public health care in Indonesia. One of the main public health challenges facing many district governments is improving nutritional standards, particularly among poorer segments of the population. Developing effective policies and strategies for improving nutrition requires a multi-sectoral approach encompassing agricultural development policy, access to markets, food security (storage) programs, provision of public health facilities, and promotion of public awareness of nutritional health. This implies a strong need for a coordinated approach involving multiple government agencies at the district level. Due to diverse economic, agricultural,and infrastructure conditions across the country, district governments’ ought to be better placed than central government both to identify areas of greatest need for public nutrition interventions, and devise policies that reflect local characteristics. However, in the two districts observed in this study—Bantul and Gunungkidul—it was clear that local government capacity to generate, obtain and integrate evidence about local conditions into the policy-making process was still limited. In both districts, decision-makers tended to rely more on intuition,anecdote, and precedent in formulating policy. The potential for evidence-based decision making was also severely constrained by a lack of coordination and communication between agencies, and current arrangements related to central government fiscal transfers, which compel local governments to allocate funding to centrally determined programs and priorities.

Context-based chemistry : creating opportunities for fluid transitions between concepts and context

Nationally and internationally, context-based programs have been implemented in an attempt to engage students in chemistry through connecting the canonical science with the real-world. In Queensland, a context-based approach to chemistry was trialled in selected schools from 2002 but there is little research that investigates how students learn in a context-based setting. This paper presents one significant finding from an ethnographic study that explored the learning that occurred in an 11th grade context-based chemistry classroom in Queensland. The study found that by providing students with the opportunity to write, fluid transitions (or to-ing and fro-ing) between concepts and context were an outcome of context-based learning.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Mixing colours: An ICT tool based on a semiotic framework for mathematical meaning-makng about ratio and fractions

This paper reports on the research and development of an ICT tool to facilitate the learning of ratio and fractions by adult prisoners. The design of the ICT tool was informed by a semiotic framework for mathematical meaning-making. The ICT tool thus employed multiple semiotic resources including topological, typological, and social-actional resources. The results showed that individual semiotic resource could only represent part of the mathematical concept, while at the same time it might signify something else to create a misconception. When multiple semiotic resources were utilised the mathematical ideas could be better learnt.

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.

Creating and enabling opportunities for increased student participation in experience based learning in professional practice

This paper explores models for enabling increased participation in experience based learning in legal professional practice. Legal placements as part of “for-credit” units offer students the opportunity to develop their professional skills in practice, reflect on their learning and job performance and take responsibility for their career development and planning. In short, work integrated learning (WIL) in law supports students in making the transition from university to practice. Despite its importance, WIL has traditionally taken place in practical legal training courses (after graduation) rather than during undergraduate law courses. Undergraduate WIL in Australian law schools has generally been limited to legal clinics which require intensive academic supervision, partnerships with community legal organisations and government funding. This paper will propose two models of WIL for undergraduate law which may overcome many of the challenges to engaging in WIL in law (which are consistent with those identified generally by the WIL Report). The first is a virtual law placement in which students use technology to complete a real world project in a virtual workplace under the guidance of a workplace supervisor. The second enables students to complete placements in private legal firms, government legal offices, or community legal centres under the supervision of a legal practitioner. The units complement each other by a) creating and enabling placement opportunities for students who may not otherwise have been able to participate in work placement by reason of family responsibilities, financial constraints, visa restrictions, distance etc; and b) enabling students to capitalise on existing work experience. This paper will report on the pilot offering of the units in 2008, the evaluation of the models and changes implemented in 2009. It will conclude that this multi-pronged approach can be successful in creating opportunities for, and overcoming barriers to participation in experiential learning in legal professional practice.

Moving towards a knowledge city : Brisbane's experience in knowledge-based urban development

In the global knowledge economy, knowledge-intensive industries and knowledge workers are extensively seen as the primary factors to improve the welfare and competitiveness of cities. To attract and retain such industries and workers, cities produce knowledge-based urban development strategies, where such strategising is also an important development mechanism for cities and their economies. This paper investigates Brisbane’s knowledge-based urban development strategies that support generation, attraction, and retention of investment and talent. The paper provides a clear understanding on the policy frameworks, and relevant applications of Brisbane’s knowledge-based urban development experience in becoming a prosperous knowledge city.

Factors related to the presentation of thin and thick nodular melanoma from a population-based cancer registry in Queensland Australia

Purpose: Worldwide, the incidence of thick melanoma has not declined, and the nodular melanoma (NM) subtype accounts for nearly 40% of newly-diagnosed thick melanoma. To assess differences between patients with thin (≤2.00 mm) and thick (≥2.01 mm) nodular melanoma, we evaluated factors such as demographics, melanoma detection patterns, tumor visibility, and physician screening for NM alone and compared clinical presentation and anatomic location of NM with superficial spreading melanoma (SSM). Methods We utilized data from a large population-based study of Queensland (Australia) residents diagnosed with melanoma. Queensland residents aged 20 to 75 years with histologically confirmed first primary invasive cutaneous melanoma were eligible for the study, and all questionnaires were conducted by telephone (response rate 77.9%). Results During this four-year period, 369 patients with nodular melanoma were interviewed, of whom 56.7% were diagnosed with tumors ≤ 2.00 mm. Men, older individuals, and those who had not been screened by a physician in the past three years were more likely to have nodular tumors of greater thickness. Thickest nodular melanoma (4 mm+) was also most common in persons who had not been screened by a doctor within the past three years (OR 3.75; 95% CI 1.47-9.59). Forty-six percent of patients with thin nodular melanoma (≤ 2.00 mm) reported a change in color, compared with 64% of patients with thin SSM and 26% of patients with thick nodular melanoma (>2.00 mm). Conclusion Awareness of factors related to earlier detection of potentially fatal nodular melanomas, including the benefits of a physician examination, should be useful in enhancing public and professional education strategies. Particular awareness of clinical warning signs associated with thin nodular melanoma should allow for more prompt diagnosis and treatment of this subtype.

Minimising speaker verification utterance length through confidence based early verification decisions

This paper presents a novel approach of estimating the confidence interval of speaker verification scores. This approach is utilised to minimise the utterance lengths required in order to produce a confident verification decision. The confidence estimation method is also extended to address both the problem of high correlation in consecutive frame scores, and robustness with very limited training samples. The proposed technique achieves a drastic reduction in the typical data requirements for producing confident decisions in an automatic speaker verification system. When evaluated on the NIST 2005 SRE, the early verification decision method demonstrates that an average of 5–10 seconds of speech is sufficient to produce verification rates approaching those achieved previously using an average in excess of 100 seconds of speech.