314 resultados para high-level features


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Driven by information accessibility-on-demand provided by the internet, education modes are changing from a teacher-led approach focused on content delivery and assessible outcomes, to a learner-based approach encouraging self-directed, peer-tutored, and cooperative learning. New pedagogies are required to extend learning beyond the classroom and traditional subject areas such as contemporary arts, in alignment with the cross disciplinary priorities of the Australian Curriculum and values of the International Baccalaureate Organisation. This research explores how partnerships with universities and cultural organisations are implicated in the generation of these new forms of pedagogy and contribute to the field of educational research within the context of Education Queensland’s Framework For Gifted Education. In particular, this paper explores a new pedagogical framework for highly capable year five to nine Queensland state school students at the intersection of arts, design and the sciences, which has arisen from an explicit secondary/ tertiary partnership between the Queensland University of Technology Creative Industries Faculty and Precincts and the Queensland Academies Young Scholars Program. The Young Scholars Program offers experiences in the International Baccalaureate and Australian Curriculum contexts to enhance outcomes via global understanding, unique industry partnerships and 21st century pedagogical innovation based not on 'content' but tacit/experiential learning concepts including immersive, creative, intellectual and social strategies. These strategies for highly capable students are centred around authentic opportunities, primary resources, transdisciplinary learning and relationships with likeminded peers including tertiary arts, design and STEM educators and students, professionals and researchers. The presentation details case studies which are hands-on real time workshops involving inquiry based challenges in the arts, design and sciences, mathematics, history, creative writing and other disciplines, with content drawn from collections from public institutions, academic research and tertiary pedagogy. Both programs implicate student collaboration and creative production as methodology/data capture for ongoing action research, in alignment with the Framework For Gifted Education’s emphasis on evidence-based practices. They also challenge gifted students “to continue their development through curricular activities that require depth of study, complexity of thinking, fast pace of learning, high-level skills development and/or creative and critical thinking (e.g. through independent investigations, tiered tasks, diverse real-world applications, mentors)”(Education Queensland, 2011:3). This presentation highlights the strengths of the ongoing collaboration between QUT Creative industries Faculty and Queensland Academies, which not only provides successful extra curricular activities for gifted students towards a place in the International Baccalaureate Program, but also provides mentoring opportunities for tertiary students in their field of endeavor to assist with their own learning, and unique research opportunities for the Faculty as it focuses on excellence in arts, design and creative education and research. Education Queensland.(2011). Framework For Gifted Education Revised Edition 2011 (accessed Nov 19 2011)

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Background Maintenance of communication is important for people with dementia living in long-term care. The purpose of this study was to assess the feasibility of using “Giraff”, a telepresence robot to enhance engagement between family and a person with dementia living in long-term care. Methods A mixed-methods approach involving semi-structured interviews, call records and video observational data was used. Five people with dementia and their family member participated in a discussion via the Giraff robot for a minimum of six times over a six-week period. A feasibility framework was used to assess feasibility and included video analysis of emotional response and engagement. Results Twenty-six calls with an average duration of 23 mins took place. Residents showed a general state of positive emotions across the calls with a high level of engagement and a minimal level of negative emotions. Participants enjoyed the experience and families reported that the Giraff robot offered the opportunity to reduce social isolation. A number of software and hardware challenges were encountered. Conclusions Participants perceived this novel approach to engage families and people with dementia as a feasible option. Participants were observed and also reported to enjoy the experience. The technical challenges identified have been improved in a newer version of the robot. Future research should include a feasibility trial of longer duration, with a larger sample and a cost analysis.

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Recent years have seen global food prices rise and become more volatile. Price surges in 2008 and 2011 held devastating consequences for hundreds of millions of people and negatively impacted many more. Today one billion people are hungry. The issue is a high priority for many international agencies and national governments. At the Cannes Summit in November 2011, the G20 leaders agreed to implement five objectives aiming to mitigate food price volatility and protect vulnerable persons. To succeed, the global community must now translate these high level policy objectives into practical actions. In this paper, we describe challenges and unresolved dilemmas before the global community in implementing these five objectives. The paper describes recent food price volatility trends and an evaluation of possible causes. Special attention is given to climate change and water scarcity, which have the potential to impact food prices to a much greater extent in coming decades. We conclude the world needs an improved knowledge base and new analytical capabilities, developed in parallel with the implementation of practical policy actions, to manage food price volatility and reduce hunger and malnutrition. This requires major innovations and paradigm shifts by the global community.

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This thesis examines the question why the automotive mode and the large technological system it creates, continues to dominate urban transport systems despite the availability of more cost-efficient alternatives. A number of theoretical insights are developed into the way these losses evolve from path dependent growth, and lead to market failure and lock-in. The important role of asymmetries of influence is highlighted. A survey of commuters in Jakarta Indonesia is used to provide a measure of transport modal lock-in (TML) in a developing country conurbation. A discrete choice experiment is used to provide evidence for the thesis central hypothesis that in such conurbations there is a high level of commuter awareness of the negative externalities generated by TML which can produce a strong level of support for its reversal. Why TML nevertheless remains a strong and durable feature of the transport system is examined with reference to the role of asymmetries of influence.

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In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

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The paper reports health related findings of the first study undertaken of rural sex workers in an income-rich nation. In-depth interviews were conducted with eighteen purposively selected women who work in the rural sex industry. Rural sex services have a unique structure which informs the experiences of sex workers. Recent advances in telecommunications technology have impacted upon the organisation and structure of the sex industry in rural environments. Notable has been the growth of escort services in rural areas, which has diversified the rural sex industry from its traditional base of brothel operations. The general absence of street prostitution in rural settings has meant that the profile of rural sex workers tends to resemble that of escorts or call girls in urban settings, with workers having a relatively high level of control over working conditions and compliance with public health initiatives. Important issues which impact upon the rural sex industry include confidentiality and the more limited market for sexual services likely to be encountered in rural settings. These issues may impact on the sexual health of rural sex workers in terms of risk practices and access to health services.

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The prospect of economically producing useful biologics in plants has greatly increased with the advent of viral vectors. The ability of viral vectors to amplify transgene expression has seen them develop into robust transient platforms for the high-level, rapid production of recombinant proteins. To adapt these systems to stably transformed plants, new ways of deconstructing the virus machinery and linking its expression and replication to chemically controlled promoters have been developed. The more advanced of these stable, inducible hyper-expression vectors provide both activated and amplified heterologous transgene expression. Such systems could be deployed in broad acre crops and provide a pathway to fully exploit the advantages of plants as a platform for the manufacture of a wide spectrum of products.

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Objective. To assess medical and nursing students’ knowledge, attitudes, and practices (KAP) regarding human immunodeficiency virus (HIV) in Fiji. Methods. A cross-sectional study of 275 medical and 252 nursing students that participated in a questionnaire survey on HIV KAP. Data was analysed according to their gender, program of study, and academic year. Results. The mean HIV knowledge (HK) and attitude scores were 16.0 and 41.3, respectively. Mean HK score was significantly higher in males compared to females. Significant positive correlations were found between HK and academic year for medical () and nursing () students and between HK and attitude scores (). The majority of students indicated fear in contracting HIV through clinical practice and felt that health care workers have the right to know a patients HIV status for their own safety. The majority would wear gloves to touch a patient if suspected of HIV. Conclusions. The study found a high level of HIV knowledge and positive attitude towards HIV patients. However, respondents also displayed negative attitudes and unacceptable practices probably due to fear. Training institutions need to ensure that students gain accurate knowledge on HIV especially on transmission routes to allay the fear of caring for HIV-infected patients.

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Semantic Web offers many possibilities for future Web technologies. Therefore, it is a need to search for ways that can bring the huge amount of unstructured documents from current Web to Semantic Web automatically. One big challenge in searching for such ways is how to understand patterns by both humans and machine. To address this issue, we present an innovative model which interprets patterns to high level concepts. These concepts can explain the patterns' meanings in a human understandable way while improving the information filtering performance. The model is evaluated by comparing it against one state-of-the-art benchmark model using standard Reuters dataset. The results show that the proposed model is successful. The significance of this model is three fold. It gives a way to interpret text mining output, provides a technique to find concepts relevant to the whole set of patterns which is an essential feature to understand the topic, and to some extent overcomes information mismatch and overload problems of existing models. This model will be very useful for knowledge based applications.

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

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The aim of the study was to assess the feasibility and effectiveness of aquatic‐based exercise in the form of deep water running ( DWR ) as part of a multimodal physiotherapy programme ( MMPP ) for breast cancer survivors. A controlled clinical trial was conducted in 42 primary breast cancer survivors recruited from community‐based P rimary C are C entres. Patients in the experimental group received a MMPP incorporating DWR , 3 times a week, for an 8‐week period. The control group received a leaflet containing instructions to continue with normal activities. Statistically significant improvements and intergroup effect size were found for the experimental group for P iper F atigue S cale‐ R evised total score ( d = 0.7, P = 0.001), as well as behavioural/severity ( d = 0.6, P = 0.05), affective/meaning ( d = 1.0, P = 0.001) and sensory ( d = 0.3, P = 0.03) domains. Statistically significant differences between the experimental and control groups were also found for general health ( d = 0.5, P < 0.05) and quality of life ( d = 1.3, P < 0.05). All participants attended over 80% of sessions, with no major adverse events reported. The results of this study suggest MMPP incorporating DWR decreases cancer‐related fatigue and improves general health and quality of life in breast cancer survivors. Further, the high level of adherence and lack of adverse events indicate such a programme is safe and feasible.

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This study responds to calls for research on work-family aspects in entrepreneurship research. Our study examined the role of work-family conflict and enhancement on small business owners’ (SBOs) wellbeing. We found work-family has negative direct effect on mental health, job and family satisfactions. Furthermore, we found that under high level of work-family conflict condition, SBOs who perceive a greater level of work-family enhancement would feel more satisfy with their life, job as well as family aspects. Interestingly, under high level of conflict, even SBOs perceive greater level of enhancement, it would not lessen the negative impact of the conflict on their mental health. These results suggest that once psychological health is harmed by work-family conflict, its negative consequences remain unchanged.

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For pedagogical change to be sustained over time, and over the span of higher education courses, it needs to be framed widely, rather than ‘tacked on’. The framing includes curriculum reform and resource provision alongside staff pedagogical development. This is especially true for initiatives (such as reflective writing and assessment) that target broad-based, high-level skills and dispositions. For various reasons, such initiatives can easily become lost because of the discipline-specific focus of a syllabus outweighs the initiative, or because lack of resources compromises a desired approach. Course improvement in higher education contexts is typically difficult and episodic. In such circumstances, we argue that a strategic and trustworthy approach is necessary where practitioner-lead pedagogic development is fostered through trust and communication and is purposefully embedded within key dimensions of curriculum integration and resource provision. This chapter describes an approach to pedagogical change where curriculum, pedagogy and resources are simultaneously and collaboratively orchestrated to provide an effective framework for sustainable and effective change. A robust conceptual model is proposed to guide the implementation of such change.

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Coastal resources are coming under increasing pressure from competition between recreational, commercial and conservation uses. This is particularly so in coastal areas adjacent to major population centres. Given high recreational and conservation values in such areas, economic activities need to be highly efficient in order to persist. Management of these industries must therefore also encourage efficient production and full utilisation of the areas available. In order to achieve this, managers must first understand the level and drivers of productivity, and how these can be influenced. In this study, by way of illustration, the focus was on the Sydney rock oyster industry within Queensland's Moreton Bay, a multiple use marine park with high recreational and conservation value adjacent to Australia's third largest city. Productivity of the oyster industry in Moreton Bay is currently low compared to historic levels, and management has an objective of reversing this trend. It is unclear whether this difference is due to oyster farmers' business choices and personal characteristics or whether varying environmental conditions in the Moreton Bay limit the capacity of the oyster industry. These require different management responses in order to enhance productivity. The study examined different productivity measures of the oyster industry using data envelopment analysis (DEA) to determine where productivity gains can be made and by how much. The findings suggest that the industry is operating at a high level of capacity utilisation, but a low level of efficiency. The results also suggest that both demographic and environmental conditions affect technical efficiency in the Bay, with water characteristics improvements and appropriate training potentially providing the greatest benefits to the industry. Methods used in this study are transferable to other industries and provide a means by which coastal aquaculture may be managed to ensure it remains competitive with other uses of coastal resources.

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Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.