691 resultados para research object
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The 'Queensland Model' grew out of three convergent agendas: educational renewal, urban redevelopment, and the Queensland state government's 'Smart State' strategy.
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This paper suggests that, while advertising has changed, advertising research has not. Indeed, questions asked of advertising research more than 20 years ago have still not been answered. The enormity of change in advertising compounded by the lack of response from researchers suggests the traditional academic advertising research model requires more than routine maintenance. It seeks an architect with vision to redesign an academic research model that is probably broken or badly outdated. Five areas of the academic research approach are identified as needing rethinking: (1) the advertising problem, (2) sample frame and subjects, (3) assumptions regarding consumer behaviour, (4) research methodologies and (5) findings. Suggestions are made for improvement. But perhaps the biggest challenge is academic leadership. This paper proposes the establishment of a blue-ribbon panel to report back on recommended changes or improvements.
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Gen Y students are digital natives (Prensky 2001) who learn in complex and diverse ways, with a variety of learning styles apparent in any given course. This paper proposes a web 2.0 conceptual learning solution–online student videos–to respond to different learning styles that exist in the classroom.
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What happens to our research once it hits the popular media? Do marketers know how to promote our research in a way that is understandable and complete, while still capturing an audience? This case study follows the dissemination of the results of a consumer ethics study via a single press release, along with the resulting media coverage, interviews and audience comments. Perhaps in their quest for a touch of controversy, the story picked up by the popular press was not the one intended by the authors. If getting the public story right is important, marketing academics need to spend as much time carefully crafting their press releases as they do writing journal manuscripts – they may not be able to rely on the ethics of media sub-editors who choose controversial headlines.
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From a ‘cultural science’ perspective, this paper traces one aspect of a more general shift, from the realist representational regime of modernity to the productive DIY systems of the internet era. It argues that collecting and archiving is transformed by this change. Modern museums – and also broadcast television – were based on determinist or ‘essence’ theory; while internet archives like YouTube (and the internet as an archive) are based on ‘probability’ theory. The paper goes through the differences between modernist ‘essence’ and postmodern ‘probability’; starting from the obvious difference that in a museum each object is selected by experts for its intrinsic properties, while on the internet you don’t know what you will find. The status of individual objects is uncertain, although the productivity of the overall archive is unlimited. The paper links these differences with changes in contemporary culture – from a Newtonian to a quantum universe, progress to risk, institutional structure to evolutionary change, objectivity to uncertainty, identity to performance. Borrowing some of its methodology from science fiction, the paper uses examples from museums and online archives, ranging from the oldest stone tool in the world to the latest tribute vid on the net.
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Issues and Approach: The high rates of co-occurring depression and substance use, and the negative impact of this on illness course and outcomes have been well established. Despite this, few clinical trials have examined the efficacy of cognitive behaviour therapy (CBT). This paper systematically reviews these clinical trials, with an aim of providing recommendations for how future research can develop a more robust evidence base for the treatment of these common comorbidities. Leading electronic databases, including PubMed (ISI) and PsychINFO (CSA), were searched for peer-reviewed journal articles using CBT for the treatment of co-occurring depression and substance use. Of the 55 articles identified, 12 met inclusion criteria and were included in the review. ---------- Key Findings: There is only a limited evidence for the effectiveness of CBT either alone or in combination with antidepressant medication for the treatment of co-occurring depression and substance use. While there is support for the efficacy of CBT over no treatment control conditions, there is little evidence that CBT is more efficacious than other psychotherapies. There is, however, consistent evidence of improvements in both depression and substance use outcomes, regardless of the type of treatment provided and there is growing evidence that that the effects of CBT are durable and increase over time during follow up. ---------- Conclusions. Rather than declaring the ‘dodo bird verdict’ that CBT and all other psychotherapies are equally efficacious, it would be more beneficial to develop more potent forms of CBT by identifying variables that mediate treatment outcomes.
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With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.
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Internet and Web services have been used in both teaching and learning and are gaining popularity in today’s world. E-Learning is becoming popular and considered the latest advance in technology based learning. Despite the potential advantages for learning in a small country like Bhutan, there is lack of eServices at the Paro College of Education. This study investigated students’ attitudes towards online communities and frequency of access to the Internet, and how students locate and use different sources of information in their project tasks. Since improvement was at the heart of this research, an action research approach was used. Based on the idea of purposeful sampling, a semi-structured interview and observations were used as data collection instruments. 10 randomly selected students (5 girls and 5 boys) participated in this research as the controlled group. The study findings indicated that there is a lack of educational information technology services, such as e-learning at the college. Internet connection being very slow was the main barrier to learning using e-learning or accessing Internet resources. There is a strong relationship between the quality of written task and the source of the information, and between Web searching and learning. The source of information used in assignments and project work is limited to books in the library which are often outdated and of poor quality. Project tasks submitted by most of the students were of poor quality.
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These cards are designed as a resource for implementing participatory action research (PAR) in social programs. Each card covers one of the five key stages of PAR as outlined in the manual 'On PAR- Using participatory Action Research to Improve Early Intervention' (Crane and O'Regan 2010).
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Process modeling is an emergent area of Information Systems research that is characterized through an abundance of conceptual work with little empirical research. To fill this gap, this paper reports on the development and validation of an instrument to measure user acceptance of process modeling grammars. We advance an extended model for a multi-stage measurement instrument development procedure, which incorporates feedback from both expert and user panels. We identify two main contributions: First, we provide a validated measurement instrument for the study of user acceptance of process modeling grammars, which can be used to assist in further empirical studies that investigate phenomena associated with the business process modeling domain. Second, in doing so, we describe in detail a procedural model for developing measurement instruments that ensures high levels of reliability and validity, which may assist fellow scholars in executing their empirical research.
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The purpose of this paper is to highlight important issues in the study of dysfunctional customer behavior and to provide a research agenda to inspire, guide, and enthuse. Through a critical evaluation of existing research, the aim is to highlight key issues and to present potentially worthy avenues for future study.
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The focus of this paper is preparing research for dissemination by mainstream print, broadcast, and online media. While the rise of the blogosphere and social media is proving an effective way of reaching niche audiences, my own research reached such an audience through traditional media. The first major study of Australian horror cinema, my PhD thesis A Dark New World: Anatomy of Australian Horror Films, generated strong interest from horror movie fans, film scholars, and filmmakers. I worked closely with the Queensland University of Technology’s (QUT) public relations unit to write two separate media releases circulated on October 13, 2008 and October 14, 2009. This chapter reflects upon the process of working with the media and provides tips for reaching audiences, particularly in terms of strategically planning outcomes. It delves into the background of my study which would later influence my approach to the media, the process of drafting media releases, and key outcomes and benefits from popularising research. A key lesson from this experience is that redeveloping research for the media requires a sharp writing style, letting go of academic justification, catchy quotes, and an ability to distil complex details into easy-to-understand concepts. Although my study received strong media coverage, and I have since become a media commentator, my experiences also revealed a number of pitfalls that are likely to arise for other researchers keen on targeting media coverage.