374 resultados para SEMANTIC WEB
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Purpose – The article aims to review a university course, offered to students in both Australia and Germany, to encourage them to learn about designing, implementing, marketing and evaluating information programs and services in order to build active and engaged communities. The concepts and processes of Web 2.0 technologies come together in the learning activities, with students establishing their own personal learning networks (PLNs). Design/methodology/approach – The case study examines the principles of learning and teaching that underpin the course and presents the students' own experiences of the challenges they faced as they explored the interactive, participative and collaborative dimensions of the web. Findings – The online format of the course and the philosophy of learning through play provided students with a safe and supportive environment for them to move outside of their comfort zones, to be creative, to experiment and to develop their professional personas. Reflection on learning was a key component that stressed the value of reflective practice in assisting library and information science (LIS) professionals to adapt confidently to the rapidly changing work environment. Originality/value – This study provides insights into the opportunities for LIS courses to work across geographical boundaries, to allow students to critically appraise library practice in different contexts and to become active participants in wider professional networks.
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Introduction: Participants may respond to phases of a workplace walking program at different rates. This study evaluated the factors that contribute to the number of steps through phases of the program. The intervention was automated through a web-based program designed to increase workday walking. Methods: The study reviewed independent variable influences throughout phases I–III. A convenience sample of university workers (n=56; 43.6±1.7 years; BMI 27.44±.2.15 kg/m2; 48 female) were recruited at worksites in Australia. These workers were given a pedometer (Yamax SW 200) and access to the website program. For analyses, step counts entered by workers into the website were downloaded and mean workday steps were compared using a seemingly unrelated regression. This model was employed to capture the contemporaneous correlation within individuals in the study across observed time periods. Results: The model predicts that the 36 subjects with complete information took an average 7460 steps in the baseline two week period. After phase I, statistically significance increases in steps (from baseline) were explained by age, working status (full or part time), occupation (academic or professional), and self reported public transport (PT) use (marginally significant). Full time workers walked more than part time workers by about 440 steps, professionals walked about 300 steps more than academics, and PT users walked about 400 steps more than non-PT users. The ability to differentiate steps after two weeks among participants suggests a differential affect of the program after only two weeks. On average participants increased steps from week two to four by about 525 steps, but regular auto users had nearly 750 steps less than non-auto users at week four. The effect of age was diminished in the 4th week of observation and accounted for 34 steps per year of age. In phase III, discriminating between participants became more difficult, with only age effects differentiating their increase over baseline. The marginal effect of age by phase III compared to phase I, increased from 36 to 50, suggesting a 14 step per year increase from the 2nd to 6th week. Discussion: The findings suggest that participants responded to the program at different rates, with uniformity of effect achieved by the 6th week. Participants increased steps, however a tapering off occurred over time. Age played the most consistent role in predicting steps over the program. PT use was associated with increased step counts, while Auto use was associated with decreased step counts.
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Stigmergy is a biological term originally used when discussing insect or swarm behaviour, and describes a model supporting environment-based communication separating artefacts from agents. This phenomenon is demonstrated in the behavior of ants and their food foraging supported by pheromone trails, or similarly termites and their termite nest building process. What is interesting with this mechanism is that highly organized societies are formed without an apparent central management function. We see design features in Web sites that mimic stigmergic mechanisms as part of the User Interface and we have created generalizations of these patterns. Software development and Web site development techniques have evolved significantly over the past 20 years. Recent progress in this area proposes languages to model web applications to facilitate the nuances specific to these developments. These modeling languages provide a suitable framework for building reusable components encapsulating our design patterns of stigmergy. We hypothesize that incorporating stigmergy as a separate feature of a site’s primary function will ultimately lead to enhanced user coordination.
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Cold-formed steel members are increasingly used as primary structural elements in the building industries around the world due to the availability of thin and high strength steels and advanced cold-forming technologies. Cold-formed lipped channel beams (LCB) are commonly used as flexural members such as floor joists and bearers. However, their shear capacities are determined based on conservative design rules. Current practice in flooring systems is to include openings in the web element of floor joists or bearers so that building services can be located within them. Shear behaviour of LCBs with web openings is more complicated while their shear strengths are considerably reduced by the presence of web openings. However, limited research has been undertaken on the shear behaviour and strength of LCBs with web openings. Hence a detailed experimental study involving 40 shear tests was undertaken to investigate the shear behaviour and strength of LCBs with web openings. Simply supported test specimens of LCBs with aspect ratios of 1.0 and 1.5 were loaded at midspan until failure. This paper presents the details of this experimental study and the results of their shear capacities and behavioural characteristics. Experimental results showed that the current design rules in cold-formed steel structures design codes are very conservative for the shear design of LCBs with web openings. Improved design equations have been proposed for the shear strength of LCBs with web openings based on the experimental results from this study.
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Cold-formed steel Lipped Channel Beams (LCB) with web openings are commonly used as floor joists and bearers in building structures. The shear behaviour of these beams is more complicated and their shear capacities are considerably reduced by the presence of web openings. However, limited research has been undertaken on the shear behaviour and strength of LCBs with web openings. Hence a detailed numerical study was undertaken to investigate the shear behaviour and strength of LCBs with web openings. Finite element models of simply supported LCBs under a mid-span load with aspect ratios of 1.0 and 1.5 were developed and validated by comparing their results with test results. They were then used in a detailed parametric study to investigate the effects of various influential parameters. Experimental and numerical results showed that the current design rules in cold-formed steel structures design codes are very conservative. Improved design equations were therefore proposed for the shear strength of LCBs with web openings based on both experimental and numerical results. This paper presents the details of finite element modelling of LCBs with web openings, validation of finite element models, and the development of improved shear design rules. The proposed shear design rules in this paper can be considered for inclusion in the future versions of cold-formed steel design codes.
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In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.
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Although the adoption of Enterprise Web 2.0 within organisations is beneficial, it could meet with employees’ resistance and the adoption process can be lengthy. The successful implementation of Enterprise Web 2.0 is based on employee involvement and adoption of such social technology. This paper is part of a larger research project that explored the adoption of Web 2.0 by individuals within enterprises. Using a qualitative study, the findings show that there are number of adoption influences including technological, individual and contextual issues. This paper presents Web 2.0 technological attributes that influence its adoption. The found attributes are: friendliness, reliability, mobility, technical compatibility, discoverability, transparency and Web 2.0 type.
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Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.
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This study explores and evaluates students’ and teachers’ experiences when using a range of Web 2.0 tools in Higher Education teaching and learning. It contributes to our understanding of how Web 2.0 learning communities are constructed, experienced and the nature of the participation therein. This research extends our knowledge and understanding of the Web 2.0 phenomena, and provides a framework that can assist with improving future Web 2.0 implementation.
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This paper details the participation of the Australian e- Health Research Centre (AEHRC) in the ShARe/CLEF 2013 eHealth Evaluation Lab { Task 3. This task aims to evaluate the use of information retrieval (IR) systems to aid consumers (e.g. patients and their relatives) in seeking health advice on the Web. Our submissions to the ShARe/CLEF challenge are based on language models generated from the web corpus provided by the organisers. Our baseline system is a standard Dirichlet smoothed language model. We enhance the baseline by identifying and correcting spelling mistakes in queries, as well as expanding acronyms using AEHRC's Medtex medical text analysis platform. We then consider the readability and the authoritativeness of web pages to further enhance the quality of the document ranking. Measures of readability are integrated in the language models used for retrieval via prior probabilities. Prior probabilities are also used to encode authoritativeness information derived from a list of top-100 consumer health websites. Empirical results show that correcting spelling mistakes and expanding acronyms found in queries signi cantly improves the e ectiveness of the language model baseline. Readability priors seem to increase retrieval e ectiveness for graded relevance at early ranks (nDCG@5, but not precision), but no improvements are found at later ranks and when considering binary relevance. The authoritativeness prior does not appear to provide retrieval gains over the baseline: this is likely to be because of the small overlap between websites in the corpus and those in the top-100 consumer-health websites we acquired.
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This project explores employees’ adoption of Web 2.0 within organisations. It shows that the adoption of Web 2.0 is a challenging and dynamic process that changes over time. The adoption is, also, influenced by a number of interrelated issues including: People Traits, Social Influence, Trust, Technological Attributes, Relevance of Web 2.0, Web 2.0 Maturity, Organisational Support, and Organisational Practice.
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We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, document-concept and document-category. A final clustering solution is obtained by exploiting associations between document pairs and hubness of the documents. Empirical analysis with various real data sets reveals that the proposed meth-od outperforms state-of-the-art text clustering approaches.
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Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.
Preparing for work in a rapidly changing environment: Student collaboration across the Web 2.0 world