999 resultados para material provenance
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Resumen tomado del autor. Incluye imágenes y material anexo
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Instructions for using the web site and the source material.
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Various templates and logos and brand related media.
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Mathematics for Electrical and Electronics Engineers (Part 2). Course material (course notes, Formula Sheet, Lecture Slides, Problem sheets) for the course as it ran in 2011/12 and 12/13. Course discontinued after 2012/13 as part of the transition from 10 to 15 credits.
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Download and edit this document to prepare your hand in. The portfolio comprises a cover sheet plus five pages of reflective writing, one page addressing each different portfolio topic This shows the cover sheet, the assessment criteria and the portfolio summary IT IS NOT THE PORTFOLIO TEMPLATE The questions shown under each sub-heading are meant to act as thinking prompts to help you in the reflective process.
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Resumen tomado de la publicación. La guía didáctica del profesor incluye solucionario. El material consta de documentos en formato .doc
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Resumen tomado parcialmente del autor. Para solicitar en préstamo este material será necesario ponerse en contacto con el centro realizador. Incluye imágenes
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Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.