Abstract
Computational journalism allows journalists to collect large collections of information chunks from separate sources. The analysis of these collections can reveal hidden relationships between of relationships, but due to their size, diversity, and varying nuances it is necessary to use both computational and human analysis. Breadcrumbs PDL is an adaptive spatial hypermedia system that brings together human cognition and machine computation in order to analyze a collection of usergenerated news clips. The project demonstrates the effectiveness of spatial hypermedia in the domain of computational journalism.

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This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. Agenetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.

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Breadcrumbs is a folksonomy of news clips, where users can aggregate fragments of text taken from online news. Besides the textual content, each news clip contains a set of metadata fields associated with it. User-defined tags are one of the most important of those information fields. Based on a small data set of news clips, we build a network of cooccurrence of tags in news clips, and use it to improve text clustering. We do this by defining a weighted cosine similarity proximity measure that takes into account both the clip vectors and the tag vectors. The tag weight is computed using the related tags that are present in the discovered community. We then use the resulting vectors together with the new distance metric, which allows us to identify socially biased document clusters. Our study indicates that using the structural features of the network of tags leads to a positive impact in the clustering process. Copyright 2012 ACM.

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In this paper the contributions of the ELLEIEC project for the international dimension of Life Long Learning (LLL) are described. In the framework of the Thematic Networks Programme, tools such as Recognition of Prior Learning (RPL), the Tuning Methodology were used in the implementation of an International Curricula Network with accreditation by two European institutions at least. Benchmarks were proposed, in order to show various tools that can be used for the establishment of dual degree programmes

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