Abstract
In this article we describe a system that is capable of self-organizing news clips collected by readers, into a personal digital library. The system then uses this information to provide a rich set of inferred relations between the clips and clusters of clips to the producers. The inferred information is in the form of news with the 'hot' topics, the relations between clips content and the interests of their readers. We describe the Breadcrumbs system which features an online news collecting tool, an inference engine and a social graph. We discuss the outcomes of our system, which allows for a better understanding of the news consumption and trends. Finally, we describe how we can use these outcomes to create a business model. © 2012 IEEE.

Abstract
In this article we present a system capable of graphically representing the interactions between students and teachers in hierarchical online forums. By defining the 'reply-to' relation between the users the system builds a graph. During forum posts mining, the system computes metrics taken from social network analysis which are then applied to the graph drawing process. This system brings up new possibilities to e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation of students; to identify key students in information passing, and to find the implicit relations between forums participants. Preliminary tests lead to the conclusions that the system is able to rapidly help in identifying situations like outliers, sources and sinks of information. It also depicts rapidly sub communities formed from forum participants. © 2012 IEEE.

Abstract
Interactive visualization systems are powerful tools in the task of exploring and understanding data. We describe two implementations of this approach, where a multidimensional network of news clips is depicted by taking advantage of its community structure. The first implementation is a multiresolution map of news clips that uses topic detection both at the clip level and at the community level, in order to assign labels to the nodes in each resolution. The second implementation is a traditional force-directed network visualization with several additional interactive aspects that provide a rich user experience for knowledge discovery. We describe a common use case for the visualization systems as a journalistic research and knowledge discovery tool. Both systems illustrate the links between news clips, induced by the co-occurrence of named entities, as well as several metadata fields based on the information contained within each node. Copyright © 2012 SciTePress - Science and Technology Publications.

Abstract
We report an integration process that involves the Moodle learning management system and an in-house developed e-portfolio system - SPD - and the institution information system. SPD is a system developed to create, evaluate and maintain digital portfolios assigned and assessed by a jury to keep a high quality level of the projects registered. The SPD system uses information imported from Moodle's database, in order to fill in its own database for users and courses and for propagating the existing session between systems. It also keeps projects ordered by development phases, determining whatever can be done and by whom, making them available for consult only after being accepted by the jury. To aid the rapid creation of projects and development of its documentation a set of pre-defined templates are made available.

Abstract
We propose a method for creating news context by taking advantage of a folksonomy of web clipping based on online news. We experiment with an ontology-based named entity recognition process, describing two alternate implementation approaches, and we study two different ways of modeling the relationships induced by the coreference of named entities on news clips. We try to establish a context by identifying the community structure for a clip-centric network and for an entity-centric network, based on a small test set from the Breadcrumbs system. Finally, we compare both models, based on the detected news communities, and show the advantages of each network specilication.

Abstract
Comparing scientific production across different fields of knowledge is commonly controversial and subject to disagreement. Such comparisons are often based on quantitative indicators, such as papers per researcher, and data normalization is very difficult to accomplish. Different approaches can provide new insight and in this paper we focus on the comparison of different scientific fields based on their research collaboration networks. We use co-authorship networks where nodes are researchers and the edges show the existing co-authorship relations between them. Our comparison methodology is based on network motifs, which are over represented patterns, or subgraphs. We derive motif fingerprints for 22 scientific fields based on 29 different small motifs found in the corresponding co-authorship networks. These fingerprints provide a metric for assessing similarity among scientific fields, and our analysis shows that the discrimination power of the 29 motif types is not identical. We use a co-authorship dataset built from over 15,361 publications inducing a co-authorship network with over 32,842 researchers. Our results also show that we can group different fields according to their fingerprints, supporting the notion that some fields present higher similarity and can be more easily compared.