Abstract
The exploration of large networks carries inherent challenges in the visualization of a great amount of data. We built an interactive visualization system for the purpose of exploring a large multidimensional network of news clips over time. These are clips gathered by users from web news sources and references to people or places are extracted from. In this paper, we present the system's capabilities and user interface and discuss its advantages in terms of the browsing and extraction of knowledge from the data. These capabilities include a textual search and associated event detection, and temporal navigation allowing the user to seek a certain date and timespan.

Abstract
In this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.

Abstract
Euclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown.

Abstract
We present a new algorithm to discover overlapping communities in networks with a scale free structure. This algorithm is based on a node evaluation function that scores the local influence of a node based on its degree and neighbourhood, allowing for the identification of hubs within a network. Using this function we are able to identify communities, and also to attribute meaningful titles to the communities that are discovered. Our novel methodology is assessed using LFR benchmark for networks with overlapping community structure and the generalized normalized mutual information (NMI) measure. We show that the evaluation function described is able to detect influential nodes in a network, and also that it is possible to build a well performing community detection algorithm based on this function.

Abstract
in this article we present a system capable of creating, managing and presenting digital portfolios. Our system innovates by using roles and states during its creation phase. This allows for high quality elements in the portfolio and promotes the students' reflection over them before full integration. The system also complies with the existing standards for e-portfolios. Moreover, it adds an extension to integrate previous created portfolios from different educational levels. In the article we show the need for such extension and describe how the system deals with integration of such diverse portfolios into a single one.

Abstract
For a long time collaborative work has been seen as an important pedagogical methodology. Lately there has been an increased interest in creating tools that allow and foster collaborative work in online and web-based environments. However, despite these efforts most of the available tools today only allow students to participate in a collaborative work. Issues like helping the teacher to create the whole collaborative activity and, helping the students to collaborate with each other are usually left out from the automatic tools. Interestingly, one of the main difficulties that hamper collaboration between students during a course work is that they do not know how to do delegate tasks, how to set deadlines and how to control the colleagues' contribution's in a democratic way. This later issue is particularly important because most collaborative systems do not offer a mechanism to differentiate the group participants in order to assess and grade them individually. In this article we propose and describe a system capable of creating group tasks while providing information that would help to individually assess each group member. The system can be configured in order to leverage the collaboration between students and guiding them in this sort of working methodology. The proposed system features two operating modes: the sequential and the simultaneous activity. It also includes the possibility to establish time limits for each assigned task; an automatic forum for mandatory comments upon referred drawbacks on a colleague's work; a versioning system associated with the simultaneous activity, and the retrieval of all logged interactions, provided in the form of a report which we believe ultimately would help the teacher to differentiate group participants in order to assess their work and grade them individually.