Abstract
Digital portfolios have recently assumed an increasing importance in e-learning. In this article we report an integrated system that can be used to publish online projects undertaken by students during their courses. The system was then integrated with the Moodle learning management system featuring the possibility to create, evaluate, publish and maintain digital portfolios assigned and corrected by the institution faculty. This integrated process ensures a high quality level of the projects registered. The system uses information imported from Moodle's database to fill in its own database for users, courses and propagates the existing session between the two systems. It also maintains the projects in specific development phases, thus, allowing asynchronous editing, assessing or commenting on projects by different students or teachers. The creation of a new project is boosted by the use of a set of pre-defined templates which in turn give a standard layout and design quality to the final view of the project.

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 and 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 specification.

Abstract
We propose a programming model for distributed concurrent systems with mobile objects in the context of a process calculus. Code mobility is induced by lexical scoping on names. Objects and messages migrate towards the site where their prefixes are lexically bound. Class definitions, on the other hand, are downloaded from the site where they are defined and are instantiated locally upon arrival. We provide several programming examples to demonstrate the expressiveness of the model. Finally, based on this model we describe an. architecture for a run-time system supporting concurrent, distributed computations and code mobility.

Abstract
In this article, we detail a system that provides contributes for analyzing and characterizing interactions that occur between participants of online communities. We adapted and applied the Social Network Analysis methodology to online discussion forums to create a dynamical interaction graph. The graph can be embedded in learning managements systems and accessed through a web page. The functionality of the system provides a suitable environment to characterize the interactions between actors and their participations in discussion forums. In the article we describe the use of the system in two real-world situations. Our conclusions lead to the verification and the rapid identification of some important situations that occur in learning communities, such as: the location of actors more or less active; distinction of positions and roles; identification of different ways of organization/interaction in groups; characterization of the interactions of a group or of a community as a whole © 2012 IEEE.

Abstract
Assessing the quality of the clustering process is fundamental in unsupervised clustering. In literature we can find three different clustering validity techniques: external criteria; internal criteria and relative criteria. In this paper, we focus on external criteria and present an algorithm that allows the implementation of external measures to assess clustering quality when the structure of the data set is unknown. To obtain an automatic partition of a data set and to reflect how documents must be grouped according to human intuition we use internal information present in data like descriptions provide by the users as tags and the distance between documents. The results show an evident correlation between manual and automatic classes indicating it is acceptable to use an automatic partition. In addition to presenting an alternative to finding the structure of the data set using meta-data such as tags, we also wanted to test the impact of their integration in the k-means++ algorithm and verify how it influences the quality of the formed clusters, suggesting a model of integration based on the occurrence of tags in document content. The experimental results indicate a positive impact when external measures are calculated, although there was no apparent correlation between the weight assigned to the tags and the quality of the obtained clusters.

Abstract
Learning objects are not a new concept, being currently produced either according to existent specifications, or in an ad hoc way. However, a system that can easily classify them for automatic storage is still missing In this article we propose a repository of learning objects which is capable of automatic classification and categorization based on a semantic organization. The classification is based on a text extraction mechanism which then uses text mining methods. We present a methodology for the initial clustering of objects, and a technique for maintaining the organization with continuous arrival of new objects.