Abstract
IPTV services consist of multiple video channels grouped in bundles, such as sports, movies or generic bundles; users typically subscribe multiple bundles, including the generic bundle. Secure IP multicast can be used to implement IPTV services, but it still has problems to be addressed. Current solutions require high computational power in video channel zapping situations, lack support for groups sourced at the users, and present a weak support for admission control in IP multicast for both sources and receivers in dynamically configured environments. This work proposes a new, secure and efficient IPTV solution that, cumulatively: (a) enforces individual access control to groups of real-time IPTV video channels; (b) enforces IP multicast admission control for both multicast senders and receivers; (c) supports user generated videos; (d) generates low signaling overheads; (e) does not introduce perceivable delays, particularly in video channel zapping situations. Moreover, this solution can be easily integrated in the IPTV architectures being developed by ETSI and ITU-T.

Abstract
One of the most hard and expensive tasks commonly attributed to e-learning is to create fresh new content. Generally, educators try to reuse pre-existent material which suffers some modification before being given to students. A repository of such educational material is therefore of great use. In this article we propose a digital repository in which content ingestion includes an unsupervised classification mechanism which organizes documents into clusters of semantic proximity. This mechanism can be afterwards enhanced with a social classification process based on tagging. We present a methodology for the evaluation of our proposal with and without using tags. © 2011 IADIS.

Abstract
We present a novel dynamic graphical representation of the interactions between students and teachers in online forums available in Moodle. By defining the relationships between the users as a graph, it is possible to apply techniques of social network analysis. This system brings up new possibilities to e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between forums participants. © 2011 IADIS.

Abstract
Many natural and artificial structures can be represented as complex networks. Computing the frequency of all subgraphs of a certain size can give a very comprehensive structural characterization of these networks. This is known as the subgraph census problem, and it is also important as an intermediate step in the computation of other features of the network, such as network motifs. The subgraph census problem is computationally hard and most associated algorithms for it are sequential. Here we present several increasingly efficient parallel strategies for, culminating in a scalable and adaptive parallel algorithm. We applied our strategies to a representative set of biological networks and achieved almost linear speedups up to 128 processors, paving the way for making it possible to compute the census for bigger networks and larger subgraph sizes.

Abstract
Complex networks are ubiquitous in real-world and represent a multitude of natural and artificial systems. Some of these networks are inherently dynamic and their structure changes over time, but only recently has the research community been trying to better characterize them. In this paper we propose a novel general methodology to characterize time evolving networks, analyzing the dynamics of their structure by labeling the nodes and tracking how these labels evolve. Node labeling is formulated as a clustering task that assigns a classification to each node according to its local properties. Association rule mining is then applied to sequences of nodes' labels to extract useful rules that best describe changes in the network. We evaluate our method using two different networks, a real-world network of the world annual trades and a synthetic scale-free network, in order to uncover evolution patterns. The results show that our approach is valid and gives insights into the dynamics of the network. As an example, the derived rules for the scale-free network capture the properties of preferential node attachment.

Abstract
Learning management systems are routinely used for presenting, solving and grading exercises with large classes. However, teachers are constrained to use questions with pre-defined answers, such as multiple-choice, to automatically correct the exercises of their students. Complex exercises cannot be evaluated automatically by the LMS and require the coordination of a set of heterogeneous systems. For instance, programming exercises require a specialized exercise resolution environment and automatic evaluation features, each provided by a different type of system. In this paper, the authors discuss an approach for the coordination of a network of eLearning systems supporting the resolution of exercises. The proposed approach is based on a pivot component embedded in the LMS and has two main roles: (1) provide an exercise resolution environment, and (2) coordinate communication between the LMS and other systems, exposing their functions as web services. The integration of the pivot component in the LMS relies on Learning Tools Interoperability (LTI). This paper presents an architecture to coordinate a network of eLearning systems and validate the proposed approach by creating such a network integrated with LMS from two different vendors.