Abstract
We present a jamming protocol for secrecy-enhanced wireless networks in which otherwise silent devices are selected as jammers to cause interference to potential eavesdroppers. This cooperative protocol includes several jammer selection policies that lead to different levels of secrecy-energy tradeoffs. Our results show that there is some advantage over selecting well-connected jammers and there is a need for a minimum number of jammers for the energy cost of jamming to payoff.

Abstract
The vision towards the Network of the Future cannot be separated from the fact that today's networks, and networking services are subject to sophisticated and very effective attacks. When these attacks first appeared, spoofing and distributed denial-of-service attacks were treated as apocalypse for networking. Now, they are considered moderate damage, whereas more sophisticated and inconspicuous attacks, such as botnets activities, might have greater and far reaching impact. As the Internet is expanding to mobile phones and smart dust and as its social coverage is liberalized towards the realization of ubiquitous computing (with communication), the concerns on security and privacy have become deeper and the problems more challenging than ever. Re-designing the Internet as the Network of the Future is self-motivating for researchers, and security and privacy cannot be provided again as separate, external, add-on, solutions. In this paper, we discuss the security and privacy challenges of the Network of the Future and try to delimit the solutions space on the basis of emerging techniques. We also review methods that help the quantification of security and privacy in an effort to provide a more systematic and quantitative treatment of the area in the future. Copyright (c) 2011 John Wiley & Sons, Ltd.

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.