Abstract
Real-time monitoring applications deployed in Low-power and Lossy Networks may generate flows sensitive to delay, where the information is useful for the destination only if it is received within a strict delay boundary. Data packets that will likely miss the application deadline could be discarded during their routing through the network or even be not transmitted at all, thus contributing for a better usage of the network resources. This paper presents RA-EEDEM, a set of modifications made to RPL that improve the End-to-End Delay (EED) estimation accuracy. The RA-EEDEM modifications include changes to the RPL metrics and to its Objective Function (OF). The results show that RA-EEDEM improves the accuracy of EED estimation while minimizing its impact on the average EED and Packet Reception Ratio (PRR).

Abstract
The need for better adaptation of networks to transported flows has led to research on new approaches such as content aware networks and network aware applications. In parallel, recent developments of multimedia and content oriented services and applications such as IPTV, video streaming, video on demand, and Internet TV reinforced interest in multicast technologies. IP multicast has not been widely deployed due to interdomain and QoS support problems; therefore, alternative solutions have been investigated. This article proposes a management driven hybrid multicast solution that is multi-domain and media oriented, and combines overlay multicast, IP multicast, and P2P. The architecture is developed in a content aware network and network aware application environment, based on light network virtualization. The multicast trees can be seen as parallel virtual content aware networks, spanning a single or multiple IP domains, customized to the type of content to be transported while fulfilling the quality of service requirements of the service provider.

Abstract
Group work is an essential activity during both graduate and undergraduate formation. Students develop a set of skills, and employ criticism which helps them to better handle future interpersonal situations. There is a vast theoretical literature and numerous case studies about group work, but we haven't yet seen much development concerning the assessment of individual group participants. It is not always easy to have the perception of each student contribution to the whole work. Nevertheless, more than frequently, the assessment of the group is transposed to each group participant, which in turn results in each student having the same final mark. We propose and describe a tool to manage and assess individual group work taking into account the amount of work, interaction, quality, and the temporal evolution of each group participant. The module features the possibility to create two types of activities: collaborative or cooperative group work. We describe the conceptual design of our tool and present the two operating modes of the module, which is based on events, alerts and conditions. We then describe the methodology for the assessment in the two operating modes and how these two major approaches can be deployed through our module into pedagogical situations.

Abstract
Network motifs are small over represented patterns that have been used successfully to characterize complex networks. Current algorithmic approaches focus essentially on pure topology and disregard node and edge nature. However, it is often the case that nodes and edges can also be classified and separated into different classes. This kind of networks can be modeled by colored (or labeled) graphs. Here we present a definition of colored motifs and an algorithm for efficiently discovering them.We use g-tries, a specialized data-structure created for finding sets of subgraphs. G-Tries encapsulate common sub-structure, and with the aid of symmetry breaking conditions and a customized canonization methodology, we are able to efficiently search for several colored patterns at the same time. We apply our algorithm to a set of representative complex networks, showing that it can find colored motifs and outperform previous methods. © 2014 Springer International Publishing Switzerland.

Abstract

Abstract
The ability to find and count subgraphs of a given network is an important non trivial task with multidisciplinary applicability. Discovering network motifs or computing graphlet signatures are two examples of methodologies that at their core rely precisely on the subgraph counting problem. Here we present the g-trie, a data-structure specifically designed for discovering subgraph frequencies. We produce a tree that encapsulates the structure of the entire graph set, taking advantage of common topologies in the same way a prefix tree takes advantage of common prefixes. This avoids redundancy in the representation of the graphs, thus allowing for both memory and computation time savings. We introduce a specialized canonical labeling designed to highlight common substructures and annotate the g-trie with a set of conditional rules that break symmetries, avoiding repetitions in the computation. We introduce a novel algorithm that takes as input a set of small graphs and is able to efficiently find and count them as induced subgraphs of a larger network. We perform an extensive empirical evaluation of our algorithms, focusing on efficiency and scalability on a set of diversified complex networks. Results show that g-tries are able to clearly outperform previously existing algorithms by at least one order of magnitude.