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.

Abstract

Abstract
Large scale information systems, such as public information systems for light-train/metro networks, must be able to fulfill contractualized Service Level Agreements (SLAs) in terms of end-to-end latencies and jitter, even in the presence of faults. Failure to do so has potential legal and financial implications for the software developers. Current middleware solutions have a hard time coping with these demands due, fundamentally, to a lack of adequate, simultaneous, support for fault-tolerance (FT) and real-time (RT) tasks. In this paper we present Stheno, a general purpose peer-to-peer (P2P) middleware system that builds on previous work from TAO and MEAD to provide: (a) configurable, transparent, FT support by taking advantage of the P2P layer topology awareness to efficiently implement Common Of The Shelf (COTS) replication algorithms and replica management strategies, and; (b) kernel-level resource reservation integrated with well-known threading strategies based on priorities to provide more robust support for soft real-time tasks. An evaluation of the first (unoptimized) prototype for the middleware shows that Stheno is able to match and often greatly exceed the SLA agreements provided by our target system, the light-train/metro information system developed and maintained by EFACEC, and currently deployed at multiple cities in Europe and Brazil. Copyright 2012 ACM.