Learning practical information communication technology skills such as network configuration and security planning requires hands-on experience with a number of different devices which may be unavailable or too costly to provide, especially for institutions under tight budget constraints. This paper describes how a specific open software technology, paravirtualization, can be used to set up open source virtual networking labs (VNLs) easily and at virtually no cost. The paper highlights how paravirtual labs can be adopted jointly by partner organizations, e.g., when the institution hosting the virtual lab provides hands-on training and students' skill evaluation as a service to partner institutions overseas. A practical VNL implementation, the open virtual lab (OVL), is used to describe the added value that open source VNLs can give to e-Learning frameworks, achieving a level of students' performance comparable or better than the one obtained when students directly interact with physical networking equipment.

This paper addresses the problem of edge restoration in digital images. Taking advantage of an ensemble approach, multiple type-1 fuzzy filters are combined to reach a decision. The fuzzy logic concept for linguistic variables and possibility theory is discussed with regard to knowledge representation and inference procedures. To improve conventional deinterlacing issues, we adopt type-1 fuzzy set concepts to design a weight-measuring approach. We demonstrate that the fuzzy ensemble approach model is well suited to image processing and provide case studies in the video-deinterlacing field. In our proposed method, five fuzzy membership functions (MFs) of linguistic variable-based fuzzy logic filters are derived from the type-1 (a.k.a. ordinary or primary) fuzzy MF. The weight-measuring process of our proposed model is used to assign weights to six candidate deinterlaced pixels (CDPs) that are interpolated according to edge direction. The use of a different MF for each direction allows the filter to characterize each pixel variation influence independently, according to its direction. The weights from all MFs are multiplied with the CDPs. The results of the empirical trials clearly show that the proposed system can successfully deal with several image types containing motion or detail elements.

Technical enhancements of mobile technologies are paving the way to the definition of high-quality and accurate geolocation solutions based on data collected and managed by GSM/3G networks. We present a technique that provides geolocation and mobility prediction both at network and service level, does not require any change to the existing mobile network infrastructure, and is entirely performed on the mobile network side, making it more robust than other positioning systems with respect to location spoofing and other terminal-based security threats. Our approach is based on a novel database correlation technique over Received Signal Strength Indication (RSSI) data, and provides a geolocation and tracking technique based on advanced map- and mobility-based filtering. The performance of the geolocation algorithm has been carefully validated by an extensive experimentation, carried out on real data collected from the mobile network antennas of a complex urban environment.

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome in the road that leads to commodization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations in fact make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. We propose a Model-Driven Engineering methodology supporting automation of BDA. Our approach lets customers declare requisites to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers' requisites, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform. The proposal is experimentally evaluated in a real-world scenario.