Abstract
Collaborative working as well as sharing resources and knowledge represent key points in today's development in all fields, including education. Know-how transfer and collaboration in learning and teaching are aspects promoted and sustained by institutional management as well as the European initiatives. Thus, leading to the idea of a federation which will facilitate engineering education. A consortium formed by five European universities decided to join efforts to provide to the community a federation, which could be used by different stakeholders interest in teaching, learning or developing new skills in the field of electronics. The proposed remote system, Virtual Instruments System in Reality, or VISIR in short, offers the possibility of working with real equipment and obtain the real-world/real-time measurements. By developing such a VISIR federation some of the constraints of using remote labs, the ones associated with development and maintenance costs, and scalability, will be minimized. This paper aims to present the initial steps for developing a VISIR Federation, which is also the primary goal of PILAR Platform Integration of Laboratories based on the Architecture of visiR project.

Abstract
"Masseiras" is an ancient Portuguese agriculture system, where soil was developed from sand dunes enriched with seaweeds over more than a century. Due to the importance for the local economy, this system evolved for greenhouse structures. In this study we compared the bacterial community composition and structure of "Masseiras" soil, aiming at assessing the potential impact of different agricultural practices. The bulk soil of two greenhouses (following or not the recommended agriculture good practices, FGP and NFGP, respectively) was compared based on their physicochemical properties and bacterial community. In both FGP and NFGP, Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Gemmatimonadetes were in a proportion of 5:1:1:1:1:1. However, the bacterial community of soil FGP was richer and more diverse than that of soil NFGP. Members of the classes Bacilli and Gemm-1, with higher relative abundance in NFGP and FGP, respectively, were those contributing most for distinguishing the bacterial communities of both soils. The differences in the structure of the bacterial communities correlated (Mantel test) with some soil physicochemical properties, such as electrical conductivity and nitrate and Zn contents, which were significantly higher in soil NFGP than in soil FGP.

Abstract
Social Media has been disrupting traditional technology mediated learning, providing students and educators with unsupervised and informal tools and spaces where authentic learning occurs. Still, the traditional LMS persists as the core element in this context, while lacking additional management, monitoring and analysis tools to handle informal learning and content. In this paper, we present an integrated methodology that combines social network analytics, sentiment analysis and topic categorization to perform social content visualizations and analysis aimed at integrated learning environments. Results provide insights on networked content dimension, type of structure, degree of popularity and degree of controversy, as well as on their educational and functional potential in the field of learning analytics.

Abstract
In the Web realm, the adoption of Cascading Style Sheets (CSS) is unanimous, being widely used for styling web documents. Despite their intensive use, this W3C specification was written for web designers with limit programming background. Thus, it lack several programming constructs, such as variables, conditional and repetitive blocks, and functions. This absence a ects negatively code reuse, and consequently, the maintenance of the styling code. In the last decade, several languages (e.g. Sass, Less) appeared to extend CSS, defined as CSS preprocessors, with the ultimate goal to bring those missing constructs and to foster stylesheets structured programming. The paper provides an introductory survey on CSS Preprocessors. It gathers information on a specific set of preprocessors, categorizes them and compares their features regarding a set of predefined criteria such as: maturity, coverage and performance. © Ricardo Queirós

Abstract
Counterfeit detection in official documents has challenged forensic experts on trying to correlate them to improve the identification of forgery authors by criminal investigators. Past counterfeit investigation on the Portuguese Police Forensic Laboratory allowed the construction of an organized set of digital images related to counterfeited documents, helping manual identification of new counterfeiters modus operandi. However, these images are usually stored in distinct resolutions, may have different sizes and could have been captured under different types of illumination. In this paper we present a methodology to automate a counterfeit identification modus operandi, by comparing a given document image with a database of previously catalogued counterfeited documents images. The proposed method ranks the identified counterfeited documents and allows the forensic experts to drive their attention to the most similar documents. It takes advantage of scalable algorithms under the OpenCV framework that compare images, match patterns and analyse textures and colours. We present a set of tests with distinct datasets with promising results.

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.