Abstract
The use of the World Wide Web has had a huge growth and there is a greater variety of web applications with an increasing importance in society and in supporting the development to all kinds of business. Often, most of websites are providing support services that must be maintained and improved over time. This maintenance and upgrade can be difficult because frequently the requirements are no longer actual and/or often not even exist documented. Websites are increasingly monitoring usage data, and this type of information is increasingly abundant. Analyzing the usage of the websites can help identify improvements and help to maintain the website and its software requirements. This paper presents REQAnalytics, a recommender system that collects the information about the usage of a website, processes it and generates recommendations to the requirements specification of the website. This research work also presents an experimental evaluation of a case study based on an online newspaper website. The results showed that REQAnalytics can produce reports in a language closer to the business, identify most and less used finictionalities, requirements that can be split in two or more requirements and give support to the maintenance of requirements of the website being analyzed. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
In the era of information overflow, the effective implementation of models for managing multiple information streams may contribute to organizations' decision-making capabilities. Even though literature has proposed several information management models, little has been described about their implementation in real-world settings. This paper presents a case study on the usage of an information management model in the implementation of a collaborative system in a supply network. The case application analysis reinforces the value of using information management models to account for cultural aspects in the mapping of information needs and in the analysis of multiple stakeholders' requirements.

Abstract
POSTDATA is a 5 year's European Research Council (ERC) Starting Grant Project that started in May 2016 and is hosted by the Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain. The context of the project is the corpora of European Poetry (EP), with a special focus on poetic materials from different languages and literary traditions. POSTDATA aims to offer a standardized model in the philological field and a metadata application profile (MAP) for EP in order to build a common classification of all these poetic materials. The information of Spanish, Italian and French repertoires will be published in the Linked Open Data (LOD) ecosystem. Later we expect to extend the model to include additional corpora. There are a number of Web Based Information Systems in Europe with repertoires of poems available to human consumption but not in an appropriate condition to be accessible and reusable by the Semantic Web. These systems are not interoperable; they are in fact locked in their databases and proprietary software, not suitable to be linked in the Semantic Web. A way to make this data interoperable is to develop a MAP in order to be able to publish this data available in the LOD ecosystem, and also to publish new data that will be created and modeled based on this MAP. To create a common data model for EP is not simple since the existent data models are based on conceptualizations and terminology belonging to their own poetical traditions and each tradition has developed an idiosyncratic analytical terminology in a different and independent way for years. The result of this uncoordinated evolution is a set of varied terminologies to explain analogous metrical phenomena through the different poetic systems whose correspondences have been hardly studied – see examples in González-Blanco & Rodríguez (2014a and b). This work has to be done by domain experts before the modeling actually starts. On the other hand, the development of a MAP is a complex task though it is imperative to follow a method for this development. The last years Curado Malta & Baptista (2012, 2013a, 2013b) have been studying the development of MAP's in a Design Science Research (DSR) methodological process in order to define a method for the development of MAPs (see Curado Malta (2014)). The output of this DSR process was a first version of a method for the development of Metadata Application Profiles (Me4MAP) (paper to be published). The DSR process is now in the validation phase of the Relevance Cycle to validate Me4MAP. The development of this MAP for poetry will follow the guidelines of Me4MAP and this development will be used to do the validation of Me4MAP. The final goal of the POSTDATA project is: i) to be able to publish all the data locked in the WIS, in LOD, where any agent interested will be able to build applications over the data in order to serve final users; ii) to build a Web platform where: a) researchers, students and other final users interested in EP will be able to access poems (and their analyses) of all databases; b) researchers, students and other final users will be able to upload poems, the digitalized images of manuscripts, and fill in the information concerning the analysis of the poem, collaboratively contributing to a LOD dataset of poetry.

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students' level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.

Abstract
On the verge of Cyber-Physical Systems (CPS), companies will need, sooner or later, to adapt their systems in order to follow the emergent visions of Industrie 4.0 and Industrial Internet demanding the digitalization of their processes, preventing the losing of their competiveness levels. The engineering of such innovative manufacturing control systems assumes a crucial challenge without which becomes hard to convince researchers and, primarily, practitioners of the proposed architecture potentials. This paper describes the engineering aspects of deploying an ADACOR (ADAptive holonic COntrol aRchitecture for distributed manufacturing systems) based CPS for a real small-scale production system. Since the solution is using agent technology, a special attention is devoted to the interface from the agent control layer to the physical resources using the industrially adopted OPC-UA (OPC Unified Architecture). At the end, some lessons learned in engineering this CPS are drawn. © 2016 IEEE.

Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.