Abstract
The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test, p value < 0.01) during CV. An accuracy of 95.8 +/- 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 +/- 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis.

Abstract
The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data quality problems) are tailored for data sources with the same schema and sharing the same data model (e.g., relational model). On the other hand, these approaches are highly dependent on a domain expert to specify the data cleaning operations. This paper extends a previously proposed data cleaning methodology that reuses cleaning knowledge specified for other data sources. The methodology is further detailed/refined by specifying the requirements that a data cleaning operations vocabulary must satisfy. Ontologies in RDF/OWL are proposed as the data model for an abstract representation of the data schemas, no matter which data model is used (e.g., relational; graph). Existing approaches, methods and techniques that support the implementation of the proposed methodology, in general, and specifically of the data cleaning operations vocabulary are also presented and discussed in this paper. Copyright

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknownunknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources. © Springer International Publishing AG 2016.

Abstract

Abstract
Decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Smart Meeting Rooms (SMR) are the election spaces where groups can perform these activities in a local or distributed setting. SMR definition has evolved and in this article we present its evolution side by side with several projects that have been developed during the last decade. We also present the features and evaluation methods present in literature and we make a discussion on the open issues. © 2015 SERSC.

Abstract
Ambient intelligence (AmI) paradigm has been the catalyst for the creation of several types of smart spaces, and the smart meeting rooms (SMR) is one of them. One of the main aims of AmI spaces is the development of middleware able to provide context aware computing specifically in SMR face-to-face, and distributed/asynchronous meeting settings are envisaged. This paper describes OLAVAmIa context aware middleware system which was tested in LAID environment, a smart meeting room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and a multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities, two tools present in LAID test bed were used. These AmI intelligent layer tools cover two application scenarios: a ubiquitous group decision-making and a service reposition in power systems. The results and insights are presented in this article.