The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

We conducted a personal opinion survey in two rounds - years 2008 and 2011 - with the aim of investigating the level of knowledge and adoption of SOA in the Italian industry. We are also interested in understanding what is the trend of SOA (positive or negative?) and what are the methods, technologies and tools really used in the industry. The main findings of this survey are the following: (1) SOA is a relevant phenomenon in Italy, (2) Web services and RESTFul services are well-known/used and (3) orchestration languages and UDDI are little known and used. These results suggest that in Italy SOA is interpreted in a more simplistic way with respect to the current/real definition (i.e., without the concepts of orchestration/choreography and registry). Currently, the adoption of SOA is medium/low with a stable/positive trend of pervasiveness.

This paper reports on empirical research based on two software products. The research goal is to ascertain the impact of the adoption of a reuse policy on customer satisfaction. The results show that when a systematic reuse policy is implemented, such as the adoption of a domain specific library: reuse is significantly positively correlated with customer satisfaction; and there is a significant increase in customer satisfaction. The results have been extended to the underlying populations, supposed normal.