Abstract
Clone detection is well established for imperative programs. It works mostly on the statement level and therefore is ill-suited for functional programs, whose main constituents are expressions and types. In this paper we introduce clone detection for functional programs using a new intermediate program representation, dubbed Functional Control Tree. We extend clone detection to the identification of non-trivial functional program clones based on the recursion patterns from the so-called Bird-Meertens formalism.

Abstract
Current software development often relies on non-trivial coordination logic for combining autonomous services, eventually running on different platforms. As a rule, however, such a coordination layer is strongly woven within the application at source code level. Therefore, its precise identification becomes a major methodological (and technical) problem and a challenge to any program understanding or refactoring process. The approach introduced in this paper resorts to slicing techniques to extract coordination data from source code. Such data are captured in a specific dependency graph structure from which a coordination model can be recovered either in the form of an ORC specification or as a collection of code fragments corresponding to the identification of typical coordination patterns in the system. Tool support is also discussed.

Abstract
Nowadays, there is a wide range of commercial LBMS (Location-Based Mobile Services) available in the market, mainly in the form of GPS-based navigation solutions, and a trend towards the display of 3D maps can be clearly observed. Given the complete disparity of ideas and a visible commercial orientation in the industry, the study of the visualisation aspects that influence user performance and experience in the exploration of urban environments using 3D maps becomes an important issue. In this work, a generic conceptual framework is proposed whose main purpose is to objectively evaluate the impact and contribution of the major visualisation elements involved (henceforth mentioned as feature vectors). With this framework in mind, an online questionnaire was developed and administered to 149 test subjects in order to measure the real impact of feature vectors. The results clearly demonstrated that certain features have clear impact on user performance, and should be taken in account in LBMS development. As an example, just by displaying buildings with a 3D appearance, subjects were able to match more accurately the real environment with the one presented on a mobile device. In general, users were able to perform the tasks entrusted to them faster, if they were provided more realistic imagery.

Abstract
People with autism spectrum disorder (ASD) find it difficult to recognize and respond to emotions conveyed by the face. Most existing methodologies to teach people with ASD to recognize expressions use still images, and do not take into account that facial expressions have movement. We propose a new approach that uses state of the art technology to solve the problem and to improve interactivity. It is based on an avatar-user interaction model with real time response, which builds upon the patient-therapist relationship: it is designed to be used by the therapist and the patient. The core technology behind it is based on a technique we have developed for real time facial synthesis of 3D characters.

Abstract
People with autism spectrum disorder (ASD) find it difficult to recognize and respond to emotions conveyed by the face. Most existing methodologies to teach people with ASD to recognize expressions use still images, and do not take into account that facial expressions have movement. We propose a new approach that uses state of the art technology to solve the problem and to improve interactivity. It is based on an avatar-user interaction model with real time response, which builds upon the patient-therapist relationship: it is designed to be used by the therapist and the patient. The core technology behind it is based on a technique we have developed for real time facial synthesis of 3D characters.

Abstract
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior knowledge of the data characteristics.