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.

Abstract
Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for non-monotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.

Abstract
Cultural heritage documents are often subject to digitization processes resulting in image material, even for textual contents. It is therefore common, in collections of valuable documents, to have descriptive information generated by the institutions, along with digitized images, transcriptions created by scholars, translations and even miscellaneous annotations. To offer a faceted access to the collection it is necessary to explore these diverse materials, integrate them according to a model that accounts for both metadata and the content and provide a comprehensive retrieval environment. In this work we have applied the MetaMedia multimedia database framework to a collection of ancient documents, processed the documents in their descriptive, textual, and image content and produced a browsing and searching system. The main challenges are the integrated management of metadata and content, the indexing of the image content, and the design of the browsing and searching interface where various views on the data are kept together. Copyright (C) 2009 Catalin Calistru et al.