Abstract

Abstract
Digital maps are very commonly used in the analysis, administration and representation of urban spaces. Despite the degree of information they can provide, they still pose some difficultives in decision making due to the three-dimensional nature of such ever-changing settings. This paper presents a collaborative solution for large virtual environment recreation aimed at urban landscape simulation. By providing an extensive interactive three-dimensional virtualization of a real-world city, multiple users may contribute with additional data, to either improve the model fidelity or to preview the impact of certain urban changes. This is achieved by employing procedural modeling methods to generate a basic three-dimensional city model from real-world data and a set of parameters, which can later be refined through human intervention. A multi-user simulation platform has been conceived, allowing the collaborative management of the model data, as well as the simulation of possible urban landscape changes, introducing therefore more advanced analysis and representation features to study and discuss the impact of certain decisions on the urban landscape.

Abstract

Abstract

Abstract
Text Categorization (TC) has attracted the attention of the research community in the last decade. Algorithms like Support Vector Machines, Naïve Bayes or k Nearest Neighbors have been used with good performance, confirmed by several comparative studies. Recently, several ensemble classifiers were also introduced in TC. However, many of those can only provide a category for a given new sample. Instead, in this paper, we propose a methodology - MECAC - to build an ensemble of classifiers that has two advantages to other ensemble methods: 1) it can be run using parallel computing, saving processing time and 2) it can extract important statistics from the obtained clusters. It uses the mean co-association matrix to solve binary TC problems. Our experiments revealed that our framework performed, on average, 2.04% better than the best individual classifier on the tested datasets. These results were statistically validated for a significance level of 0.05 using the Friedman Test. © 2012 Springer-Verlag.

Abstract
Location-based games have become more popular thanks to the growth of mobile device's technology. This paper presents a framework for the development of location-based augmented reality games and wARms, an augmented-reality location-based mobile game prototype based on said framework that uses the player's real position and orientation in order to play against others. The game shows how modern mobile device's sensors can be used for providing new and unusual gaming experiences. (C) 2012 The Authors. Published by Elsevier B. V. Selection and/or peer-review under responsibility of the scientific programme committee of VS-Games 2012