Abstract
Along with the evolution of the Internet and its ever-increasing use, a significant trend to develop solutions to provide information is noticeable. This scenario increases information decentralization in certain contexts. Nowadays, information regarding music is available online in several online locations. In this context, this paper reports on the development of a web platform that centralizes existing information regarding events, retrieving other contextually related data available. Validated with both information retrieval quality and interface usability metrics, this project attained a more effective and complete concert search service according to the data provided to the user. It has revealed itself capable of retrieving information on more events, when compared to other platforms without a data centralization approach. According to the usability survey results attained, such as an 89 SUS scale score, it was proved that the developed service is provided with a simple and intuitive interface as well.

Abstract
The growing use of smartphones has revolutionized the way people live, fostering the use of mobile application in the most diverse situations. These applications take advantage of the mobile device's capabilities to provide the user with useful and contextualized information, being equipped with increasingly intuitive interfaces, and offering richer contents in an attractive manner. Augmented reality emerges as one of the technologies that can be used in these applications, allowing for an improved user experience. This paper describes a tourism-oriented mobile application, in this case to be used in a botanical garden, which uses current mobile device's capabilities to provide the visitor with several ways to obtain the desired information. The results obtained from this application are shown, including images of the implemented features, and highlighting the results related to the use of augmented reality.

Abstract
With an ever increasing number of vehicles traveling the roads, traffic problems such as congestions and increased travel times became a hot topic in the research community, and several approaches have been proposed to improve the performance of the traffic networks. This paper introduces the Inverted Ant Colony Optimization (IACO) algorithm, a variation of the classic Ant Colony algorithm that inverts its logic by converting the attraction of ants towards pheromones into a repulsion effect. IACO is then used in a decentralized traffic management system, where drivers become ants that deposit pheromones on the followed paths; they are then repelled by the pheromone scent, thus avoiding congested roads, and distributing the traffic through the network. Using SUMO (Simulation of Urban MObility), several experiments were conducted to compare the effects of using IACO with a shortest time algorithm in artificial and real world scenarios - using the map of a real city, and corresponding traffic data. The effect of the behavior caused by this algorithm is a decrease in traffic density in widely used roads, leading to improvements on the traffic network at a local and global level, decreasing trip time for drivers that adhere to the suggestions made by IACO as well as for those who do not. Considering different degrees of adhesion to the algorithm, IACO has significant advantages over the shortest time algorithm, improving overall network performance by decreasing trip times for both IACO-compliant vehicles (up to 84%) and remaining vehicles (up to 71%). Thus, it benefits individual drivers, promoting the adoption of IACO, and also the global road network. Furthermore, fuel consumption and CO2 emissions from both vehicle types decrease significantly when using IACO (up to 49%).

Abstract
Fitness assignment is one of the biggest challenges in evolutionary art. Interactive evolutionary computation approaches put a significant burden on the user, leading to human fatigue. On the other hand, autonomous evolutionary art systems usually fail to give the users the opportunity to express and convey their artistic goals and preferences. Our approach empowers the users by allowing them to express their intentions through the design of fitness functions. We present a novel responsive interface for designing fitness function in the scope of evolutionary ant paintings. Once the evolutionary runs are concluded, further control is given to the users by allowing them to specify the rendering details of selected pieces. The analysis of the experimental results highlights how fitness function design influences the outcomes of the evolutionary runs, conveying the intentions of the user and enabling the evolution of a wide variety of images. © 2014 Springer-Verlag.

Abstract
Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice. This paper presents a comparative study of three ensemble methods (TreeBagger, LPBoost and Subspace) using a clinical dataset with 25% missing values to predict the overall survival of women with breast cancer. To complete the absent values, the k-nearest neighbor (k-NN) algorithm was used with four distinct neighbor values, trying to determine the best one for this particular scenario. Tests were performed for each of the three ensemble methods and each k-NN configuration, and their performance compared using a Friedman test. Despite the complexity of this challenge, the produced results are promising and the best algorithmconfiguration (TreeBagger using 3 neighbors) presents a prediction accuracy of 73%. © Springer International Publishing Switzerland 2014.

Abstract
Nowadays, a large number of people consume music from the web. Web sites and online services now typically contain millions of music tracks, which complicates search, retrieval, and discovery of music. Music recommender systems can address these issues by recommending relevant and novel music to a user based on personal musical tastes. In this paper, we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real-time on a commercial web site, specialized in content from the very Long Tail of music content. We compare it against two stand-alone recommender systems, the first system based on usage and the second one based on content data (namely, audio and textual tags). The results show that the proposed hybrid recommender shows advantages with respect to usage-based and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty. © 2012, Springer-Verlag London.