Analyzing autostereoscopic environment confgurations for the design of videogames

Stereoscopic devices are becoming more popular every day. The 3D visualization that these displays ofer is being used by videogame designers to enhance the user’s game experience. Autostereoscopic monitors ofer the possibility of obtaining this 3D visualization without the need for extra device. This fact makes them more attractive to videogame developers. However, the confguration of the cameras that make it possible to obtain an immersive 3D visualization inside the game is still an open problem. In this paper, some system confgurations that create autostereoscopic visualization in a 3D game engine were evaluated to obtain a good accommodation of the user experience with the game. To achieve this, user tests that take into account the movement of the player were carried out to evaluate diferent camera confgurations, namely, dynamic and static converging optical axis and parallel optical axis. The purpose of these tests is to evaluate the user experience regarding visual discomfort resulting from the movement of the objects, with the purpose of assessing the preference for one confguration or the other. The results show that the users tend to have a preference trend for the parallel optical axis confguration set. This confguration seems to be optimal because the area where the moving objects are focused is deeper than in the other confgurations

Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.Fil: Rubiolo, Mariano. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Caliusco, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Coronel, M.. Universidad Tecnológica Nacional; ArgentinaFil: Gareli Fabrizi, M.. Universidad Tecnológica Nacional; Argentin

An efficient evolutionary algorithm for the orienteering problem

This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem, each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject to a limitation on the total route distance. To solve this problem, we propose an evolutionary algorithm, whose key characteristic is to maintain unfeasible solutions during the search. Furthermore, it includes a novel solution codification for the Orienteering Problem, a novel heuristic for node inclusion in the route, an adaptation of the Edge Recombination crossover developed for the Travelling Salesperson Problem, specific operators to recover the feasibility of solutions when required, and the use of the Lin-Kernighan heuristic to improve the route lengths. We compare our algorithm with three state-of-the-art algorithms for the problem on 344 benchmark instances, with up to 7397 nodes. The results show a competitive behavior of our approach in instances of low-medium dimensionality, and outstanding results in the large dimensionality instances reaching new best known solutions with lower computational time than the state-of-the-art algorithms.MTM2015-65317-P, TIN2016-78365-R, IT-609-13, IT-928-16, UFI BETS 201

Genetic Algorithms to Simplify Prognosis of Endocarditis

This ongoing interdisciplinary research is based on the application of genetic algorithms to simplify the process of predicting the mortality of a critical illness called endocarditis. The goal is to determine the most relevant features (symptoms) of patients (samples) observed by doctors to predict the possible mortality once the patient is in treatment of bacterial endocarditis. This can help doctors to prognose the illness in early stages; by helping them to identify in advance possible solutions in order to aid the patient recover faster. The results obtained using a real data set, show that using only the features selected by employing a genetic algorithm from each patient’s case can predict with a quite high accuracy the most probable evolution of the patient

Software Domain Model Integration Methodology for Formal Specifications

Using formal methods to create automatic code generation systems is one of the goals of Knowledge Based Software Engineering (KBSE) groups. The research of the Air Force Institute of Technology KBSE group has focused on the utilization of formal languages to represent domain model knowledge within this process. The code generation process centers around correctness preserving transformations that convert domain models from their analysis representations through design to the resulting implementation code. The diversity of the software systems that can be developed in this manner is limited only by the availability of suitable domain models. Therefore it should be possible to combine existing domain models when no single model is able to completely satisfy the requirements by itself. This work proposes a methodology that can be used to integrate domain models represented by formal languages. The integration ensures that the correctness of each input model is maintained while adding the desired functionality to the integrated model. Further, because of the inherent knowledge captured in the domain models, automated tool support can be developed to assist the application engineer in this process