Time filter

Source Type

Porto, Portugal

Baptista R.,INESCTEC | Coelho A.,INESCTEC | De Carvalho C.V.,ISEP
Proceedings of the European Conference on Games-based Learning | Year: 2015

Recently, Serious Games (SG) achieved a recognized position as a learning tool in several contexts. SG provide constructive learning environments in which errors can be made without real life penalties and where students get instant feedback from their actions when facing challenges. These challenges should be in accordance with the intended learning goals and they should adapt and/or be repeated according to the learner's level. This aspect is decisive in the acquisition of knowledge, experience and professional skills through the simulation of different situations and contexts. The effectiveness of competences' training is directly related to the success in their acquisition but, above all, it is related to the ability to apply them to successfully perform a given task. However, an optimal game design methodology for competence training is yet to be created. This article presents a study that identifies the most appropriate game categories to develop specific skills and competences. It considers a taxonomy with eight game categories (Action, Strategy, Playing, Sports, Management Simulation, Adventure, Puzzle and Quiz) that were matched with the Education Competences and Educational Competency Wheel. Analysing 116 serious games allowed identifying which categories were more efficient in the training of a specific competence and therefore should be reused in the same scope.

De Sa C.R.,INESCTEC | De Sa C.R.,Leiden University | Soares C.,INESCTEC | Knobbe A.,Leiden University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms. © 2013 Springer-Verlag.

Carvalho L.,INESCTEC | Roriz P.,INESCTEC | Frazao O.,INESCTEC | Frazao O.,University of Porto | And 2 more authors.
Journal of Physics: Conference Series | Year: 2015

Cross-bite, as a malocclusion effect, is defined as a transversal changing of the upper dental arch, in relation to the lower arch, and may be classified as skeletal, dental or functional. As a consequence, the expansion of maxilla is an effective clinical treatment used to correct transversal maxillary discrepancy. The maxillary expansion is an ancient method used in orthodontics, for the correction of the maxillary athresia with posterior crossbite, through the opening of the midpalatal suture (disjunction), using orthodontic- orthopaedic devices. Same controversial discussion arises among the clinicians, about the effects of each orthodontic devices as also about the technique to be employed. The objective of this study was to compare the strain field induced by two different orthodontic devices, named disjunctor with and without a connecting bar, in an acrylic model jaw, using fiber Bragg grating sensors to measure the strain patterns. The orthodontic device disjunctor with the bar, in general, transmits higher forces and strain to teeth and maxillae, than with the disjunctor without bar. It was verified that the strain patterns were not symmetric between the left and the right sides as also between the posterior and anterior regions of the maxillae. For the two devices is also found that in addition a displacement in the horizontal plane, particularly in posterior teeth, also occurs a rotation corresponding to a vestibularization of the posterior teeth and their alveolar processes. © Published under licence by IOP Publishing Ltd.

Costa C.M.,INESCTEC | Sobreira H.M.,INESCTEC | Sousa A.J.,INESCTEC | Veiga G.M.,INESCTEC
Robotics and Autonomous Systems | Year: 2016

Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1°-3° degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise. © 2015 Elsevier B.V. All rights reserved.

Discover hidden collaborations