Interactive game for the training of portuguese vowels

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Semantic adaptability for the systems interoperability

In the current global and competitive business context, it is essential that enterprises adapt their knowledge resources in order to smoothly interact and collaborate with others. However, due to the existent multiculturalism of people and enterprises, there are different representation views of business processes or products, even inside a same domain. Consequently, one of the main problems found in the interoperability between enterprise systems and applications is related to semantics. The integration and sharing of enterprises knowledge to build a common lexicon, plays an important role to the semantic adaptability of the information systems. The author proposes a framework to support the development of systems to manage dynamic semantic adaptability resolution. It allows different organisations to participate in a common knowledge base building, letting at the same time maintain their own views of the domain, without compromising the integration between them. Thus, systems are able to be aware of new knowledge, and have the capacity to learn from it and to manage its semantic interoperability in a dynamic and adaptable way. The author endorses the vision that in the near future, the semantic adaptability skills of the enterprise systems will be the booster to enterprises collaboration and the appearance of new business opportunities

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Neural Mechanisms Underlying the Perception of Three-Dimensional Shape from Texture: Adaptation and Aftereffects

Input into the visual system is two-dimensional (2D) and yet we effortlessly perceive the world around us as three-dimensional (3D). How we are able to accurately extract 3D shape information from the 2D representations that fall on the retina remains largely unknown. Although much research has been conducted that investigates higher levels of form processing (i.e. face recognition), less is known about the mechanisms that underlie the perception of simple 3D shape. Previous studies in our lab have shown that our ability to perceive 3D shape from texture cues relies on the visibility of orientation flows -- patterns that run parallel to the surface curvature of a 3D shape. Using the psychophysical technique of selective adaptation, we have further characterized the neural mechanisms that underlie the accurate perception of 3D shape. In Experiment One, we examined whether orientation flows that are defined by second order contours convey 3D shape, whether they induce 3D shape aftereffects, and whether these aftereffects are invariant to the patterns that define the orientation flows. Aftereffects were obtained and 3D shape was conveyed using stimuli in which orientation flows were defined by two classes of second order contours, and adapting to second order stimuli caused 3D shape aftereffects in first order stimuli. These results can be explained by the adaptation of 3D shape-selective neurons in extrastriate regions that invariantly extract first- and second order orientation flows from striate and extrastriate signals. In Experiment Two, we were interested in determining to what extent these neural mechanisms are invariant to differences in spatial frequency. We chose adapting/test stimuli that differed in spatial frequency by a factor of three, consistent with documented frequency bandwidths of V1 and V2 neurons. Shape aftereffects were obtained, indicating that these neural mechanisms are invariant to differences in spatial frequency by a factor of 3. Furthermore, these neural mechanisms are invariant to the patterns in which spatial frequency was varied (i.e., stimuli in which the orientation flows were created by first- or second order properties). Both of these properties are indicative of neurons that are located in extrastriate cortex. In Experiment Three, we were interested in testing to what extent these neural mechanisms were selective for retinal position by misaligning adapting and test stimuli by 2°, which corresponded to a single convexity or concavity in our corrugated surfaces. Our results suggest that 3D shape-selective mechanisms that respond to luminance modulated orientation flows appear to be sensitive to shifts in position of 2°. Overall, our results indicate that there are 3D shape mechanisms that are pattern invariant, invariant to differences in spatial frequencies by a factor of 3, and that exhibit position selectivity to shifts in retinal position of 2°. Taken together, these results implicate 3D shape mechanisms that are located in extrastriate cortex