Abstract

Abstract
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog. The most used inference method for ProbLog is exact inference combined with tabling. Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles. At a second step, inference requires handling these cycles in order to create a noncyclic Boolean representation of the probabilistic information. Finally, the Boolean representation is compiled to a data structure where inference can be performed in linear time. Previous work has illustrated that there are two limiting factors for ProbLog’s exact inference. The first factor is the target compilation language and the second factor is the handling of the cycles. In this paper, we address the second factor by presenting an iterative deepening algorithm which handles cycles and produces solutions to problems that previously ProbLog was not able to solve. Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling factor. © Springer International Publishing AG 2017.

Abstract

Abstract
This paper addresses the problem of decision making in the context of financial markets, more specifically, the problem of forecasting the correct trading action for a certain future horizon. We study and compare two alternative ways of addressing these forecasting tasks: (a) using standard numeric prediction models to forecast the variation on the prices of the target asset and, on a second stage, transform these numeric predictions into a decision according to some predefined decision rules; and (b) use models that directly forecast the right decision thus ignoring the intermediate numeric forecasting task. The objective of our study is to determine if both strategies provide identical results or if there is any particular advantage worth being considered that may distinguish each alternative in the context of financial markets.

Abstract
Deaf people face serious communication problems. The use of smartphones has been explored as a solution for breaking communication barriers and enhancing their communication, by providing access to basic services. This paper explores the usage of iconographic interfaces in smartphones as a means for contributing to further autonomy for deaf people. We applied the model for asynchronous and non-verbal communication through iconographic and interactive flows to develop the MyCarMobile application, a travel assistance android mobile application for deaf people. Our research explores a solution which enables travel assistance services without involving audio, using an iconographic interface to report road accidents. A user centered design approach was applied in the development of the prototype and usability tests were performed with eleven deaf users, in order to validate the mobile application. The results revealed a good performance and user satisfaction when interacting with the application.

Abstract
We consider the problem of robust model predictive control for linear sampled data dynamical systems subject to state and control constraints and additive and bounded disturbances. We propose a rigid tube model predictive control algorithm utilizing recent and topologically compatible notions for the sampled data forward reach sets as well as robust positively invariant sets. The proposed method inherits almost all desirable features associated with rigid tube model predictive control of discrete-time systems, and, in addition, it ensures robust constraint satisfaction and safety in a continuous-time sense.