Abstract
Markov Logic is an expressive and widely used knowledge representation formalism that combines logic and probabilities, providing a powerful framework for inference and learning tasks. Most Markov Logic implementations perform inference by transforming the logic representation into a set of weighted propositional formulae that encode a Markov network, the ground Markov network. Probabilistic inference is then performed over the grounded network. Constructing, simplifying, and evaluating the network are the main steps of the inference phase. As the size of a Markov network can grow rather quickly, Markov Logic Network (MLN) inference can become very expensive, motivating a rich vein of research on the optimization of MLN performance. We claim that parallelism can have a large role on this task. Namely, we demonstrate that widely available Graphics Processing Units (GPUs) can be used to improve the performance of a state-of-the-art MLN system, Tuffy, with minimal changes. Indeed, comparing the performance of our GPU-based system, TuGPU, to that of the Alchemy, Tuffy and RockIt systems on three widely used applications shows that TuGPU is up to 15x times faster than the other systems.

Abstract
We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.

Abstract
Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

Abstract
ICT in healthcare businesses has been growing in Portugal in the past few decades. The implementation of large scale information systems in hospitals, the deployment of electronic prescription and electronic patient records applications are just a few examples. Telemedicine is another emergent and widely used ICT solution to smooth the communication between patients and healthcare professionals, by allowing video and voice transfer over the Internet. Although there are several implementations of telemedicine solutions, they usually have some drawbacks, namely: i) too specific for a purpose; ii) based on proprietary applications; iii) require additional software installation; iv) and usually have associated costs. In this paper we propose a telemedicine solution based on WebRTC Application Programming Interface (API) to transmit video and voice in real time over the Internet, through a web browser. Besides microphone and webcam control, we have also included two additional functionalities that may be useful to both patients and healthcare professionals during the communication, namely i) bidirectional sending files capability and ii) shared whiteboard which allows free drawing. The proposed solution uses exclusively open source software components and requires solely a WebRTC compatible web browser, like Google Chrome or Firefox. We have made two types of tests in healthcare environment: i) a bidirectional patient-doctor communication; ii) and connecting at one end an external USB medical device with an integrated webcam. The results were promising, since they revealed the potential of using WebRTC API to control microphone and webcam in a telemedicine application, as well as the appropriateness and acceptance of the features included. (C) 2016 Published by Elsevier B.V.

Abstract
Forgery detection of official documents is a continuous challenge encountered by documents' forensic experts. Among the most common counterfeited documents we may find citizen cards, passports and driving licenses. Forgers are increasingly resorting to more sophisticated techniques to produce fake documents, trying to deceive criminal polices and hamper their work. Having an updated past counterfeited documents image catalogue enables forensic experts to determine if a similar technique or material was already used to forge a document. Thus, through the modus operandi characterization is possible to obtain more information about the source of the counterfeited document. In this paper we present an information system to manage counterfeited documents images that includes a two-fold approach: (i) the storage of images of past counterfeited documents seized by questioned documents forensic experts of the Portuguese Scientific Laboratory in a structured database; and (ii) the automation of the counterfeit identification by comparing a given fraudulent document image with the database images of previously catalogued counterfeited documents. In general, the proposed information system aims to smooth the counterfeit identification and to overcome the error prone, manual and time consuming tasks carried on by forensic experts. Hence, we have used a scalable algorithm under the OpenCV framework, to compare images, match patterns and analyse textures and colours. The algorithm was tested on a subset of counterfeited Portuguese citizen cards, presenting very promising results.