Abstract
Metabolomics is paramount for precision agriculture. Knowing the metabolic state of the vine and its implication for grape quality is of outermost importance for viticulture and wine industry. The MetBots system is a metabolomics precision agriculture platform, for automated monitoring of vineyards, providing geo-referenced metabolic images that are correlated and interpreted by an artificial intelligence self-learning system for aiding precise viticultural practices. Results can further be used to analyze the plant metabolic response by genome-scale models. In this research, we introduce the system main components: (i) robotic platform; (ii) autonomous navigation; (iii) sampling arm manipulation; (iv) spectroscopy systems; and (v) non-invasive, real-time metabolic hyper-spectral imaging monitoring of vineyards. The full potential of the Metbots system is revealed when metabolic data and images are analyzed by big data AI and systems biology vine plant models, establishing a new age of molecular biology precision agriculture. © Springer Nature Switzerland AG 2019.

Abstract
Semantic segmentation consists in predicting whether any given pixel is part of the object of interest or not. Two types of errors are therefore possible: false positives and false negatives. For visualization and emphasis purposes, we might want to put special effort into reducing one type of error in detriment of the other. A common practice is to define the two types of errors as a relative trade-off using a cost matrix. However, it might be more natural for humans to define the trade-off in terms of an absolute constraint on one type of errors while trying to minimize the other. Previously, we suggested possible approaches to introduce this absolute trade-off in binary classifiers. Extending to semantic segmentation, we propose a threshold on the sigmoid layer and modifications to gradient descent such as adding a new term to the loss function and training in two phases. The latter produced the more resilient results, with a simple threshold being sufficient in most cases.

Abstract
Nowadays, one of the problems associated with Unmanned Systems is the gap between research community and end-users. In order to emend this problem, the Portuguese Navy Research Center (CINAV) conducts the REX 2016 (Robotic Exercises). This paper describes the trials that were presented in this exercise, divided in two phases. The first phase happened at the Naval Base in Lisbon, with the support of divers and RHIBs (Rigid-Hulled Inflatable Boats), and the second phase, also with divers' support, at the coast of Lisbon-Cascais. It counted with many participants and research groups, including INESC-TEC, UNINOVA, TEKEVER and UAVISION. There are several advantages of doing this exercise, including for the Portuguese Navy, but also for partners. For the Navy, because it is an opportunity of being in contact with recent market technologies and researches. On the other hand, it is an opportunity for the partners to test their systems in a real environment, which usually is a difficult action to accomplish. Therefore, the paper describes three of the most relevant experiments: underwater docking stations, UAV and USV cooperation and Tracking targets from UAVs.

Abstract
Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. For this end, we compared the performance of different remotely-sensed variables to discriminate burned areas, by applying a simple change-point analysis procedure on time-series of MODIS imagery for the northern half of Portugal, without external information (e.g. active fire maps). Overall, our results highlight the importance of adopting a multi-indicator consensus approach for mapping and detecting wildfire disturbances at a regional scale, that allows to profit from spectral indices capturing different aspects of the Earth's surface, and derived from distinct regions of the electromagnetic spectrum. Finally, we argue that the framework here described can be used: (i) in a wide variety of geographical and environmental contexts; (ii) to support the identification of the best possible remotely-sensed functional indicators of wildfire disturbance; and (iii) for improving and complementing incomplete wildfire databases.

Abstract
The crime is spread in every daily newspaper, and particularly on criminal investigation reports produced by several Police departments, creating an amount of data to be processed by Humans. Other research studies related to relation extraction (a branch of information retrieval) in Portuguese arisen along the years, but with few extracted relations and several computer methods approaches, that could be improved by recent features, to achieve better performance results. This paper aims to present the ongoing work related to SEM (Simple Event Model) ontology population with instances retrieved from crime-related documents, supported by an SVO (Subject, Verb, Object) algorithm using hand-crafted rules to extract events, achieving a performance measure of 0.86 (F-Measure).

Abstract