Abstract
Sea exploration is important for countries with large areas in the ocean under their control, since in the future it may be possible to exploit some of the resources in the seafloor. The sea exploration problem was presented by Pedroso et al. [13] (unpublished); we maintain most of the paper's structure, to provide the needed theoretical background and context. In the sea exploration problem, the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The goal is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is the first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface. Results on a benchmark test set are presented and analyzed, confirming the merit of the approach proposed. In this paper, additional methods are presented, along with a small topological result and subsequent proof of the convergence of these same methods to the optimal solution, when we have instant access to the ground truth and the underlying function is piecewise continuous.

Abstract
One of the areas with the potential to be explored in quantum computing (QC) is machine learning (ML), giving rise to quantum machine learning (QML). In an era when there is so much data, ML may benefit from either speed, complexity or smaller amounts of storage. In this work, we explore a quantum approach to a machine learning problem. Based on the work of Mari et al., we train a set of hybrid classical-quantum neural networks using transfer learning (TL). Our task was to solve the problem of classifying full-image mammograms into malignant and benign, provided by BCDR. Throughout the course of our work, heatmaps were used to highlight the parts of the mammograms that were being targeted by the networks while evaluating different performance metrics. Our work shows that this method may hold benefits regarding the generalization of complex data; however, further tests are needed. We also show that, depending on the task, some architectures perform better than others. Nonetheless, our results were superior to those reported in the state-of-the-art (accuracy of 84% against 76.9%, respectively). In addition, experiments were conducted in a real quantum device, and results were compared with the classical and simulator.

Abstract
The projects funded under the European Horizon 2020 program have responded to the challenges facing small enterprises and have provided a framework for different actors (e.g., universities, R&D centers, SMEs) to collaborate and find innovative approaches to address the challenges of digital transformation. This study conducts a bibliometric analysis of the scientific production supported by this project, between 2014 and 2021, evaluating 114 projects, which have associated 2312 scientific production items and 1460 deliverables. The results demonstrate that scientific production is mostly carried out collaboratively with project partners and is mainly published in peer-reviewed journals. The research demonstrates that resources, such as Horizon 2020, provide a useful adjunct to other databases as a basis for bibliometric and related analyses.

Abstract
Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.

Abstract
<p>The Alto do Sobrido (AS) and Ribeiro da Serra (RS) Mines are old Sb-Au explorations. These are located in Gondomar, Portugal, on the inverse limb of the well-known structure called Valongo Anticline. In the AS Mine, the mineralization occurs near the contact between the Schist-Greywacke Complex (CXG) (Precambrian and/or Cambrian(?)) and the breccia of the base of the Carboniferous. In the RS Mine, the mineralization occurs only on the CXG. In both mines, the Sb-Au mineralization occurs in quartz veins and some stockworks.</p><p>A spatial correlation between the Sb-Au mineralization and the post-orogenic granites occurs in the Dúrico-Beirã Region according to Gumiel & Arribas (1987). Couto et al. (2007) also acquired data that suggests a genetic connection between this mineralization and non-outcropping granites. These granites may have been the source of fluids and a heat source that improved hydrothermal circulation and they have been observed in one of the RS Mine’s galleries.</p><p>With this hypothesis in mind, we intend to compare the data from a radiometric survey, which is a method that is radiometrically sensitive to K, Th and U at the near-surface, to the data from a gravimetric survey, which is a method that is sensitive to density anomalies at greater depths, in order to show if these granites could have chemically influenced its embedding rocks.</p><p>To make this comparison, we used the residual anomaly map from our gravimetric survey and the four maps obtained in the radiometric survey (total concentrations, K, eTh and eU). Firstly, we normalized all the grid maps to obtain grids with values between -1 and 1. Once this was complete, we multiplied each of the four radiometry maps to the residual anomaly map, obtaining the comparison maps.</p><p>On the resulting maps, we can observe high values in 3 different areas. The first corresponds to a lower value of gravimetric anomaly and a lower value of concentrations of all the elements. This area is located where the hypothesized non-outcropping granites are situated. The second area corresponds to high values on both methods. This matches the location of the lithologies from the Middle Ordovician to the Carboniferous, which are rocks of higher densities and higher concentration values of K, eTh and eU. The third area consists of lower gravimetric anomalies and lower concentrations of K and eU, and coincides with the location of the Ordovician quartzites. This area isn’t as visible on the eTh map, which is consistent with what was observed on the field.</p><p>We consider this approach to be a practical method to correlate the results of these two methods and an attempt to understand how the granite located at depth could have influenced these lithologies that today outcrop.</p><p><strong>References</strong></p><p>Gumiel, P., Arribas, A., 1987. Antimony Deposits in the Iberian Peninsula. Economic Geology, Volume 82, pp 1453-1463.</p><p>Couto, H., Borges, F. S., Roger, G., 2007. Late Palaeozoic orogenic gold-antimony deposits from the Dúrico-Beirã area (North Portugal) and their relation with hidden granitic apexes. Ninth Biennial SGA Meeting, Dublin. pp 609-612.</p>

Abstract
This position paper provides an overview of the most important practices in the field of Artificial Intelligence (AI) used in educational contexts, with a focus on the main platforms used for teaching (LMS) to support the development of a research work at EduardoMondlane University (UEM) in Mozambique. To that end, definitions and descriptions of relevant terms, a brief historical overview of Artificial Intelligence (AI) in education and an overview of the common goals and practices of using computational methods in educational contexts are provided. The state of the art regarding the adaptation and use of Artificial Intelligence is presented and we discuss the potential benefits and the open challenges. The paper also presents the methodology and key steps which will be developed at UEM to achieve the research goals.