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

Abstract
Today, cloud platforms for Internet of Everything (IoE) are facilitating organizational and industrial growth, and have different requirements based on their different purposes. Usual task scheduling algorithms for distributed environments such as group of clusters, networks, and clouds, focus only on the shortest execution time, regardless of the power consumption. Network energy can be optimized if tasks are properly scheduled to be implemented in virtual machines, thus achieving green computing. In this research, Dynamic Voltage Frequency Dcaling (DVFS) is used in two different ways, to select a suitable candidate for scheduling the tasks with the help of an Artificial Intelligence (AI) approach. First, the GIoTDVFS_SFB method based on sorting processor elements in Cloud has been considered to handle Task Scheduling problem in the Clouds system. Alternatively, the GIoTDVFS_mGA microgenetic method has been used to select suitable candidates. The proposed mGA and SFB methods are compared with SLAbased suggested for Cloud environments, and it is shown that the Makespan and Gain in benchmarks 512 and 1024 are optimized in the proposed method. In addition, the Energy Consumption (EC) of Real PM (RPMs) against the numeral of Tasks has been considered with that of PAFogIoTDVFS and EnergyAwareDVFS methods in this area. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.