Abstract
Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.

Abstract
Environment understanding in real-world scenarios has gained an increased interest in research and industry. The advances in data capture and processing allow a high-detailed reconstruction from a set of multi-view images by generating meshes and point clouds. Likewise, deep learning architectures along with the broad availability of image datasets bring new opportunities for the segmentation of 3D models into several classes. Among the areas that can benefit from 3D semantic segmentation is the automotive industry. However, there is a lack of labeled 3D models that can be useful for training and use as ground truth in deep learning-based methods. In this work, we propose an automatic procedure for the generation and semantic segmentation of 3D cars that were obtained from the photogrammetric processing of UAV-based imagery. Therefore, sixteen car parts are identified in the point cloud. To this end, a convolutional neural network based on the U-Net architecture combined with an Inception V3 encoder was trained in a publicly available dataset of car parts. Then, the trained model is applied to the UAV-based images and these are mapped on the photogrammetric point clouds. According to the preliminary image-based segmentation, an optimization method is developed to get a full labeled point cloud, taking advantage of the geometric and spatial features of the 3D model. The results demonstrate the method's capabilities for the semantic segmentation of car models. Moreover, the proposed methodology has the potential to be extended or adapted to other applications that benefit from 3D segmented models.

Abstract
Virtual power plants (VPPs) have emerged as a way to coordinate and control the growing number of distributed energy resources (DERs) within power systems. Typically, VPP models have focused on financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this article is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles, and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the tradeoffs between comfort, cost, and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey-Clarke-Grove mechanism. This article develops a stochastic mixed-integer linear programming model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar photovoltaic (PV) units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings.

Abstract

Abstract
The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning methods (including Transformers) for several medical applications based on the types of tasks that may integrate several works pipelines of the medical domain. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

Abstract
Social networks are the main marketing entity of the hotel industry of the 21st century. To understand the impact that social networks have on the hotel sector, both from the point of view of the hotel and the consumer is becoming essential. The aim of this research is to find out how 5 stars Slovakian hotels present themselves in 3 different social networks Instagram, Facebook and Twitter and to compare them with each other. For that purpose a descriptive analyses of the most relevant features of this social networks was conducted. The study offers relevant insight for marketing practitioners in the hospitality industry