Abstract
The process of building and deploying Machine Learning (ML) models includes several phases and the training phase is taken as one of the most time-consuming. ML models with time series datasets can be used to predict users positions, behaviours or mobility patterns, which implies paths crossing by well-defined positions, and thus, in these cases, syntactic similarity can be used to reduce these models training time. This paper uses the case study of a Mobile Network Operator (MNO) where users mobility are predicted through ML and the use of syntactic similarity withWord2Vec (W2V) framework is tested with Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models. Experimental results show that by using framework W2V in these architectures, the training time task is reduced in average between 22% to 43%. Also an improvement on the validation accuracy of mobility prediction of about 3 percentage points in average is obtained.

Abstract
Home Energy Management Systems (HEMS) are becoming progressively more researched and employed to invert the continuously increasing trend in (electrical) energy consumption in buildings. One of the critical aspects of any HEMS is the real-time monitoring of all variables related to the management system, as well as the real-time control of schedulable electric appliances. This paper describes a data acquisition system implemented in a residential house in the South of Portugal. With the small amount of data collected, a Radial Basis Function (RBF) model, designed by a Multi-objective Genetic Algorithm (MOGA) framework, to forecast total electric consumption was developed. Results show that, even with these little data, the model can be used in a predictive control scheduling mechanism for HEMS. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.

Abstract
Companies and brands are increasingly using social media networks as one of the main channels of disseminating products and services, due to the exponential growth that these platforms have had in the last few years. Universities and Higher Education Institutions are also using the contents published on social networks as a way of advertising the institution itself and its training offer. Content marketing for social media has increasingly become one of the most used strategies by companies and brands to increase engagement and attract new followers on their social networks. The main goal of this paper is to develop a content marketing strategy for School of Business Sciences (ESCE) of Polytechnic Institute of Viana do Castelo’s social networks as Facebook and Instagram that can generate an increase in the school’s awareness and followingly increase the number of new students. This study also aims to create greater identification of students with ESCE, to improve the engagement of its social networks with their followers and to get more interaction from users who do not usually interact with ESCE’s social network profiles. Subsequently, content marketing strategy was developed, and it analyzed the results obtained with the statistical analysis of ESCE’s social network profile. With the results obtained with this study, it was concluded that the application of a social media content marketing strategy for a higher education school had very positive results, on increasing the engagement in social networks by the followers of ESCES’s social networks. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15%, an mAP of 51.46% and an inference time of 16.44 ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 ms.

Abstract
Numerous machine learning applications involve dealing with imbalanced domains, where the learning focus is on the least frequent classes. This imbalance introduces new challenges for both the performance assessment of these models and their predictive modeling. While several performance metrics have been established as baselines in balanced domains, some cannot be applied to the imbalanced case since the use of the majority class in the metric could lead to a misleading evaluation of performance. Other metrics, such as the area under the precision-recall curve, have been demonstrated to be more appropriate for imbalance domains due to their focus on class-specific performance. There are, however, many proposed implementations for this particular metric, which could potentially lead to different conclusions depending on the one used. In this research, we carry out an experimental study to better understand these issues and aim at providing a set of recommendations by studying the impact of using different metrics and different implementations of the same metric under multiple imbalance settings.