Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Abstract
Following the reduced number of female students at ISTEC Porto computer science and engineering courses, we tried to find out what are the factors that might be causing this fact. This gender inequality was found looking back at the data from the last 15 years, clearly showing the gap between male and female students managing to conclude the degree. To point out the reasons and probable explanation a literature review was made to figure out conceivable paths to change this status quo. The article concludes that gender inequality resides on cultural differences as well as in the lack of information about the real role played by women in technology companies, particularly IT companies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Abstract
Autonomous underwater vehicles (AUV) are increasing in popularity and importance for the realization of underwater explorations. Nowadays, these types of vehicles are implemented in underwater environments to accomplish tasks for military, scientific and industrial purposes. These vehicles can use imaging sonars that are effective in detecting the AUV's distance to an obstacle. The main goals of this paper were to extract meaningful information gathered by sonar, use it to map the surrounding environment, and locate the vehicle on the estimated map. To accomplish these goals, the system is composed of a constant false alarm rate (CFAR) algorithm to filter the sonar information, a feature extractor that filters the first obstacle for each sonar beam in a 360 degrees revolution, an Octomap to build the estimated map and a Particle Filter (PF) to locate the vehicle in the environment. This system was developed using a set of measurements in a rectangular tank where the AUV was in static positions and in motion.

Abstract
Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria-Hausa, Igbo, Nigerian-Pidgin, and Yoruba-consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing, and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

Abstract
The introduction of IoT technologies in industrial settings is changing the way manufacturing companies perceive their production processes, allowing for more accurate monitoring of production-related parameters. Due to the introduction of IoT technologies and associated enhancements in terms of data acquisition, processing, and availability, it becomes possible for manufacturing companies to tackle complex issues, such as the efficiency of production. In this paper, the development of an Industrial IoT solution targeting the fish canning industrial sector is described, as an approach to identify and monitor raw input material losses as well as water and energy waste, an endemic issue of the sector, promoting an increased efficiency in the production processes. This paper mainly focuses on the description of the implementation of the proposed IoT technologies for monitoring waste and loss, and its deployment in three distinct environments, including at a fish canning manufacturing company.