Jorge Morais

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

Abstract
Building evacuation simulation allows for a better assessment of fire safety conditions in existing buildings, which is why it is of interest to develop an easy-to-use Web platform that helps fire safety technicians in this assessment. To achieve this goal, the geometric and physical representation of the building and installed fire safety devices are necessary, as well as the modelling of occupant movement. Although these are widely studied areas, in this paper, we present two new model approaches, either for the physical and geometric representation of a building or for the occupant’s movement simulation, during a building evacuation process. To test both models, we develop a multi-agent Web simulator platform. The tests carried out show the suitability of the model approaches herein presented.

Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

Abstract
Considering the growing volume of information and services available on the web, it has become essential to provide websites and applications with tools, such as recommender systems, capable of helping users to obtain the information and services appropriate to their interests. Due to the complexity of web adaptation and the ability of multi-agent systems to deal with complex problems, the use of multi-agent approaches in recommender systems has been increasing. In the present work, we make a thorough review of the use of multi-agent-based recommender systems. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns the diversity of domains, different types of approaches and contribution to the performance improvement of the recommender systems.