Jorge Morais

Abstract
Traditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Modern agriculture faces important challenges for feeding a fast-growing planet's population in a sustainable way. One of the most important challenges faced by agriculture is the increasing destruction caused by pests to important crops. It is very important to control and manage pests in order to reduce the losses they cause. However, pest detection and monitoring are very resources consuming tasks. The recent development of computer vision-based technology has made it possible to automatize pest detection efficiently. In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi) is considered the key-pest of the crop. This paper presents olive fly detection using the lightweight YOLO-based model for versions 7 and 8, respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection models were trained, validated, and tested using two different image datasets collected in various locations of Portugal and Greece. The images are constituted by sticky yellow trap photos and by McPhail trap photos with olive fly exemplars. The performance of the models was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny model best performance is 88.3% of precision, 85% of Recall, 90% of mAP.50, and 53% of mAP.95. The YOLOV8n model best performance is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50 YOLO8n model achieved worst results than YOLOv7-tiny for a dataset without negative images (images without olive fly exemplars). Aiming at installing an experimental prototype in the olive grove, the YOLOv8n model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.

Abstract
AbstractSeveral approaches have been proposed to obtain successful models to solve complex next-item recommendation problem in non-prohibitive computational time, such as by using heuristics, designing architectures, and applying information filtering techniques. In the current technological scenario of artificial intelligence, sequential recommender systems have been gaining attention and they are a highly demanding research area, especially using deep learning in their development. Our research focuses on an efficient and practical model for managing sequential session-based recommendations of specific products for users using the wine and movie domains as case studies. Through an innovative recommender model called X-Model4Rec – eXtensible Model for Recommendation, we explore the user's dynamic taste profile using architectures with transformer and multi-head attention mechanisms to solve the next-item recommendation problem. The performance of the proposed model is compared to that of classical and baseline recommender models on two real-world datasets of wines and movies, and the results are better for most of the evaluation metrics.

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 Protege tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.