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.

Abstract
Recent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users' dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic.   Received: 15 December 2024 | Revised: 12 June 2025 | Accepted: 30 June 2025   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement The data that support the findings of this study are openly available in X-Wines Research Project at https://sites.google.com/farroupilha.ifrs.edu.br/xwines.   Author Contribution Statement Rogério Xavier de Azambuja: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, and Project administration. A. Jorge Morais: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, and Project administration. Vítor Filipe: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, and Project administration.

Abstract

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.