Abstract

Abstract
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.

Abstract
News articles typically include reporting events to inform on what happened. These reporting events are not part of the story being told but are nonetheless a relevant part of the news and can pose a challenge to the computational processing of news narratives. They compose a reporting narrative, which is the present study's focus. This paper aims to demonstrate through selected use cases how a comprehensive annotation scheme with suitable tags and links can properly represent the reporting events and the way they relate to the events that make the story. In addition, we put forward a proposal for their visual representation that enables a systematic and detailed analysis of the importance of reporting events in the news structure. Finally, we describe some lexico-grammatical features of reporting events, which can contribute to their automatic detection.

Abstract
Entre as metodologias de ensino que vem ganhando espaço no ambiente escolar destaca-se a Robótica Educacional (RE), que é capaz de propiciar uma inserção tecnológica de maneira prática e melhorar a dinâmica de ensino em sala de aula. Visando a disseminação dessa ferramenta, a Universidade Federal Rural do Semiárido (UFERSA), em parceria com a Petrobras, desenvolveu o projeto de extensão Robot em Ação, cujo objetivo foi levar a RE para escolas públicas do município de Mossoró (RN). Neste projeto, os participantes passaram pelo processo de inserção tecnológica, com aulas que utilizaram a RE como metodologia de ensino. Dessa forma, este artigo apresenta o relato deste projeto junto com a análise da perspectiva de vida dos alunos das escolas, antes e após a sua participação no mesmo. Os resultados obtidos destacam os benefícios da robótica educacional e o impacto positivo oferecido pelo projeto na vida desses alunos.

Abstract
The significance of software testing cannot be overstated, as its poor implementation often leads to problematic and faulty software applications. This problem comes from a mismatch in the required industry skills, the learning requirements of students, and the current teaching methodology for testing in higher and vocational education institutes. This project aims to create seamless teaching materials for testing education that is in line with industry standards and learning needs. Considering the diverse socioeconomic environment that will benefit from this project, a consortium of partners ranging from universities to small businesses has been assembled. The project starts with research into sense-making and cognitive models for learning and doing testing. Additionally, a study will be conducted to identify the training and knowledge transfer requirements for testing within the industry. Based on the research findings and study outcomes, teaching capsules for software testing will be developed, taking into account the cognitive models of students and the needs of the industry. After the effectiveness validation of these capsules, these capsules and the instructional material will be available to other researchers and professors to improve testing education.

Abstract
Colorectal cancer is the third most common cancer and the second cause of cancer-related deaths in the world. Colonoscopic surveillance is extremely important to find cancer precursors such as adenomas or serrated polyps. Identifying small or flat polyps can be challenging during colonoscopy and highly dependent on the colonoscopist's skills. Deep learning algorithms can enable improvement of polyp detection rate and consequently assist to reduce physician subjectiveness and operation errors. This study aims to compare YOLO object detection architecture with self-attention models. In this study, the Kvasir-SEG polyp dataset, composed of 1000 colonoscopy annotated still images, were used to train (700 images) and validate (300images) the performance of polyp detection algorithms. Well-defined architectures such as YOLOv4 and different YOLOv5 models were compared with more recent algorithms that rely on self-attention mechanisms, namely the DETR model, to understand which technique can be more helpful and reliable in clinical practice. In the end, the YOLOv5 proved to be the model achieving better results for polyp detection with 0.81 mAP, however, the DETR had 0.80 mAP proving to have the potential of reaching similar performances when compared to more well-established architectures.