Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.

Abstract
The water supply and sewage sector (WSS) is essential for promoting health and providing the population with drinking water and the adequate disposal of effluents. Assessing the evolution of performance in WSS allows for highlighting the best and worst results achieved, identifying benchmarks, and pinpointing sources of improvement for water services. Brazil has a large population and immense freshwater reserves that are unevenly distributed throughout the territory. This situation emanates a challenge that requires the efficient management of water resources. This study develops a composite indicator framework based on the robust Benefit-of-the-Doubt (BoD) approach to estimate the performance of municipalities of the Brazilian State of Santa Catarina from 2009 to 2021, considering financial, operational, and quality dimensions associated with the provision of WSS services. Subsequently, the Global Malmquist Index (GMI) is applied to assess the performance evolution of the municipalities over time. The BoD results enable the quantification of the relative contribution of each sub-indicator to the performance score, allowing the assessment of the strengths and weaknesses of each municipality. The GMI results show an average performance loss of 3.3% in Santa Catarina state and considerable variability among municipalities, with scores ranging from losses of 54.2% to gains of 109.3% in the period analysed.

Abstract
Memorising passwords poses a significant challenge for individuals, leading to the increasing adoption of password managers, particularly browser password managers. Despite their benefits to users’ daily routines, the use of these tools introduces new vulnerabilities to web and network security. This paper aims to investigate these vulnerabilities and analyse the security mechanisms of browser-based password managers integrated into Google Chrome, Microsoft Edge, Opera GX, Mozilla Firefox, and Brave. Through malware development and deployment, Dvorak is capable of extracting essential files from the browser’s password manager for subsequent decryption. To assess Dvorak functionalities we conducted a controlled security analysis across all aforementioned browsers. Our findings reveal that the designed malware successfully retrieves all stored passwords from the tested browsers when no master password is used. However, the results differ depending on whether a master password is used. A comparison between browsers is made, based on the results of the malware. The paper ends with recommendations for potential strategies to mitigate these security concerns. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
Enterprise Architecture (EA) is a coherent set of principles, methods, and models that express the structure and behavior of an enterprise and its IT landscape. EA mining uses data mining techniques to automate EA models' extraction. Ontologies help to define concepts and the relationships among these concepts to describe a domain of interest. This paper presents an extensible ontology for EA mining to extract models using Application Program Interface (API) log files as the data source. The ontology development follows the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and uses OntoUML 2.0 language to ensure its expressiveness and readability. To validate its theoretical feasibility and contribution to EA modeling, it presents a simulation of the ontology application through a controlled scenario using data structures similar to an industrial case. Then, the ontology is verified and validated, checking quality ontology criteria using specialized tools for syntactic and semantic model checking, which also aids in avoiding ontology anti-patterns.

Abstract
Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids' protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bilevel method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria.

Abstract
Identifying participants in narratives is important to understand and extract meaning from unstructured texts. This paper investigates the use of DBpedia and Wikifier for this task. We tested these two knowledge base platforms to evaluate their performance in recognizing and extracting entities in Portuguese-language journalistic narrative texts. The results show that both DBpedia and Wikifier present similar results in identifying participants, around 0.40 in the f1-score. The objective of this paper is to study the potential of knowledge bases to improve the understanding of narratives, in addition to suggesting directions for future research in this domain.