Abstract
One of the principles of digital democracy is to actively inform citizens and mobilize them to participate in the political debate. This paper introduces a tool that processes public political documents to make information accessible to citizens and specific professional groups. In particular, we investigate and develop artificial intelligence techniques for text mining from the Portuguese Diário da Assembleia da República to partition, analyze, extract and synthesize information contained in the minutes of parliamentary sessions. We also developed dashboards to show the extracted information in a simple and visual way, such as summaries of speeches and topics discussed. Our main objective is to increase transparency and accountability between elected officials and voters, rather than characterizing political behavior. © (2024), (SciELO-Scientific Electronic Library Online). All Rights Reserved.

Abstract

Abstract
Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients' responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts' decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

Abstract
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

Abstract
Single Page Applications (SPAs) are characterised by changing a webpage's content without forcing a reload. While they increase the interactivity of web pages, they can also raise accessibility concerns, such as new content being displayed without screen reader users being aware. Another concern raised impacts the ability of current web accessibility evaluation tools to be able to assess SPAs. Current tools work by loading the DOM and assessing it. For SPAs, which change the DOM in response to user interaction, it is possible that evaluation tools do not have access to a significant part of a page's content. In this paper, we introduce QualState, a tool that can browse the different states of a SPA, and provide the DOM of the different states to QualWeb, an automated web accessibility evaluation tool, so their accessibility can be evaluated. We assessed QualState in a small set of SPAs and concluded that it increases the number of elements evaluated and improves QualWeb's ability to identify accessibility barriers.

Abstract
Owing to their similarities with giant exoplanets, brown dwarf companions of stars provide insights into the fundamental processes of planet formation and evolution. From their orbits, several brown dwarf companions are found to be more massive than theoretical predictions given their luminosities and the ages of their host stars1-3. Either the theory is incomplete or these objects are not single entities. For example, they could be two brown dwarfs each with a lower mass and intrinsic luminosity1,4. The most problematic example is Gliese 229 B (refs. 5,6), which is at least 2-6 times less luminous than model predictions given its dynamical mass of 71.4 +/- 0.6 Jupiter masses (MJup) (ref. 1). We observed Gliese 229 B with the GRAVITY interferometer and, separately, the CRIRES+ spectrograph at the Very Large Telescope. Both sets of observations independently resolve Gliese 229 B into two components, Gliese 229 Ba and Bb, settling the conflict between theory and observations. The two objects have a flux ratio of 0.47 +/- 0.03 at a wavelength of 2 mu m and masses of 38.1 +/- 1.0 and 34.4 +/- 1.5 MJup, respectively. They orbit each other every 12.1 days with a semimajor axis of 0.042 astronomical units (au). The discovery of Gliese 229 BaBb, each only a few times more massive than the most massive planets, and separated by 16 times the Earth-moon distance, raises new questions about the formation and prevalence of tight binary brown dwarfs around stars. Analysis of the cool brown dwarf Gliese 229 B suggests that it is actually a close binary of two less massive brown dwarfs, explaining its low luminosity and settling the conflict between theoretical predictions and measurements.