Abstract
Side-channel attacks exploit involuntary information leakages from devices, such as power consumption, electromagnetic radiation, visible light, or acoustic emanations. The objective of these attacks is to gain unauthorized access to data, such as passwords or other sensitive information. In particular, the keyboard acoustic side-channel attacks take advantage of the sound emanating from keystrokes to decipher which keys have been pressed by the user by comparing the produced sounds with a recorded dataset. This paper proposes a prototype that generates false and random keystroke sounds that are mixed with real keystroke sounds, to mitigate the impact of a keyboard acoustic side-channel attack. It includes three security modes low, medium, and high, each offering different levels of protection and user customization. This is accomplished by generating random key sounds and also randomizing the number of key sounds to play and the time between the generated key sounds, during the real keystroke sound. To test the proposed prototype, a dataset was generated and used to assess the security modes execution. The results show that the sound of the real keystrokes is scrambled with the fake keystrokes sounds, making it difficult to decipher which keys have been pressed by the user.

Abstract
This work aims to contribute to a deeper understanding of cryptocurrencies, which have emerged as a unique form within the financial market. While there are numerous cryptocurrencies available, most individuals are only familiar with Bitcoin. This knowledge gap and the lack of literature on the subject motivated the present study to shed light on the key characteristics of cryptocurrencies, along with their advantages and disadvantages. Additionally, we seek to investigate the integration of cryptocurrencies within the financial market by applying a dynamic equicorrelation model. The analysis covers ten cryptocurrencies from June 2(nd), 2016 to May 25(th), 2021. Through the implementation of the dynamic equicorrelation model, we have reached the conclusion that the degree of integration among cryptocurrencies primarily depends on factors such as trading volume, global stock index performance, energy price fluctuations, gold price movements, financial stress index levels, and the index of US implied volatility.

Abstract
Recent advances in deep learning have promoted the advent of many computational systems capable of performing intelligent actions that, until then, were restricted to the human intellect. In the particular case of human languages, these advances allowed the introduction of applications like ChatGPT that are capable of generating coherent text without being explicitly programmed to do so. Instead, these models use large volumes of textual data to learn meaningful representations of human languages. Associated with these advances, concerns about copyright and data privacy infringements caused by these applications have emerged. Despite these concerns, the pace at which new natural language processing applications continued to be developed largely outperformed the introduction of new regulations. Today, communication barriers between legal experts and computer scientists motivate many unintentional legal infringements during the development of such applications. In this paper, a multidisciplinary team intends to bridge this communication gap and promote more compliant Portuguese NLP research by presenting a series of everyday NLP use cases, while highlighting the Portuguese legislation that may arise during its development. © 2024 ELRA Language Resource Association.

Abstract
A novel non-destructive analytical method for early diagnosis of two bacterial diseases, Pseudomonas syringae and Xanthomonas euvesicatoria, in tomato plants, using ultraviolet-visible (UV-Vis) transmittance spectroscopy and chemometric models, is developed. Plant-pathogen interactions caused tissue damage that generated non-linear data patterns compared to the control set (healthy samples), which challenges traditional discrimination models, even when employing non-linear discriminant approaches. Alternatively, an authentication task to conduct oneclass classification relying on a data-driven version of soft independent modeling of class analogy (DD-SIMCA) is a wise choice due to its quadratic approach, proper to deal with non-linear data. DD-SIMCA detached the target class (control healthy plant leaflet tissues) from all other samples (target class and non-target class of plant leaflet tissues inoculated with two bacteria, even before the manifestation of macroscopic lesions associated with the diseases) by capturing the main similarities within the samples of the target class through the full distance that acts as a classification analytical signal, reaching 100 % sensitivity in the training and validation sets. Multivariate curve resolution - alternating least-squares (MCR-ALS) constrained analysis allowed the description of the bacterial inoculation process on diseased tissues through pure spectral signatures. DD-SIMCA results indicate that non-target class of samples with higher proximity to the acceptance boundary suggested that they were at earlier stages of infection when compared to more distant ones, presenting lower full distance values. These findings reveal that a handheld UV-Vis transmittance spectrometer is sufficiently sensitive to be used in acquiring biological data with suitable chemometric models for early disease diagnosis and prompt intervention.

Abstract
The high rates of mortality provoked by cardiovascular disorders (CVDs) have been rated by the OMS in the top among non-communicable diseases, killing about 18 million people annually. It is crucial to detect arrhythmias or cardiovascular events in an early way. For that purpose, novel portable acquisition devices have allowed long-term electrocardiographic (ECG) recording, being the most common way to discover arrhythmias of a random nature such as atrial fibrillation (AF). Nonetheless, the acquisition environment can distort or even destroy the ECG recordings, hindering the proper diagnosis of CVDs. Thus, it is necessary to assess the ECG signal quality in an automatic way. The proposed approach exploits the feature and meta-feature extraction of 5-s ECG segments with the ability of machine learning classifiers to discern between high- and low-quality ECG segments. Three different approaches were tested, reaching values of accuracy close to 83% using the original feature set and improving up to 90% when all the available meta-features were utilized. Moreover, within the high-quality group, the segments belonging to the AF class outperformed around 7% until a rate over 85% when the meta-features set was used. The extraction of meta-features improves the accuracy even when a subset of meta-features is selected from the whole set.

Abstract
Underwater scenarios pose additional challenges to perception systems, as the collected imagery from sensors often suffers from limitations that hinder its practical usability. One crucial domain that relies on accurate underwater visibility assessment is underwater pipeline inspection. Manual assessment is impractical and time-consuming, emphasizing the need for automated algorithms. In this study, we focus on developing learning-based approaches to evaluate visibility in underwater environments. We explore various neural network architectures and evaluate them on data collected within real subsea scenarios. Notably, the ResNet18 model outperforms others, achieving a testing accuracy of 93.5% in visibility evaluation. In terms of inference time, the fastest model is MobileNetV3 Small, estimating a prediction within 42.45 ms. These findings represent significant progress in enabling unmanned marine operations and contribute to the advancement of autonomous underwater surveillance systems.