Abstract
Ensuring privacy while sharing sensitive data is critical, particularly in fields such as healthcare, and everywhere compliance with data protection regulations is required. Anonymization and pseudonymization techniques are essential for preserving individual privacy but it is challenging to select the most appropriate methods given particular privacy and utility requirements. We conducted a focus group during the EuroPLoP 2024 conference that aimed to obtain feedback on patterns that we documented in this space and on a pattern map we outlined, and to identify patterns related to anonymization or pseudonymization of data that have not yet been documented. Some of the patterns we documented were not known by participants. On the other hand, we found some techniques that are potentially privacy-preserving patterns that have not yet been documented, and framed these techniques according to the category in our pattern map. Although the results suggest that our current patterns address some recurring privacy challenges, further exploration and documentation of the techniques are necessary to capture the full range of privacy-preserving solutions.

Abstract

Abstract
Advancements in the energy efficiency and computational power of embedded devices allow developers to equip resource-constrained systems with a greater number of features and more complex behavior. As complexity of a system grows, so does the difficulty in demonstrating its overall correctness. Formal methods have been successfully applied in a variety of verification and validation scenarios, but their wide adoption in the industry and academia is still lackluster. Among the explanations listed in the literature for the low adoption of these techniques are the perceived difficulty of getting into formal practices and how formal tools are not usually aimed at practical use cases. Striving to address these issues, we present MARS, an open-source domain-specific language for the safe instrumentation of runtime verification monitors into real-time resource-constrained distributed systems. Our main objective with MARS is to ease the integration of runtime verification monitors in distributed applications while also providing developers with evidence of their correct instrumentation in the context of systems where dependability and temporal requirements need to be respected even under extreme resource constraints. We present the language syntax, the set of tools embedded into its compiler, its functionalities, and a use case to exemplify its use in a practical distributed application. © 2024 IEEE.

Abstract
Estimating turbulence parameters is essential during commissioning and optimising adaptive optics or fringe tracking systems. It also gained new relevance with free-space optical communication applications. The estimation of such parameters is done under the assumption of stationarity. Yet, the stationarity time scale of the atmospheric turbulence is unknown. The breakdown of this assumption leads to incorrect estimates and added error terms. In this paper, we illustrate stationarity detection with unit root testing and the pitfalls of its application to turbulence parameter time series.

Abstract
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Abstract
Objectives: The purpose of this study was to field-test a recently developed AR-based serious game designed to promote SSE self-efficacy, called Spot. Methods: Thirty participants played the game and answered 3 questionnaires: a baseline questionnaire, a second questionnaire immediately after playing the game, and a third questionnaire 1 week later (follow-up). Results: The majority of participants considered that the objective quality of the game was high, and considered that the game could have a real impact in SSE promotion. Participants showed statistically significant increases in SSE self-efficacy and intention at follow-up. Of the 24 participants that had never performed a SSE or had done one more than 3 months ago, 12 (50.0%) reported doing a SSE at follow-up. Conclusions: This study provides supporting evidence to the use of serious games in combination with AR to educate and motivate users to perform SSE. Spot seems to be an inconspicuous but effective strategy to promote SSE, a cancer prevention behavior, among healthy individuals. Practice implications: Patient education is essential to tackle skin cancer, particularly melanoma. Serious games, such as Spot, have the ability to effectively educate and motivate patients to perform a cancer prevention behavior.