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.

Abstract
Canvas have for long been embraced as a popular design tool. Initially aimed towards, business model development, the model of a one page, visual and collaborative tool has spread to the design of many different artifacts. Digital platforms, with its conjugation of business, technical, and social facets have benefited from the canvas model for its design practices, from both scholars and practitioners. Nonetheless, the recent push for more industry-specific and holistic digital platform research agenda is bound to have an impact in the tools used for platform design. In this paper, we apply a literature review method to examine existing canvas, inspired by the Business Model Canvas, as tools for the design of digital platforms. Using conceptual platform design research as a frame of reference, we review eight canvas specific for digital platform design, highlighting four critical limitations in their application regarding (1) adopted broad platform conceptualizations; (2) a restricted focus on business elements; (3) a lack of focus on platform evolution; and (4) a lack of guidance in the translation of canvas to explicit platform design propositions and requirements. By addressing these limitations, we set a path for the evolution of canvas as collaborative tools that can better support the more comprehensive and nuanced approaches required for the design of digital platforms acting in an evermore non-linear, volatile, uncertain, complex, and ambiguous environments.

Abstract
While a wide range of resources is available on orchestration techniques and best practices for containerized software systems, many are not documented clearly or in detail. This complicates the process of selecting the most suitable methods for various usage scenarios. To address this gap, we documented a set of orchestration patterns. This paper reports the results of a focus group conducted during the EuroPLoP 2024 conference, where we aimed to obtain feedback on that group of patterns and on a wider pattern map we outlined. We also aimed to identify container orchestration patterns that have not yet been documented. We found that participants knew most of the patterns we included on the pattern map. Additionally, one of the practices mentioned by the participants (Node Balancing) was previously documented as a pattern by us with the name of Service Balancing. Finally, we found important insights into container orchestration patterns, expanding our pattern map to include eight new proto-patterns.

Abstract
This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

Abstract
This paper provides an overview on recent work on Team Automata, whereby a network of automata interacts by synchronising actions from multiple senders and receivers. We further revisit this notion of synchronisation in other well known concurrency models, such as Reo, BIP, Choreography Automata, and Multiparty Session Types. We address realisability of Team Automata, i.e., how to infer a network of interacting automata from a global specification, taking into account that this realisation should satisfy exactly the same properties as the global specification. In this analysis we propose a set of interesting directions of challenges and future work in the context of Team Automata or similar concurrency models.