Abstract

Abstract
Language models have become foundational to many widely used systems. However, these seemingly advantageous models are double-edged swords. While they excel in tasks related to resource-rich languages like English, they often lose the fine nuances of language forms, dialects, and varieties that are inherent to languages spoken in multiple regions of the world. Languages like European Portuguese are neglected in favor of their more popular counterpart, Brazilian Portuguese, leading to suboptimal performance in various linguistic tasks. To address this gap, we introduce the first open-source translation model specifically tailored for European Portuguese, along with a novel dataset specifically designed for this task. Results from automatic evaluations on two benchmark datasets demonstrate that our best model surpasses existing open-source translation systems for Portuguese and approaches the performance of industry-leading closed-source systems for European Portuguese. By making our dataset, models, and code publicly available, we aim to support and encourage further research, fostering advancements in the representation of underrepresented language varieties.

Abstract
Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.

Abstract

Abstract
Ensuring robustness in time series classification remains a critical challenge for safety-sensitive domains like clinical decision systems. While current evaluation practices focus on accuracy measures, they fail to address model stability under semantically meaningful input deformations. We propose tsMIST (Time Series Model Sensitivity Test), a novel morphing-based framework that systematically evaluates classifier resilience through controlled interpolation between adversarial class prototypes. By calculating the switchThreshold – defined as the minimal morphing distance required to flip predictions – our method reveals critical stability patterns across synthetic benchmarks with tunable class separation and 17 medical time series datasets. Key findings show convolutional architectures (ROCKET) maintain optimal thresholds near 50% morphing (48.2±3.1%), while feature-based models (Catch22) exhibit premature decision flips at 22.7% deformation (±15.4%). In clinical scenarios, tsMIST detected critical ECG misclassifications triggered by =12% signal variation – vulnerabilities undetected by accuracy measures. Our results establish that robustness measures must complement accuracy for responsible AI in high-stakes applications. This work advances ML evaluation practices by enabling systematic sensitivity analysis, with implications for model auditing and deployment in safety-critical domains. © 2025 Elsevier B.V., All rights reserved.

Abstract
We describe RebeCaos, a user-friendly web-based front-end tool for the Rebeca language, based on the Caos library for Scala. RebeCaos can simulate different operational semantics of (timed) Rebeca, thus facilitating the dissemination and awareness of Rebeca, providing insights into the differences among existing semantics for Rebeca, and supporting quick experimentation of new Rebeca variants (e.g., when the order of received messages is preserved). The tool also comes with initial reachability analyses for Rebeca models (e.g., the possibility of reaching deadlocks or desirable states). We illustrate the RebeCaos tool by means of a ticket service use case from the timed Rebeca literature.