Abstract

Abstract

Abstract
Accurate segmentation of grapevines in imagery acquired from unmanned aerial vehicles (UAVs) is important for precision viticulture, as it supports vineyard management by monitoring grapevine health, growth, and environmental stress. However, the structural diversity of vineyards, including differences in training systems, row curvatures, and foliage density, presents challenges for grapevine segmentation methods. This study evaluates the performance of deep learning (DL) models-Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet) and U-Net-each combined with different backbones for grapevine segmentation in UAV-based RGB orthophoto mosaics. Data were collected under a range of vineyard conditions and scenarios from Portugal's Douro and Vinhos Verdes regions, providing a representative dataset across multiple vineyard configurations. The DL models were trained, tested, and evaluated using orthorectified RGB imagery, and their segmentation accuracy was compared to thresholding techniques. The results show that DL models, particularly U-Net, achieved accurate grapevine segmentation and reduced over-segmentation and false detections that are common in thresholding methods. FPN models with Inception-v4 and Xception backbones performed well in vineyards with inter-row vegetation, while PSPNet models showed segmentation limitations. Overall, DL-based segmentation models demonstrated advantages over thresholding approaches, demonstrating their suitability for UAV-based grapevine segmentation in diverse and challenging vineyard environments. These results support the scalability of DL-based segmentation for vineyard monitoring applications and indicate that improved segmentation accuracy can contribute to decision support in precision viticulture.

Abstract
Validation is a central activity when developing formal specifications. Similarly to coding, a possible validation technique is to define upfront test cases or scenarios that a future specification should satisfy or not. Unfortunately, specifying such test cases is burdensome and error prone, which could cause users to skip this validation task. This paper reports the results of an empirical evaluation of using pre-trained large language models (LLMs) to automate the generation of test cases from natural language requirements. In particular, we focus on test cases for structural requirements of simple domain models formalized in the Alloy specification language. Our evaluation focuses on the state-of-theart GPT-5 model, but results from other closed- and open-source LLMs are also reported. The results show that, in this context, GPT-5 is already quite effective at generating positive (and negative) test cases that are syntactically correct and that satisfy (or not) the given requirement, and that can detect many wrong specifications written by humans.

Abstract
Analyzing time series of counts often encounters the challenge of missing data, which can significantly hinder the accuracy and reliability of statistical models. This study addresses this issue by employing Poisson first-order integer-valued au-toregressive (PoINAR) models in conjunction with the Gibbs sampler with data augmentation. This method is particularly effective as it accounts for both the mechanisms behind missing data and the intrinsic serial correlation within the time series. Two distinct approaches to data augmentation are explored and compared in this work and illustrated using both simulated and real data.

Abstract