Abstract
This article aims to explore menopausal women's views on empowerment in menopause-related femvertising on social media and to examine its outcomes for both women and brands. It includes a qualitative study comprising in-depth interviews with menopausal women who were active social media users (n = 15). The data were subject to content analysis using NVIVO software. The results reveal that menopause empowerment strategies on social media are perceived by women as a source of knowledge, facilitating social support, focusing on self-worth enhancement, and deconstructing stereotypes and taboos. Despite positive impacts such as self-esteem and self-confidence, these messages can also induce discomfort and feelings of segregation. Although the study highlights potential benefits for brands, including improved image and engagement, it also identifies risks such as skepticism, distrust, and customer loss. This research contributes to the femvertising and branding literature by addressing the largely overlooked segment of menopausal women. It highlights knowledge dissemination as a critical and previously underexplored dimension of femvertising and demonstrates that menopause empowerment carries distinct dynamics and consequences for both women and advertising brands, shedding light on the complexity of femvertising strategies. The findings can assist brands and social organizations aiming to develop more effective strategies for engaging menopausal audiences.

Abstract
Explainable AI (xAI) emerged as one of the ways of addressing the interpretability issues of the so-called black-box models. Most of the xAI artifacts proposed so far were designed, as expected, for human users. In this work, we posit that such artifacts can also be used by computer systems. Specifically, we propose a set of metrics derived from LIME explanations, that can eventually be used to ascertain the quality of each output of an underlying image classification model. We validate these metrics against quantitative human feedback, and identify 4 potentially interesting metrics for this purpose. This research is particularly useful in concept drift scenarios, in which models are deployed into production and there is no new labelled data to continuously evaluate them, becoming impossible to know the current performance of the model.

Abstract

Abstract
Cybersecurity attacks, such as poisoning and evasion, can intentionally introduce false or misleading information in different forms into data, potentially leading to catastrophic consequences for critical infrastructures, like water supply or energy power plants. While numerous studies have investigated the impact of these attacks on model-based prediction approaches, they often overlook the impurities present in the data used to train these models. One of those forms is missing data, the absence of values in one or more features. This issue is typically addressed by imputing missing values with plausible estimates, which directly impacts the performance of the classifier. The goal of this work is to promote a Data-centric AI approach by investigating how different types of cybersecurity attacks impact the imputation process. To this end, we conducted experiments using four popular evasion and poisoning attacks strategies across 29 real-world datasets, including the NSL-KDD and Edge-IIoT datasets, which were used as case study. For the adversarial attack strategies, we employed the Fast Gradient Sign Method, Carlini & Wagner, Project Gradient Descent, and Poison Attack against Support Vector Machine algorithm. Also, four state-of-the-art imputation strategies were tested under Missing Not At Random, Missing Completely at Random, and Missing At Random mechanisms using three missing rates (5%, 20%, 40%). We assessed imputation quality using MAE, while data distribution shifts were analyzed with the Kolmogorov-Smirnov and Chi-square tests. Furthermore, we measured classification performance by training an XGBoost classifier on the imputed datasets, using F1-score, Accuracy, and AUC. To deepen our analysis, we also incorporated six complexity metrics to characterize how adversarial attacks and imputation strategies impact dataset complexity. Our findings demonstrate that adversarial attacks significantly impact the imputation process. In terms of imputation assessment in what concerns to quality error, the scenario that enrolees imputation with Project Gradient Descent attack proved to be more robust in comparison to other adversarial methods. Regarding data distribution error, results from the Kolmogorov-Smirnov test indicate that in the context of numerical features, all imputation strategies differ from the baseline (without missing data) however for the categorical context Chi-Squared test proved no difference between imputation and the baseline.

Abstract
Software testing is an integral part of modern software development. Testing frameworks are part of the toolset of any software language allowing programmers to test their programs in order to detect bugs. Unfortunately, there is no work on testing in attribute grammars. In this paper we combine the powerful property-based testing technique with the attribute grammar formalism. In such property-based attribute grammars, properties are defined on attribute instances. Properties are tested on large sets of randomly generated (abstract syntax) trees by evaluating their attributes. We present an implementation that relies on strategies to express property-based attribute grammars. Strategies are tree-based recursion patterns that are used to encode logic quantifiers defining the properties.

Abstract
Acquiring high-quality annotated data remains one of the most significant challenges in Natural Language Processing (NLP), especially for supervised learning approaches. In scenarios where pre-existing labeled data is unavailable, common solutions like crowdsourcing and zero-shot approaches often fall short, suffering from limitations such as the need for large datasets and a lack of guarantees regarding annotation quality. Traditionally, data for human annotation has been selected randomly, a practice that is not only costly and inefficient but also prone to bias, particularly in imbalanced datasets where minority classes are underrepresented. To address these challenges, this work introduces an automatic and informed data selection architecture designed to minimize the volume of required annotations while maximizing the diversity and representativeness of the selected data. Among the evaluated methods, Reverse Semantic Search (RSS) demonstrated superior performance, consistently outperforming random sampling in imbalanced scenarios and enhancing the effectiveness of trained classifiers. Furthermore, we compared RSS with other clustering-based approaches, providing insights into their respective strengths and weaknesses.