Abstract
The manufacturing process of thermoregulation products with polyester (PES) fabric and conductive polymers such as poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) with proper wearability, comfort, and high performance is still a challenge due to low adhesion, environment instability and nonuniform coatings. This study presents a simple and effective method for producing thermoregulatory PES fabrics using the Joule heating effect. Textiles treated with dielectric barrier discharge (DBD) plasma were functionalized with PEDOT:PSS incorporating secondary dopants, such as dimethyl sulfoxide (DMSO) and glycerol (GLY). PEDOT:PSS was used because it does not compromise the mechanical properties of base materials. DBD plasma treatment was applied to PES to improve the substrate's functional groups and consequently increase adhesion and homogeneity of the PEDOT:PSS on the substrate. The polymer were applied to the textiles by dip-pad-drycure method ensuring uniform distribution and homogeneous heating of the materials. The samples' conductivity, impedance, potential and Joule effect, and their morphological, chemical and thermal properties were studied. Control samples without plasma treatment and secondary dopants were also prepared. The results showed that the DBD-treated samples, coated with 5 layers of PEDOT:PSS, doped with DMSO 7 % (w/v), displayed the best conductivity and Joule effect performance reaching 44.3 degrees C after 1 h.

Abstract

Abstract
Virtual presence demands ultra-low latency, a factor that centralized architectures, by their nature, cannot minimize. Local peer-to-peer architectures offer a compelling alternative, but also pose unique challenges in terms of network infrastructure.This paper introduces a prototype leveraging Conflict-Free Replicated Data Types (CRDTs) to enable real-time collaboration in a shared virtual environment. Using this prototype, we investigate latency, synchronization, and the challenges of decentralized coordination in dynamic non-Byzantine contexts.We aim to question prevailing assumptions about decentralized architectures and explore the practical potential of P2P in advancing virtual presence. This work challenges the constraints of mediated networks and highlights the potential of decentralized architectures to redefine collaboration and interaction in digital spaces. © 2025 Copyright is held by the owner/author(s).

Abstract
This research addresses the challenge of generating synthetic data that resembles real-world data while preserving privacy. With privacy laws protecting sensitive information such as healthcare data, accessing sufficient training data becomes difficult, resulting in an increased difficulty in training Machine Learning models and in overall worst models. Recently, there has been an increased interest in the usage of Generative Adversarial Networks (GAN) to generate synthetic data since they enable researchers to generate more data to train their models. GANs, however, may not be suitable for privacy-sensitive data since they have no concern for the privacy of the generated data. We propose modifying the known Conditional Tabular GAN (CTGAN) model by incorporating a privacy-aware loss function, thus resulting in the Private CTGAN (PCTGAN) method. Several experiments were carried out using 10 public domain classification datasets and comparing PCTGAN with CTGAN and the state-of-the-art privacy-preserving model, the Differential Privacy CTGAN (DP-CTGAN). The results demonstrated that PCTGAN enables users to fine-tune the privacy fidelity trade-off by leveraging parameters, as well as that if desired, a higher level of privacy.

Abstract
The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim superior accuracy for the proposed approaches. Nevertheless, concerns are rising about the reliability and generalizability of these results due to limitations in experimental setups. This paper addresses a critical limitation: the number and representativeness of the datasets used. We investigate the impact of dataset selection bias, particularly the practice of cherry-picking datasets, on the performance evaluation of forecasting methods. Through empirical analysis with a diverse set of benchmark datasets, our findings reveal that cherry-picking datasets can significantly distort the perceived performance of methods, often exaggerating their effectiveness. Furthermore, our results demonstrate that by selectively choosing just four datasets — what most studies report — 46% of methods could be deemed best in class, and 77% could rank within the top three. Additionally, recent deep learning-based approaches show high sensitivity to dataset selection, whereas classical methods exhibit greater robustness. Finally, our results indicate that, when empirically validating forecasting algorithms on a subset of the benchmarks, increasing the number of datasets tested from 3 to 6 reduces the risk of incorrectly identifying an algorithm as the best one by approximately 40%. Our study highlights the critical need for comprehensive evaluation frameworks that more accurately reflect real-world scenarios. Adopting such frameworks will ensure the development of robust and reliable forecasting methods. Copyright © 2025, Association for the Advancement of Artificia Intelligence (www.aaai.org). All rights reserved.

Abstract
Software testing plays a fundamental role in software engineering, involving the systematic evaluation of software to identify defects, errors, and vulnerabilities from the early stages of the development process. Education in software testing is essential for students and professionals, as it promotes quality and favours the construction of reliable software solutions. However, motivating students to learn software testing may be a challenge. To overcome this, educators may incorporate some strategies into the teaching and learning process, such as real-world examples, interactive learning, and gamification. Gamification aims to make learning software testing more engaging for students by creating a more enjoyable experience. One approach that has proven effective is to use serious games. This paper presents a novel serious game to teach white-box testing test case design techniques, named GAMFLEW (GAMe For LEarning White-box testing). It describes the design, game mechanics, and its implementation. It also presents a preliminary evaluation experiment with students to assess the usability, learnability, and perceived problems, among other aspects. The results obtained are encouraging.