Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

Abstract
This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models' performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

Abstract

Abstract
The increasing integration of distributed energy generation into the electrical grid has led to changes in the structure and organization of energy markets over the past years. Market trading has become increasingly demanding due to the different types of production profiles. A forecast of the total production of all assets is made to bid for energy. Whenever there are differences between the forecast and the actual produced energy, a deviation occurs, which is assigned to the agent responsible for its settlement. This article proposes the application of a linear regression algorithm supported by a clustering method to forecast energy production. Based on the historical production profile of the installations in each cluster, it is possible to predict the production pattern for a period with no available data, thus standardizing this data for other assets belonging to the same cluster.

Abstract
Polarization-based fiber sensors rely on the dynamics of the Stokes vector at the output of the optical fiber to probe stimuli that induce polarization variations. However, these sensors often suffer from limitations in sensitivity, precision, and reproducibility. In this work, we address these challenges by incorporating concepts from the Mueller matrix formalism to enhance the capabilities of such sensors. Specifically, we measure the Mueller matrix in the polarization basis that describes how the polarization evolves inside the optical fiber. Leveraging this formalism, we configure the system as a precise sensor to detect deformations along the fiber. By utilizing the Fisher Information framework, we significantly improve accuracy and resolution, enabling the detection of subtle perturbations with greater precision. This study introduces a novel approach for precise polarization control and advanced fiber-based sensing applications.

Abstract