Abstract
Traditional recurrent neural networks operate as passive observers of data, unable to modify the information they learn from despite errors that may arise from suboptimal input representations. We introduce Read & Write LSTM (read-write LSTM), a new variant within the family of read & write machine learning (RW-ML) architectures that address this fundamental limitation by integrating input modification directly into the backpropagation process. Read-write LSTM establishes a dynamic feedback loop where input representations evolve alongside model weights through gradient transformation mechanisms. Our approach introduces a principled gradient scaling framework with an adaptive correction rate that carefully controls the extent of data modification, preserving data integrity while enhancing representational power. We comprehensively evaluate read-write LSTM against traditional LSTMs and state-of-the-art transformer models on the M4 competition and Numenta Anomaly Benchmark datasets, demonstrating significant improvements in forecasting accuracy. Notably, read-write LSTM consistently out-performs standard LSTM models in over 70% of time series with complex patterns and achieves superior performance on 55% of anomaly-rich datasets. Through extensive experimentation and analysis, we establish both the theoretical foundations and practical benefits of integrating data modification with neural computation, paving the way for a new generation of adaptive learning systems that actively reshape their inputs rather than merely adapting to them.

Abstract
Data-driven decision models based on Artificial Intelligence (AI) are increasingly adopted across domains. However, these models are susceptible to bias that can result in unfair or discriminatory outcomes. Recent research has explored causal discovery methods as a promising way to understand and improve fairness in decision-making systems. In this work, we investigate how different conditional independence tests used in constraint-based causal discovery algorithms, specifically the PC algorithm, affect fairness and performance. We perform an empirical evaluation on several datasets, including Portuguese public contracts, COMPAS, and the German Credit dataset. Using seven conditional independence tests, we assess model behavior under fairness (demographic parity, accuracy parity, equalized odds and predictive rate parity) and performance (accuracy, F1-score, AUC) metrics. Our findings reveal that some tests, due to their statistical properties, fail to expose unfairness detectable via causal structures, even when performance metrics appear acceptable. Furthermore, we highlight significant differences in computational efficiency among the tests, with x2-Adf, sp-mi, and sp-x2 being the least efficient. This study underscores the need for careful selection of conditional independence tests in causal discovery to ensure both fairness and reliability in data-driven decision systems.

Abstract
Hyper-parameter optimization in machine learning models is critical for achieving peak performance. Over the past few years, numerous researchers have worked on this optimization challenge. They primarily focused on batch learning tasks where data distributions remain relatively unchanged. However, addressing the properties of data streams poses a substantial challenge. With the rapid evolution of technology, the demand for sophisticated techniques to handle dynamic data streams is becoming increasingly urgent. This paper introduces a novel adaptation of the Fish School Search (FSS) Algorithm for online hyper-parameter optimization, the FSS-SPT. The FSS-SPT is a solution designed explicitly for the dynamic context of data streams. One fundamental property of the FSS-SPT is that it can change between exploration and exploitation modes to cope with the concept drift and converge to reasonable solutions. Our experiments on different datasets provide compelling evidence of the superior performance of our proposed methodology, the FSS-SPT. It outperformed existing algorithms in two machine learning tasks, demonstrating its potential for practical application.

Abstract
Hierarchical document classification is essential for structuring large-scale textual corpora in domains such as digital libraries and academic repositories. While recent advances in large language models (LLMs) have opened new possibilities for text classification, their applicability to hierarchical settings under real-world constraints remains underexplored. This study investigates both generative and discriminative transformer-based models, evaluating their effectiveness across multiple inference strategies: zero-shot baseline, local fine-tuning, and a global approach using category-specific models. Experiments on two real-world hierarchical datasets provide a comprehensive comparison of classification accuracy, F1-macro scores, and inference times. The results highlight that, although generative LLMs can deliver competitive (yet variable) performance at higher levels of the hierarchy, their high inference costs hinder their use in time-sensitive applications. In contrast, fine-tuned discriminative models-particularly BERT-based architectures-consistently offer a more favorable trade-off between performance and efficiency.

Abstract
Traditional machine learning techniques assume that data is drawn from a stationary source. This assumption is challenged in contexts with data streams for presenting constant and potentially infinite sequences whose distribution is prone to change over time. Based on these settings, detecting changes (a.k.a. concept drifts) is necessary to keep learning models up-to-date. Although state-of-the-art detection methods were designed to monitor the loss of predictive models, such monitoring falls short in many real-world scenarios where the true labels are not readily available. Therefore, there is increasing attention to unsupervised concept drift detection methods as approached in this paper. In this work, we present an unsupervised and interpretable method based on Radial Basis Function Networks (RBFN) and Markov Chains (MC), referred to as RMIDDM (Radial Markov Interpretable Drift Detection Method). In our method, RBF performs, in the intermediate layer, an activation process that implicitly produces groups of observations collected over time. Simultaneously, MC models the transitions between groups to support the detection of concept drifts, which happens when the active group changes and its probability exceeds a given threshold. A set of experiments with synthetic datasets and comparisons with state-of-the-art algorithms demonstrated that the proposed method can detect drifts at runtime in an efficient, interpretable, and independent way of labels, presenting competitive results and behavior. Additionally, to show its applicability in a real-world scenario, we analyzed new COVID-19 cases, deaths, and vaccinations to identify new waves as concept drifts and generate Markov models that allow understanding of their interaction.

Abstract