Modern Machine Learning (ML) models pose challenges such as long development cycles, high costs, and difficult model selection. Understanding how dataset properties influence performance in a model-agnostic and interpretable way remains limited. This work analyzes the relationship between dataset characteristics and model behavior across diverse algorithms, introducing symbolic regression models that transparently estimate accuracy from dataset features and simple model identifiers. Results show that features such as noisiness, redundancy, and class distribution consistently affect performance. A symbolic model using only dataset features achieved modest accuracy ($R^{2}=0.361$), while including model identification improved predictions ($R^{2}=0.534$). These findings provide interpretable insights into the data-model relationship, supporting preprocessing decisions (e.g., feature selection, class balancing) and informing meta-learning strategies for algorithm recommendation.

Federated Learning (FL) offers a robust framework for training Machine Learning (ML) models across distributed devices while preserving data privacy. However, concerns remain about whether FL-trained models learn in the same way as their centralized-data counterparts. This paper examines the impact of FL on model behavior, utilizing Explainable AI (XAI) techniques and correlation metrics to compare feature importance. By applying two XAI metrics across four public datasets and evaluating multiple FL strategies, we assess how closely federated models align with traditionally trained ones. Although models often achieve similar classification performance (Matthews's Correlation Coefficient (MCC) > 0.9), we observe significant differences in attribution patterns, especially in async approaches, with correlation scores below 0.7 Spearman's Rank Correlation Coefficient (SRCC) on several occasions and below 0.4 SRCC for the worst scenario. These findings suggest that identical performance does not always imply equivalent learning. Furthermore, since these findings were obtained under Independent and Identically Distributed (IID) settings, it is expected that under non-IID settings, the results might be even worse, underscoring the need for explainability-driven validation tools to ensure the reliability, fairness, and trustworthiness of FL models in practice.

The rapid growth of mobile network traffic and the emergence of complex applications, such as self-driving cars and augmented reality, demand ultra-low latency, high throughput, and massive device connectivity, which traditional network design approaches struggle to meet. These issues were initially addressed in 5th Generation (5G) and Beyond-5G (B5G) networks, where Artificial Intelligence (AI), particularly Deep Learning (DL), is proposed to optimize the network and to meet these demanding requirements. However, the resource constraints and time limitations inherent in telecommunication networks raise questions about the practicality of deploying large Deep Neural Networks (DNNs) in these contexts. This paper analyzes the costs of implementing DNNs by comparing them with shallow ML models across multiple datasets and evaluating factors such as execution time and model interpretability. Our findings demonstrate that shallow ML models offer comparable performance to DNNs, with significantly reduced training and inference times, achieving up to 90% acceleration. Moreover, shallow models are more interpretable, as explainability metrics struggle to agree on feature importance values even for high-performing DNNs.

Artificial intelligence (AI) is a fundamental pillar in developing next-generation networks. Federated learning (FL) emerges as a promising solution to address data privacy concerns during AI model training within the network. However, training AI models on user equipment raises challenges regarding battery consumption, unreliable connections, and communication overhead. This paper proposes Zenoh, a data-centric communication middleware, as an alternative to the traditional Message Passing Interface (MPI) for FL applications. Zenoh’s decentralized nature and low communication overhead make it suitable for resource-constrained devices and unreliable network connections. The paper compares Zenoh and MPI in a realistic FL scenario, demonstrating Zenoh’s potential to outperform MPI in terms of flexibility, communication efficiency, and system complexity.

The growth of Machine Learning (ML) has led to the commercialization of applications like data analytics, autonomous systems, and security diagnostics. These models are becoming widespread across various domains. However, security and privacy issues accompany this growth. Although actively researched, there's fragmentation in analyzing and defining ML models' resilience. This work examines the resilience of shallow ML models against typical data poisoning attacks. Our study assessed their strengths in adversarial scenarios using the MNIST dataset in a CAPTCHA context. Results show notable resilience, with accuracy and generalization maintained despite malicious inputs, offering insights to strengthen future ML systems. Understanding the mechanisms enabling this resilience can aid in fortifying the security of future ML systems.

The use of mobile technologies is important for Supply Chain Management (SCM) because these technologies allow for a ubiquitous flow of information, higher agility and risk reduction in supply chains. In food markets, these issues are particularly relevant due to food safety risks. The main goal of this paper is to analyze the use of mobile technology for management and risk control in the Brazilian beef supply chain, since Brazil is one of the main producers and beef exporters in the world. The research method was a single case study. Results show the actual level of mobile technology use; drivers and barriers to mobile technology adoption and how mobile technology is applied to beef traceability and risk reduction along the chain. The authors propose a framework that links the issues of mobile technology use for SCM and risk control, considering the context of a developing country such as Brazil.