Cybersecurity in robotics stands out as a key aspect within Regulation (EU) 2024/1689, also known as the Artificial Intelligence Act, which establishes specific guidelines for intelligent and automated systems. A fundamental distinction in this regulatory framework is the difference between robots with Artificial Intelligence (AI) and those that operate through automation systems without AI, since the former are subject to stricter security requirements due to their learning and autonomy capabilities. This work analyzes cybersecurity tools applicable to advanced robotic systems, with special emphasis on the protection of knowledge bases in cognitive architectures. Furthermore, a list of basic tools is proposed to guarantee the security, integrity, and resilience of these systems, and a practical case is presented, focused on the analysis of robot knowledge management, where ten evaluation criteria are defined to ensure compliance with the regulation and reduce risks in human-robot interaction (HRI) environments.

Legal texts, characterized by complex and specialized terminology, present a significant challenge for Language Models. Adding an underrepresented language, such as Spanish, to the mix makes it even more challenging. While pre-trained models like XLM-RoBERTa have shown capabilities in handling multilingual corpora, their performance on domain specific documents remains underexplored. This paper presents the development and evaluation of MEL, a legal language model based on XLM-RoBERTa-large, fine-tuned on legal documents such as BOE (Bolet\'in Oficial del Estado, the Spanish oficial report of laws) and congress texts. We detail the data collection, processing, training, and evaluation processes. Evaluation benchmarks show a significant improvement over baseline models in understanding the legal Spanish language. We also present case studies demonstrating the model's application to new legal texts, highlighting its potential to perform top results over different NLP tasks.

We are 600 million Spanish speakers. We launched the #Somos600M Project because the diversity of the languages from LATAM, the Caribbean and Spain needs to be represented in Artificial Intelligence (AI) systems. Despite being the 7.5% of the world population, there is no open dataset to instruction-tune large language models (LLMs), nor a leaderboard to evaluate and compare them. In this paper, we present how we have created as an international open-source community the first versions of the instruction and evaluation datasets, indispensable resources for the advancement of Natural Language Processing (NLP) in our languages.

By introducing elements of information mining to tax analysis, by means of data mining software and advanced computational concepts of artificial intelligence, the problem of tax evader's crime against public property has been addressed. Through an empirical approach from a hypothetical case of use, induction algorithms, neural networks and bayesian networks are applied to determine the feasibility of its heuristic application by the tax public administrator. Different strategies are explored to facilitate the work of local and regional federal tax inspectors, considering their limited computational capabilities, but equally effective for those social scientist committed to handcrafting tax research. ----- Apresentando a introdu\c{c}\~ao de elementos de explora\c{c}\~ao de informa\c{c}\~oes para an\'alise fiscal, por meio de software de minera\c{c}\~ao de dados e conceitos avan\c{c}ados computacionais de intelig\^encia artificial, foi abordado o problema do crime de sonegador fiscal contra o patrim\^onio p\'ublico. Atrav\'es de uma abordagem emp\'irica a partir de um caso hipot\'etico de uso, os algoritmos de indu\c{c}\~ao, redes neurais e redes bayesianas s\~ao aplicados para determinar a viabilidade de sua aplica\c{c}\~ao heur\'istica pelo administrador p\'ublico tribut\'ario. Diferentes estrat\'egias s\~ao exploradas para facilitar o trabalho dos inspectores tribut\'arios federais locais e regionais, tendo em conta as suas capacidades computacionais limitados, mas igualmente eficaz para aqueles cientista social comprometido com a investiga\c{c}\~ao fiscal.