Aitor García-Pablos

Extended Reality (XR) is evolving rapidly, offering new paradigms for humancomputer interaction. This position paper argues that integrating Large Language Models (LLMs) with XR systems represents a fundamental shift toward more intelligent, context-aware, and adaptive mixed-reality experiences. We propose a structured framework built on three key pillars: (1) Perception and Situational Awareness, (2) Knowledge Modeling and Reasoning, and (3) Visualization and Interaction. We believe leveraging LLMs within XR environments enables enhanced situational awareness, real-time knowledge retrieval, and dynamic user interaction, surpassing traditional XR capabilities. We highlight the potential of this integration in neurorehabilitation, safety training, and architectural design while underscoring ethical considerations such as privacy, transparency, and inclusivity. This vision aims to spark discussion and drive research toward more intelligent, human-centric XR systems.

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.

With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches to Aspect Based Sentiment Analysis obtain good results for the domain/language their are trained on, but having manually labelled data for training supervised systems for all domains and languages are usually very costly and time consuming. In this work we describe W2VLDA, an almost unsupervised system based on topic modelling, that combined with some other unsupervised methods and a minimal configuration, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic-devices).