
Holistic framework for AI in critical network infrastructures
This document establishes the main foundations of the AI4REALNET project, in particular, the following key outcomes: - The formal specification of domain-specific use cases (UCs), replicating real-world operating scenarios involving human operator

Position paper on AI for the operation of critical energy and mobility network infrastructures
This position paper outlines AI4REALNET’s approach to applying AI in network infrastructure operations, translating application needs into algorithmic proposals for effective human-AI collaboration in decision-making processes.

Non-Markov Decision Processes and Reinforcement Learning
We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.

Data Storytelling and Big Data value chain in Natural Disaster Management
This lecture explores the concepts of Data Storytelling and the Big Data value chain in the context of Natural Disaster Management.

Augmentation-free unsupervised approach for point clouds
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.

ToDY: Time of Day/Year: Dataset for visual time of day and season classification
The dataset provides training and valiation data for classifying images by time of day and season (time of year). The images are taken from the Skyfinder dataset, containing webcam images along with timestamps and geolocation.