A tourist falls ill and needs to go to the hospital in a city, she has not visited before. When she crosses the hospital entrance, a likeable robot approaches her and immediately asks about the reason for visiting the hospital. Thereafter the robot accompanies her to the reception desk where an AI receptionist avatar is waiting. She has a fever and is coughing, suspecting she has the COVID-19 virus. The AI receptionist registers her and asks some questions about the symptoms. Then the AI asks the robot to accompany the patient to the radiologist room where additional tests will be done. A medical doctor with the help of an AI diagnostic system identifies the illness and prescribes the treatment.
In this futurist example, “Collaborative AI” (CAI) appears in different moments in the hospital episode, when the robot approaches the patient; when the robot accompanies the patient to the AI receptionist avatar; when the AI avatar talking with the person refers her to a specialist medical doctor; when the robot accompanies the patient to the radiologist room; and when the medical doctor makes a diagnosis with the help of the AI diagnostic system.
This asset is the open dataset used for the experiment and for develop the proof of concept. It is an open dataset of SCADA data for Energy Management Systems.
This asset is the open dataset used for the experiment and for develop the proof of concept. It is an open dataset of RTE data for Energy grids.
XLM-RoBERTa model which was trained during experiments in order to create models capable of detecting check-worthy claims in the widest achievable range.
The dataset provides energy consumption readings from a specific device, identified by UUID. The data captures detailed information, including the exact timestamp of the reading, energy consumed, and voltage, among other parameters. All readings are taken...
The Semantic Middleware is a solution of middleware that acts as a connector mediating data exchanges between shop-floor data producers (publishers) and data consumers (subscribers), thus contributing to enhance the level of interoperability of the moder...
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content
Jupyter Notebook providing the execution of a simulation of a given Grid2Op agent using the PowSyBl backend.
A fully peer-to-peer framework to define and test graph neural network fragments for decentralized devices to classify themselves.
This metadata document provides information about the Soil Productivity Map. This map was generated following the methodology presented in Schaetzl, R. J., Krist Jr, F. J., & Miller, B. A. (2012). A taxonomically based ordinal estimate of soil productivit...
Machine learning algorithm for delineation and zoning blueberry fields using UAV multispectral images and calculated vegetation index maps. This work has been carried out in the scope of the H2020 FlexiGroBots project, which has been funded by the Euro...