
Learning Agents in AI
Learning agents are a shining example of scientific advancement in the field of artificial intelligence.


Reasoning Mechanisms in AI
Artificial Intelligence (AI) systems are designed to mimic human intelligence and decision-making processes, and reasoning is a critical component of these capabilities.


Types of Reasoning in Artificial Intelligence
In today's tech-driven world, machines are being designed to mimic human intelligence and actions.

VCIO based description of systems for AI trustworthiness characterisation



Labelling initiatives, codes of conduct and other self-regulatory mechanisms for artificial intelligence applications
AI has emerged as a critical area of interest to numerous stakeholders across the world.

STRATEGIC RESEARCH & INNOVATION ROADMAP OF TRUSTWORTHY AI
This document is the first version of the Strategic Research and Innovation Roadmap of the TAILOR project, focussed on Trustworthy Artificial Intelligence through Learning, Optimization and Reasoning.


Handbook of Trustworthy AI
The main goal of the Handbook of Trustworthy AI (HTAI) is to provide to non-experts, researchers and students, an overview of the problem related to the developing of ethical and trustworthy AI systems.

Human activity recognition in time series in PIR sensor networks with reinforcement learning
Recognizing human activity from IoT events is essential for automatic light scene setting. A challenge with IoT systems is the diversity among customers.

EdgeAI: Leveraging SNNs, Transformers, and Autoencoders for Near-Sensors Intelligence
This talk will explore the cutting-edge advancements in EdgeAI for biosignal analysis, focusing on innovative algorithms such as Spiking Neural Networks (SNNs), Transformers, and Autoencoders.

Functional and Non-Functional Requirements in Edge Ai Systems
This presentation highlights the critical elements defining edge AI solutions’ performance and effectiveness.

Pioneering the Future of Edge AI
In today’s fast-evolving technological landscape, edge AI stands at the forefront of innovation, enabling data processing at the edge, closer to the source of information.

Features Disentanglement for Explainable Convolutional Neural Networks
Explaining AI decisions in in critical sectors like healthcare, autonomous vehicles, and
environmental monitoring is crucial for ensuring safety, trust, and transparency.