
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

An Open Dataset of Synthetic Speech
This paper introduces a multilingual, multispeaker dataset composed of synthetic and natural speech, designed to foster research and benchmarking in synthetic speech detection.

Decentralized-gnn
A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes.

Word-Class Embeddings for Multiclass Text Classification
Code for Word-Class Embeddings (WCEs), a form of supervised embeddings especially suited for multiclass text classification.

Neighborhood Contrastive Learning for Novel Class Discovery
A holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data.

PandA: Unsupervised learning of parts and appearances in the feature maps of GANs
We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.