1.5k citations
- Internet Interdisciplinary InstituteES17 papers
- Universitat Rovira i VirgiliES10 papers
- Universitat Autònoma de BarcelonaES8 papers
- Universitat Politècnica de CatalunyaES8 papers
- Universitat Pompeu FabraES8 papers
- Universitat de BarcelonaES7 papers
- University of ZurichCH6 papers
- Worldsensing (Spain)ES6 papers
- Universidad de ZaragozaES5 papers
- Barcelona Supercomputing CenterES4 papers
- Institució Catalana de Recerca i Estudis AvançatsES4 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
7 papers · 1 filter
Recurrent Few-Shot model for Document Verification
Maxime Talarmain, Carlos Boned, Sanket Biswas +1
General-purpose ID, or travel, document image- and video-based verification systems have yet to achieve good enough performance to be considered a solved problem. There are several…
Language-Agnostic Modeling of Source Reliability on Wikipedia
Jacopo D'Ignazi, Andreas Kaltenbrunner, Yelena Mejova +4
Over the last few years, verifying the credibility of information sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designe…
Security, Trust and Privacy challenges in AI-driven 6G Networks
Helena Rifa-Pous, Victor Garcia-Font, Carlos Nunez-Gomez +1
The advent of 6G networks promises unprecedented advancements in wireless communication, offering wider bandwidth and lower latency compared to its predecessors. This article explo…
Analyzing News Engagement on Facebook: Tracking Ideological Segregation and News Quality in the Facebook URL Dataset
Emma Fraxanet, Andreas Kaltenbrunner, Fabrizio Germano +1
The Facebook Privacy-Protected Full URLs Dataset was released to enable independent, academic research on the impact of Facebook's platform on society while ensuring user privacy.…
LangBiTe: A Platform for Testing Bias in Large Language Models
Sergio Morales, Robert Clarisó, Jordi Cabot
The integration of Large Language Models (LLMs) into various software applications raises concerns about their potential biases. Typically, those models are trained on a vast amoun…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…