312 citations · 317 across the 5 of their papers we have counts for
5 papers
QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption
Mattia Sabella, Alberto Archetti, Pietro Pinoli +2
Tabular machine learning systems are frequently trained on data affected by non-uniform corruption, including noisy measurements, missing entries, and feature-specific biases. In p…
Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?
Matteo Spreafico, Ludovica Tassini, Camilla Sancricca +1
Large language models have recently demonstrated their exceptional capabilities in supporting and automating various tasks. Among the tasks worth exploring for testing large langua…
Analyzing social media with crowdsourcing in Crowd4SDG
Carlo Bono, Mehmet Oğuz Mülâyim, Cinzia Cappiello +6
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being post…
Knowledge-driven Data Ecosystems Towards Data Transparency
Sandra Geisler, Maria-Esther Vidal, Cinzia Cappiello +9
A Data Ecosystem offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues amo…
Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions
Florian Daniel, Pavel Kucherbaev, Cinzia Cappiello +2
Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourci…