2 citations · 6 across the 5 of their papers we have counts for
5 papers
Synthetic Data Augmentation for Multi-Task Chinese Porcelain Classification: A Stable Diffusion Approach
Ziyao Ling, Silvia Mirri, Paola Salomoni +1
The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porce…
Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties
Ziyao Ling, Giovanni Delnevo, Paola Salomoni +1
Chinese porcelain holds immense historical and cultural value, making its accurate classification essential for archaeological research and cultural heritage preservation. Traditio…
Modeling CoVid-19 Diffusion with Intelligent Computational Techniques is not Working. What Are We Doing Wrong?
Marco Roccetti, Giovanni Delnevo
As Europe is experiencing a second violent CoVid-19 storm, with the PCR-based testing system deteriorating due to the high volumes of people to be tested daily, there is a general…
The Barrier of meaning in archaeological data science
Luca Casini, Marco Roccetti, Giovanni Delnevo +2
Archaeologists, like other scientists, are experiencing a data-flood in their discipline, fueled by a surge in computing power and devices that enable the creation, collection, sto…
Categorical data as a stone guest in a data science project for predicting defective water meters
Giovanni Delnevo, Marco Roccetti, Luca Casini
After a one-year long effort of research on the field, we developed a machine learning-based classifier, tailored to predict whether a mechanical water meter would fail with passag…