no code implementations • 7 Dec 2022 • Irene Amerini, Aris Anagnostopoulos, Luca Maiano, Lorenzo Ricciardi Celsi
However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential.
no code implementations • 13 Jun 2022 • Marianna Maranghi, Aris Anagnostopoulos, Irene Cannistraci, Ioannis Chatzigiannakis, Federico Croce, Giulia Di Teodoro, Michele Gentile, Giorgio Grani, Maurizio Lenzerini, Stefano Leonardi, Andrea Mastropietro, Laura Palagi, Massimiliano Pappa, Riccardo Rosati, Riccardo Valentini, Paola Velardi
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database.
no code implementations • 8 Sep 2021 • Luca Maiano, Irene Amerini, Lorenzo Ricciardi Celsi, Aris Anagnostopoulos
To mitigate this limitation, in this work we propose two different solutions based on transfer learning and multitask learning to determine whether a video has been uploaded from or downloaded to a specific social platform through the use of shared features with images trained on the same task.
no code implementations • 17 Feb 2020 • Tesi Aliaj, Aris Anagnostopoulos, Stefano Piersanti
Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches.
no code implementations • 16 Feb 2020 • Aris Anagnostopoulos, Carlos Castillo, Adriano Fazzone, Stefano Leonardi, Evimaria Terzi
In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.
no code implementations • 20 Jun 2019 • Na Zhu, Aris Anagnostopoulos, Ioannis Chatzigiannakis
Public educational systems operate thousands of buildings with vastly different characteristics in terms of size, age, location, construction, thermal behavior and user communities.
no code implementations • 31 May 2019 • Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini, Chris Schwiegelshohn
Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention.
no code implementations • NeurIPS 2016 • Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian
In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph.