no code implementations • EMNLP (Louhi) 2020 • Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Robert-Jan Sips, Zoltan Szlanik, Selene Baez
The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health.
no code implementations • 30 Sep 2024 • Samuel Kernan Freire, Tianhao He, Chaofan Wang, Evangelos Niforatos, Alessandro Bozzon
In the shift towards human-centered manufacturing, our two-year longitudinal study investigates the real-world impact of deploying Cognitive Assistants (CAs) in factories.
1 code implementation • 5 Apr 2024 • Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i. e., model zoos.
no code implementations • 19 Jul 2022 • Ziyu Li, Rihan Hai, Alessandro Bozzon, Asterios Katsifodimos
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes.
1 code implementation • 9 May 2022 • Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, Alessandro Bozzon
We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
no code implementations • 5 Oct 2021 • Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Himanshu Verma, Andrea Mauri, Illah Nourbakhsh, Alessandro Bozzon
Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition.
no code implementations • 4 May 2021 • Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans
To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.
no code implementations • 21 Jan 2021 • Evgeny Krivosheev, Fabio Casati, Alessandro Bozzon
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment.
no code implementations • 11 Nov 2020 • Evgeny Krivosheev, Burcu Sayin, Alessandro Bozzon, Zoltán Szlávik
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters.
no code implementations • 27 Oct 2020 • Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints.
no code implementations • 6 Nov 2019 • Agathe Balayn, Alessandro Bozzon, Zoltan Szlavik
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed.
no code implementations • 6 Nov 2019 • Agathe Balayn, Alessandro Bozzon
Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making.
1 code implementation • 25 Jul 2018 • Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon, Marco Brambilla
In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.
Social and Information Networks Computers and Society
no code implementations • 5 Sep 2017 • Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M. J. Tax
In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.