Search Results for author: Aurora Saibene

Found 7 papers, 1 papers with code

SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

no code implementations SemEval (NAACL) 2022 Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen

The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI), which explores the detection of misogynous memes on the web by taking advantage of available texts and images.

A multi-artifact EEG denoising by frequency-based deep learning

no code implementations26 Oct 2023 Matteo Gabardi, Aurora Saibene, Francesca Gasparini, Daniele Rizzo, Fabio Antonio Stella

These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements.

Denoising EEG +1

The evolution of AI approaches for motor imagery EEG-based BCIs

no code implementations11 Oct 2022 Aurora Saibene, Silvia Corchs, Mirko Caglioni, Francesca Gasparini

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination.

EEG Motor Imagery

Novel EEG-based BCIs for Elderly Rehabilitation Enhancement

no code implementations8 Oct 2021 Aurora Saibene, Francesca Gasparini, Jordi Solé-Casals

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life.

EEG Motor Imagery

Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content

1 code implementation15 Jun 2021 Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Elisabetta Fersini

Two further binary labels have been collected from both the experts and the crowdsourcing platform, for memes evaluated as misogynistic, concerning aggressiveness and irony.

Genetic algorithm for feature selection of EEG heterogeneous data

no code implementations12 Mar 2021 Aurora Saibene, Francesca Gasparini

Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.

Benchmarking EEG +1

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