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Found 10 papers, 1 papers with code
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
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.
SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults
In this paper, SER_AMPEL, a multi-source dataset for speech emotion recognition (SER) is presented.
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset
The aim of this work is to define a speech emotion recognition (SER) model able to recognize positive, neutral and negative emotions in natural conversations of Italian elderly people.
The evolution of AI approaches for motor imagery EEG-based BCIs
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.
Inner speech recognition through electroencephalographic signals
This work focuses on inner speech recognition starting from EEG signals.
Personalized PPG Normalization based on Subject Heartbeat in Resting State Condition
The effectiveness of the proposed normalization is evaluated in comparison with other normalization procedures in a binary classification task, where cognitive load and relaxing state are considered.
Novel EEG-based BCIs for Elderly Rehabilitation Enhancement
The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life.
Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content
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
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.
