0000000000080721
AUTHOR
La Cascia M.
T100: A modern classic ensemble to profile irony and stereotype spreaders
In this work we propose a novel ensemble model based on deep learning and non-deep learning classifiers. The proposed model was developed by our team for participating at the Profiling Irony and Stereotype Spreaders (ISSs) task hosted at PAN@CLEF2022. Our ensemble (named T100), include a Logistic Regressor (LR) that classifies an author as ISS or not (nISS) considering the predictions provided by a first stage of classifiers. All these classifiers are able to reach state-of-the-art results on several text classification tasks. These classifiers (namely, the voters) are a Convolutional Neural Network (CNN), a Support Vector Machine (SVM), a Decision Tree (DT) and a Naive Bayes (NB) classifie…
Dynamic Perception Map of Urban Area for Social Surveillance
We propose to develop a new social platform for surveil- lance purposes whose main goal is to reduce the citizens’ fear of crime while helping them to make sense of their fears through interactions with the other members of the social surveillance network. Dynamic perception maps will be estimated by the social surveillance system in order to measure the sense of safety of the citizens. These maps will be useful to increase citizens awareness of their surroundings, and highlight “hot area” to make more enjoyable in the urban area.