0000000000272919
AUTHOR
Garlisi D.
An SVM Ensamble Approach to Detect Irony and Stereotype Spreaders on Twitter
The problem we address in this work is classifying whether a Twitter user has spread Irony and Stereotype or not. We used a text vectorization layer to generate Bag-Of-Words sequences. Then such sequences are passed to three different text classifiers (Decision Tree, Convolutional Neural Network, Naive Bayes). Our final classifier is an SVM. To test and validate our approach we used the dataset provided for the author profiling task organized by PAN@CLEF 2022. Our team (missino) submitted the predictions on the provided test set to participate at the shared task. Over several cross fold validation our approach was able to reach a maximum binary accuracy on the best validation split equal to…
Dynamic Adaptation of LoRaWan Traffic for Real-time Emergency Operations
Modern standards for IoT communications support fast deployment, large coverage in the order of kilometers, and physical layer adaptations to increase link robustness under time-varying propagation and interference conditions. A possible use of such IoT technologies is in case of emergency scenarios where first responders (FRs) arrive after a disastrous event. Indeed, an important challenge for emergency management is the need to (re)establish real-time communication capabilities and to offer integrated decision making facilities based on information gathered by FRs acting on the crisis site. In this paper, we present a system architecture based on LoRaWAN technology for connecting emergenc…