Riccardo Cantini

Riccardo Cantini

PhD student in Information and Communication Technologies

DIMES, University of Calabria

Biography

Riccardo Cantini is a PhD student in Information and Communication Technologies at the Department of Computer Science, Modeling, Electronics and Systems Engineering of the University of Calabria, since 2019. Currently he is contract professor of Distributed Systems and Cloud/Edge Computing for the Internet of Things, master’s degree course in Computer Engineering for the IoT. He is also a member of the Scalable Computing and Cloud Laboratory (SCALab) at the University of Calabria since 2018. He was co-supervisor of several Computer Engineering theses, mainly in the field of social media and big data analysis, machine learning and deep learning.

His research interests include social media and big data analysis, deep learning, natural language processing, sentiment analysis, edge/fog computing, parallel and distributed data analysis.

Interests

  • Machine and Deep Learning
  • Social Big Data Analysis
  • Parallel and Distributed Data Analysis

Education

  • M.Sc. in Computer Engineering, 2019

    University of Calabria

  • B.Sc. in Computer Engineering, 2016

    University of Calabria

Recent Posts

Let's play with MNIST! Generate digits with Convolutional Variational Autoencoders

This post is dedicated to the development of a Flask web application capable of drawing digits through the use of a generative model. This model is obtained by training a convolutional variational autoencoder on the MNIST dataset of handwritten digits using Keras+Tensorflow.
Let's play with MNIST! Generate digits with Convolutional Variational Autoencoders

Personality detection using BERT

In this post I show how to leverage BERT, a transformer-based language representation model, in order to identify the personality type of users based on their writing style and the content of their posts, according to the Myers-Briggs indicator (MBTI).
Personality detection using BERT

Play with BERT! Text classification using Huggingface and Tensorflow

In what follows, I’ll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (negative or positive) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as toxic, severe toxic, obscene, threat, insult and identity hate.
Play with BERT! Text classification using Huggingface and Tensorflow

Recent Publications

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A Weighted Artificial Bee Colony Algorithm for Influence Maximization

Social media platforms are increasingly used to convey advertising campaigns for products or services. A key issue is to identify an …
A Weighted Artificial Bee Colony Algorithm for Influence Maximization

A visual tool for reducing returns in e-commerce platforms

Nowadays, the number of people who prefer to make online purchases on e-commerce platforms is constantly increasing. Online shopping …
A visual tool for reducing returns in e-commerce platforms

Learning sentence-to-hashtags semantic mapping for hashtag recommendation on microblogs

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and …
Learning sentence-to-hashtags semantic mapping for hashtag recommendation on microblogs

Recent & Upcoming Talks

RTSI 2021

6th online Forum on Research and Technologies for Society and Industry Innovation for a smart world - RTSI 2021
RTSI 2021

Contact

  • riccardo.cantini@unical.it
  • Cubo 41C, 5th floor, via P. Bucci, Rende (CS), Calabria 87036
  • Office hours: send an email for booking an online meeting on Microsoft Teams