Gianni Barlacchi

TL;DR: In this paper, the authors describe the richest open multi-source dataset ever released on two geographical areas composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino, which is an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.

TL;DR: The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino and makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems.

TL;DR: In this paper, a Recurrent Neural Network (RNN) was used to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase.

TL;DR: Scikit-mobility is a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits, and is efficient and easy to use as it extends pandas, a popular Python library for data analysis.

TL;DR: E ERNESTA (Enhanced Readability through a Novel Event-based Simplification Tool), the first sentence simplification system for Italian, specifically developed to improve the comprehension of factual events in stories for children with low reading skills, achieves promising results.