Search Results for author: Luca Pappalardo

Found 18 papers, 11 papers with code

Trajectory Test-Train Overlap in Next-Location Prediction Datasets

1 code implementation7 Mar 2022 Massimiliano Luca, Luca Pappalardo, Bruno Lepri, Gianni Barlacchi

Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading.

Marketing

Generating Synthetic Mobility Networks with Generative Adversarial Networks

1 code implementation22 Feb 2022 Giovanni Mauro, Massimiliano Luca, Antonio Longa, Bruno Lepri, Luca Pappalardo

We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks.

Data Augmentation

Coach2vec: autoencoding the playing style of soccer coaches

no code implementations29 Jun 2021 Paolo Cintia, Luca Pappalardo

Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics.

Sports Analytics

Understanding peacefulness through the world news

1 code implementation1 Jun 2021 Vasiliki Voukelatou, Ioanna Miliou, Fosca Giannotti, Luca Pappalardo

Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers.

BIG-bench Machine Learning

An interactive dashboard for searching and comparing soccer performance scores

no code implementations16 Apr 2021 Paolo Cintia, Giovanni Mauro, Luca Pappalardo, Paolo Ferragina

The performance of soccer players is one of most discussed aspects by many actors in the soccer industry: from supporters to journalists, from coaches to talent scouts.

Explaining the difference between men's and women's football

no code implementations5 Jan 2021 Luca Pappalardo, Alessio Rossi, Giuseppe Pontillo, Michela Natilli, Paolo Cintia

Women's football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men's football.

Applications

A Survey on Deep Learning for Human Mobility

1 code implementation4 Dec 2020 Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more.

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

1 code implementation1 Dec 2020 Filippo Simini, Gianni Barlacchi, Massimiliano Luca, Luca Pappalardo

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment.

An individual-level ground truth dataset for home location detection

1 code implementation17 Oct 2020 Luca Pappalardo, Leo Ferres, Manuel Sacasa, Ciro Cattuto, Loreto Bravo

Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data.

Computers and Society Physics and Society

Automatic Pass Annotation from Soccer VideoStreams Based on Object Detection and LSTM

1 code implementation13 Jul 2020 Danilo Sorano, Fabio Carrara, Paolo Cintia, Fabrizio Falchi, Luca Pappalardo

In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i. e., passes, from video streams.

object-detection Object Detection

Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data

6 code implementations8 Jul 2019 Luca Pappalardo, Gianni Barlacchi, Filippo Simini, Roberto Pellungrini

The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms.

Physics and Society

Open the Black Box Data-Driven Explanation of Black Box Decision Systems

no code implementations26 Jun 2018 Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Luca Pappalardo, Salvatore Ruggieri, Franco Turini

We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.

Decision Making

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

1 code implementation14 Feb 2018 Luca Pappalardo, Paolo Cintia, Paolo Ferragina, Emanuele Massucco, Dino Pedreschi, Fosca Giannotti

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e. g., tackles, passes, shots, etc.).

BIG-bench Machine Learning

Human Perception of Performance

no code implementations5 Dec 2017 Luca Pappalardo, Paolo Cintia, Dino Pedreschi, Fosca Giannotti, Albert-Laszlo Barabasi

Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports.

Effective injury forecasting in soccer with GPS training data and machine learning

no code implementations23 May 2017 Alessio Rossi, Luca Pappalardo, Paolo Cintia, Marcello Iaia, Javier Fernandez, Daniel Medina

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players.

BIG-bench Machine Learning

Quantifying the relation between performance and success in soccer

no code implementations2 May 2017 Luca Pappalardo, Paolo Cintia

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success.

BIG-bench Machine Learning

Data-driven generation of spatio-temporal routines in human mobility

1 code implementation16 Jul 2016 Luca Pappalardo, Filippo Simini

DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory.

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