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Soccer & data cup - Expo Dubai 2020

Soccer & Data Cup at Expo Dubai 2020 has been a 3-days international hybrid marathon of Sport Analytics combining fundamental techniques of data analysis and Artificial Intelligence. The event covers the subject area of Data Science, and aims to raise young people's awareness to the new frontiers of the complex analysis of digital data in the sport area.

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

With the technological advent of the last few decades, it is possible to record a huge quantity of data from athletes. Wearable devices, video analysis systems, tracking systems, and questionnaires are only a few examples of the devices used currently to record data in sports. These data can be used for scouting, performance analysis, and tactical analysis, but an increased interest is in assessing the risk of injuries.

Healthy Twitter discussions? Time will tell

As the volume of online content and discussions grows, the amount of misinformation grows with it. The most extreme type of misinformation (content created with malicious intent), which includes fabricated or manipulated data, can be automatically identified in certain domains (e.g., bot detection, image deep fake analysis) and is the target of extensive research.

Ask “Who”, Not “What”: Bitcoin Volatility Forecasting with Twitter Data

Cryptocurrency market, after its surge in 2009, gained immense popularity among not only small-scale investors, but also large hedge funds. The ever increasing popularity of cryptocurrencies attracted professional investors who started constructing portfolios using cryptocurrencies, however, the vast majority of the market share still belongs to individuals.

Ordinal Quantification Methods Inspired by Astro-particle Physics

A TNA experience on ordinal quantification

Trans-national access (TNA) @ ISTI-CNR, Pisa, Italy

Generating Synthetic Mobility Networks with Generative Adversarial Networks

Look at these people. They look gorgeous, don’t they? Well, too bad they do not exist. These images are completely synthetic. How is it possible, by the way? Well, thanks to a Deep Learning architecture called Generative Adversarial Networks (GANs) [1]. Long story short, these architectures are able to capture the probability distribution of a training set (of images, in this case) and to replicate for creating a new sample with the same probability distribution (therefore realistic) but not belonging to the training set. In a nutshell this architecture is made up of two building blocks.

Factors Affecting Performance and Recovery in Team Sports: A Multidimensional Perspective

In a team sports’ season, players likely experience congested fixture schedules, characterized by multiple games within a brief time period. To face such a dense modern competitive schedule, players often undergo an increasing number of training sessions. The combination of multiple games and numerous training sessions within a short time period could induce marked psychophysiological stress on the athletes, making the recovery between the events a crucial effective element of the whole training process.

The world news tells us about peace

Previous research (recently published in EPJ Data Science) demonstrates that the official Global Peace Index (GPI) [1] can be captured at a higher frequency through GDELT [2], a digital news database. Undoubtedly, digital data harnessed with AI tools contribute to advances in well-being studies, including peace.

The world news tells us about peace

Previous research (recently published in EPJ Data Science) demonstrates that the official Global Peace Index (GPI) [1] can be captured at a higher frequency through GDELT [2], a digital news database. Undoubtedly, digital data harnessed with AI tools contribute to advances in well-being studies, including peace.