In the past decade, machine learning based decision systems have been widely used in a plethora of applications ranging from credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the application of these systems may bring myriad benefits, their use might involve some ethical and legal risks, such as codifying biases; jeopardizing transparency and privacy, reducing accountability.
La Soccer data challenge è una competizione aperta a tutti gli appassionati di dati e calcio, che si svolgerà a Pisa il 12 e 13 ottobre 2018.
Per 30 ore consecutive, le squadre partecipanti si sfideranno sullo sviluppo di una soluzione per l’analisi di partite di calcio.
A loro disposizione avranno i dati di una intera stagione di serie A: oltre 500mila record, un dataset che contiene tutti gli eventi di gioco di ogni partita, definiti in ogni possibile aspetto.
La squadra vincitrice si aggiudicherà un premio di 5.000 euro.
The rapid growth of the Internet and the Web, the speed with which global communication and trade now takes place, and the fast spreading around the world of news and information as well as epidemics, trends, financial crises and social: these are all signals that mankind has entered a new era, a new techno-social ecosystem whose inner mechanisms are different from before, and largely unveiled.
MoKMaSD 2015 aims at bringing together practitioners and researchers from academia, industry and research institutions to present research results and exchange experience, ideas, and solutions for modelling and analysing complex systems and using knowledge management and discovery methodologies in various domain areas such as social systems, ecology, biology, medicine, smart cities, governance, education and social software engineering.
In the last years we witnessed to a shift from static network analysis to a dynamic networks analysis, i.e., the study of networks whose structure change over time. As time goes by, all the perturbations which occur on the network topology due to the rise and fall of nodes and edges have repercussions on the network phenomena we are used to observe. As an example, evolution over time of social interactions in a network can play an important role in the diffusion of an infectious disease.
Aula Gerace, Department of Computer Science, Pisa, Italy
Science 2.0 refers to the rapid and systematic changes in doing Research and organising Science driven by the rapid advances in ICT and digital technologies combined with a growing demand to do Science for Society (actionable research) and in Society (co-design of knowledge). Nowadays, teams of researchers around the world can easily access a wide range of open data across disciplines and remotely process them on the Cloud, combining them with their own data to generate knowledge, develop information products for societal applications, and tackle complex integrative complex problems that could not be addressed a few years ago.
The summer school "Constraint Programming meets Data Mining" provides an intensive training opportunity to learn the essentials of recent research on constraint solving, machine learning and data mining, and the key aspects related to their integration.
Social mining has the potential to provide a privacy-respectful social microscope, or socioscope,
needed to observe the hidden mechanisms of socio-economic complexity.
There is a fertile ground for further aggregation of researchers and stakeholders, towards the
creation of a European Laboratory on Big Data Analytics and Social Mining, capable of boosting
research and innovation in the deployment of big data analytics, social mining and privacy/trust
technologies to face global challenges.