New data and methods for migration studies: going beyond traditional data sources
The Paris School of Economics, SoBigData++ consortium, HumMingBird consortium and Institut Convergences Migrations are jointly organising a two-day workshop aimed at bringing together migration scholars from various disciplines from these institutions and beyond. The conference is devoted to investigating and showcasing new methods to study human migration based on non-traditional data sources and methods.
Privacy in statistical databases is about finding tradeoffs to the tension between the increasing societal and economical demand for accurate information and the legal and ethical obligation to protect the privacy of individuals and enterprise which are the respondents providing the statistical data. In the case of statistical databases, the motivation for respondent privacy is one of survival: data collectors cannot expect to collect accurate information from individual or corporate respondents unless these feel the privacy of their responses is guaranteed.
ITADATA2022 aims to discuss and shape the future of Big Data and Data Science in Italy and abroad, considering the multidisciplinary, complex, heterogeneous, and data-centric environment in which modern distributed systems are operating. The ability to timely manage and analyze large amounts of data, and to guarantee low-latency access to the data themselves increasingly become critical requirements and are at the heart of modern business processes.
The purpose of , is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning.
Also, this year the workshop will seek submissions addressing uncovered important issues in specific fields related to eXplainable AI (XAI), such as privacy and fairness, application in real case studies, benchmarking, explanation of decision systems based on time series and graphs which are becoming more and more important in nowadays applications.
CLEF 2022 is the 13th CLEF conference continuing the popular CLEF campaigns which have run since 2000 contributing to the systematic evaluation of information access systems, primarily through experimentation on shared tasks.
The CNRS, the Gephi Consortium and the University of Aalborg are organizing a one-week datathon (29 August to 2 September) to develop and consolidate the codebase of the Gephi software (gephi.org). During the week, we will also organize several side-events on Gephi and visual network analysis.
Pisa is the home of the first edition of the “AI & Society Summer School”, organized by the Italian National PHD program in Artificial Intelligence, PhD-AI.it. The Summer School is dedicated to the PhD students of the “AI & Society” branch of PhD-AI.it, and open to PhD students of the other branches. Five thrilling days of lectures, panel, poster sessions and proactive project work, to advance the frontier of AI research together with internationally renown scientists.
Online speech is the object of many regulation attempts, AI-based detection of problematic social media content being one of the most important but also of the most controversial. Apart from the technical difficulties to classify content automatically – well known in the data science community – there are also tensions between different conceptions of online speech.
The seminars for the PhD in Data Science cover several advanced topics in deep learning: meta learning (i.e., “learning to learn”), continual learning (i.e., learning from a continuous stream of tasks), and data engineering for deep learning (i.e., preparing data for being used in deep learning pipelines).
This event is a collaboration between the London Interdisciplinary Social Science Doctoral Training Partnership (LISS DTP), the Department of War Studies at King’s College London and the SoBigData project (plusplus.sobigdata.eu). Organised as an introductory, interactive seminar, it is designed to allow PhD students and junior researchers to explore and critically engage with innovative approaches to digital methods.
The recent advancement of deep learning has had a profound influence on many fields, including finance. Mathematical and quantitative finance provide a plentitude of challenging prediction problems that can be used as benchmarks for deep and reinforcement learning algorithms. Specifically, financial markets represent a complex interplay of agents interacting through auction-market mechanisms at different time scales and with different objectives.
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