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  • JournalArticle

    The Role of the GDPR in Designing the European Strategy on Artificial Intelli...

    Starting from an analysis of the EU Reg. n. 2016/679 on General Data Protection Regulation (GDPR), the Author deals with the opportunity to translate the current strategies on...
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  • Method

    STS-EPR - Trajectory generator

    Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. Existing models focus on...
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  • Experiment

    Simulation of individual mobility using STS-EPR

    In this experiment we simulate the mobility of individuals in New York City, London, and Tokyo, using STS-EPR. For each city, we compare the synthetic trajectories generated...
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  • JournalArticle

    Big Data and Due Process Toward a Framework to Redress Predictive Privacy Harms

    The rise of “Big Data” analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create...
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  • ConferencePaper

    Private traits and attributes are predictable from digital records of human b...

    We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes...
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  • JournalArticle

    Big other Surveillance capitalism and the prospects of an information civiliz...

    This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The...
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  • TrainingMaterial

    Compressed and Learned Data Structures Seminar

    In this seminar cycle, students are guided in the direct usage of a powerful C++ library implementing many state-of-the-art compressed data structures for big data. Other than...
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    • ZIP
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    • TXT
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  • JournalArticle

    Fairer machine learning in the real world

    Mitigating discrimination without collecting sensitive data Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used...
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  • ConferencePaper

    Beyond Distributive Fairness in Algorithmic Decision Making

    Beyond Distributive Fairness in Algorithmic Decision Making Feature Selection for Procedurally Fair Learning With widespread use of machine learning methods in numerous...
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  • ConferencePaper

    Why Are Learned Indexes So Effective

    A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends...
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  • JournalArticle

    The PGM-index a fully-dynamic compressed learned index with provable worst-ca...

    We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case. In the (static)...
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  • ConferencePaper

    Measuring What Counts The case of Rumour Stance Classification

    Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies...
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  • JournalArticle

    Explaining misclassification and attacks in deep learning via random forests

    Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...
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  • BookChapter

    Information Technology Privacy and the Protection of Personal Data

    Information technology allows us to generate, store, and process huge quantities of data. Search engines, satellites, sensor networks, scientists, security agencies,...
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  • ConferencePaper

    Efficient detection of Byzantine attacks in federated learning using last lay...

    Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...
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  • ConferencePaper

    Fair detection of poisoning attacks in federated learning

    Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....
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  • JournalArticle

    Conformity a Path-Aware Homophily measure for Node-Attributed Networks

    Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...
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  • JournalArticle

    Co Bridges Pair wise Visual Connection and Comparison for Multi item Data Str...

    In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports...
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  • JournalArticle

    Automating and utilizing equal-distribution data classification

    Data classification, i.e., organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of...
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  • JournalArticle

    Attention dynamics on the Chinese social media Sina Weibo during the COVID-19...

    COVID-19 was first detected in Hubei province of China and has had severe impact on the life in the country since then. We investigate how this epidemic has influenced...
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