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Electronic Government and the Information Systems Perspective, 1st ed. 2021 10th International Conference, EGOVIS 2021, Virtual Event, September 27-30, 2021, Proceedings Information Systems and Applications, incl. Internet/Web, and HCI Series

Langue : Anglais

Coordonnateurs : Kö Andrea, Francesconi Enrico, Kotsis Gabriele, Tjoa A Min, Khalil Ismail

Couverture de l’ouvrage Electronic Government and the Information Systems Perspective
This volume LNCS 12926 constitutes the papers of the 10th International Conference on Electronic Government and the Information Systems Perspective, EGOVIS 2021, held in September 2021 as part of the DEXA 2021 conference. The event was held virtually due to COVID-19 pandemic.
The 13 full papers presented were carefully reviewed and selected from 21 submissions and focus on information systems and ICT aspects of e-government. The papers are organized in 3 topical sections: e-government theoretical background and cases; identity management and legal issues; artificial intelligence and machine learning in e-government context.
Government as a Platform? Constitutive Elements of Public Service Platforms.- A Solution to Support Integrity in the Lawful Interception Ecosystem.- Can Tech4Good Prevent Domestic Violence and Femicides? An Intelligent System Design Proposal for Restraining Order Implementations.- Toward Smart City Architecture Principles: A Cornerstone in the Case of Smart City Duisburg.- Analysing the Surface Urban Heat Island effect with Copernicus data.- Vulnerability of State-Provided Electronic Identification: The Case of ROCA in Estonia.- Transparency by default: GDPR Patterns for Agile Development.- Anonymization between minimization and erasure: The perspectives of French and Italian data protection authorities.- A Legal Framework for Digital Transformation: A Proposal based on a Comparative Case Study.- Classification and association rules in Brazilian Supreme Court judgments on pre-trial detention.- Social Security Reform in Brazil: a Twitter sentiment analysis.- Demystifying Tax Evasion Using Variational Graph Autoencoders.- Using Machine Learning for Risk Classification in Brazilian Federal Voluntary Transfers.

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