A multilingual dataset of COVID-19 vaccination attitudes on Twitter

in Data in Brief (2022), 44

Modal characterisation of simulation relations in probabilistic concurrent games

in Science of Computer Programming (2022), 215

Proceedings of the 13th Asia-Pacific Symposium on Internetware

Book published by ACM (2022)

Effective attributed network embedding with information behavior extraction

in PeerJ Computer Science (2022), 8

Provably Improving Election Verifiability in Belenios

in Electronic Voting 6th International Joint Conference, E-Vote-ID 2021 Virtual Event, October 5–8, 2021, Proceedings (2021, October)

Belenios is an online voting system that provides a strong notion of election verifiability, where no single party has to be trusted, and security holds as soon as either the voting registrar or the ... [more ▼]

Belenios is an online voting system that provides a strong notion of election verifiability, where no single party has to be trusted, and security holds as soon as either the voting registrar or the voting server is honest. It was formally proved to be secure, making the assump- tion that no further ballots are cast on the bulletin board after voters verified their ballots. In practice, however, revoting is allowed and voters can verify their ballots anytime. This gap between formal proofs and use in practice leaves open space for attacks, as has been shown recently. In this paper we make two simple additions to Belenios and we formally prove that the new version satisfies the expected verifiability properties. Our proofs are automatically performed with the Tamarin prover, under the assumption that voters are allowed to vote at most four times. [less ▲]

Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation

in Scientometrics (2021)

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a ... [more ▼]

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded. [less ▲]

CABEAN 2.0: Efficient and Efficacious Control of Asynchronous Boolean Networks

in Proceedings of the 24th International Symposium on Formal Methods (FM 2021) (2021)

Membership Inference Attacks against GANs by Leveraging Over-representation Regions

in Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security (CCS'21) (2021)

Proceedings of the AI4Health Lecture Series (2021)

Scientific Conference (2021)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [less ▲]

Hilbert Sinkhorn Divergence for Optimal Transport

in Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR'21 (2021)