Unsupervised network embedding beyond homophily

in Transactions on Machine Learning Research (2022)

A multilingual dataset of COVID-19 vaccination attitudes on Twitter

in Data in Brief (2022), 44

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

in ACM Transactions on the Web (2022), 16(2), 1-29

THS-GWNN: a deep learning framework for temporal network link prediction

in Frontiers of Computer Science (2022), 16(2), 162304

Functional scenario classification for Android applications using GNNs

in Proceedings of the 13th Asia-Pacific Symposium on Internetware (2022)

Exploring Spillover Effects for COVID-19 Cascade Prediction

in Entropy (2022), 24(2),

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.

in Cells (2022), 11(16),

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for ... [more ▼]

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition. [less ▲]

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 ▲]

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 ▲]