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Showing results 1 to 20 of 162
   A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin; Pang, Jun  ; et alin ACM Transactions on the Web (2022), 16(2), 71-729 Detailed reference viewed: 103 (0 UL)Modal characterisation of simulation relations in probabilistic concurrent games; Pang, Jun in Science of Computer Programming (2022), 215 Detailed reference viewed: 27 (1 UL)THS-GWNN: a deep learning framework for temporal network link prediction; Pang, Jun ; in Frontiers of Computer Science (2022), 16(2), 162304 Detailed reference viewed: 111 (2 UL) Exploring Spillover Effects for COVID-19 Cascade Prediction; Chen, Xihui ; Zhong, Zhiqiang et alin Entropy (2022), 24(2), Detailed reference viewed: 27 (2 UL)Provably Improving Election Verifiability in BeleniosBaloglu, Sevdenur ; Bursuc, Sergiu ; Mauw, Sjouke et alin 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 ▲] Detailed reference viewed: 108 (22 UL)Election Verifiability Revisited: Automated Security Proofs and Attacks on Helios and BeleniosBaloglu, Sevdenur ; Bursuc, Sergiu ; Mauw, Sjouke et alin IEEE 34th Computer Security Foundations Symposium, Dubrovnik 21-25 June 2021 (2021, June) Election verifiability aims to ensure that the outcome produced by electronic voting systems correctly reflects the intentions of eligible voters, even in the presence of an adversary that may corrupt ... [more ▼] Election verifiability aims to ensure that the outcome produced by electronic voting systems correctly reflects the intentions of eligible voters, even in the presence of an adversary that may corrupt various parts of the voting infrastructure. Protecting such systems from manipulation is challenging because of their distributed nature involving voters, election authorities, voting servers and voting platforms. An adversary corrupting any of these can make changes that, individually, would go unnoticed, yet in the end will affect the outcome of the election. It is, therefore, important to rigorously evaluate whether the measures prescribed by election verifiability achieve their goals. We propose a formal framework that allows such an evaluation in a systematic and automated way. We demonstrate its application to the verification of various scenarios in Helios and Belenios, two prominent internet voting systems, for which we capture features and corruption models previously outside the scope of formal verification. Relying on the Tamarin protocol prover for automation, we derive new security proofs and attacks on deployed versions of these protocols, illustrating trade-offs between usability and security. [less ▲] Detailed reference viewed: 177 (15 UL)Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article VectorisationKozlowski, Diego ; Dusdal, Jennifer ; Pang, Jun et alin 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 ▲] Detailed reference viewed: 93 (20 UL)CABEAN: a software for the control of asynchronous Boolean networks; Pang, Jun in Bioinformatics (2021), 36(6), 879-881 Detailed reference viewed: 77 (1 UL)Towards optimal decomposition of Boolean networks; Pang, Jun ; in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021), 18(6), 2167-2176 Detailed reference viewed: 30 (0 UL)Proceedings of the AI4Health Lecture Series (2021)Schommer, Christoph ; Sauter, Thomas ; Pang, Jun et alScientific 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 ▲] Detailed reference viewed: 57 (3 UL)Preface of the special issue ‘Logic, argumentation and AI’ in JLC; Pang, Jun ; in Journal of Logic and Computation (2021), 31(8), 1901-1902 Detailed reference viewed: 35 (0 UL)Membership Inference Attacks against GANs by Leveraging Over-representation RegionsHu, Hailong ; Pang, Jun in Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security (CCS'21) (2021) Detailed reference viewed: 66 (6 UL)Effective Link Prediction with Topological and Temporal Information using Wavelet Neural Network Embedding; Pang, Jun ; in Computer Journal (2021), 64(3), 325-336 Detailed reference viewed: 52 (6 UL)Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial NetworksHu, Hailong ; Pang, Jun in Proceedings of the 37th Annual Computer Security Applications Conference (ACSAC'21) (2021) Detailed reference viewed: 37 (8 UL)Hilbert Sinkhorn Divergence for Optimal Transport; ; et al in Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR'21 (2021) Detailed reference viewed: 42 (3 UL)Temporal Networks Based Industry Identification for Bitcoin Users; ; Pang, Jun et alin Proceedings of 16th International Conference on Wireless Algorithms, Systems, and Applications (WASA'21) (2021) Detailed reference viewed: 36 (2 UL)CABEAN 2.0: Efficient and Efficacious Control of Asynchronous Boolean Networks; Pang, Jun in Proceedings of the 24th International Symposium on Formal Methods (FM 2021) (2021) Detailed reference viewed: 40 (0 UL)An Exploratory Study of COVID-19 Information on Twitter in the Greater RegionChen, Ninghan ; Zhong, Zhiqiang ; Pang, Jun in Big Data and Cognitive Computing (2021), 5(1), 5 Detailed reference viewed: 98 (6 UL)Proceedings of the 6th Global Conference on Artificial Intelligence (GCAI 2020)Danoy, Grégoire ; Pang, Jun ; in 6th Global Conference on Artificial Intelligence (2020, May) Detailed reference viewed: 155 (18 UL)Proceedings of the AI4Health Lecture Series (2020)Schommer, Christoph ; Sauter, Thomas ; Pang, Jun et alScientific Conference (2020) 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 ▲] Detailed reference viewed: 45 (2 UL) |
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