References of "Pang, Jun 50002807"
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See detailA multilingual dataset of COVID-19 vaccination attitudes on Twitter
Chen, Ninghan UL; Chen, Xihui UL; Pang, Jun UL

in Data in Brief (2022), 44

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See detailA Large-scale Empirical Analysis of Ransomware Activities in Bitcoin
Wang, Kai; Pang, Jun UL; Chen, Dingjie et al

in ACM Transactions on the Web (2022), 16(2), 71-729

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See detailModal characterisation of simulation relations in probabilistic concurrent games
Zhang, Chenyi; Pang, Jun UL

in Science of Computer Programming (2022), 215

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See detailTHS-GWNN: a deep learning framework for temporal network link prediction
Mo, Xian; Pang, Jun UL; Liu, Zhiming

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

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See detailProceedings of the 13th Asia-Pacific Symposium on Internetware
Mei, Hong; Lv, Jian; Jin, Zhi et al

Book published by ACM (2022)

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See detailExploring Spillover Effects for COVID-19 Cascade Prediction
Chen, Ninghan; Chen, Xihui UL; Zhong, Zhiqiang UL et al

in Entropy (2022), 24(2),

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See detailEffective attributed network embedding with information behavior extraction
Hu, Ganglin; Pang, Jun UL; Mo, Xian

in PeerJ Computer Science (2022), 8

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See detailFunctional scenario classification for Android applications using GNNs
Li, Guiyin; Zhu, Fengyi; Pang, Jun UL et al

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

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See detailProvably Improving Election Verifiability in Belenios
Baloglu, Sevdenur UL; Bursuc, Sergiu UL; Mauw, Sjouke UL et al

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 ▲]

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See detailElection Verifiability Revisited: Automated Security Proofs and Attacks on Helios and Belenios
Baloglu, Sevdenur UL; Bursuc, Sergiu UL; Mauw, Sjouke UL et al

in 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 ▲]

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See detailSemantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
Kozlowski, Diego UL; Dusdal, Jennifer UL; Pang, Jun UL et al

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 ▲]

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See detailPreface of the special issue ‘Logic, argumentation and AI’ in JLC
Dong, Huimin; Pang, Jun UL; Wang, Yi

in Journal of Logic and Computation (2021), 31(8), 1901-1902

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See detailCABEAN 2.0: Efficient and Efficacious Control of Asynchronous Boolean Networks
Su, Cui; Pang, Jun UL

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

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See detailTemporal Networks Based Industry Identification for Bitcoin Users
Han, Weili; Chen, Dingjie; Pang, Jun UL et al

in Proceedings of 16th International Conference on Wireless Algorithms, Systems, and Applications (WASA'21) (2021)

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See detailMembership Inference Attacks against GANs by Leveraging Over-representation Regions
Hu, Hailong UL; Pang, Jun UL

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

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See detailTowards optimal decomposition of Boolean networks
Su, Cui; Pang, Jun UL; Paul, Soumya

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021), 18(6), 2167-2176

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See detailProceedings of the AI4Health Lecture Series (2021)
Schommer, Christoph UL; Sauter, Thomas UL; Pang, Jun UL et al

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 ▲]

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See detailStealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks
Hu, Hailong UL; Pang, Jun UL

in Proceedings of the 37th Annual Computer Security Applications Conference (ACSAC'21) (2021)

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See detailHilbert Sinkhorn Divergence for Optimal Transport
Li, Qian; Wang, Zhichao; Li, Gang et al

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

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See detailCABEAN: a software for the control of asynchronous Boolean networks
Su, Cui; Pang, Jun UL

in Bioinformatics (2021), 36(6), 879-881

Detailed reference viewed: 86 (3 UL)