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See detailDeepCity: A Feature Learning Framework for Mining Location Check-Ins
Pang, Jun UL; Zhang, Yang

in Proceedings of the 11th International Conference on Web and Social Media (ICWSM'17) (2017)

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See detailQuantifying location sociality
Pang, Jun UL; Zhang, Yang

in Proc. 28th ACM Conference on Hypertext and Social Media - HT'17 (2017)

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See detailFormal modelling and analysis of receipt-free auction protocols in applied pi
Dong, Naipeng; Jonker, Hugo; Pang, Jun UL

in Computers & Security (2017), 65

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See detailShould We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Wang, Jingyi; Sun, Jun; Yuan, Qixia UL et al

in Proceedings of 20th International Conference on Fundamental Approaches to Software Engineering (2017)

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See detailDoes #like4like indeed provoke more likes?
zhang, Yang; Ni, Minyue; Han, Weili et al

in Proceedings of the 16th IEEE/WIC/ACM International Conference on Web Intelligence (WI'17) (2017)

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See detailA new decomposition method for attractor detection in large synchronous Boolean networks
Mizera, Andrzej; Pang, Jun UL; Qu, Hongyang et al

in Proceedings of the 3rd International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (2017)

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See detailwalk2friends: Inferring Social Links from Mobility Profiles
Backes, Michael; Humbert, Mathias; Pang, Jun UL et al

in Proceedings of the 24th ACM International Conference on Computer and Communications Security (2017)

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See detailSemantic annotation for places in LBSN through graph embedding
Wang, Yan; Qin, Zongxu; Pang, Jun UL et al

in Proceedings of the 26th ACM International Conference on Information and Knowledge Management - CIKM'17 (2017)

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See detailA verification framework for stateful security protocols
Li, Li; Dong, Naipeng; Pang, Jun UL et al

in Proceedings of the 19th International Conference on Formal Engineering Methods (2017)

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See detailParallel Approximate Steady-state Analysis of Large Probabilistic Boolean Networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of the 31st ACM Symposium on Applied Computing (2016, April)

Probabilistic Boolean networks (PBNs) is a widely used computational framework for modelling biological systems. The steady-state dynamics of PBNs is of special interest in the analysis of biological ... [more ▼]

Probabilistic Boolean networks (PBNs) is a widely used computational framework for modelling biological systems. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. However, obtaining the steady-state distributions for such systems poses a significant challenge due to the state space explosion problem which arises in the case of large PBNs. The only viable way is to use statistical methods. In the literature, the two-state Markov chain approach and the Skart method have been proposed for the analysis of large PBNs. However, the sample size required by both methods is often huge in the case of large PBNs and generating them is expensive in terms of computation time. Parallelising the sample generation is an ideal way to solve this issue. In this paper, we consider combining the Gelman & Rubin method with either the two-state Markov chain approach or the Skart method for parallelisation. The first method can be used to run multiple independent Markov chains in parallel and to control their convergence to the steady-state while the other two methods can be used to determine the sample size required for computing the steady-state probability of states of interest. Experimental results show that our proposed combinations can reduce time cost of computing stead-state probabilities of large PBNs significantly. [less ▲]

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See detailSelected and extended papers from ACM SVT 2014
Pang, Jun UL; Stoelinga, Marielle

in Science of Computer Programming (2016)

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See detailASSA-PBN 2.0: A software tool for probabilistic Boolean networks.
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016)

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See detailFast simulation of probabilistic Boolean networks.
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of 14th International Conference on Computational Methods in Systems Biology (2016)

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See detailImproving BDD-based attractor detection for synchronous Boolean networks.
Yuan, Qixia UL; Qu, Hongyang; Pang, Jun UL et al

in SCIENCE CHINA Information Sciences (2016), 59(8), 0801011-08010116

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See detailOn impact of weather on human mobility in cities
Pang, Jun UL; Zablotskaia, Polina; Zhang, Yang

in Proceedings of the 17th International Conference on Web Information System Engineering (2016)

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See detailAn Empirical Study on User Access Control in Online Social Networks
Ni, Minyue; Zhang, Yang UL; Han, Weili et al

in Proceedings of the 21st ACM Symposium on Access Control Models and Technologies (SACMAT'16) (2016)

Detailed reference viewed: 28 (2 UL)
See detailEditorial (ICFEM 14 special issue, part II)
Merz, Stephan; Pang, Jun UL; Dong, Jin Song

in Formal Aspects of Computing (2016), 28(5), 723-724

Detailed reference viewed: 22 (3 UL)
See detailEditorial (ICFEM 14 special issue, part I)
Merz, Stephan; Pang, Jun UL; Dong, Jin Song

in Formal Aspects of Computing (2016), 28(3), 343-344

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See detailModeling city locations as complex networks: An initial study
Zhou, Lu UL; Zhang, Yang; Pang, Jun UL et al

in Proceedings of the 5th International Workshop on Complex Networks and their Applications (2016)

Detailed reference viewed: 74 (16 UL)