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1. Learning a Partially-Known Discrete Event System

   https://doi.org/10.1109/ACCESS.2020.2983074  

   Wendell Bates, Ira; Karimoddini, Ali; Karimadini, Mohammad (January 2020, IEEE Access)
2. Information control in networked discrete event systems

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   Wang, Fei; Lin, Feng (May 2024, Information Sciences)
3. Stochastic Failure Prognosis of Discrete Event Systems

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4. Efficient Protocol Testing Under Temporal Uncertain Event Using Discrete-event Network Simulations

   https://doi.org/10.1145/3490028  

   Vu, Minh; Xu, Lisong; Elbaum, Sebastian; Sun, Wei; Qiao, Kevin (April 2022, ACM Transactions on Modeling and Computer Simulation)

   Testing network protocol implementations is difficult mainly because of the temporal uncertain nature of network events. To evaluate the worst-case performance or detect the bugs of a network protocol implementation using network simulators, we need to systematically simulate the behavior of the network protocol under all possible cases of the temporal uncertain events, which is time consuming. The recently proposed Symbolic Execution based Interval Branching (SEIB) simulates a group of uncertain cases together in a single simulation branch and thus is more efficient than brute force testing. In this article, we argue that the efficiency of SEIB could be further significantly improved by eliminating unnecessary comparisons of the event timestamps. Specifically, we summarize and present three general types of unnecessary comparisons when SEIB is applied to a general network simulator, and then correspondingly propose three novel techniques to eliminate them. Our extensive simulations show that our techniques can improve the efficiency of SEIB by several orders of magnitude, such as from days to minutes. 

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5. Event Representation with Sequential, Semi-Supervised Discrete Variables

   https://doi.org/10.18653/v1/2021.naacl-main.374  

   Rezaee, Mehdi; Ferraro, Francis (January 2021, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

   null (Ed.)

   Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly. 

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