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Combating self-learning worms by using predators., , , and . WCNIS, page 607-611. IEEE, (2010)TabFact: A Large-scale Dataset for Table-based Fact Verification., , , , , , , and . ICLR, OpenReview.net, (2020)Logic2Text: High-Fidelity Natural Language Generation from Logical Forms., , , , , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 2096-2111. Association for Computational Linguistics, (2020)Comprehensive Survey on Person Identification: Queries, Methods, and Datasets., , , , and . MORE@ICMR, page 3:1-3:6. ACM, (2024)CloudAP: Improving the QoS of Mobile Applications with Efficient VM Migration., , , , , and . HPCC/EUC, page 1374-1381. IEEE, (2013)Defending passive worms in unstructured P2P networks based on healthy file dissemination., , and . Computers & Security, 28 (7): 628-636 (2009)TabFact: A Large-scale Dataset for Table-based Fact Verification, , , , , , , and . (2019)cite arxiv:1909.02164Comment: Table-based Fact Verification Dataset on Structured Information.Global Analysis of a SEIQV Epidemic Model for Scanning Worms with Quarantine Strategy., , , and . Int. J. Netw. Secur., 17 (4): 423-430 (2015)Worm Propagation Modeling and Analysis on Network., , , and . CIS (2), volume 3802 of Lecture Notes in Computer Science, page 476-481. Springer, (2005)Combating Good Point Set Scanning-based Self-learning Worms by Using Predators., , , and . Int. J. Netw. Secur., 15 (3): 171-178 (2013)