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2022.
[31] Yamaguchi F, Lottmann M, Rieck K. Generalized vulnerability extrapolation using abstract syntax trees. In: Proc. of the 28th Annual
Computer Security Applications Conf. Orlando: ACM, 2012. 359–368. [doi: 10.1145/2420950.2421003]
[32] Neuhaus S, Zimmermann T, Holler C, Zeller A. Predicting vulnerable software components. In: Proc. of the 14th ACM Conf. on
Computer and Communications Security. Alexandria: ACM, 2007. 529–540. [doi: 10.1145/1315245.1315311]
[33] Joern. 2019. https://github.com/ShiftLeftSecurity/
[34] Ferrante J, Ottenstein KJ, Warren JD. The program dependence graph and its use in optimization. ACM Trans. on Programming
Languages and Systems, 1987, 9(3): 319–349. [doi: 10.1145/24039.24041]
[35] Sinha S, Harrold MJ, Rothermel G. System-dependence-graph-based slicing of programs with arbitrary interprocedural control flow. In:
Proc. of the 21st Int’l Conf. on Software Engineering. Los Angeles: ACM, 1999. 432–441. [doi: 10.1145/302405.302675]
[36] Wang HT, Ye GX, Tang ZY, Tan SH, Huang SF, Fang DY, Feng YS, Bian LZ, Wang Z. Combining graph-based learning with
automated data collection for code vulnerability detection. IEEE Trans. on Information Forensics and Security, 2020, 16: 1943–1958.
[doi: 10.1109/TIFS.2020.3044773]
[37] Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In:
Proc. of the 27th Int’l Conf. on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2013. 3111–3119.
[38] Sohn K. Improved deep metric learning with multi-class N-pair loss objective. In: Proc. of the 30th Int’l Conf. on Neural Information
Processing Systems. Barcelona: Curran Associates Inc., 2016. 1857–1865.
[39] van den Oord A, Li YZ, Vinyals O. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2019.
[40] Wu ZR, Xiong YJ, Yu SX, Lin DH. Unsupervised feature learning via non-parametric instance discrimination. In: Proc. of the 2018
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 3733–3742. [doi: 10.1109/CVPR.2018.
00393]
[41] Bachman P, Hjelm RD, Buchwalter W. Learning representations by maximizing mutual information across views. In: Proc. of the 33rd
Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 15535–15545.
[42] Hénaff OJ, Srinivas A, De Fauw J, Razavi A, Doersch C, Ali Eslami SM, van den Oord A. Data-efficient image recognition with
contrastive predictive coding. In: Proc. of the 37th Int’l Conf. on Machine Learning. JMLR.org, 2020. 4182–4192.
[43] Baevski A, Zhou H, Mohamed A, Auli M. Wav2vec 2.0: A framework for self-supervised learning of speech representations. In: Proc. of
the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 12449–12460.
[44] Conneau A, Baevski A, Collobert R, Mohamed A, Auli M. Unsupervised cross-lingual representation learning for speech recognition.
arXiv:2006.13979, 2020.
[45] Tian YL, Krishnan D, Isola P. Contrastive multiview coding. In: Proc. of the 16th European Conf. on Computer Vision. Glasgow:
Springer, 2020. 776–794. [doi: 10.1007/978-3-030-58621-8_45]
[46] Hjelm RD, Fedorov A, Lavoie-Marchildon S, Grewal K, Bachman P, Trischler A, Bengio Y. Learning deep representations by mutual
information estimation and maximization. arXiv:1808.06670, 2019.
[47] Han TD, Xie WD, Zisserman A. Video representation learning by dense predictive coding. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on
Computer Vision Workshop. Seoul: IEEE, 2019. 1483–1492. [doi: 10.1109/ICCVW.2019.00186]
[48] He KM, Fan HQ, Wu YX, Xie SN, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proc. of the 2020
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 9726–9735. [doi: 10.1109/CVPR42600.2020.00975]
[49] Misra I, van der Maaten L. Self-supervised learning of pretext-invariant representations. In: Proc. of the 2020 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 6706–6716. [doi: 10.1109/CVPR42600.2020.00674]
[50] Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: Proc. of the 37th
Int’l Conf. on Machine Learning. JMLR.org, 2020. 1597–1607.
[51] Khosla P, Teterwak P, Wang C, Sarna A, Tian YL, Isola P, Maschinot A, Liu C, Krishnan D. Supervised contrastive learning. In: Proc. of
the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 18661–18673.
[52] Wu YM, Dou SH, Zou DQ, Yang W, Qiang WZ, Jin H. Obfuscation-resilient Android malware analysis based on contrastive learning.
arXiv:2107.03799, 2022.
[53] Gunel B, Du JF, Conneau A, Stoyanov V. Supervised contrastive learning for pre-trained language model fine-tuning. arXiv:2011.01403,
2021.
[54] Lin GJ, Zhang J, Luo W, Pan L, De Vel O, Montague P, Xiang Y. Software vulnerability discovery via learning multi-domain knowledge
bases. IEEE Trans. on Dependable and Secure Computing, 2021, 18(5): 2469–2485. [doi: 10.1109/TDSC.2019.2954088]
[55] Lin GJ, Wen S, Han QL, Zhang J, Xiang Y. Software vulnerability detection using deep neural networks: A survey. Proc. of the IEEE,