Add: Methodology section 12 references

arxiv-style/references.bib

@@ -1,27 +1,117 @@
-@inproceedings{kour2014real,
+@inproceedings{vaswani2017attention,
-  title={Real-time segmentation of on-line handwritten arabic script},
+  title={Attention Is All You Need},
-  author={Kour, George and Saabne, Raid},
+  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
-  booktitle={Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
-  pages={417--422},
+  volume={30},
-  year={2014},
+  year={2017},
-  organization={IEEE}
+  url={https://arxiv.org/abs/1706.03762}
 }
 
-@inproceedings{kour2014fast,
+@inproceedings{ho2020denoising,
-  title={Fast classification of handwritten on-line Arabic characters},
+  title={Denoising Diffusion Probabilistic Models},
-  author={Kour, George and Saabne, Raid},
+  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
-  booktitle={Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
-  pages={312--318},
+  volume={33},
-  year={2014},
+  pages={6840--6851},
-  organization={IEEE},
+  year={2020},
-  doi={10.1109/SOCPAR.2014.7008025}
+  url={https://arxiv.org/abs/2006.11239}
 }
 
-@inproceedings{keshet2016prediction,
+@inproceedings{austin2021structured,
-  title={Prediction-Based, Prioritized Market-Share Insight Extraction},
+  title={Structured Denoising Diffusion Models in Discrete State-Spaces},
-  author={Keshet, Renato and Maor, Alina and Kour, George},
+  author={Austin, Jacob and Johnson, Daniel D and Ho, Jonathan and Tarlow, Daniel and van den Berg, Rianne},
-  booktitle={Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings 12},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
-  pages={81--94},
+  volume={34},
-  year={2016},
+  pages={17981--17993},
-  organization={Springer}
+  year={2021},
+  url={https://arxiv.org/abs/2107.03006}
+}
+
+@article{shi2024simplified,
+  title={Simplified and Generalized Masked Diffusion for Discrete Data},
+  author={Shi, Juntong and Han, Ke and Wang, Zinan and Doucet, Arnaud and Titsias, Michalis K},
+  journal={arXiv preprint},
+  eprint={2406.04329},
+  archivePrefix={arXiv},
+  year={2024},
+  url={https://arxiv.org/abs/2406.04329}
+}
+
+@inproceedings{hang2023efficient,
+  title={Efficient Diffusion Training via Min-SNR Weighting Strategy},
+  author={Hang, Tianyu and Gu, Shuyang and Li, Chen and Bao, Jianmin and Chen, Dong and Hu, Han and Geng, Xin and Guo, Boxin},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  pages={7407--7417},
+  year={2023},
+  doi={10.1109/ICCV51070.2023.00702},
+  url={https://arxiv.org/abs/2303.09556}
+}
+
+@inproceedings{kollovieh2023tsdiff,
+  title={Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting},
+  author={Kollovieh, Marcel and Ansari, Abdul Fatir and Bohlke-Schneider, Michael and Fatir Ansari, Abdul and Salinas, David},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  volume={36},
+  year={2023},
+  url={https://arxiv.org/abs/2307.11494}
+}
+
+@article{sikder2023transfusion,
+  title={TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers},
+  author={Sikder, M. F. and Ramachandranpillai, R. and Heintz, F.},
+  journal={arXiv preprint},
+  eprint={2307.12667},
+  archivePrefix={arXiv},
+  year={2023},
+  url={https://arxiv.org/abs/2307.12667}
+}
+
+@inproceedings{song2021score,
+  title={Score-Based Generative Modeling through Stochastic Differential Equations},
+  author={Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P and Kumar, Abhishek and Ermon, Stefano and Poole, Ben},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2021},
+  url={https://arxiv.org/abs/2011.13456}
+}
+
+@inproceedings{shi2025tabdiff,
+  title={TabDiff: A Mixed-type Diffusion Model for Tabular Data Generation},
+  author={Shi, Juntong and Xu, Minkai and Hua, Harper and Zhang, Hengrui and Ermon, Stefano and Leskovec, Jure},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2025},
+  url={https://arxiv.org/abs/2410.20626}
+}
+
+@inproceedings{yuan2025ctu,
+  title={CTU-DDPM: Generating Industrial Control System Time-Series Data with a CNN-Transformer Hybrid Diffusion Model},
+  author={Yuan, Yusong and Sha, Yun and Zhao, Wei and Zhang, Kun},
+  booktitle={Proceedings of the 2025 International Symposium on Artificial Intelligence and Computational Social Sciences (ACM AICSS)},
+  pages={123--132},
+  year={2025},
+  doi={10.1145/3776759.3776845},
+  url={https://dl.acm.org/doi/10.1145/3776759.3776845}
+}
+
+@misc{sha2026ddpm,
+  title={DDPM Fusing Mamba and Adaptive Attention: An Augmentation Method for Industrial Control Systems Anomaly Data},
+  author={Sha, Yun and Yuan, Yusong and Wu, Yonghao and Zhao, Haidong},
+  year={2026},
+  month={jan},
+  note={SSRN Electronic Journal},
+  eprint={6055903},
+  archivePrefix={SSRN},
+  doi={10.2139/ssrn.6055903},
+  url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6055903}
+}
+
+@techreport{nist2023sp80082,
+  title={Guide to Operational Technology (OT) Security},
+  author={{National Institute of Standards and Technology}},
+  institution={NIST},
+  type={Special Publication},
+  number={800-82 Rev. 3},
+  year={2023},
+  month={sep},
+  doi={10.6028/NIST.SP.800-82r3},
+  url={https://csrc.nist.gov/pubs/sp/800/82/r3/final}
 }