Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.24963/ijcai.2017/443.Xu) was visible in the surrounding text but could not be confirmed against doi.org as printed.
Paper page Integrity report arXiv Try DOI
Evidence text
URL https://doi.org/10.24963/ijcai. 2017/443. Xu, N., Liu, Y ., and Geng, X. Label enhancement for label distribution learning.IEEE Trans. Knowl. Data Eng., 33(4):1632–1643, 2021. doi: 10.1109/ TKDE.2019.2947040. URL https://doi.org/10. 1109/TKDE.2019.2947040. Xu, S., Shang, L., Shen, F., Yang, X., and Pedrycz, W. Incomplete label distribution learning via label correla- tion decomposition.Inf. Fusion, 113:102600, 2025. doi: 10.1016/J.INFFUS.2024.102600. URL https://doi. org/10.1016/j.inffus.2024.102600. Yan, Y ., Fu, H., Li, Y ., Xie, J., Ma, J., Yang, G., and Zhu, L. A simple data augmentation for feature distribution skewed federated learning. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, pp. 25749–25758. Computer Vision Foundation / IEEE, 2025. doi: 10.1109/CVPR52734.2025.02398. URL https://openaccess.thecvf.com/ content/CVPR2025/html/Yan_A_Simple_ Data_Augmentation_for_Feature_ Distribution_Skewed_Federated_ Learning_CVPR_2025_paper.html. You, Q., Luo, J., Jin, H., and Yang, J. Building a large scale dataset for image emotion recognition: The fine print and 11 Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity the benchmark. In Schuurmans, D. and Wellman, M. P. (eds.),Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pp. 308–314. AAAI Press, 2016. doi: 10. 1609/AAAI.V30I1.9987. URL https://doi.org/ 10.1
Evidence payload
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"reconstructed_doi": "10.24963/ijcai.2017/443.Xu",
"ref_index": 7,
"resolved_title": null,
"verdict_class": "incontrovertible"
}