[1] P. Bharati and A. Pramanik, “Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey,” Computational Intelligence in Pattern Recognition, vol. 999, pp. 657–668, Aug. 2019, doi: https://doi.org/10.1007/978-981-13-9042-5_56.
[2] Su, D., & Mai, Y. (2004). Liquidity and Asset Pricing: Empirical Research on the Relationship Between Turnover Rates and Expected Returns in China's Stock Market. Economic Research, 2004(02), 95-105.
[3] Yin, H., & Wu, X. (2019). The Predictive Role of High-Frequency Investor Sentiment on Intraday Stock Returns. China Industrial Economics, 2019(08), 80-98.
[4] Huang, Y., Liu, Q., & Xu, J. (2024, May). Adversarial combinatorial bandits with switching cost and arm selection constraints. In IEEE INFOCOM 2024-IEEE Conference on Computer Communications (pp. 371-380). IEEE.
[5] Xing, Z., & Zhao, W. (2024). Block-Diagonal Guided DBSCAN Clustering. IEEE Transactions on Knowledge and Data Engineering.
[6] Zhao, Haodong, Jinming Hu, Peixuan Li, Fangqi Li, Jinrui Sha, Peixuan Chen, Zhuosheng Zhang, and Gongshen Liu. "NSmark: Null Space Based Black-box Watermarking Defense Framework for Pre-trained Language Models." arXiv e-prints (2024): arXiv-2410.
[7] Xing, Z., & Zhao, W. (2024, March). Unsupervised action segmentation via fast learning of semantically consistent actoms. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 6, pp. 6270-6278).
[8] Sang, Y., Xie, M., Bai, X., & Guo, F. (2024). Does natural resource dependence influence the impact of financial technologies on corporate ESG and digital governance in China's listed enterprises?. Resources Policy, 91, 104948.
[9] Ke, Z., & Yin, Y. (2024, November). Tail risk alert based on conditional autoregressive var by regression quantiles and machine learning algorithms. In 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE) (pp. 527-532). IEEE.
[10] Xing, Z., & Zhao, W. (2024). Calibration-Free Indoor Positioning via Regional Channel Tracing. IEEE Internet of Things Journal.
[11] Guo, F., Mo, H., Wu, J., Pan, L., Zhou, H., Zhang, Z., ... & Huang, F. (2024). A hybrid stacking model for enhanced short-term load forecasting. Electronics, 13(14), 2719.
[12] Chen, Y., Liu, L., & Fang, L. (2024). An Enhanced Credit Risk Evaluation by Incorporating Related Party Transaction in Blockchain Firms of China. Mathematics, 12(17), 2673.
[13] Guo, F., Wu, J. Z., & Pan, L. (2023, July). An Empirical Study of AI Model’s Performance for Electricity Load Forecasting with Extreme Weather Conditions. In International Conference on Science of Cyber Security (pp. 193-204). Cham: Springer Nature Switzerland.
[14] Hu, Z., Yu, R., Zhang, Z., Zheng, H., Liu, Q., & Zhou, Y. (2024). Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms. arXiv preprint arXiv:2412.18202.
[15] Yu, Q., Xu, Z., & Ke, Z. (2024, November). Deep learning for cross-border transaction anomaly detection in anti-money laundering systems. In 2024 6th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 244-248). IEEE.
[16] Zhang, J., & Liu, L. (2006). Turnover Rates and Stock Returns: Liquidity Premium or Speculative Bubble? Economics (Quarterly), 2006(02), 871-892.
[17] Zhou, H., Xu, Y., Li, P., & Chen, J. (2019). Empirical Research on the Impact of Black Swan Events on Stock Prices from the Perspective of Investor Sentiment. Journal of Beijing University of Posts
[18] Gong, H., & Wang, M. (2020, July). A duality approach for regret minimization in average-award ergodic markov decision processes. In Learning for Dynamics and Control (pp. 862-883). PMLR.
[19] Yu, Q., Ke, Z., Xiong, G., Cheng, Y., & Guo, X. (2024, December). Identifying money laundering risks in digital asset transactions based on ai algorithms. In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC) (pp. 1081-1085). IEEE.
[20] Chen, K., Jiang, J., Liu, Q., & Li, X. (2018). Air Quality, Investor Sentiment, and Stock Returns. Management Science, 31(06), 145-160.
[21] Chen, C., Chen, H., & Zheng, Z. (2014). Behavioral Finance Explanations for Momentum Effects. Systems Engineering Theory and Practice, 34(03), 613-622.
[22] Zhang, W., Shen, Y., Wu, L., Peng, Q., Wang, J., Zhuang, Y., & Lu, W. (2024). Self-contrast: Better reflection through inconsistent solving perspectives. arXiv preprint arXiv:2401.02009.
[23] Zhao, H., Du, W., Guo, J., & Liu, G. (2023). A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network. arXiv preprint arXiv:2303.16031.
[24] Zhang, W., Cheng, Z., He, Y., Wang, M., Shen, Y., Tan, Z., ... & Zhuang, Y. (2024). Multimodal self-instruct: Synthetic abstract image and visual reasoning instruction using language model. arXiv preprint arXiv:2407.07053.
[25] Wan, W., Zhou, F., Liu, L., Fang, L., & Chen, X. (2021). Ownership structure and R&D: The role of regional governance environment. International Review of Economics & Finance, 72, 45-58.
[26] Liu, S., & Zhu, M. (2023). Learning multi-agent behaviors from distributed and streaming demonstrations. Advances in Neural Information Processing Systems, 36, 53552-53564.
[27] Feng, H., Liu, Q., Liu, H., Tang, J., Zhou, W., Li, H., & Huang, C. (2024). Docpedia: Unleashing the power of large multimodal model in the frequency domain for versatile document understanding. Science China Information Sciences, 67(12), 1-14.
[28] Wang, Y., Guo, Y., Wei, Z., Huang, Y., & Liu, X. (2019, November). Traffic flow prediction based on deep neural networks. In 2019 International Conference on Data Mining Workshops (ICDMW) (pp. 210-215). IEEE.
[29] Liu, S., & Zhu, M. (2023). Meta inverse constrained reinforcement learning: Convergence guarantee and generalization analysis. In The Twelfth International Conference on Learning Representations.
[30]
[31] Ouyang, K., Fu, S., Chen, Y., & Chen, H. (2025, June). Dynamic Graph Neural Evolution: An Evolutionary Framework Integrating Graph Neural Networks with Adaptive Filtering. In 2025 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
[32] Pan, P., Chen, L., He, Q., Yuan, K., Wang, H., & Zhang, W. (2026). FinSCRA: An LLM-Powered Multi-Chain Reasoning Framework for Interpretable Node Classification on Text-Attributed Graphs. Preprints. https://doi.org/10.20944/preprints202602.0209.v1
[33] Zhao, Z., Yuan, K., Wang, Z., Shen, J., & Huang, Y. (2026). CSSA: A Cross‐Modal Semantic‐Structural Alignment Framework via LLMs and Graph Contrastive Learning for Fraud Detection of Online Payment. Preprints. https://doi.org/10.20944/preprints202602.0543.v1
[34] Ouyang, K., Ke, Z., Fu, S., Liu, L., Zhao, P., & Hu, D. (2024). Learn from global correlations: Enhancing evolutionary algorithm via spectral gnn. arXiv preprint arXiv:2412.17629.
[35] Zheng, H., Lin, Y., He, Q., Zou, Y., & Wang, H. (2026, January 30). Blockchain Payment Fraud Detection with a Hybrid CNN-GNN-LSTM Model.Https://Www.researchgate.net/Publication/400235797_Blockchain_Payment_Fraud_Detection_with_a_Hybrid_CNN-GNN-LSTM_Model. https://doi.org/10.13140/RG.2.2.26663.20641
[36] Lizi Chen, Yue Zou, Pengfei Pan, et al. Cascading Credit Risk Assessment in Multiplex Supply Chain Networks. Authorea. January 16, 2026.DOI: 10.22541/au.176858311.10362606/v1
[37] Ouyang, Kaichen, Dedai Wei, Xinye Sha, Juntao Yu, Yuanli Zhao, Minyu Qiu, Shengwei Fu, Ali Asghar Heidari, and Huiling Chen. "Beaver behavior optimizer: A novel metaheuristic algorithm for solar PV parameter identification and engineering problems." Journal of Advanced Research (2025).
[38] Keyu Yuan, Yuqing Lin, Wenjun Wu, et al. Detection of Blockchain Online Payment Fraud Via CNN-LSTM. Authorea. January 15, 2026,doi:10.22541/au.176851576.63306241/v1.
[39] Wei, D., Wang, Z., Kang, H., Sha, X., Xie, Y., Dai, A., & Ouyang, K. (2025). A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks. Scientific Reports, 15(1), 30095.
[40] Ouyang, Kaichen, Dedai Wei, Shengwei Fu, Shaowei Gu, Xinye Sha, Juntao Yu, Jiaquan Yu, Ali Asghar Heidar, Zhennao Cai, and Huiling Chen. "Multi-objective Red-billed Blue Magpie Optimizer: A Novel Algorithm for Multi-objective UAV Path Planning." Results in Engineering (2025): 106785.
[41] Yao, J., Li, C., & Xiao, C. (2024). Swift sampler: Efficient learning of sampler by 10 parameters. Advances in Neural Information Processing Systems, 37, 59030-59053.
[42] Peng, Yong, Shaowei Gu, Yunbin Liang, Kaichen Ouyang, Yingli Li, Kui Wang, Guohua Wu, and Chaojie Fan. "Wave Optics Optimizer: A novel meta-heuristic algorithm for engineering optimization." Communications in Nonlinear Science and Numerical Simulation (2025): 109337.
[43] Ouyang, K., Fu, S., Chen, Y., Cai, Q., Heidari, A. A., & Chen, H. (2024). Escape: an optimization method based on crowd evacuation behaviors. Artificial Intelligence Review, 58(1), 19.
[44] Xiao, C., & Liu, Y. (2025). A multifrequency data fusion deep learning model for carbon price prediction. Journal of Forecasting, 44(2), 436-458.
[45] Wei, Dedai, Zimo Wang, Minyu Qiu, Juntao Yu, Jiaquan Yu, Yurun Jin, Xinye Sha, and Kaichen Ouyang. "Multiple objectives escaping bird search optimization and its application in stock market prediction based on transformer model." Scientific Reports 15, no. 1 (2025): 5730.
[46] Zhao, P., Liu, X., Su, X., Wu, D., Li, Z., Kang, K., ... & Zhu, A. (2025). Probabilistic Contingent Planning Based on Hierarchical Task Network for High-Quality Plans. Algorithms, 18(4), 214.
[47] Chen, X., Xiao, C., Cao, W., Zhang, W., & Liu, Y. (2025). Framework and Pathway for the Construction of a Unified Data-Element Market in China. Strategic Study of Chinese Academy of Engineering, 27(1), 40-50.
[48] Yi, Q., He, Y., Wang, J., Song, X., Qian, S., Yuan, X., ... & Ni, J. (2025). Score: Story coherence and retrieval enhancement for ai narratives. arXiv preprint arXiv:2503.23512.
[49] Lin, Z., Zhao, K., Zhang, S., Yu, P., & Xiao, C. (2025). CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards. arXiv preprint arXiv:2512.23971.
[50] Wang, J., Zhang, Z., He, Y., Song, Y., Shi, T., Li, Y., ... & He, L. (2024). Enhancing code llms with reinforcement learning in code generation. arXiv e-prints, arXiv-2412.
