|
[1]
|
肖国煜. 信息系统等级保护测评实践[J]. 信息网络安全, 2011(7): 86-88.
|
|
[2]
|
郑南宁. 人工智能新时代[J]. 智能科学与技术学报, 2019, 1(1): 1-3.
|
|
[3]
|
郭天霞. AI 对工作流程和工作质量的影响: 自动化, 错误减少和优化[J]. 文化与社会心理学, 2023(12): 1-7.
|
|
[4]
|
王志宏, 杨震. 人工智能技术研究及未来智能化信息服务体系的思考[J]. 电信科学, 2017. 33(5): 1-11.
|
|
[5]
|
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z. and Zhang, Y. (2024) A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly. High-Confidence Computing, 4, Article 100211. [Google Scholar] [CrossRef]
|
|
[6]
|
Vaswani, A. (2017) Attention Is All You Need. https://arxiv.org/abs/1706.03762
|
|
[7]
|
An, J., Ding, W. and Lin, C. (2023) Chatgpt: Tackle the Growing Carbon Footprint of Generative AI. Nature, 615, 586-586. [Google Scholar] [CrossRef] [PubMed]
|
|
[8]
|
Touvron, H., et al. (2023) Llama: Open and Efficient Foundation Language Models.
|
|
[9]
|
Bai, J., et al. (2023) Qwen Technical Report.
|
|
[10]
|
Glm, T., et al. (2024) ChatGLM: A Family of Large Language Models from GLM-130b to GLM-4 All Tools.
|
|
[11]
|
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., et al. (2023) Parameter-Efficient Fine-Tuning of Large-Scale Pre-Trained Language Models. Nature Machine Intelligence, 5, 220-235. [Google Scholar] [CrossRef]
|
|
[12]
|
Mao, Y., et al. (2024) A Survey on Lora of Large Language Models.
|
|
[13]
|
Yang, A., et al. (2023) Baichuan 2: Open Large-Scale Language Models.
|
|
[14]
|
Devlin, J. (2018) Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding.
|
|
[15]
|
Patil, R., Boit, S., Gudivada, V. and Nandigam, J. (2023) A Survey of Text Representation and Embedding Techniques in NLP. IEEE Access, 11, 36120-36146. [Google Scholar] [CrossRef]
|
|
[16]
|
Yacouby, R. and Axman, D. (2020) Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, Online, November 2020, 79-91. [Google Scholar] [CrossRef]
|
|
[17]
|
Voulodimos, A., Doulamis, N., Doulamis, A. and Protopapadakis, E. (2018) Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018, 1-13. [Google Scholar] [CrossRef] [PubMed]
|
|
[18]
|
Kaur, R. and Singh, S. (2023) A Comprehensive Review of Object Detection with Deep Learning. Digital Signal Processing, 132, Article 103812. [Google Scholar] [CrossRef]
|
|
[19]
|
Sharma, S. and Guleria, K. (2022) Deep Learning Models for Image Classification: Comparison and Applications. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, 28-29 April 2022, 1733-1738. [Google Scholar] [CrossRef]
|
|
[20]
|
Li, L., Zhou, T., Wang, W., Li, J. and Yang, Y. (2022) Deep Hierarchical Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, 18-24 June 2022, 1236-1247. [Google Scholar] [CrossRef]
|
|
[21]
|
Tsourounis, D., Kastaniotis, D., Theoharatos, C., Kazantzidis, A. and Economou, G. (2022) SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification. Journal of Imaging, 8, Article 256. [Google Scholar] [CrossRef] [PubMed]
|
|
[22]
|
Mori, S., Suen, C.Y. and Yamamoto, K. (1992) Historical Review of OCR Research and Development. Proceedings of the IEEE, 80, 1029-1058. [Google Scholar] [CrossRef]
|
|
[23]
|
Peng, Q. and Tu, L. (2023) Paddle-OCR-Based Real-Time Online Recognition System for Steel Plate Slab Spray Marking Characters. Journal of Control, Automation and Electrical Systems, 35, 221-233. [Google Scholar] [CrossRef]
|
|
[24]
|
Wang, R.J., Li, X. and Ling, C.X. (2018) PELEE: A Real-Time Object Detection System on Mobile Devices. Advances in Neural Information Processing Systems, 2018, Article No. 31.
|
|
[25]
|
Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324. [Google Scholar] [CrossRef]
|