生活中的人工智慧

1. ^ Xu, B., & Albert, E. (2014). Media censorship in China. Council on Foreign Relations, 25, 243. 2. ^ Xu, X., Mao, Z. M., & Halderman, J. A. (2011, March). Internet censorship in China: Where does the filtering occur?. In International Conference on Passive and Active Network Measurement (pp. 133-142). Springer, Berlin, Heidelberg. 3. ^ Huang, H., Wang, X., & Wang, H. (2020). NER‐RAKE: An improved rapid automatic keyword extraction method for scientific literatures based on named entity recognition. Proceedings of the Association for Information Science and Technology, 57(1), e374. 4. ^ Song, Y., Kim, E., Lee, G. G., & Yi, B. K. (2004). POSBIOTM-NER in the Shared Task of BioNLP/NLPBA2004. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP) (pp. 103-106). 5. ^ Liao, S. (2019). “# IAmGay# What About You?”: Storytelling, Discursive Politics, and the Affective Dimension of Social Media Activism against Censorship in China. International Journal of Communication, 13, 21. 6. ^ Chaudhary, A., Mittal, H., & Arora, A. (2019, February). Anomaly detection using graph neural networks. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 346-350). IEEE. 7. ^ Dey, N., Ashour, A. S., & Nguyen, G. N. (2020). Recent advancement in multimedia content using deep learning. 8. ^ Froehlich, D. E., Rehm, M., & Rienties, B. C. (2020). Mixed methods social network analysis. Mixed methods social network analysis: Theories and methodologies in learning and education, 1-10. 9. ^ Hu, M., Peng, J., Zhang, W., Hu, J., Qi, L., & Zhang, H. (2021). An intent recognition model supporting the spoken expression mixed with Chinese and English. Journal of Intelligent & Fuzzy Systems, 40(5), 10261- 10272. 10. ^ Sparr, M. (2022). Explicit User Manipulation in Reinforcement Learning Based Recommender Systems. arXiv preprint arXiv:2203.10629. 11. ^ Papakyriakopoulos, O., Serrano, J. C. M., & Hegelich, S. (2020). Political communication on social media: A tale of hyperactive users and bias in recommender systems. Online Social Networks and Media, 15, 100058. 12. ^ Paasonen, S. (2016). Fickle focus: Distraction, affect and the production of value in social media. First Monday. 13. ^ Xie, J. Q., Rost, D. H., Wang, F. X., Wang, J. L., & Monk, R. L. (2021). The association between excessive social media use and distraction: An eye movement tracking study. Information & Management, 58(2), 103415.