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Ross, J. A. (2017). Aerobic dance and cheerleading. Athletic footwear and orthoses in sports medicine, 389-396.Search in Google Scholar
Synytsya, T., Synytsya, S., Rybalko, L., Kornosenko, O., & Zhamardiy, V. (2024). Evaluation of sports achievements of participants in cheerleading competitions by rationalizing refereeing. Revista Romaneasca pentru Educatie Multidimensionala, 16(1), 326-342.Search in Google Scholar
Cherepov, E., Kalugina, G., Sevostianov, D., & Smirnova, L. (2020). Development of coordination abilities in cheerleaders at the stage of initial preparation. Journal of Physical Education and Sport, 20(2), 666-671.Search in Google Scholar
Grigoroiu, C., Pricop, A., Wesselly, T., & Netolitzchi, M. (2019). Optimizing the physical training of the female students in the cheerleading team of the university politehnica of Bucharest. Gymnasium, 20(2), 68-84.Search in Google Scholar
Artemyeva, G., & Moshenska, T. (2018). Role and importance of choreography in gymnastic and dance sports. Slobozhanskyi herald of science and sport, (4 (66)), 27-30.Search in Google Scholar
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021). Ai choreographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 13401-13412).Search in Google Scholar
Wang, C., & An, C. (2023). Characteristic Analysis of Cheerleading Self-selected Action Arrangement Elements Based on BP Neural Network. Applied Mathematics and Nonlinear Sciences.Search in Google Scholar
Lee, S., Hwang, S., Oakley, I., & Lee, K. (2024, July). Expanding the Design Space of Vision-based Interactive Systems for Group Dance Practice. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (pp. 2768-2787).Search in Google Scholar
Liu, Y., & Sra, M. (2024, July). DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (pp. 920-938).Search in Google Scholar
Pataranutaporn, P., Mano, P., Bhongse-Tong, P., Chongchadklang, T., Archiwaranguprok, C., Hantrakul, L., ... & Klunchun, P. (2024, May). Human-AI Co-Dancing: Evolving Cultural Heritage through Collaborative Choreography with Generative Virtual Characters. In Proceedings of the 9th International Conference on Movement and Computing (pp. 1-10).Search in Google Scholar
De Filippo, A., & Milano, M. (2023, November). Robotic Choreography Creation Through Symbolic AI Techniques. In International Conference on Entertainment Computing (pp. 346-351). Singapore: Springer Nature Singapore.Search in Google Scholar
Bonny Banerjee & Murchana Baruah. (2024). Attention-Based Variational Autoencoder Models for Human-Human Interaction Recognition via Generation. Sensors (Basel, Switzerland)(12),3922-3922.Search in Google Scholar
Ruilong Wang,Ming Wang,Qianchuan Zhao,Yanling Gong,Lingchen Zuo,Xuehan Zheng & He Gao. (2024). A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning. Biomimetics (Basel, Switzerland)(4).Search in Google Scholar
Meng Wang,Jianmin Wang,Zhiwei Rong,Liuying Wang,Zhenyi Xu,Liuchao Zhang... & Kang Li. (2024). A bidirectional interpretable compound-protein interaction prediction framework based on cross attention. Computers in biology and medicine108239-108239.Search in Google Scholar
Gong Changfei,Huang Yuling,Luo Mingming,Cao Shunxiang,Gong Xiaochang,Ding Shenggou... & Zhang Yun. (2024). Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images. Radiation Oncology(1),37-37.Search in Google Scholar