Editorial
RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First]
@ARTICLE{10.4108/eai.8-4-2022.173790, author={Shuang Gao and Xiaowei Wang}, title={RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First]}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={SIS}, year={2022}, month={1}, keywords={}, doi={10.4108/eai.8-4-2022.173790} }
- Shuang Gao
Xiaowei Wang
Year: 2022
RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First]
SIS
EAI
DOI: 10.4108/eai.8-4-2022.173790
Abstract
We, the Publisher, have retracted the following article: Shuang Gao, Xiaowei Wang (2022). Feature extraction of dance movement based on deep learning and deformable part model. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172783 The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany. From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission. Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright © 2022 Shuang Gao et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.