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Authors
Wang, Penglei
Fan, Xin
Yang, Qimeng
Tian, Shengwei
Yu, Long
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Funding
Funding for this research was provided by:
Tianshan Talent Training Program (NO. 2023TSYCLJ0023)
Natural Science Foundation of Xinjiang Uygur Autonomous Region (NO. 2023D01C176)
Xinjiang Uygur Autonomous Region Universities Fundamental Research Funds Scientific Research Project (NO. XJEDU2022P018)
Major science and technology programs in the autonomous region (No. 2023A03001)
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Article History
Received: 20 July 2024
Accepted: 18 January 2025
First Online: 4 February 2025
Declarations
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: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
: We strictly use public datasets for our research, ensuring that no private or sensitive data is involved without explicit consent. When dealing with visual data, we employ advanced anonymization techniques to remove any personal identifiers, thereby protecting individuals’ privacy and adhering to ethical standards. This process ensures that the visual data we use does not inadvertently reveal personal information, and it aligns with the best practices for data privacy. We are fully aware of the potential risks associated with the misuse of translation technology. If employed maliciously, such technology could pose risks, including but not limited to, the dissemination of biased or discriminatory information. We strive to prevent any form of discrimination or bias in our models by continuously monitoring and testing our systems, ensuring they do not perpetuate or amplify harmful stereotypes or biases.
