Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference between CP infants' frequency of human movement and that of the healthy group, which improves prediction performance. However, the existing deep learning-based methods did not use the frequency information of infants' movement for CP prediction. This paper proposes a frequency attention informed graph convolutional network and validates it on two consumer-grade RGB video datasets, namely MINI-RGBD and RVI-38 datasets. Our proposed frequency attention module aids in improving both classification performance and system interpretability. In addition, we design a frequency-binning method that retains the critical frequency of the human joint position data while filtering the noise. Our prediction performance achieves stateof-the-art research on both datasets. Our work demonstrates the effectiveness of frequency information in supporting the prediction of CP non-intrusively and provides a way for supporting the early diagnosis of CP in the resource-limited regions where the clinical resources are not abundant.
Data sharing of remote sensing images promotes the development of remote sensing research and applications in various industries, but the sharing of remote sensing images has security issues such as piracy, leakage, and theft. The emergence of the blockchain has made it possible to solve the above problems. However, the hash function adopted by the blockchain can only verify the changes in the binary level of remote sensing images, and cannot reflect the changes of the content of remote sensing images. Aiming at the security problems in remote sensing image sharing, this paper combines blockchain and subject-sensitive hashing to build an authentication method for remote sensing images. In our method, subject-sensitive hash algorithm is used to calculate the hash value of remote sensing images, instead of using cryptographic hash function. Experiments show that our method is not only robust to operations that do not change the content of remote sensing images, but also can detect content tampering of the images.
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