With the rapid development of information technology, artificial intelligence technology and the financial industry began to deeply integrate up. Algorithmic trading, credit card fraud detection and a series of other new technologies being applied to the financial industry all require a large amount of data support. However, due to the increasing amount of online financial data, it is difficult for the majority of investors and financial industry practitioners to obtain the required information in a timely manner. Entity recognition technology, as the basis of natural language processing, can quickly extract effective information from the massive financial texts and can provide effective help for investors and financial industry practitioners. In this paper, we propose a neural network model based on Bert-BiLSTM-CRF, which is applied to recognize financial entities. Through experimental analysis, the model achieves more than 95% of all indicators. Compared with the conventional model, the model has superior performance.
The implementation of the "double reduction" and "double increase" policy has been reflected in the physical education examination. Physical education exams add more difficult sports, such as basketball and soccer, which are more difficult than traditional sports, require higher posture, and require professional guidance. However, there is a shortage of physical education personnel in China, which is far from meeting the needs of schools. In response to the above problem this book proposes, a sports posture assessment system based on open pose algorithm. By comparing the assessor's pose with the standard pose, the matching of key frames is performed, and the method of calculating vector similarity is used to define the standardized evaluation criteria of sports pose for differentiated comparison. The system is proven to be effective in evaluating the motion stance and providing users with improvement suggestions to improve their performance.
KEYWORDS: Material fatigue, Eye, Mouth, Video, Detection and tracking algorithms, Video acceleration, Facial recognition systems, Deep learning, Matrices, Instrument modeling
In order to avoid fatigue driving, the driver fatigue detection technology is studied by extracting facial fatigue feature parameters. Use the optimized SSD to extract facial features, use PFLD to detect key points of the face, and detect the key points and spatial attitude angles of the eyes, mouth, and head of the face; calculate the face fatigue feature parameters based on time series The matrix is input to GRU for fatigue driving detection. Compared with other eight methods in the case of low computing power, it has a high accuracy rate and detection speed, which meets the needs of the fatigue driving detection system.
KEYWORDS: Video, Transformers, Performance modeling, Data modeling, Visual process modeling, Feature extraction, Optical flow, Video coding, Image segmentation, Deep learning
Deepfake open source technology has lowered the threshold for AI face swapping to a very low level, making it possible to swap faces with one click. The cost of "disinformation" is greatly reduced, so that some deeply faked pictures and videos can be spread on social networks The social network can spread explosively. However, in the defense layer, there are almost no standardized and automated detection tools for deepfake. There is no such tool. Therefore, whether for individuals or platforms, the time window for fighting fake and disinformation is very short, but it is very difficult. In this paper, we use the Transformer model as a base, improve the model and optimize the structure of the model, so that the model can extract the depth features of the video and build a more accurate and efficient deepfake inspection method.
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