License plate recognition technology use widely in intelligent traffic management and control. Researchers have been committed to improving the speed and accuracy of license plate recognition for nearly 30 years. This paper is the first to propose combining the attention mechanism with YOLO-v5 and LPRnet to construct a new license plate recognition model (LPR-CBAM-Net). Through the attention mechanism CBAM (Convolutional Block Attention Module), the importance of different feature channels in license plate recognition can be re-calibrated to obtain proper attention to features. Force information to achieve the purpose of improving recognition speed and accuracy. Experimental results show that the model construction method is superior in speed and accuracy to traditional license plate recognition algorithms. The accuracy of the recognition model of the CBAM model is increased by two percentage points to 97.2%, and the size of the constructed model is only 1.8 M, which can meet the requirements of real-time execution of embedded low-power devices. The codes for training and evaluating LPR-CBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.

Privacy-sensitive data encounter immense security and usability challenges in processing, analyzing, and sharing. Meanwhile, traditional privacy data desensitization methods suffer from issues such as poor quality and low usability after desensitization. Therefore, a text data desensitization model that combines Transformer and Wasserstein Text convolutional Generative Adversarial Network (Trans-WTGAN) is proposed. Transformer as the generator and its self-attention mechanism can handle long-range dependencies, enabling the generated of higher-quality text; Text Convolutional Neural Network (TextCNN) integrates the idea of Wasserstein as the discriminator to enhance the stability of model training; and the strategy gradient scheme of reinforcement learning is employed. Reinforcement learning utilizes the policy gradient scheme as the updating method of generator parameters, ensuring the generated data retains the original key features and maintains a certain level of usability. The experimental results indicate that the proposed model scheme holds a greater advantage over existing methods in terms of text quality and structural consistency, can guarantee the desensitization effect, and ensures the usability of the privacy-sensitive data to a certain extent after desensitization, facilitates the simulation of the development environment for the use of real data and the analysis and sharing of data.