Long-term stress not only affects physical health but also leads to the deterioration of mental state. To prevent such adverse effects, it is very important to detect and manage stress promptly. Therefore, it is particularly necessary to develop effective stress detection methods. This study utilizes the photoplethysmogram (PPG) signal in the WESAD database to develop a deep learning model for detecting psychological stress. The PPG signal is preprocessed by data filtering, down sampling, and sliding windows. The local features of the PPG signal are extracted using a convolutional neural network (CNN). Then, the Transformer model is used to analyze and capture the correlation between features. The Transformer is based on the attention mechanism, which enables the model to focus on important local features. Lastly, the long short-term memory (LSTM) is utilized to process the temporal relationship between features and detect stress status. Evaluations on binary, ternary, and quaternary stress classification tasks show significant performance: 96.07%, 92.27%, and 87.04% accuracy are achieved, respectively. Comparison with other recent research findings demonstrates the superiority of the proposed method. The proposed method does not require manual feature extraction engineering, simplifies the data processing process, and improves the accuracy of stress detection, indicating its potential in real-life and practical applications.

This study focuses on developing and validating interpretable deep learning models for depression detection using the proprietary Database. The preprocessing of the proprietary database involves noise reduction, normalization, and segment extraction. The paper proposes a Deep ResNet Network that utilizes overlapping ECG signal segments to capture underlying patterns related to depression. To enhance the understanding of important areas of the input ECG signal in the model, the SHAP (SHapley Additive exPlanations) framework was introduced to increase the interpretability of the results. Independent testing confirmed the robustness and reliability of the Deep ResNet Network, which achieved an accuracy of 92.63%, establishing it as a promising and interpretable tool for future depression diagnosis.

With the development of deep learning technologies, object detection algorithms have made significant progress in terms of detection speed and detection performance. However, the detection speed of current detection networks still does not meet the requirements of real-world applications in some scenarios. In this paper, we propose a faster non-maximum suppression (FNMS) algorithm that reduces the processing time by a large margin while achieving the same detection precision compared with the traditional non-maximum suppression (NMS) algorithm. Moreover, an attempt is made to adopt additional lightweight network structures to improve the speed of the detection network. By combining our FNMS algorithm with other network optimization strategies, we are able to improve the detection speed of YOLO v3 on the DOTA dataset by 165%.

Captcha, which can prevent computer programs from attacking websites, has been the most important security technology for many years. The most popularly deployed Captcha is the text-based scheme. The vast majority of the existing text Captchas are designed with English letters and Arabic numerals. Recently, text Captchas with large character sets are being increasingly popular. From the perspective of attackers, larger character set means greater solution space and better theoretical security. However, the security of Captchas with large character sets in real world has never been studied comprehensively. In this article, we introduce a simple, fast, and effective deep learning method to attack these newly emerging Captchas. Taking 11 Chinese Captchas as representatives, we ran our experimental attack on each of them. Our attack achieved high success rates, ranging from 34.7 to 86.9 percent at an average speed of 0.175 seconds on these schemes. All of the results show that the Chinese text Captcha can be easily broken, demonstrating that text Captchas with large character sets are also insecure in existing forms. As a substitute, we proposed a 3D image-based scheme combining semantic comprehension and dragging action. The preliminary experimental results show that it is more robust than current text-based schemes.