To address the issues of Contrastive Learning （CL） methods struggling to distinguish similar chest X-ray samples and detect tiny lesions in medical images， a dual-branch distribution consistency contrastive learning model （TCL） was proposed. Firstly， inpainting and outpainting data augmentation strategies were employed to strengthen the model’s focus on lung textures， thereby improving the model’s ability to recognize complex structures. Secondly， a collaborative learning approach was used to further enhance the model’s sensitivity to tiny lesions in lungs， thereby capturing lesion information from different perspectives. Finally， the heavy-tailed characteristic of Student-t distribution was utilized to differentiate hard negative samples， so as to constrain the consistency of distributions among different augmented views and samples， thereby reinforcing the learning of feature relationships among hard negatives and other samples， and reducing the influence of hard negatives on the model. Experimental results on four chest X-ray datasets， including pneumoconiosis， NIH （National Institutes of Health）， Chest X-Ray Images （Pneumonia）， and COVID-19 （Corona Virus Disease 2019）， demonstrate that compared to MoCo v2 （Momentum Contrastive Learning） model， TCL model improves the accuracy by 6.14%， 3.08%， 0.65%， and 4.67%， respectively， and in terms of transfer performance on COVID-19 dataset， TCL model achieves improvements of 4.10%， 0.61%， and 8.41%， respectively， at label rate of 5%， 20%， and 50%. Furthermore， CAM （Class Activation Mapping） visualization verifies that TCL model focuses on critical pathological regions effectively， confirming the model’s effectiveness.

Convolutional Neural Network （CNN） is one of the important research directions in the field of computer vision based on deep learning at present. It performs well in applications such as image classification and segmentation， target detection. Its powerful feature learning and feature representation capability are admired by researchers increasingly. However， CNN still has problems such as incomplete feature extraction and overfitting of sample training. Aiming at these issues， the development of CNN， classical CNN network models and their components were introduced， and the methods to solve the above issues were provided. By reviewing the current status of research on CNN models in image classification， the suggestions were provided for further development and research directions of CNN.

To solve the problems that the index value of cryptographic modules is not fixed, the index system is hardly built, and the security assurance ability can not be quantitatively assessed, a security assurance capability assessment for cryptographic module was proposed. The description on indexes by interval number was applied to illustrate the security attribute of cryptographic modules. This paper determined the weight vector of each period point by entropy weight coefficient method combined with expert decision weight method. According to the interval multi-attribute decision methodology, a feasible methodology was adopted to solve the interval Information Assurance (IA) capability evaluation problem of cryptographic modules. Finally, through analyzing two kinds of cryptographic modules, the experimental results show that the proposed method is feasible.

To solve the problem of transmitting credentials and other resources through unsafe physical channels during an Automated Trust Negotiation (ATN), a transmission scheme for credentials and resources was proposed based on magic cube algorithm. Through the magic cube algorithm, a transformation sequence was formed in terms of the request or the resource of negotiation initiator, followed by the digital digest to generate the information transformation sequence. According to the logical expression composed of credentials which represent the condition negotiation success, the information transformation sequence was shuffled to form an information transmission sequence, which was sent to the negotiation receiver. The information transmission sequence was reciprocally transformed by the negotiation receiver according to his own credentials. This scheme has many features of the one-round credential exchange, and little network cost. The example shows that the scheme is feasible, and the experimental results show that the scheme has good security and efficiency and low information transmission capacity.