The rapid development of Internet technology has brought great convenience to our production life, and the ensuing security problems have become increasingly prominent. These problems threaten users’ privacy and pose significant security risks to the normal conduct of many aspects of society, such as politics, economy, culture, and people’s livelihood. The growth of the information transmission rate expands the scope of attacks and provides a more attack environment for intruders. Abnormal detection is an effective security protection technology that can monitor network transmission in real-time, effectively sense external attacks, and provide response decisions for relevant managers. The development of machine learning has also led to the development of abnormal traffic detection technology. The goal has been to use powerful and fast learning algorithms to deal with changing threats and respond in real-time. Most of the current abnormal detection research is based on simulation, using public and well-known datasets. On the one hand, the dataset contains high-dimensional massive data, which traditional machine learning methods cannot be processed. On the other hand, the labeled data scale is far behind the application requirements, and the dataset’s labels are all manually labeled, so the labeling cost is exceptionally high. This paper proposes a semi-supervised Double Deep Q-Network (SSDDQN)-based optimization method for network abnormal traffic detection, mainly based on Double Deep Q-Network (DDQN), a representative of Deep Reinforcement Learning algorithm. In SSDDQN, the current network first adopts the autoencoder to reconstruct the traffic features and then uses a deep neural network as a classifier. The target network first uses the unsupervised learning algorithm K-Means clustering and then uses deep neural network prediction. The experiment uses NSL-KDD and AWID datasets for training and testing and performs a comprehensive comparison with existing machine learning models. The experimental results show that SSDDQN has certain advantages in time complexity and achieved good results in various evaluation metrics.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug–disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model’s effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug–disease network analysis, laying a solid foundation for future drug discovery.

Smart clothing is widely used for social safety, health monitoring, and sports monitoring. Current research focuses on the use of various materials or sensors to implement smart clothes with different functions, which implies that the functionality of smart clothing depends on the number of sensors used. For existing smart clothing systems, the greatest attention has been given to information processing algorithms and assembly of sensors, and the interaction between users and systems is ignored. To address this gap, this paper considers a multifunctional smart clothing system constructed with several sensors. The smart clothing system proposed in this paper mainly consists of a hardware module and a software module. Four types of sensors are incorporated into the hardware module to monitor heart rate, blood oxygen saturation, body temperature, locating information, and activity states; the software module includes the three-dimensional model based on the user and the feedback system based on digital twin technology. The digital twin technology can map the fundamental states of users in terms of the monitoring indexes from the hardware module, and give correspondent advice to users. This novel smart clothing system overcomes the lack of an interaction function in existing methods and introduces digital twin technology into smart wearable devices for the first time.

An image-based virtual try-on system transfers an in-shop garment to the corresponding garment region of a reference person, which has huge application potential and commercial value in online clothing shopping. Existing methods have difficulty preserving garment texture and body details because of rough garment alignment and imperfect detail-retention strategies. To address this problem, we propose a virtual try-on network based on semantic constraints and flow alignment. The key idea of the framework is as follows: 1) a global-local semantic predictor (GLSP) is proposed to generate a reasonable target semantic map, which clearly guides the correct alignment of the in-shop garment with the body and the generation of try-on result; and 2) a novel appearance flow-based garment alignment network (AFGAN) is proposed to align the in-shop garment with the body, which is important to preserve maximum garment detail and ensure natural and realistic warping; and 3) we propose a synthesis strategy to integrate the aligned garment and the human body to preserve maximum body detail for generating a realistic result and preventing cross-occlusion and pixel confusion between different body parts. Experiments on the existing benchmark dataset demonstrate that the proposed method achieves the best performance on qualitative and quantitative experiments among the state-of-the-art virtual try-on techniques.

Multi-pose virtual try-on has become a research focus for online clothes shopping due to the fixed-pose virtual try-on methods that cannot provide a different pose try-on effect. The challenge of multi-pose virtual try-on is that the detailed information of a generated image is difficult to obtain in the pose transformation and garment distortion. To solve the issue, we propose a multi-pose virtual try-on method via appearance flow and feature filtering (VTON-MP). First, a segmentation generation network of 2D keypoints about the target pose is used to predict the body semantic distribution of the target pose. Second, the desired garment is distorted to correspond to the body posture using the appearance flow figure alignment network (AFFAN). Third, latent useless feature weights are restrained using a filtering-enhancement block (FEB), and effective appearance feature weights are enhanced. Finally, the spatial relationship of body parts in the resulting image is further optimized using spatially-adaptive instance normalization (SAIN). Compared to state-of-the-art methods of subjective and objective experiments on the MPV dataset, the proposed VTONMP achieves the best performance in terms of SSIM, PSNR and FID. The experimental results demonstrate that the proposed algorithm can better retain image details (head, hands, arms, and trousers).

Retinal vessel segmentation is an important task in medical image analysis that can aid doctors in diagnosing various eye diseases. However, due to the complexity and blurred boundaries of retinal vessel structures, existing methods face many challenges in practical applications. To overcome these challenges, this paper proposes a retina vessel segmentation algorithm based on an attention mechanism, called CAS-UNet. Firstly, the Cross-Fusion Channel Attention mechanism is introduced, and the Structured Convolutional Attention block is used to replace the original convolutional block of U-Net to achieve channel enhancement for retinal blood vessels. Secondly, an Additive Attention Gate is added to the skip-connection layer of the network to achieve spatial enhancement for retinal blood vessels. Finally, the SoftPool pooling method is used to reduce information loss. Experimental results using the CHASEDB1 and DRIVE datasets show that the proposed algorithm achieves an accuracy of 96.68% and 95.86%, and a sensitivity of 83.21% and 83.75%, respectively. The proposed CAS-UNet thus outperforms the existing U-Net-based classic algorithms.