Cross-Domain Car Detection Model with Integrated Convolutional Block Attention Mechanism

Car detection, particularly through camera vision, has become a major focus in the field of computer vision and has gained widespread adoption. While current car detection systems are capable of good detection, reliable detection can still be challenging due to factors such as proximity between the car, light intensity, and environmental visibility. To address these issues, we propose a cross-domain car detection model that we apply to car recognition for autonomous driving and other areas. Our model includes several novelties: 1)Building a complete cross-domain target detection framework. 2)Developing an unpaired target domain picture generation module with an integrated convolutional attention mechanism. 3)Adopting Generalized Intersection over Union (GIOU) as the loss function of the target detection framework. 4)Designing an object detection model integrated with two-headed Convolutional Block Attention Module(CBAM). 5)Utilizing an effective data enhancement method. To evaluate the model's effectiveness, we performed a reduced will resolution process on the data in the SSLAD dataset and used it as the benchmark dataset for our task. Experimental results show that the performance of the cross-domain car target detection model improves by 40% over the model without our framework, and our improvements have a significant impact on cross-domain car recognition

Multimodal Sentiment Analysis: A Survey

Multimodal sentiment analysis has become an important research area in the field of artificial intelligence. With the latest advances in deep learning, this technology has reached new heights. It has great potential for both application and research, making it a popular research topic. This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology. Finally, it looks ahead to future research directions. It should be noted that this review provides constructive suggestions for promising research directions and building better performing multimodal sentiment analysis models, which can help researchers in this field.Comment: It needs to be returned for major modification

Classifying Crime Types using Judgment Documents from Social Media

The task of determining crime types based on criminal behavior facts has become a very important and meaningful task in social science. But the problem facing the field now is that the data samples themselves are unevenly distributed, due to the nature of the crime itself. At the same time, data sets in the judicial field are less publicly available, and it is not practical to produce large data sets for direct training. This article proposes a new training model to solve this problem through NLP processing methods. We first propose a Crime Fact Data Preprocessing Module (CFDPM), which can balance the defects of uneven data set distribution by generating new samples. Then we use a large open source dataset (CAIL-big) as our pretraining dataset and a small dataset collected by ourselves for Fine-tuning, giving it good generalization ability to unfamiliar small datasets. At the same time, we use the improved Bert model with dynamic masking to improve the model. Experiments show that the proposed method achieves state-of-the-art results on the present dataset. At the same time, the effectiveness of module CFDPM is proved by experiments. This article provides a valuable methodology contribution for classifying social science texts such as criminal behaviors. Extensive experiments on public benchmarks show that the proposed method achieves new state-of-the-art results.Comment: 5 page

Impact of vaccination on kinetics of neutralizing antibodies against SARS-CoV-2 by serum live neutralization test based on a prospective cohort

ABSTRACTHow much the vaccine contributes to the induction and development of neutralizing antibodies (NAbs) of breakthrough cases relative to those unvaccinated-infected cases is not fully understood. We conducted a prospective cohort study and collected serum samples from 576 individuals who were diagnosed with SARS-CoV-2 Delta strain infection, including 245 breakthrough cases and 331 unvaccinated-infected cases. NAbs were analysed by live virus microneutralization test and transformation of NAb titre. NAbs titres against SARS-CoV-2 ancestral and Delta variant in breakthrough cases were 7.8-fold and 4.0-fold higher than in unvaccinated-infected cases, respectively. NAbs titres in breakthrough cases peaked at the second week after onset/infection. However, the NAbs titres in the unvaccinated-infected cases reached their highest levels during the third week. Compared to those with higher levels of NAbs, those with lower levels of NAbs had no difference in viral clearance duration time (P>0.05), did exhibit higher viral load at the beginning of infection/maximum viral load of infection. NAb levels were statistically higher in the moderate cases than in the mild cases (P<0.0001). Notably, in breakthrough cases, NAb levels were highest longer than 4 months after vaccination (Delta strain: 53,118.2 U/mL), and lowest in breakthrough cases shorter than 1 month (Delta strain: 7551.2 U/mL). Cross-neutralization against the ancestral strain and the current circulating isolate (Omicron BA.5) was significantly lower than against the Delta variant in both breakthrough cases and unvaccinated-infected cases. Our study demonstrated that vaccination could induce immune responses more rapidly and greater which could be effective in controlling SARS-CoV-2