Based on logistics-field blog post data from Weibo from November 2019 to May 2022, the user behaviors of express logistics services in the context of the Coronavirus epidemic are profiled. Using grounded theory and abstract clustering methods, five user behaviors and 22 subject contents are abstracted, and the corresponding user profile is generated. This paper further discusses the subject contents, the subject evolution, and the analysis of group differences. The results show that user satisfaction with logistics services was similar, and the dissatisfaction was diversified with obvious escalation. Variables of transportation efficiency and logistics guarantee were the main factors affecting the evaluation, and the development of the epidemic affected the concerns and attitudes of the subject contents, which had obvious group differences at different degrees.
Convolutional neural networks have made remarkable achievements in artificial intelligence, such as blockchain, speech recognition, and image understanding. However, improvement in model performance is accompanied by a substantial increase in the computational and parameter overhead, leading to a series of problems, such as a slow inference speed, large memory consumption, and difficulty of deployment on mobile devices. Knowledge distillation serves as a typical model compression method, and can transfer knowledge from the teacher network to the student network to improve the latter’s performance without any increase in the number of parameters. A method for extracting representative knowledge for distillation has become the core issue in this field. In this paper, we present a new knowledge distillation method based on intermediate correlation operation, which with the help of data augmentation captures the learning and transformation process of image features during each middle layer stage of the network. We model this feature transform procedure using a correlation operation to extract a new representation from the teacher network to guide the training of the student network. The experimental results demonstrate that our method achieves the best performance on both the CIFAR-10 and CIFAR-100 datasets, in comparison to previous state-of-the-art methods.
Text semantic matching is the basis of many natural language processing tasks. Text semantic matching techniques are required in many scenarios, such as search, question, and answer systems. In practical application scenarios, the efficiency of text semantic matching is crucial. Although the representational learning semantic-matching model is less accurate than the interactive model, it is more efficient. The key to improve the performance of learning-based semantic-matching models is to extract sentence vectors with high-level semantic features. On this basis, this paper presents the design of a feature-fusion module and feature-extraction module based on the ERINE model to obtain sentence vectors with multidimensional semantic features. Further, the performance of the model is improved to obtain semantic information by designing a loss function of semantic prediction. Finally, the accuracy on the Baidu Qianyan dataset reaches 0.851, which indicates good performance.
Instance segmentation is an important task in computer vision. In recent years, the development of meta- and few-shot learning has promoted the combination of computer vision learning tasks, which has overcome the bottleneck of detection and classification with regard to objects that are difficult to manually label and those with high labeling costs. Although great progress has been made with few-shot semantic segmentation and object detection, instance segmentation based on few-shot learning has not become a research hotspot until very recently. Beginning with an overview of few-shot instance segmentation, existing approaches are divided into categories of anchor-based and anchor-free algorithms. The architectures and primary technologies behind those approaches are respectively discussed, and common datasets and evaluation indices are described. Additionally, advantages and disadvantages of algorithm performance are analyzed, and future development directions and challenges are presented.
中国综合性科技类核心期刊(北大核心)
