Underwater object detection plays an essential role in ocean exploration, and the increasing amount of underwater object image data makes the study of advanced underwater object detection algorithms of great practical significance. However, there are problems with colour offset, low contrast, and target blur in underwater image data. An underwater object detection algorithm based on Faster R-CNN is proposed to solve these problems. First, the Swin Transformer is used as the backbone network of the algorithm. Second, by adding the path aggregation network, the deep feature map and the shallow feature map are superimposed and fused. Third, online hard example mining, makes the training process more efficient. Fourth, the ROI pooling is improved to ROI align, eliminating the two quantization errors of ROI pooling and improving the detection performance. Compared with other algorithms, the proposed algorithm’s based on improved Faster-RCNN on URPC2018 dataset is improved to 80.54%, and basically solve the problem of missed detection and false detection of objects of different sizes in a complex environment.

Practice teaching is an inseparable part of professional courses for computer majors, which helps to cultivate capability of coding and engineering for students. Progress assessment and result assessment are two common ways to assess the practice teaching. But only by class attendance and programming result, it is not scientific and reasonable for all students to get the final assessment result. And it is not scientific to evaluate teaching quality only by classroom observations or instructional supervision too. How to assess the practice teaching from multiple perspectives scientifically is key point of this paper. A new assessment called fuzzy comprehensive evaluation from different targets collecting from five assessment means is adopted to complete the task. Based on five targets of grade 1 and corresponding targets of grade 2, one fuzzy matrix is constructed with membership determining and one quantitative result is obtained based on calculation of fuzzy matrix. This assessment method breaks through the knowledge barrier and puts emphasis on competence assessment and teaching evaluation, which improves teaching quality in the teaching process. Adopting this assessment method, students' learning effects can be assessed objectively and fairly, which will result in inspiring students' passion for independent learning and helping them to build employment challenge selfconfidence with optimistic and positive attitudes. And teachers will get effective feedback and professional suggestions from experts, students and management department to improve their practice teaching in the future.

The domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to promote the spread and development of historical culture, and it is becoming a necessary means to promote our traditional culture. This paper will build a knowledge graph of ancient Chinese history and culture in order to facilitate the public to more quickly and accurately understand the relevant knowledge of ancient Chinese history and culture. The construction process is as follows: firstly, use crawler technology to obtain text and table data related to ancient history and culture on Baidu Encyclopedia (similar to Wikipedia) and ancient Chinese history and culture related pages. Among them, the crawler technology crawls the semi-structured data in the information box (InfoBox) in the Baidu Encyclopedia to directly construct the triples required for the knowledge graph, crawls the introductory text information of the entries in Baidu Encyclopedia, and specialized historical and cultural websites (history Chunqiu.com, On History.com) to extract unstructured entities and relationships. Secondly, entity recognition and relationship extraction are performed on an unstructured text. The entity recognition part uses the Bidirectional Long Short-Term Memory-Convolutional Neural Networks-Conditions Random Field (BiLSTM-CNN-CRF) model for entity extraction. The relationship extraction between entities is performed by using the open source tool DeepKE (information extraction tool with language recognition ability developed by Zhejiang University) to extract the relationships between entities. After obtaining the entity and the relationship between the entities, supplement it with the triple data that were constructed from the semi-structured data in the existing knowledge base and Baidu Encyclopedia information box. Subsequently, the ontology construction and the quality evaluation of the entire constructed knowledge graph are performed to form the final knowledge graph of ancient Chinese history and culture.

Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient.

With rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$F 1 value have been significantly improved.