Aiming at the problem that the number of parameters， calculation amount and accuracy of general gesture recognition algorithms are difficult to balance， a lightweight gesture recognition algorithm for basketball referee was proposed. The proposed algorithm was reconstructed on the basis of YOLOV5s （You Only Look Once Version 5s） algorithm： Firstly， the Involution operator was used to replace CSP1_1 （Cross Stage Partial 1_1） convolution operator to expand the context information capturing range and reduce the kernel redundancy. Secondly， the Coordinate Attention （CA） mechanism was added after the C3 module to obtain stronger gesture feature extraction ability. Thirdly， a lightweight content aware upsampling operator was used to improve the original upsampling module， and the sampling points were concentrated in the object area and the background part was ignored. Finally， the Ghost-Net with SiLU （Sigmoid Weighted Liner Unit） as the activation function was used for lightweight pruning. Experimental results on the self-made basketball referee gesture dataset show that the calculation amount， number of parameters and model size of this lightweight gesture recognition algorithm for basketball referee are 3.3 GFLOPs， 4.0×10⁶ and 8.5 MB respectively， which are only 79%， 44% and 40% of those of YOLOV5s algorithm， mAP@0.5 of the proposed algorithm is 91.7%， and the detection frame rate of the proposed algorithm on the game video with a resolution of 1 920×1 280 reaches 89.3 frame/s， verifying that the proposed algorithm can meet the requirements of low error， high detection rate and lightweight.

Control logic injection attack against Programmable Logic Controller （PLC） manipulate the physical process by tampering with the control program， thereby achieving the purpose of affecting the control process or destroying the physical facilities. Aiming at PLC control logic injection attacks， an intrusion detection method based on automatic whitelist rules generation was proposed， called PLCShield （Programmable Logic Controller Shield）. Based on the fact that PLC control program carries comprehensive and complete physical process control information， the proposed method mainly includes two stages： firstly， by analyzing the PLC program’s configuration file， instruction function， variable attribute， execution path and other information， the detection rules such as program attribute， address， value range and structure were extracted； secondly， combining actively requesting a “snapshot” of the PLC’s running and passively monitoring network traffic was used to obtain real-time information such as the current running status of PLC and the operation and status in the traffic， and the attack behavior was identified by comparing the obtained information with the detection rules. Four PLCs of different manufacturers and models were used as research cases to verify the feasibility of PLCShield. Experimental results show that the attack detection accuracy of the proposed method can reach more than 97.71%. The above prove that the proposed method is effective.

In order to solve the problems of low detection efficiency and poor detection precision caused by various surface defects and numerous small defects of section steel， a detection algorithm for surface defects of section steel， namely Steel-YOLOv3， was proposed on the basis of the deformable convolution and multi-scale dense feature pyramid. Firstly， the deformable convolution was used to replace the convolutional layers of part of the residual units in Darknet53 network， which strengthened the feature learning ability of feature extraction network for multi-type defects on the surface of section steel. Secondly， a multi-scale dense feature pyramid module was designed， which means that a shallower prediction scale was added to the 3 prediction scales of the original YOLOv3 algorithm and the multi-scale feature maps were connected across layers， thereby enhancing the ability to characterize dense small defects. Finally， according to the defect size distribution characteristics of section steel， the K-means dimension clustering method was used to optimize the scales of anchor boxes， and the anchor boxes were evenly distributed to 4 corresponding prediction scales. Experimental results show that Steel-YOLOv3 algorithm has a detection mean Average Precision （mAP） of 89.24%， which is improved by 3.51%， 26.46%， 12.63% and 5.71% compared with those of Faster Region-based Convolutional Neural Network （Faster R-CNN）， Single Shot multibox Detector （SSD）， YOLOv3 and YOLOv5 algorithms respectively. And the detection rate of tiny spalling defects is significantly improved by the proposed algorithm. Moreover， the proposed algorithm can detect 25.62 images per second， which means the requirement of real-time detection can be met and the algorithm can be applied to the online detection for the surface defects of section steel.

Entity relation extraction is a critical basic step of question answering, knowledge graph construction and information extraction in the medical field. In view of the fact that there is no open dataset available in the process of building knowledge graph specialized for cardiovascular disease, a professional training set for entity relation extraction of specialized cardiovascular disease knowledge graph was constructed by collecting some medical guidelines for cardiovascular disease and performing the corresponding professional labeling of the categories of entities and relations. Based on this dataset, firstly, Bidirectional Encoder Representation from Transformers and Convolutional Neural Network (BERT-CNN) model was proposed to realize the relation extraction in Chinese corpus. Then, the improved Bidirectional Encoder Representation from Transformers and Convolutional Neural Networks based on whole word mask (BERT(wwm)-CNN) model was proposed to improve the performance of relation extraction in Chinese corpus, according to the fact that word instead of character is the fundamental unit in Chinese. Experimental results show that, the improved BERT(wwm)-CNN model has the accuracy of 0.85, the recall of 0.80 and the F ₁ value of 0.83 on the constructed relation extraction dataset, which are better than those of the comparison models, Bidirectional Encoder Representation from Transformers and Long Short Term Memory (BERT-LSTM) and BERT-CNN, verifying the superiority of the improved BERT(wwm)-CNN.

The traditional blind detection methods of image copy-paste forgery are time consuming, of high computation cost and low detection precision. A blind detection algorithm of copy-paste image forgery based on Mean Shift (MS) was proposed in this paper, which extracted Speed Up Robust Feature (SURF) points and then performed feature matching utilizing the method of best bin first in order to filter redundant points and locate the copy-paste forgery regions preliminarily. Pixels with the same or similar attributes would be segmented in the same region after implementing MS. The copy-paste regions could be detected according to the position dependency between matched feature point with its segmented region of MS and the detection result would be further refined by comparing the similarity of edge histogram and HSV (Hue-Saturation-Value) color histogram among the segmented regions of matched SURF and its neighborhood, and those regions with large similarity were included in the forged region. The experimental results show that the copy-paste forgery regions are detected accurately in the image with clear outline and rich details, and the proposed algorithm can robustly and efficiently detect the copy-paste forgery regions of image.

Considering the problems caused by the traditional association rules visualization approaches, including being unable to display the frequent pattern and relationships of items, unitary express, especially being not conducive to represent multi-schema association rules, a new visualizing algorithm for multi-valued association rules mining was proposed. It introduced the redefinition and classification of multi-valued attribute data by using conceptual lattice and presented the multi-valued attribute items of frequent itemset and association rules with concept lattice structure. This methodology was able to achieve frequent itemset visualization and multi-schema visualization of association rules, including the type of one to one, one to many, many to one, many to many and concept hierarchy. At last, the advantages of these new methods were illustrated with the help of experimental data obtained from demographic data of a province, and the source data visualization, frequent pattern and association relation visual representation of the demographic data were also achieved. The practical application analysis and experimental results prove that the schema has more excellent visual effects for frequent itemset display and authentical multi-schema association rules visualization.