In order to explore the feasibility of a self-supervised feature extraction method in skeletal X-ray image anomaly detection， an anomaly detection method for skeletal X-ray images based on self-supervised feature extraction was proposed. The self-supervised learning framework and Vision Transformer （ViT） model were combined for feature extraction in skeletal anomaly detection， and anomaly detection classification was carried out by linear classifiers， which can effectively avoid the dependence of supervised models on large-scale labeled data in feature extraction stage. Experiments were performed on publicly available skeletal X-ray image datasets， the skeletal anomaly detection models based on pre-trained Convolutional Neural Network （CNN） and self-supervised feature extraction were evaluated with accuracy. Experimental results show that self-supervised feature extraction model has better effect than the general CNN models， its classification results in seven parts are similar to those of supervised CNN models， but the abnormal detection accuracy for elbow， finger and humerus achieved optimal values， and the average accuracies increases by 5.37 percentage points compared to ResNet50. The proposed method is easy to implement and can be used as a visual assistant tool for radiologist initial diagnosis.

Prompt paradigm is widely used to zero-shot Natural Language Processing （NLP） tasks. However， the existing zero-shot Relation Extraction （RE） model based on Prompt paradigm suffers from the difficulty of constructing answer space mappings and dependence on manual template selection， which leads to suboptimal performance. To address these issues， a zero-shot RE model via multi-template fusion in Prompt was proposed. Firstly， the zero-shot RE task was defined as the Masked Language Model （MLM） task， where the construction of answer space mapping was abandoned. Instead， the words output by the template were compared with the relation description text in the word embedding space to determine the relation class. Then， the part of speech of the relation description text was introduced as a feature， and the weight between this feature and each template was learned. Finally， this weight was utilized to fuse the results output by multiple templates， thereby reducing the performance loss caused by the manual selection of Prompt templates. Experimental results on FewRel （Few-shot Relation extraction dataset） and TACRED （Text Analysis Conference Relation Extraction Dataset） show that， the proposed model significantly outperforms the current state-of-the-art model， RelationPrompt， in terms of F1 score under different data resource settings， with an increase of 1.48 to 19.84 percentage points and 15.27 to 15.75 percentage points， respectively. These results convincingly demonstrate the effectiveness of the proposed model for zero-shot RE tasks.

Concerning the problems of centralized functions of notary nodes and low cross?chain transaction efficiency in notary mechanism， a cross?chain interaction safety model based on notary groups was proposed. Firstly， notary nodes were divided into three kinds of roles， i.e. transaction verifiers， connectors and supervisors， and multiple transactions with consensus were packaged to a big deal by transaction verification group， and the threshold signature technique was used to sign it. Secondly， the confirmed transactions were placed in a cross?chain wait?to?be?transferred pool， some transactions were selected randomly by the connectors， and the technologies such as secure multiparty computation and fully homomorphic encryption were used to judge the authenticity of these transactions. Finally， if the hash values of all eligible transactions were true and reliable as well as verified by the transaction verification group， a batch task of multiple cross?chain transactions was able to be continued by the connector and be interacted with the blockchain in information. Security analysis shows that the proposed cross?chain mechanism is helpful to protect the confidentiality of information and the integrity of data， realizes the collaborative computing of data without leaving the database， and guarantees the stability of the cross?chain system of blockchain. Compared with the traditional cross?chain interaction security model， the complexity of the number of signatures and the number of notary groups that need to be assigned decreases from O ( n ) to O ( \mathrm{1} ) .

In view of the fact that the existing algorithms cannot effectively be applied to multi-factor trajectory outlier detection, this paper proposed a new method named TOD-KPCA (Trajectory Outlier Detection method based on Kernel Principal Component Analysis). Firstly, in order to enhance the effect of trajectory feature extraction, the method used KPCA to do the space transformation for trajectories and converted nonlinear space to a high dimension linear space. Furthermore, in order to improve the accuracy of outlier detection, the method used one-class Support Vector Machine (SVM) to do unsupervised learning and prediction with trajectory feature data. Finally, the method detected those trajectories with abnormal behavior. The proposed algorithm was tested on the Atlantic hurricane data. The experimental results show that the proposed algorithm can effectively extract trajectory features, and compared with the same algorithm, the proposed algorithm has better detection results in terms of multi-factor trajectory outlier detection.

Concerning the impact on network performance of the mobile nodes and the constantly changing data transmission rate, the authors proposed a new backoff scheme for IEEE802.15.4, which used Probability Judgment based on Network Load and Exponentially Weighted Moving Average (PJNL_EWMA) method. According to a realtime monitoring of current network status by probability judgment of network load, this method dynamically adjusted backoff exponent by EWMA when Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) began. Compared with the IEEE802.15.4 standard protocol and MBS (Memorized Backoff Scheme)+EWMA algorithm, the simulation experiments on NS2 platform show that the PJNL_EWMA algorithm not only improves the throughput of the network, but also reduces the packet loss rate and the collision ratio, significantly improving the network performance.

The judgment of reachability is one of the fundamental issues in Petri net analysis. The paper analyzed the existing method and the method based on constraint programming for the reachability of Petri net, and then proposed the judgment method for reachability problem based on constraint optimization. The method was based on the state equation method, separately using the constraint programming and the optimization to seek the feasible solution and the optimal solution, thereby decreased the searching path and attained the purpose of reducing the solution space of the state equation. Finally an example was given to prove that the algorithm can improve the determination efficiency.

Considering the basic need of the attack resistance test for trustworthiness, controllability and effectiveness of attack operation, a Denial of Service (DoS) Attack Script Language (DASL) was designed based on Domain Specific Language (DSL), which could be used to develop DoS attacks simply, quickly and conveniently. In this article, attack unit was defined, the domain specific syntactic was constructed based on the analysis of attack samples, the semantic function was realized based on LIBNET, and the interpreter of DASL was designed on the basis of ANTLR. The experimental results show that, attacks developed by DASL were effective and controllable. And DASL can lower the complexity of development, reduce the amount of code to write, increase the efficiency of development and provide powerful support for DoS penetration testing.

An approach to detecting continuous curves from uniformly distributed random background was presented. A dynamic programming algorithm was employed to detect the most notable curve from the points according to curve continuity. And an application to detect the Yellow River's mainstream line was demonstrated with multispectral image. The mainstream points extracted by feature extraction methods were scattered on the river because of the spectral similarity of mainstream and non-mainstream. The spatial continuity between mainstream points makes this approach feasible. And experimental results indicate that this approach is valid and superior to curve fitting.