In order to improve the crowdsourced testing data sharing system in the cloud environment and solve the problems of data security and privacy protection in the field of crowdsourced testing， a Crowdsourced Testing Task Privacy Protection （CTTPP） scheme based on blockchain and CP-ABE （Ciphertext-Policy Attribute-Based Encryption） policy hiding was proposed. Blockchain technology and attribute based encryption were combined to improve the privacy of crowdsourced testing data sharing by the proposed scheme. Firstly， the terminal internal nodes were used to construct an access tree to express the access policy， and the exponentiation operation and bilinear pairing operation in CP-ABE were used to realize policy hiding， so as to improve the privacy protection ability of data sharing in the crowdsourced testing scenarios. Secondly， the blockchain smart contract was called to automatically verify the legitimacy of data visitors， and completed the verification of task ciphertext access rights together with the cloud server to further improve the security of crowdsourced testing tasks. The performance test results show that the average encryption and decryption time is shorter， and the calculation overhead of encryption and decryption is lower than the same type of access tree policy hiding algorithm. In addition， when the frequency of decryption requests reaches 1 000 transactions per second， the processing capacity of blockchain is saturated gradually， and the maximum processing delay for data uplinking and data querying is 0.80 s and 0.12 s， so the proposed scheme is suitable for lightweight commercial crowdsourced testing application scenarios.

A weakly supervised action localization method， which integrated snippet contrastive learning， was proposed to address the issue of misclassification of snippets at action boundaries in existing attention-based methods. First， an attention mechanism with three branches was introduced to measure the possibility of each video frame being an action instance， context， or background. Second， the Class Activation Sequences （CAS） corresponding to each branch were constructed based on the obtained attention values. Then， positive and negative sample pairs were generated using a snippet mining algorithm. Finally， the network was guided through snippet contrastive learning to correctly classify hard snippets. Experimental results indicated that at an Intersection over Union （IoU） of 0.5， the mean Average Precisions （mAP） of the proposed method on THUMOS14 and ActivityNet1.3 datasets are 33.9% and 40.1% respectively， with improvements of 1.1 and 2.9 percentage points compared to the DGCNN （Dynamic Graph modeling for weakly-supervised temporal action localization Convolutional Neural Network） weakly supervised action localization model， validating the effectiveness of the proposed method.

A Contrastive Hypergraph Transformer for session-based recommendation （CHT） model was proposed to address the problems of noise interference and sample sparsity in the session-based recommendation itself. Firstly， the session sequence was modeled as a hypergraph. Secondly， the global context information and local context information of items were constructed by the hypergraph transformer. Finally， the Item-Level （I-L） encoder and Session-Level （S-L） encoder were used on global relationship learning to capture different levels of item embeddings， the information fusion module was used to fuse item embedding and reverse position embedding， and the global session representation was obtained by the soft attention module while the local session representation was generated with the help of the weight line graph convolutional network on local relationship learning. In addition， a contrastive learning paradigm was introduced to maximize the mutual information between the global and local session representations to improve the recommendation performance. Experimental results on several real datasets show that the recommendation performance of CHT model is better than that of the current mainstream models. Compared with the suboptimal model S²-DHCN （Self-Supervised Hypergraph Convolutional Networks）， the proposed model has the P@20 of 35.61% and MRR@20 of 17.11% on Tmall dataset， which are improved by 13.34% and 13.69% respectively； the P@20 reached 54.07% and MRR@20 reached 18.59% on Diginetica dataset， which are improved by 0.76% and 0.43% respectively； verifying the effectiveness of the proposed model.

To solve the problem that representation of interest preferences based on the Recurrent Neural Network （RNN） is incomplete and inaccurate in session recommendation， a Session Recommendation method based on Graph Model and Attention Model （SR?GM?AM） was proposed. Firstly， the graph model used global graph and session graph to obtain neighborhood information and session information respectively， and used Graph Neural Network （GNN） to extract item graph features， which were passed through the global item representation layer and session item representation layer to obtain the global? level embedding and the session?level embedding， and the two levels of embedding were combined into graph embedding. Then， attention model used soft attention to fuse graph embedding and reverse position embedding， target attention activated the relevance of the target items， as well as attention model generated session embedding through linear transformation. Finally， SR?GM?AM outputted the recommended list of the N items for the next click through the prediction layer. Comparative experiments of SR?GM?AM and Lossless Edge?order preserving aggregation and Shortcut graph attention for Session?based Recommendation （LESSR） were conducted on two real public e?commerce datasets Yoochoose and Diginetica， and the results showed that SR?GM?AM had the highest P@20 of 72.41% and MRR@20 of 35.34%， verifying the effectiveness of it.