To address the challenges of privacy leakage in identity information and transaction data within consortium blockchain， a Privacy Protection Method for Consortium Blockchain based on SM2 Linkable Ring Signature （PPMCB-SM2LRS） was proposed. Firstly， to overcome the issues of insufficient security and poor traceability in existing Linkable Ring Signature （LRS） scheme， it was redesigned in combination with SM2 digital signature， aiming to enhance the privacy protection of counterparty identities while enabling the traceability of malicious users. Secondly， based on the optimized Paillier homomorphic encryption algorithm， a hierarchical encryption strategy was proposed to realize the “visible unavailability” of private data， so as to improve the privacy and confidentiality of transaction data verification in consortium chain. Security analysis demonstrates that the proposed method is correct， unforgeable， conditionally anonymous and linkable. Experimental results show that compared with similar LRS schemes， PPMCB-SM2LRS has lower computational overhead， and the average time spent in the signature generation and verification stages is significantly reduced； additionally， it adheres to the principle of autonomous controllability in cryptographic technology development.
A Multi-Behavior Recommendation based on Cascading Residual graph convolutional network （CRMBR） model was proposed to address the problems of data sparsity and neglecting the complex connections among multiple behaviors in multi-behavior recommendation research. Firstly， the global embeddings of users and items were learned from a unified isomorphic graph constructed from the interactions of all behaviors and used as initialization embeddings. Secondly， the embeddings of different types of behaviors were refined continuously to improve the user preferences by capturing the connections among different behaviors through cascading residual blocks. Finally， user and item embeddings were aggregated through two different aggregation strategies， respectively， and optimized using Multi-Task Learning （MTL）. Experimental results on several real datasets show that the recommendation performance of CRMBR model is better than that of the current mainstream models. Compared with the advanced benchmark model — Multi-Behavior Hierarchical Graph Convolutional Network （MB-HGCN）， the proposed model has the Hit Rate （HR@20） and Normalized Discount Cumulative Gain （NDCG@20） improved by 3.1% and 3.9% on Tmall dataset， increased by 15.8% and 16.9% on Beibei dataset， and improved by 1.0% and 3.3% on Jdata dataset， respectively， which validates the effectiveness of the proposed model.
A Multi-View and Multi-Scale Contrastive Learning for graph collaborative filtering （MVMSCL） model was proposed to address the limitations of single view and the data sparsity in graph collaborative filtering recommendation methods. Firstly， an initial interaction diagram was constructed on the basis of user-item interactions， and multiple potential intentions in user-item interactions were considered to build multi-intention decomposition view. Secondly， the adjacency matrix was improved using high-order relationships to construct a collaborative neighbor view. Thirdly， the irrelevant noise interactions were removed to construct the adaptively enhanced initial interaction diagram and multi-intention decomposition view. Finally， contrastive learning paradigms with local， cross-layer， and global scales were introduced to generate self-supervised signals， thereby improving the recommendation performance. Experimental results on three public datasets， Gowalla， Amazon-book and Tmall， demonstrate that the recommendation performance of MVMSCL surpasses that of the comparison models. Compared with the optimal baseline model DCCF （Disentangled Contrastive Collaborative Filtering framework）， MVMSCL has the Recall@20 increased by 5.7%， 14.5% and 10.0%， respectively， and the Normalized Discounted Cumulative Gain NDCG@20 increased by 4.6%， 17.9% and 11.5%， respectively.
In view of the inaccuracy of action classification and localization caused by the independent processing of video clips as single action instances in the existing weakly supervised action localization studies， a weakly supervised action localization method that integrates temporal and global contextual feature enhancement was proposed. Firstly， the temporal feature enhancement branch was constructed to enlarge the receptive field by using dilated convolution， and the attention mechanism was introduced to capture the temporal dependency between video clips. Secondly， an EM （Expectation-Maximization） algorithm based on Gaussian Mixture Model （GMM） was designed to capture video context information. At the same time， global contextual feature enhancement was performed by using binary walk propagation. As the result， high-quality Temporal Class Activation Maps （TCAMs） were generated as pseudo labels to supervise the temporal enhancement branch online. Thirdly， the momentum update network was used to obtain a cross-video dictionary that reflects the action features between videos. Finally， cross-video contrastive learning was used to improve the accuracy of action classification. Experimental results show that the proposed method has the mean Average Precision （mAP） of 42.0% and 42.2% on THUMOS’14 and ActivityNet v1.3 datasets when the Intersection-over-Union （IoU） is 0.5， and compared with CCKEE （Cross-video Contextual Knowledge Exploration and Exploitation）， the proposed method has the mAP improved by 2.6 and 0.6 percentage points， respectively， proving the effectiveness of the proposed method.
Image caption methods based on Transformer use multi-head attention to calculate attention weights on the entire input sequence， and lack hierarchical feature extraction capabilities. Additionally， two-stage image caption methods limit model performance. To address the above issues， an image caption method based on Swin Transformer and Multi-Scale feature Fusion （STMSF） was proposed. In the encoder of this method， Agent Attention was used to maintain global context modeling capability while improving computational efficiency. In the decoder of this method， Multi-Scale Cross Attention （MSCA） was proposed to combine cross-attention and depthwise separable convolution， which obtained multi-scale features and fused multi-modal features better. Experimental results on the MSCOCO dataset show that compared to SCD-Net （Semantic-Conditional Diffusion Network） method， STMSF has the BLEU4 （BiLingual Evaluation Understudy with 4-grams） and CIDEr （Consensus-based Image Description Evaluation） metrics improved by 1.1 and 5.3 percentage points， respectively. The above comparison experimental results as well as ablation experimental results show that the proposed single-stage STMSF can improve model performance effectively and generate high-quality image caption sentences.
Smart contracts on blockchain platforms are decentralized applications to provide secure and trusted services to multiple parties on the chain. Smart contract vulnerability detection can ensure the security of these contracts. However， the existing methods for detecting smart contract vulnerabilities encountered issues of insufficient feature learning and low vulnerability detection accuracy when dealing with imbalanced sample sizes and incomplete semantic information mining. Moreover， these methods cannot detect new vulnerabilities in contracts. A smart contract vulnerability detection method based on Echo State Network （ESN） was proposed to address the above problems. Firstly， different semantic and syntactic edges were learned on the basis of contract graph， and feature vectors were obtained through Skip-Gram model training. Then， ESN was combined with transfer learning to achieve transfer and extension of new contract vulnerabilities in order to improve the vulnerability detection rate. Finally， experiments were conducted on the smart contract dataset collected on Etherscan platform. Experimental results show that the accuracy， precision， recall， and F1-score of the proposed method reach 94.30%， 97.54%， 91.68%， and 94.52%， respectively. Compared with Bidirectional Long Short-Term Memory （BLSTM） network and Bidirectional Long Short-Term Memory with ATTention mechanism （BLSTM-ATT）， the proposed method has the accuracy increased by 5.93 and 11.75 percentage points respectively， and the vulnerability detection performance is better. The ablation experiments also further validate the effectiveness of ESN for smart contract vulnerability detection.
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.
In response to the facts that most of Attribute-Based Searchable Encryption （ABSE） schemes are designed on the basis of non-national encryption algorithms and are unable to resist internal Algorithm Substitution Attack （ASA）， an SM9-based Attribute-Based Searchable Encryption with Cryptographic Reverse Firewall （SM9ABSE-CRF） was proposed. This scheme extends the SM9 algorithm to the ABSE field， realizes fine-grained data access control， and introduces Cryptographic Reverse Firewall （CRF） technology to effectively resist ASA. SM9ABSE-CRF was analyzed under the Decisional Bilinear Diffie-Hellman （DBDH） assumption， and the deployment of CRF was formally proved to maintain functionality， preserving security， and resisting exfiltration. Theoretical analysis and simulation results show that compared to the ABSE scheme providing CRF — cABKS-CRF （consistent Attribute-Based Keyword Search system with CRF）， SM9ABSE-CRF has higher security and demonstrates notable performance advantages during index and trapdoor generation phases.
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.
In the operation and maintenance of high-end manufacturing enterprises， the spare parts demand occurs randomly， accompanied by a large number of zero demand periods. At the same time， the corresponding sparse parts demand data is of small scale and has intermittent and distribution with lump formation characteristics. Consequently， most of current time series forecasting methods are hard to effectively predict the demand trends. To solve this problem， a predictability evaluation and joint forecasting method for intermittent time series was proposed. Firstly， a new intermittent-similarity metric was proposed. In this metric， the frequency and positions of the "0" element occurring in the two sequences were counted， while the metrics such as maximal information coefficient and average demand interval were combined to evaluate the tendency information and fluctuation pattern of the sequences effectively and realize the quantification of the predictability of the intermittent time series. Then， based on this metric， an intermittent-similarity hierarchical clustering method was constructed to adaptively select the sequences with high similarity and strong predictability as well as eliminate extremely sparse and unpredictable sequences. Moreover， the structured information between the sequences was explored and utilized， a Multi-output Support Vector Regression （M-SVR） model was constructed， thereby achieving the joint prediction of intermittent time series with small-scale data. Finally， the experiments were conducted on two public datasets （UCI （University of California Irvine） gift retail dataset and Huawei computer accessory dataset） and a real-world spare parts after-sales dataset of a large manufacturing enterprise， respectively. The results show that compared with several representative time series forecasting methods， the proposed method can effectively exploit the predictability of various kinds of intermittent sequences and improve the prediction accuracy of intermittent time series with small-scale data. Therefore， the proposed method provides a new solution for the spare parts demand forecasting of manufacturing enterprises.
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.
