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.

The existing multi-tampering type image forgery detection algorithms using noise features often can not effectively detect the feature difference between tampered areas and non-tampered areas， especially for copy-move tampering type. To this end， a dual-stream image tampering forensics network fusing residual feedback and self-attention mechanism was proposed to detect tampering artifacts such as unnatural edges of RGB pixels and local noise inconsistence respectively through two streams. Firstly， in the encoder stage， multiple dual residual units integrating residual feedback were used to extract relevant tampering features to obtain coarse feature maps. Secondly， further feature reinforcement was performed on the coarse feature maps by the improved self-attention mechanism. Thirdly， the mutual corresponding shallow features of encoder and deep features of decoder were fused. Finally， the final features of tempering extracted by the two streams were fused in series， and then the pixel-level localization of the tampered area was realized through a special convolution operation. Experimental results show that the F1 score and Area Under Curve （AUC） value of the proposed network on COVERAGE dataset are better than those of the comparison networks. The F1 score of the proposed network is 9.8 and 7.7 percentage points higher than that of TED-Net （Two-stream Encoder-Decoder Network） on NIST16 and Columbia datasets， and the AUC increases by 1.1 and 6.5 percentage points， respectively. The proposed network achieves good results in copy-move tampering type detection， and is also suitable for other tampering type detection. At the same time， the proposed network can locate the tampered area at pixel level accurately， and its detection performance is superior to the comparison networks.

Nested entities pose a challenge to the task of entity-relation joint extraction. The existing joint extraction models have the problems of generating a large number of negative examples and high complexity when dealing with nested entities. In addition， the interference of nested entities on triplet prediction is not considered by these models. To solve these problems， a forest-based entity-relation joint extraction method was proposed， named EF2LTF （Entity Forest to Layering Triple Forest）. In EF2LTF， a two-stage joint training framework was adopted. Firstly， through the generation of an entity forest， different entities within specific nested entities were identified flexibly. Then， the identified nested entities and their hierarchical structures were combined to generate a hierarchical triplet forest. Experimental results on four benchmark datasets show that EF2LTF outperforms methods such as joint entity and relation extraction with Set Prediction Network （SPN） model， joint extraction model for entities and relations based on Span — SpERT （Span-based Entity and Relation Transformer） and Dynamic Graph Information Extraction ++ （DyGIE++）^{on F1 score. It is verified that the proposed method not only enhances the recognition ability of nested entities， but also enhances the ability to distinguish nested entities when constructing triples， thereby improving the joint extraction performance of entities and relations.}

Image inpainting is a common method of image tampering. Image inpainting methods based on deep learning can generate more complex structures and even new objects， making image inpainting forensics more challenging. Therefore， an end-to-end U-shaped Feature Pyramid Network （FPN） was proposed for image inpainting forensics. Firstly， multi-scale feature extraction was performed through the from-top-to-down VGG16 module， and then the from-bottom-to-up feature pyramid architecture was used to carry out up-sampling of the fused feature maps， and a U-shaped structure was formed by the overall process. Next， the global and local attention mechanisms were combined to highlight the inpainting traces. Finally， the fusion loss function was used to improve the prediction rate of the repaired area. Experimental results show that the proposed method achieves an average F1-score and Intersection over Union （IoU） value of 0.791 9 and 0.747 2 respectively on various deep inpainting datasets. Compared with the existing Localization of Diffusion-based Inpainting （LDI）， Patch-based Convolutional Neural Network （Patch-CNN） and High-Pass Fully Convolutional Network （HP-FCN） methods， the proposed method has better generalization ability， and also has stronger robustness to JPEG compression.

When using the slicing method to measure the point cloud volumes of irregular objects， the existing Polygon Splitting and Recombination （PSR） algorithm cannot split the nearer contours correctly， resulting in low calculation precision. Aiming at this problem， a multi-contour segmentation algorithm — Improved Nearest Point Search （INPS） algorithm was proposed. Firstly， the segmentation of multiple contours was performed through the single-use principle of local points. Then， Point Inclusion in Polygon （PIP） algorithm was adopted to judge the inclusion relationship of contours， thereby determining positive or negative property of the contour area. Finally， the slice area was multiplied by the thickness and the results were accumulated to obtain the volume of irregular object point cloud. Experimental results show that on two public point cloud datasets and one point cloud dataset of chemical electron density isosurface， the proposed algorithm can achieve high-accuracy boundary segmentation and has certain universality. The average relative error of volume measurement of the proposed algorithm is 0.043 6%， which is lower than 0.062 7% of PSR algorithm， verifying that the proposed algorithm achieves high accuracy boundary segmentation.

Aiming at the problems of strong interference and low detection precision of the existing safety helmet wearing detection， an algorithm of safety helmet detection based on improved YOLOv5 （You Only Look Once version 5） model was proposed. Firstly， for the problem of different sizes of safety helmets， the K-Means++ algorithm was used to redesign the size of the anchor box and match it to the corresponding feature layer. Secondly， the multi-spectral channel attention module was embedded in the feature extraction network to ensure that the network was able to learn the weight of each channel autonomously and enhance the information dissemination between the features， thereby strengthening the network ability to distinguish foreground and background. Finally， images of different sizes were input randomly during the training iteration process to enhance the generalization ability of the algorithm. Experimental results show as follows： on the self-built safety helmet wearing detection dataset， the proposed algorithm has the mean Average Precision （mAP） reached 96.0%， the the Average Precision （AP） of workers wearing safety helmet reached 96.7%， and AP of workers without safety helmet reached 95.2%. Compared with the YOLOv5 algorithm， the proposed algorithm has the mAP of helmet safety-wearing detection increased by 3.4 percentage points， and it meets the accuracy requirement of helmet safety-wearing detection in construction scenarios.

Focusing on the prediction of student grade in the undergraduate teaching of higher education， a prediction algorithm based on course Knowledge Graph （KG） was proposed. Firstly， a course KG representing course information was constructed. Then， the neighbor-based methods and the KG representation learning-based methods were used to calculate the similarity of the courses on the knowledge level based on the KG， and those knowledge similarities among courses were integrated into the traditional grade prediction framework Collaborative Filtering （CF）. Finally， the performance of the algorithm with fusing KG and the common prediction algorithm in different data sparsities were compared in experiments. Experimental results show that in the data sparse scenario， compared with the traditional CF algorithm， the neighbor-based algorithm has the Root Mean Square Error （RMSE） reduced by about 11% and the Mean Absolute Error （MAE） reduced by about 9%； and compared with the traditional CF algorithm， KG representation learning-based algorithm has the RMSE reduced by about 17.55% and the MAE reduced by about 11.40%. Experimental results indicate that the CF algorithm using KG can significantly reduce the prediction error， which proves that the KG can be used as information supplement in the lack of historical data， thus helping CF to obtain better prediction results.

Concerning the collision avoidance problem for multi-robot system, a collision avoidance algorithm based on improved artificial coordinating field was advanced. Firstly, a method was adopted, which made obstacle convex and chose new target initiatively to resolve the deadlock problem with artificial coordinating field in non-protruding polygon obstacle surroundings. Secondly, a repelling force was modeled based on velocity and distance to overcome the problem of low space utilization ratio with artificial coordinating field, especially in the situation that the target was near by the obstacle. Lastly, a force mixer was designed, and it was applied to avoidance movement tremble. The experimental results indicate that, the algorithm is effective and reliable to resolve collision avoidance problem for multi-robot system, and it improves the adaptability of multi-robot system for complicated environment.

As microblog application grows rapidly, how to extract users' interested popular topic from massive microblog information automatically becomes a challenging research area. This paper studied and proposed a topic extraction algorithm of Chinese microblog based on extended topic model. In order to deal with data sparse problem of microblog, the content related microblog text would be firstly clustered to generate synthetic document. Based on the assumption that posting relationship among microblogs implied topical correlation, the traditional LDA (Latent Dirichlet Allocation) topic model was extended to model the posting relationship among microblogs. At last, Mutual Information (MI) measurement was utilized to calculate topic vocabulary after extracting topics by proposing extended LDA topic model for topic recommendation. Furthermore, a prototype system for domain-specific topical mining system, named BTopicMiner, was implemented so as to verify the effectiveness of the proposed algorithm. The experimental result shows that the proposed algorithm can extract topics from microblogs more accurately. Meanwhile, the semantic similarity between automatically calculated topic vocabulary and manually selected topic vocabulary exceeds 75% while automatically calculating topic vocabulary based on MI.

Social annotation is a form of folksonomy, which allows Web users to categorize Web resource with text tags freely. It usually implicates fundamental and valuable semantic information of Web resources. Consequently, social annotation is helpful to improve the quality of information retrieval when applied to information retrieval system. This paper investigated and proposed an improved text classification algorithm based on social annotation. Because social annotation is a kind of folksonomy and social tags are usually generated arbitrarily without any control or expertise knowledge, there has been significant variance in the quality of social tags. Under this consideration, the paper firstly proposed a quantitative approach to measure the quality of social tags by utilizing the semantic similarity between Web pages and social tags. After that, the social tags with relatively low quality were filtered out based on the quality measurement and the remained social tags with high quality were applied to extend traditional vector space model. In the extended vector space model, a Web page was represented by a vector in which the components were the words in the Web page and tags tagged to the Web page. At last, the support vector machine algorithm was employed to perform the classification task. The experimental results show that the classification result can be improved after filtering out the social tags with low quality and embedding those high quality social tags into the traditional vector space model. Compared with other classification approaches, the classification result of F1 measurement has increased by 6.2% on average when using the proposed algorithm.

Web service choreography and orchestration defines the global interaction of composite Web service and the local behavior of each participant from global and local perspectives, respectively. The conformance of each participant's local behavior to global interaction is the guarantee of the correctness of Web service composition. The paper firstly presented a set of definitions to formally describe the global interaction of composite Web service, the local behavior of each participant and the mapping rules between them based on process algebra. Accordingly, two formal judgmental rules for the conformance of each participant's local behavior to global interaction were proposed. The two formal rules were based on the relationship between the transition of global interaction and local process and bisimulation theorem. At last, the conformance formal checking approach was described through a case study. The result of the case study shows that the proposed conformance definition of Web service composition and conformance checking approach can formally check the conformance of Web service composition effectively. As a result, the correctness of Web service composition can be guaranteed.

Concerning that the performance of Mobile Ad Hoc NETwork (MANET) with normal route method degrades obviously when being under attack in battlefield, a new STHR route strategy which integrating AODV（MANET route method） and Spay & Waiter protocols（DTN route method）to achieve high survivability was proposed in this paper. And to reduce the total network load, the method for choosing DTN nodes was designed. At the situation of hard network attacking, the target node may not be searched by AODV search extending ring. Then the zone covering nodes called ZCSEN in this paper would be used to start DTN route progress. In this way, the performance of MANET becomes stable even when it is under attack. The experimental results by 'ONE' show that the delivery rate of STHR is better than AODV and the delay time is shorter than Spay & Waiter route protocol.

Three series of similarity measures formulas between Vague sets based on three-dimensional representation and (tx, fx) expansion and fuzzy set operations were presented. The thoughts and an example of applying the similarity measures between Vague sets to the network information filtered problem were given. This example shows that the new formulas are practicable.