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In recent years, blockchain has attracted increasing attention in both academic and industrial circles, and the privacy of blockchain remains a controversial issue when deploying blockchain in different applications. This paper systematically reviewed the current privacy protection schemes and mechanisms in blockchain, and gave insights into the security and privacy of blockchain. Firstly, this paper briefly introduced the main functions, types and privacy definitions of blockchain technology, and analyzed the privacy issues it faced. Then, from a technical perspective, this paper divided blockchain privacy protection technology into four categories: decentralized identity authentication, privacy protocol, encryption technology and obfuscation technology, as well as eight sub-categories: decentralized identity authentication, secure multi-party computation, differential privacy, homomorphic encryption, zero-knowledge proof, coin mixing technology, ring signature and anonymous communication. This paper studied them separately, and summarized the mechanisms of different privacy protection technologies on permissioned and permissionless blockchains. Finally, this paper conducted qualitative research on the main characteristics and attributes of the eight privacy protection technologies, and discussed future research directions of privacy protection technologies. The research provided reference for blockchain developers to choose appropriate privacy protection schemes and for researchers to choose research directions for blockchain privacy protection. [ABSTRACT FROM AUTHOR]

The purpose of image stitching is to obtain a high-definition, seamless panoramic image. Existing methods rely on the accuracy of feature matching, which will misalign images and produce errors such as artifacts and distortions.This paper proposed a new unsupervised real scene image stitching network that can adapt to real scene stitching in the presence of moving targets, ensuring no loss of accuracy in the panorama. It included two networks of alignment and reconstruction, excluding the negative influence of moving targets and misleading regions on the transformation matrix through content branching, and optimizing image details by constraining the reconstruction process through edge branching to achieve high-definition and artifact-free stitching effects. The experimental results show that the method’s RMSE, PSNR, and SSIM reached 1.81, 26.56, and 0.85, respectively. The objective evaluation indexes are better than other classical algorithms overall, and the user research results also indicate that the method obtains higher definition of panoramic images. The method effectively accomplishes unsupervised image stitching in real scenes and can be generalized to stitching tasks in other scenes. [ABSTRACT FROM AUTHOR]

The preferred semantics in the abstract argumentation framework is the most important semantics for judging the acceptability of arguments. The existing preferred expansion solving methods mostly use marker mapping to solve, which depends on the definition of markers, conversion rules, and the markers of adjacent arguments. Each iteration of the algorithm will generate a new abstract argumentation framework, resulting in high time and space complexity . This paper proposed preferred expansion algorithm based on dynamic programming, and added the judgment of the acceptability of arguments to the dynamic programming, and obtained the maximum admissible set in the argumentation framework by preferred expansion. This paper conducted experiments based on the random abstract argumentation framework and the data set provided by ICCMA,and compared the proposed algorithm with algorithms such as Heureka and ArgSemSAT The results show that, to solve the same number of preferred expansions, the algorithm consumes less time and reduces the time and space complexity. [ABSTRACT FROM AUTHOR]

Constrained multi-objective evolutionary algorithms (CMOEAs) can deal with multiple conflicting objective functions and constraints simultaneously and guide the population towards the optimal solution in the feasible domain, which has received extensive attention from researchers. This paper firstly introduces the relevant definitions of constrained multi-objective optimization problems (CMOPs) and three classifications of multi-objective evolutionary algorithms (MOEAs); secondly, it systematically analyzes the current constraint processing mechanisms in CMOEAs, and condenses the current four main constraint processing methods; Then, the research progress of CMOEAs is reviewed in detail from three aspects: dominance-based, index-based, and decomposition-based; finally, the challenges and future research directions of CMOEAs are pointed out. [ABSTRACT FROM AUTHOR]

To solve the problems that the objects with different semantic classes are identified as co-salient objects in current co-saliency detection methods, this paper proposed a CNN and semantic correction-based co-saliency detection method (CSCCD). The proposed method first adopted the guided super pixel filter to process the super pixels obtained by SLIC and the saliency results generated by DSS, which showed clear object boundaries. Then it utilized Mask R-CNN to extract semantic features. It proposed the definitions of image semantic feature and semantic consistency. It also defined the image group semantic correction to solve the problem that detected the objects with different pose belonging to a semantic class as different semantic classes. With the concept, this paper defined image group semantic correlation class, solving semantic correlation problem of multiple images. It generated the final co-saliency detection results by fusing the saliency detection regions with the image group semantic consistent regions. The experimental results on public benchmark datasets show that this algorithm can effectively highlight the whole and outline of the object, and its comprehensive performance in objective quantification is obviously improved. [ABSTRACT FROM AUTHOR]

In order to understand the development of outlier detection technique detailly, this paper overviewed the outlier detection techniques in recent years. Firstly, it introduced and summarized the definition of outliers, the causes of outlier and classification of the outlier mining algorithms. Next, it overviewed outlier detection methods based on neighbor, based on distributed framework and based on deep learning. Especially, it discussed the most representative methods in outlier detection, and pointed out the merits and drawbacks. In the end, this paper concluded and discussed the future direction of outlier detection techniques. [ABSTRACT FROM AUTHOR]

With the advancement of data acquisition technology, spatiotemporal data with geographic location information are growing rapidly, so it is urgent to explore effective spatiotemporal data modeling methods. Spatiotemporal sequence prediction is one of the basic methods in spatiotemporal data modeling and widely used in many fields. At present, there is no Chinese literature on its review, so it is of great significance to summarize these methods. For the spatiotemporal sequence prediction problem, this paper reviewed its application background and development history. Then it introduced some related definitions and characteristics. According to its category, the spatiotemporal sequence prediction methods, based on traditional methods, classical machine learning methods and deep learning methods are analyzed respectively as well as their application scope, advantages and disadvantages. Finally, this paper made a review and prospect over the research directions of spatiotemporal sequence prediction, laying a theoretical foundation for researchers to further study this problem [ABSTRACT FROM AUTHOR]

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task. [ABSTRACT FROM AUTHOR]

Focusing on problems of the accuracy recommendation system that the recommended commodity type is relatively single, and commodities are mostly popular goods and lack of freshness, the novelty recommendation is gradually gaining attention. However, current researches don't combine item features when designing algorithms, which make them unable to distinguish and select items with higher novelty for different users. In order to improve the performance of the recommendation system, this paper improved the method based on random walk and designed a new recommendation algorithm by fusing novelty features. This algorithm further analyzed features of items and gave the formal definition of the novelty from perspectives of user interest expansion and prediction. This paper analyzed user demands, constructed new transition probability, generated personalized recommendation lists and improved the novelty of the lists. The experimental results show that the proposed algorithm has less influence on the accuracy than existing methods and has significant improvement on novelty indexes. It concludes that by fusing novel features, this system can improve the recommendation contents effectively while taking into account the accuracy. [ABSTRACT FROM AUTHOR]

To solve the problem of low efficiency of large data clustering, this paper proposed a fast grid density clustering algorithm SGBSCAN. Firstly, this paper gave the definition of square neighborhood density clustering, and used the square neighborhood instead of the circular neighborhood to reduce the time complexity. Secondly, this paper proposed the concept of grid of square neighborhood density clustering, and determined the density relationship between core points and data points in high density region quickly. Finally, this paper proposed the grid density cluster, and used the relationship between the grid to accelerate the formation of density clusters. It applied this algorithm to 16 data sets and compared with the existing literature algorithms. The results show that the algorithm has a significant improvement in clustering efficiency. The larger the data volume, the more obvious the efficiency of the algorithm, and the algorithm is suitable for multidimensional data clustering. [ABSTRACT FROM AUTHOR]

The network reliability is an important field of the complex network research. Hypernetworks which can describe some complex systems effectively belong to the scope of the complex network research. Based on the topological structures of hypernetworks: hypergraphs, this paper presented the definition of the all-terminal reliability of hypernetworks with edge failure, and proposed two basic methods for calculating the reliability, namely state enumeration and the factorization method. According to the factorization method, some hypernetworks with special structures could be simplified. As an application of the hypernetwork reliability, this paper studied the number of connected spanning sub-networks. Compared with the ordinary complex networks, the result concludes that the reliability of hypernetworks can't be re placed by the reliability of ordinary complex networks that transform from corresponding hypernetworks. This research is a preliminary exploration of the hypernetwork reliability, which has broad research spaces and application prospects. [ABSTRACT FROM AUTHOR]

In order to solve the high cost and low efficiency of buffer overflow detection in software development due to the influence of variable changes, this paper proposed a method for analyzing the impact of unsafe function calls based on data topology, this method analyzes the impact of variable changes on unsafe function calls with the granularity of variable and path. Extracted information such as variable definitions, references, variable reference, variable dependency, unsafe function calls information and function calls relationship by statically analyzing source code to build a variable dependency set, analyzed data topology of changed variables, and combined distribution of variables in the stack to obtain the variable impact domain. Established a variable impact analysis model by combining function call relations information to obtain path domain. Finally obtained the influence of variable changes on unsafe function calls according to the variable impact domain and path impact domain. Experimental results show that this method is more accurate in defining the impact of variable changes on unsafe function calls. This method has high comprehensibility and usability, can effectively improve the accuracy and efficiency of software regression testing. [ABSTRACT FROM AUTHOR]

To expand the application field of designated confirmer signature, this paper introduced a concept of decentralized attribute-based designated confirmer signature (DABDCS) by combing the concepts of attribute-based and decentralization with designated confirmer signatures. It presented the formal definition and security model of DABDCS, and proposed a concrete scheme. The scheme was based on Cramer et al. 's witness hiding protocol, in which Schnorr identification was chosen as its basis L.: protocol, and Shamir threshold scheme as its secret sharing scheme. Based on the threshold signature scheme, it proposed a decentralized attribute-based designated confirmer signature scheme without bilinear pairings, and proved its securities under the random oracle model. The proposed scheme can be applied to multiple-party communication environments, such as electronic bidding and so on. [ABSTRACT FROM AUTHOR]

In order to overcome the drawback of large computational overhead in the existing schemes, this paper proposed a new certificateless multi-signature scheme on the nature of broadcasting by using bilinear pairing, and given the formal definition and security model of certificateless broadcast multi-signature. Under the computational Diffie-Hellman difficulty assumption, it proved that the new scheme satisfied the unforgeability under the random oracle model. Finally, the efficiency analysis and simulation experiments show that the new scheme has higher efficiency than the similar schemes while increasing the number of signatures. In addition, the multi-message certificateless broadcast multi-signature scheme is extended and satisfies the correctness and security. [ABSTRACT FROM AUTHOR]

Aiming at the problems oflog detection accuracy and long delay of DDoS (distributed denial-of-service) attacks in the software definition networks, this paper proposed a real-time DDoS security system of software definition networks based on kernel functions. Firstly, it abstracted the packet header fields of software definition networks periodly, and formed the abstracted information as matrices; then, it adopted the Mahalanobis distance to analyze the significant change of continuous feature vectors, and it designed two kernel functions to evaluate the behavior flows of attacks; lastly, it identified the attackers by the spectral clustering technique and the covariance statistical information. Experimental results based on the real software definition networks show that the proposed security system realizes a good detection accuracy, and performs a reasonable processing time. [ABSTRACT FROM AUTHOR]

In view of the existing endocardium and epicardium contour of left ventricle segmentation methods, there are some problems such as the poor regularity of segmented contour, segmenting the fuzzy edge of ventricular membrane incompletely and low segmentation efficiency. This paper proposed a new double well potential function and anisotropy gradient vector flow (AGVF) energy function improved level set model, with the fusion of zero level set and k level set of the double level set, the division of left ventricular inner and outer membrane method at the same time. First of all, with the modified Hough change detection algorithm, positioned ventricular endocardium and epicardium initial position; then, it used the double level set model to segment the endocardium and epicardium simultaneously. The experimental results show that the proposed method can segment smooth endocardium and epicardium contour of left ventricle. The analysis of experimental datum demonstrates that the proposed method can segment endocardium and epicardium contour of left ventricle confirms to the clinical definition, and increases averagely the overlap rate of the endocardium and epicardium contour on MRI images to 0. 956 9. [ABSTRACT FROM AUTHOR]

Most of traditional fault diagnosis research ignored characteristics of local structure. For that problem under PMC model, this paper introduced the definition of node diagnosis. And through the study by nodes diagnosis, it obtained the sufficient condition of the node diagnosiability and got a newt-diagnosis algorithm called STFDA. Finally, it performed an analysis from the angle of node diagnosis of n-dimensional hypercube network and n-dimensional star network in order to test the validity of the sufficient conditions. This algorithm performed a fault diagnosis to the two kinds of network at last. Indeed, the implementation of the sufficient condition and algorithm got the help of a new structure called ST. And the algorithm's time complexity was proved to be 0 ( N δ ), with δ equal to the maximum degree of node in network, which was significantly reduced compared with others algorithm. [ABSTRACT FROM AUTHOR]

Combined with the structural support theory, this paper probed into some properties of the network structure. After describing the definition of supportiveness, it put forward the conclusion that the total network supportiveness and re-supportiveness were equal in MSSN, then it proved this conclusion by using formula. With the reference of the PageRank calculation method in the random walk model, it implemented an analogue simulation about the rumors spreading of different nodes supportiveness, as well as the rumor refuting situation. The results show that different nodes supportiveness has different propagation characteristics and different supportiveness of node has significant impact on the rumor infection and recovery. [ABSTRACT FROM AUTHOR]

When the user stores the data on a dishonest server, the integrity of the data needs to be verified, so the proof-of-retrievability (POR) system is proposed. Aiming at the problem of identity-based cryptosystem used in most of the existing solutions, a more intuitive and flexible attribute-based cryptosystem is adopted to design an attribute-based data recoverability proof scheme. This paper firstly proposed definition and model of an attribute-based proof of retrievability with public verifiability, then, constructed an attribute-based proof of retrievability with public verifiability, finally, it proves its correctness and security. [ABSTRACT FROM AUTHOR]