Joint entity-relation extraction methods based on “pre-training + fine-tuning” paradigm rely on large-scale annotated data. In the small sample scenarios of ancient Chinese books where data annotation is difficult and costly， the fine-tuning efficiency is low and the extraction performance is poor； entity nesting and relation overlapping problems are common in ancient Chinese books， which limit the effect of joint entity-relation extraction； pipeline extraction methods have error propagation problems， which affect the extraction effect. In response to the above problems， a joint entity-relation extraction method for ancient Chinese books based on prompt learning and global pointer network was proposed. Firstly， the prompt learning method of span extraction reading comprehension was used to inject domain knowledge into the Pre-trained Language Model （PLM） to unify the optimization goals of pre-training and fine-tuning， and the input sentences were encoded. Then， the global pointer networks were used to predict and jointly decode the boundaries of subject and object and the boundaries of subject and object of different relationships， so as to align into entity-relation triples， and complete the construction of PTBG （Prompt Tuned BERT with Global pointer） model. As the results， the problem of entity nesting and relation overlapping was solved， and the error propagation problem of pipeline decoding was avoided. Finally， based on the above work， the influence of different prompt templates on extraction performance was analyzed. Experimental results on Records of the Grand Historian dataset show that compared with OneRel model before and after injecting domain knowledge， the PTBG model has the F1-value increased by 1.64 and 1.97 percentage points respectively. It can be seen that the PTBG model can better extract entity-relation jointly in ancient Chinese books， and provides new research ideas and approaches for low-resource， small-sample deep learning scenarios.

Existing Graph Convolutional Network （GCN） methods are based on the assumption of homophily， which cannot be directly applied to heterophilic graph representation learning， and many studies on heterophilic graph representation learning are limited by message-passing mechanism， which leads to the problem of over-smoothing due to the confusion and over-squeezing of node features. To address these issues， a semi-supervised heterophilic graph representation learning model based on Graph Transformer，named HPGT（HeteroPhilic Graph Transformer）， was proposed. Firstly， the path neighborhood of a node was sampled using the degree connection probability matrix， then the heterophilic connection patterns of nodes on the path were adaptively aggregated through the self-attention mechanism， which were encoded to obtain the structural information of nodes， and the original attribute information and structural information of nodes were used to construct the self-attention module of the Transformer layer. Secondly， the hidden layer representation of each node itself was separated from those of its neighboring nodes and updated to avoid the node aggregating too much information about itself through the self-attention module， and then the representation and the neighborhood representation of nodes were connected to get the output of a single Transformer layer； in addition， the outputs of all Transformer layers were connected to get the final node hidden layer representation so as to prevent the loss of information in middle layers. Finally， the linear layer and Softmax layer were used to map the hidden layer representations of nodes to the predictive labels of nodes. In the comparison experiments with the model without Structural Encoding （SE）， SE based on degree connection probability provides effective deviation information for self-attention modules of Transformer layers， and improves the average accuracy of HPGT by 0.99% to 11.98%. Compared with the comparative models， on the heterophilic datasets （Texas， Cornell， Wisconsin， and Actor）， the node classification accuracies of HPGT are improved by 0.21% to 1.69%， and on homophilic datasets （Cora， CiteSeer， and PubMed）， the node classification accuracies reach 0.837 9， 0.746 7 and 0.886 2， respectively. The experimental results show that HPGT has a strong ability for heterogeneous graph representation learning， and is particularly suitable for node classification tasks of strong heterophilic graphs.

In view of the phenomenon that automatic sentence segmentation and punctuation task in ancient book information processing relies on large-scale annotated corpora， and considering that training high-quality， large-scale samples is expensive and these samples are difficult to obtain， a prompt learning method for ancient text sentence segmentation and punctuation based on span-extracted prototypical network was proposed. Firstly， structured prompt information was incorporated into the support set to form an effective prompt template， so as to improve the model's learning efficiency. Then， combined with a punctuation position extractor and a prototype network classifier， the misjudgment impact and the interference from non-punctuation labels in traditional sequence labeling method were effectively reduced. Experimental results show that on Records of the Grand Historian dataset， the F1 score of the proposed method is 2.47 percentage points higher than that of the Siku-BERT-BiGRU-CRF （Siku - Bidirectional Encoder Representation from Transformer - Bidirectional Gated Recurrent Unit - Conditional Random Field） method. In addition， on the public multi-domain ancient text dataset CCLUE， the precision and F1 score of this method reach 91.60% and 93.12% respectively， indicating that the method can perform sentence segmentation and punctuation in multi-domain ancient text effectively and automatically by using a small number of training samples. Therefore， the proposed method offers new thought and approach for conducting in-depth research on automatic sentence segmentation and punctuation， as well as for enhancing the model's learning efficiency， in multi-domain ancient text.

On the basis of the classical multiple knapsack problem， the Heterogeneous Multiple Knapsack Problem （HMKP） was proposed， which was abstracted from the commonalities of typical logistics service scenarios. And， an Imperialist Competitive Algorithm （ICA） was designed and customized to solve HMKP. As the origin ICA is easy to fall into the local optimum and the optimal solution of the 0-1 knapsack problem is usually near the constraint boundary， Two-Point Automutation Strategy （TPAS） and Jump out of Local Optimum Algorithm （JLOA） were designed to improve ICA， and a Binary Imperialist Competitive Algorithm （BICA） for 0-1 knapsack problem was presented. BICA showed comprehensive and efficient optimization ability in solving 35 numerical examples of 0-1 knapsack problem. BICA based on Best-Matched Value （BMV） was able to find the ideal optimal solutions of 19 out of 20 examples with 100% success rate in the first test set， and the ideal optimal solutions of 12 out of 15 examples were found by the above algorithm with 100% success rate in the second test set， achieving the best performance of all the comparison algorithms. The numerical analysis results show that BICA maintains the multipolar development strategy in the optimization evolution and relies on the unique population evolution method to search the ideal solution in the solution space efficiently. Subsequently， aiming at the strong constraint and high complexity of HMKP， a Multiple Level Binary Imperialist Competitive Algorithm （MLB-ICA） for solving HMKP was put forward based on BICA. Finally， the numerical experiments and performance evaluation of MLB-ICA were carried out on a high dimensional HMKP test set constructed by combining multiple typical numerical examples of 0-1 knapsack problems. The results showed that the solving time of MLB-ICA is longer than that of Gurobi solver， but the solving accuracy of MLB-ICA is 28% higher than that of Gurobi solver. It can be seen that MLB-ICA can solve high-dimensional complicated HMKP efficiently with low computational cost within acceptable time， and provides a feasible algorithm design scheme for ICA to solve super-large scale combinatorial optimization problems.

Existing tree decoder is only suitable for solving single variable problems， but has no good effect of solving multivariate problems. At the same time， most mathematical solvers select truth expression wrongly， which leads to learning deviation occurred in training. Aiming at the above problems， a Graph to Equation Tree （GET） model based on expression level-by-level aggregation and dynamic selection was proposed. Firstly， text semantics was learned through the graph encoder. Then， subexpressions were obtained by aggregating quantities and unknown variables iteratively from bottom of the equation tree layer by layer. Finally， combined with the longest prefix of output expression， truth expression was selected dynamically to minimize the deviation. Experimental results show that the precision of proposed model reaches 83.10% on Math23K dataset， which is 5.70 percentage points higher than that of Graph to Tree （Graph2Tree） model. Therefore， the proposed model can be applied to solution of complex multivariate mathematical problems， and can reduce influence of learning deviation on experimental results.

With the development and maturity of deep learning， the quality of neural machine translation has increased， yet it is still not perfect and requires human post-editing to achieve acceptable translation results. Interactive Machine Translation （IMT） is an alternative to this serial work， that is performing human interaction during the translation process， where the user verifies the candidate translations produced by the translation system and， if necessary， provides new input， and the system generates new candidate translations based on the current feedback of users， this process repeats until a satisfactory output is produced. Firstly， the basic concept and the current research progresses of IMT were introduced. Then， some common methods and state-of-the-art works were suggested in classification， while the background and innovation of each work were briefly described. Finally， the development trends and research difficulties of IMT were discussed.

Aiming at the problem that the deraining methods based on tensor product wavelet cannot capture high-frequency rain streaks in all directions， a Dual U-Former Network （DUFN） based on non-separable lifting wavelet was proposed. Firstly， the isotropic non-separable lifting wavelet was used to capture high-frequency rain streaks in all directions. In this way， compared with tensor product wavelets such as Haar wavelet， which can only capture high-frequency rain streaks in three directions， DUFN was able to obtain more comprehensive rain streak information. Secondly， two U-Nets composed of Transformer Blocks （TBs） were connected in series at various scales， so that the semantic features of the shallow decoder were transferred to the deep stage， and the rain streaks were removed more thoroughly. At the same time， the scale-guide encoder was used to guide the coding stage by using the information of various scales in the shallow layer， and Gated Fusion Module （GFM） based on CBAM （Convolutional Block Attention Module） was used to make the fusion process put more focus on the rain area. Experimental results on Rain200H， Rain200L， Rain1200 and Rain12 synthetic datasets show that the Structure SIMilarity （SSIM） of DUFN is improved by 0.009 7 on average compared to that of the advanced method SPDNet （Structure-Preserving Deraining Network）. And on Rain200H， Rain200L and Rain12 synthetic datasets， the Peak Signal-to-Noise Ratio （PSNR） of DUFN is improved by 0.657 dB averagely. On real-world dataset SPA-Data， PSNR and SSIM of DUFN are improved by 0.976 dB and 0.003 1 respectively compared with those of the advanced method ECNetLL （Embedding Consistency Network+Layered Long short-term memory）. The above verifies that DUFN can improve the rain removal performance by enhancing the ability to capture high-frequency information.

Due to the introduction of MonoDepth2， unsupervised monocular ranging has made great progress in the field of visible light. However， visible light is not applicable in some scenes， such as at night and in some low-visibility environments. Infrared thermal imaging can obtain clear target images at night and under low-visibility conditions， so it is necessary to estimate the depth of infrared image. However， due to the different characteristics of visible and infrared images， it is unreasonable to migrate existing monocular depth estimation algorithms directly to infrared images. An infrared monocular ranging algorithm based on multiscale feature fusion after improving the MonoDepth2 algorithm can solve this problem. A new loss function， edge loss function， was designed for the low texture characteristic of infrared image to reduce pixel mismatch during image reprojection. The previous unsupervised monocular ranging simply upsamples the four-scale depth maps to the original image resolution to calculate projection errors， ignoring the correlation between scales and the contribution differences between different scales. A weighted Bi-directional Feature Pyramid Network （BiFPN） was applied to feature fusion of multiscale depth maps so that the blurring of depth map edge was solved. In addition， Residual Network （ResNet） structure was replaced by Cross Stage Partial Network （CSPNet） to reduce network complexity and increase operation speed. The experimental results show that edge loss is more suitable for infrared image ranging， resulting in better depth map quality. After adding BiFPN structure， the edge of depth image is clearer. After replacing ResNet with CSPNet， the inference speed is improved by about 20 percentage points. The proposed algorithm can accurately estimate the depth of the infrared image， solving the problem of depth estimation in night low-light scenes and some low-visibility scenes， and the application of this algorithm can also reduce the cost of assisted driving to a certain extent.

In feature learning process， the existing hashing methods cannot distinguish the importance of the feature information of each region， and cannot utilize the label information to explore the correlation between modalities. Therefore， an Adaptive Hybrid Attention Hashing for deep cross-modal retrieval （AHAH） model was proposed. Firstly， channel attention and spatial attention were combined by the weights obtained by autonomous learning to strengthen the attention to the relevant target area and weaken the attention to the irrelevant target area. Secondly， the similarity between modalities was expressed more finely through the statistical analysis of modality labels and quantification of similarity degrees to numbers between 0 and 1 by using the proposed similarity measurement method. Compared with the most advanced method Multi-Label Semantics Preserving Hashing （MLSPH） on four commonly used datasets MIRFLICKR-25K， NUS-WIDE， MSCOCO， and IAPR TC-12， when the hash code length is 16 bit， the proposed method has the retrieval mean Average Precision （mAP） increased by 2.25%， 1.75%， 6.8%， and 2.15%， respectively. In addition， ablation experiments and efficiency analysis also prove the effectiveness of the proposed method.

Concerning the issue that the traditional price prediction model for agricultural product cannot predict the market price of apple quickly and accurately under the big data scenario， an apple price prediction method based on distributed neural network was proposed. Firstly， the relative factors that affect the market price of apple were studied， and the historical price of apple， historical price of alternatives， household consumption level and oil price were selected as the input of the neural network. Secondly， a distributed neural network prediction model containing price fluctuation law was constructed to implement the short-term prediction for the market price of apple. Experimental results show that the proposed model has a high prediction accuracy， and the average relative error is only 0.50%， which satisfies the requirements of apple market price prediction. It indicates that the distributed neural network model can reveal the price fluctuation law and development trend of apple market price through the characteristic of self-learning. The proposed method not only can provide scientific basis for stabilizing apple market order and macroeconomic regulation of market price， but also can reduce the harms brought by price fluctuations， helping farmers to avoid the market risks.

There are many illegal Web pages on the Internet, which may have pornographic, violent, gambling or reactionary content. Without being filtered effectively, they will exercise a malign influence on the searching services. An improved K-Nearest Neighbors (KNN) classification algorithm to promote the recognition accuracy was proposed and implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which made it distributed and parallel. Through experiments and comparison with the existing work, it is proved that the proposed recognition method improves the accuracy and efficiency greatly. The algorithm is implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which makes it distributed and parallel. Through experiments and comparison with existing work, it is proved that the recognition method we propose improves the accuracy and efficiency greatly.

The influence of social network is closely related with its structural characteristics. Based on the data from Sina microblog, the distributions of the number of followers and followings were analyzed and found that the number of followers and followings both were power-law distributed. The distance characteristic between different pairs of nodes was discussed, and it was found and proved that there was "small-world" phenomenon in the microblog network. At last, the links between nodes in the network were investigated and found that the formation of the link satisfied triple closure principle. The investigation results on the above three topics are important for us to explore the relationship between the influence of micro-blog and the structural characteristics of its underlying social network, as well as to the design of mechanisms to control the influence.

An improved Principal Component Analysis (PCA) method was proposed with the synchronous multi-dimensional data stream anomaly analysis techniques. In this method, the problem of the original data stream variation tendency was mapped to the eigenvector space, and the steady-state eigenvector was solved, then the abnormal changes of the synchronous multi-dimensional data stream could be diagnosed by the relationship between the instantaneous eigenvector and the steady-state eigenvector. This method was applied to the abnormality diagnosis of the tunnel strain monitoring data stream, and the real-time monitoring and warning system for the tunnel strain was realized by using VC++. The experimental results show that the proposed method can reflect the changes of the aperiodic variables timely and realize the anomaly monitoring and early warning for multi-dimensional data stream effectively.

Multiple feature fusion based tracking is one of the most active research topic in tracking field, but the tracking accuracy needs improving in complex environment and most of them use single fusion rule. In this paper, a new adaptive fusion strategy was proposed for multi-feature fusion. First, the local background information was introduced to strengthen the description of the target, and then the feature weight was calculated by a variety of criteria in the fusion process. In addition, the framework of mean shift was considered to realize target tracking. An extensive number of comparative experimental results show that the proposed algorithm is more stable and robust than the single fusion rule.

The Artificial Fish Swarm Algorithm (AFSA) has low search speed and it is difficult to obtain accurate value. To solve the problems, an improved algorithm based on social learning mechanism was proposed. In the latter optimization period, the authors used convergence and divergence behaviors to improve the algorithm. The two acts had fast search speed and high optimization accuracy, meanwhile, the divergence behavior enhanced the population diversity and the ability of skipping over the local extremum. To a certain extent, the improved algorithm enhanced the search performance. The experimental results show that the proposed algorithm is feasible and efficacious.

In response to the issue that GPS is unable to carry out Location-Based Service (LBS) in indoor environment, a LBS indoor channel allocation model, credibility evaluation and control method was presented in this paper, which integrated GPS, Wi-Fi, ZigBee and Bluetooth technologies. It solved the problem arising from combination channel allocation, including the evaluation of the traffic load, the available Radio Frequency (RF), and non-overlapping RF channels number of each node. Each Access Point(AP)'s signal strength built the prediction model with reference point. The optimization algorithm was designed to determine and select the credibility of combination channel based on the energy evaluation. It adaptively selected neighbors with highest comprehensive effects to participate in iterative optimization. The simulation result indicates this method can effectively inhibit the proliferation of communication interference error in the network, reduce the positioning complexity, and improve the positioning accuracy in addition to improving scalability and robustness of the entire network.

The sparse representations of signal theory has been extensively and deeply researched in recent years, and been widely applied to image processing. For the huge computation of over-complete dictionary structure and sparse decomposition, a new self-adaptive method for image denoising based on sparse decomposition and clustering was proposed. Firstly, an overcomplete dictionary was designed by training samples with a modified K-means clustering algorithm. In the training process, atoms of the dictionary were updated adaptively in every iterative step to better fit the sparse representation of the samples. Secondly, the sparse representation of the test image was obtained by using the dictionary combined with Orthogonal Matching Pursuit (OMP) algorithm, so as to achieve image denoising. The experimental results show that in terms of image denoising and computational complexity, the performance of the proposed method is better than the traditional dictionary training algorithm.

Current urban traffic noise mapping systems are not ideal for big scale project distributed computing in dynamic network. This paper proposed a noise mapping distributed computation method based on loosely-coupled services and the mechanism of Service Object Oriented Architecture (SOOA), investigated the generation approach of noise propagation calculation service, and introduced the deployment and management of services in the proposed system. At last, a demonstration indicated that the distributed computation approach considerably reduced the overhead of calculation and supplied flexible system architecture at the same time. The experimental results show that the imbalance of parallel subtasks will affect the parallel efficiency. Under normal circumstances, parallel efficiency can reach over 85%.

The most commonly used technology in weak signal detection is using correlation operations to detect whether a known periodic signal exists; however, it is always very complicated and cannot be applied widely. To solve this problem, an envelope analysis based algorithm was proposed from the perspective of mathematical morphology. In this algorithm, salient points were selected from low level envelope to form a higher level envelope of the signal, and finally it converged at the peak position of every target signal in parallel. No priori knowledge about the target signal was needed here and it was also less sensitive of white Gaussian noise. This algorithm is effective in the simulation with signal-to-noise ratio of -10dB, and the measured data demonstrate that it is good at detecting weak signal.

Password-based authentication and key exchange protocol have been widely used in various network services due to easy memory of password. Unfortunately, password-based authentication scheme also suffers from attacks because of the low entropy of password. In the year 2011, Islam et al.（ISLAM SK H, BISWAS G P. Improved remote login scheme based on ECC. IEEE-International Conference on Recent Trends in Information Technology. Washington, DC: IEEE Computer Society, 2011： 1221-1226）proposed an improved remote login scheme based on Elliptic Curve Cryptography (ECC).Whereas, the scheme was vulnerable to stolen-verifier and impersonation attacks and failed to provide mutual authentication. Therefore, the authors proposed a password-based Remote Authentication with Key Agreement (RAKA) protocol using ECC to tackle the problems in Islam et al.'s scheme. RAKA was based on Elliptic Curve Discrete Logarithm Problem (ECDLP) and needed to compute six elliptic curve scale multiplications and seven hash function operations during a protocol run. The efficiency improves by about 15%〖BP(〗 percent〖BP)〗. It is more secure and efficient than Islam et al.'s scheme.

For evaluating the two values of the average queue length and the queue waiting time, the core indicators of congestion control algorithms, more accurately in the network with priority scheduling service, a computational model, which includes the data arriving process, the data leaving process and the priority scheduling service, was designed by using the three statistical methods: the Pareto distribution, the Poisson random process and the weighted average method. And the computational function of curve shape parameter was deduced by using the matrix method. By comparing the simulation results produced from a test bed with the results of the computational model, it is found that the deviation is small, which proves that the new model can predict the status of network correctly.

Session Initiation Protocol (SIP) provides authentication and session key agreement to ensure the security of the successive session. In 2010, Yoon et al. (YOON E-J, YOO K-Y. A three-factor authenticated key agreement scheme for SIP on elliptic curves. NSS '10: 4th International Conference on Network and System Security. Piscataway: IEEE, 2010: 334-339.) proposed a three-factor authenticated key agreement scheme named TAKASIP for SIP. However, the scheme is vulnerable to insider attack, server-spoofing attack, off-line password attack, and losing token attack. Moreover, it does not provide mutual authentication. To overcome these flaws of TAKASIP, a new three-factor authentication scheme named ETAKASIP based on Elliptic Curve Cryptosystem (ECC) was proposed. ETAKASIP, on the basis of elliptic curve discrete logarithm problem, provides higher security than TAKASIP. It needs 7 elliptic curve scalar multiplication operations, 1 additional operation and up to 6 Hash operations, and of high efficiency.

Collaborative filtering is the most popular personalized recommendation technology at present. However, the existing algorithms are limited to the user-item rating matrix, which suffers from sparsity and cold-start problems. Neighbours' similarity only considers the items which users evaluate together, but ignores the correlation of item attribute and user characteristic. In addition, the traditional ones have taken users' interests in different time into equal consideration. As a result, they lack real-time nature. Concerning the above problems, this paper proposed a nonlinear combinatorial collaborative filtering algorithm consequently. In order to obtain more accurate nearest neighbour sets, it improved neighbours' similarity calculated approach based on item attribute and user characteristic respectively. Furthermore, the initial prediction rating fills in the rating matrix, so makes it much denser. Lastly, it added time weight to the final prediction rating, so then let users' latest interests take the biggest weight. The experimental results show that the optimized algorithm can increase prediction precision, by way of reducing sparsity and cold-start problems, and realizing real-time recommendation effectively.

According to the research of distributed SIP flooding attack detection and defense, in combination with the characteristics of IP-based distributed flood attack and SIP messages, the two-level defense architecture against SIP distributed flooding attacks (TDASDFA) was presented. Two-level defensive components made up TDASDFA logically: the First level Defense Subsystem (FDS) and the Second level Defense Subsystem (SDS). FDS coarse-grained detected and defended SIP signaling stream to filter out non-VoIP messages and discard SIP messages of the IP addresses exceeding the specified rate to ensure service availability| SDS fine-grained detected and defended SIP messages using a mitigation method based on security level to identify the cunning attacks and low-flow attacks with obvious features of malicious DoS attacks. FDS and SDS detected and defended network status in real-time together to weaken SIP distributed flooding attacks. The experimental results show that TDASDFA can detect and defend SIP distributed flooding attacks, and reduces the probability of SIP proxy server or IMS server being attacked when the network is on the abnormity.

Mobile sink can efficiently collect data and extend the network lifetime. However, the existing researches about data collection based on mobile sink mainly focus on path-constrained mobile sink. Hence, a path-controlled traversal model for mobile sink data collection was constructed, and a data collection algorithm for mobile sink based on optimal-path traveling was proposed. The algorithm discretized the continuous path problem by local Voronoi grid, used the amount of data collected and system energy consumption as performance metric, combined taboo search algorithm to achieve the maximum amount of data collected and the minimum of network energy consumption traversing. Theoretically and experimentally, it is concluded that the proposed algorithm is able to solve the optimal-path traveling of data collection problem using path-controlled mobile sink.

The major disadvantages of the current software reliability models were discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model were proposed and an improved model was established. Lastly, the simulation results show that compared with classic models，the improved model has better prediction precision，better generalization ability and lower dependence on the number of sample, which is more applicable for software reliability prediction.

With regard to the copy-move forgery of image region, this paper proposes a detection algorithm based on gray level co-occurrence matrix. Firstly, we divided the detected image into multiple overlapping blocks with same sizes, represented the textural features of each block with the statistics of its gray level co-occurrence matrix, and got the feature vector of the image. Secondly, we sorted the feature vector by dictionary, and located the tampered region by utilizing the displacement vectors of image blocks. Lastly, experimental results show that our algorithm performs better than the classical detection algorithm based on Principal Component Analysis (PCA) in terms of the processing against rotate operation and of efficiency.

The template-based feature in Threading Building Blocks (TBB) simplifies parallel design and is suitable for efficient design of multi-core parallelism. Since fractal computing is CPU-intensive, it is practicable to parallelize fractal computation under TBB. As to the workload unbalance problem in parallelism, a balance method based on sampling execution time was presented to estimate workload. The proposed method realized the task partition through the workload estimate from sampling execution time, and TBB task scheduler was invoked for parallel process. The experimental results show that the proposed method has high estimation accuracy and low time rate so as to effectively achieve workload balance, and good speedups are available through TBB design.

The existing certificateless public key signature schemes are based on elliptic curve or Tate pairing. The proposed scheme was certificateless public signature scheme without pairing. New scheme was proved to be unforgery under random oracle model. New scheme avoids the using of certificate in certificate-based public key signature scheme, removes key escrow in ID-based signature scheme, needs simpler algorithm, and is convenient to practical application.

A graph-based image segmentation method was presented. Firstly, use region growing technique to find initial over-segmentation. Secondly, use these regions as nodes to create the graph. Finally, use minimum cut method to merge these regions. The proposed method has two advantages: one is using minimum cut criterion to merge regions, which can contain global information; the other is using regions as nodes to create the graph, which can greatly reduce nodes of the graph. The experimental results show the effectiveness of the approach.