The portfolio problem is a hot issue in the field of quantitative trading. An Integrated Deep Reinforcement Learning Portfolio Model （IDRLPM） was proposed to address the shortcomings of existing deep reinforcement learning-based portfolio models that cannot achieve adaptive trading strategies and effectively utilize supervised information. Firstly， multi-agent method was used to construct multiple base agents and design reward functions with different trading styles to represent different trading strategies. Secondly， integrated learning method was used to fuse the features of strategy network of the base agents to obtain the integrated agent adaptive to market environment. Then， a trend prediction network based on Convolutional Block Attention Module （CBAM） was embedded in the integrated agent， and the output of the trend prediction network guided integrated strategy network to adaptively select the proportion of trades. Finally， under the alternating iterative training of supervised deep learning and reinforcement learning， IDRLPM effectively utilized supervised information from training data to enhance model profitability. The Sharpe Ratio （SR） of IDRLPM reaches 1.87 and 1.88， and the Cumulative Return （CR） reaches 2.02 and 1.34 in Shanghai Stock Exchange （SSE） 50 constituent stocks and China Securities Index （CSI） 500 constituent stocks； compared with the Ensemble Deep Reinforcement Learning （EDRL） trading model， the SR improves by 105% and 55%， and the CR improves by 124% and 79%. The experimental results show that IDRLPM can effectively solve the portfolio problem.

Aiming at the problem of high matching cost in the existing complex event matching processing methods， a complex event matching algorithm ReCEP was proposed， which uses event buffers （ordered event lists） for recursive traversal. Different from the existing method that uses automaton to match on the event stream， this method decomposes the constraints in the complex event query mode into different types， and then recursively verifies the different constraints on the ordered list. Firstly， according to the query mode， the related event instances were cached according to the event type. Secondly， the query filtering operation was performed to the event instances on the ordered list， and an algorithm based on recursive traversal was given to determine the initial event instance and obtain candidate sequence. Finally， the attribute constraints of the candidate sequence were further verified. Experimental testing and analysis results based on simulated stock transaction data show that compared with the current mainstream matching methods SASE and Siddhi， ReCEP algorithm can effectively reduce the processing time of query matching， has overall performance better than both of the two methods， and has the query matching efficiency improved by more than 8.64%. It can be seen that the proposed complex event matching method can effectively improve the efficiency of complex event processing.

The core of the Blockchain as a Service(BaaS) platform is how to deploy the blockchain network on the cloud computing platform. Fabric deployment can be divided into static components and dynamic chaincodes according to the component startup time, and chaincode deployment is the core and the most complex part of Fabric cloudification. Because the Fabric has no interfaces for Kubernetes,the current solutions in the industry implement chaincode deployment through a series of auxiliary technologies, but these solutions do not incorporate the chaincodes into the Kubernetes management environment along with static components. In response to the existing problems of BaaS scheme, the following works were mainly done:1) a comprehensive study of the underlying infrastructure, especially of the Kubernetes platform with high availability in the production environment; 2) the cloud deployment of Kubernetes on Fabric was designed and implemented, especially in the chaincode part, a brand-new container control plug-in was used to realize the support for Kubernetes at the code level and complete the goal of incorporating chaincodes into Kubernetes environment management; 3) the functional computing service was used to manage the Fabric chaincodes to realize a brand-new chaincode execution mode, which means changing from the "start-wait-call-wait" mode to the "start-call-exit" mode. The above works in Fabric cloud deployment, especially in chaincode deployment management, have certain reference value for the optimization of the BaaS platform based on Fabric and Kubernetes.

Damage to sensors in Industrial Internet of Things （IIoT） system due to continuous use and normal wear leads to hidden anomalies in the collected and recorded sensing data. To solve this problem， an anomaly detection algorithm based on Local Sensitive Bloom Filter （LSBF） model was proposed， namely LSBFAD. Firstly， the Spatial Partition based Fast Johnson-Lindenstrauss Transform （SP-FJLT） was used to perform hash mapping to the data， then the Mutual Competition （MC） strategy was used to reduce noise， and finally the Bloom filter was constructed by 0-1 coding. In simulation experiments conducted on three benchmark datasets including SIFT， MNIST and FMA， the false detection rate of LSBFAD algorithm is less than 10%. Experimental results show that compared with the current mainstream anomaly detection algorithms， the proposed anomaly detection algorithm based on LSBF has higher Detection Rate （DR） and lower False Alarm Rate （FAR） and can be effectively applied to anomaly detection of IIoT data.

In view of the problems of low efficiency, error propagation, and entity redundancy in traditional entities and relations annotation methods, and for the fact that there is the characteristic of "the overlapping relationship between one entity (main-entity) and multiple entities at the same time" in corpuses of some domains, a new annotation method for joint extraction of domain entities and relations was proposed. First, the main entity was marked as a fixed label, each other entity in the text that has relation with the main-entity was marked as the type of relation between the corresponding two entities. This way that entities and relations were simultaneously labeled was able to save at least half of the cost of annotation. Then, the triples were modeled directly instead of modeling entities and relations separately, and, the triple data were able to be obtained through label matching and mapping, which alleviated the problems of overlapping relation extraction, entity redundancy, and error propagation. Finally, the field of crop diseases and pests was taken as the example to conduct experiments, and the Bidirectional Encoder Representations from Transformers (BERT)-Bidirectional Long Short-Term Memory (BiLSTM)+Conditional Random Field (CRF) end-to-end model was tested the performance on the dataset of 1 619 crop diseases and pests articles. Experimental results show that this model has the F1 value 47.83 percentage points higher than the pipeline method based on the traditional annotation method+BERT model; compared with the joint learning method based on the new annotation method+BiLSTM+CRF model, Convolutional Neural Network (CNN)+BiLSTM+CRF or other classic models, the F1 value of the model increased by 9.55 percentage points and 10.22 percentage points respectively, which verify the effectiveness of the proposed annotation method and model.

The performance of swarm intelligent algorithms in solving large-scale permutation and combinatorial optimization problems is influenced by the large search space, so a Solution Space Dynamic Compression (SSDC) strategy was proposed to cut down the search space of the algorithms dynamically. In the proposed strategy, two times of initial solutions of the the permutation and combination optimization problem were obtained by the intelligent algorithm firstly. Then the repetitive segments of the two solutions were recognized and merged together. And the new points after merging were taken into the original solution space to perform the compression and update of the solution space. In the next intelligent algorithm solving process, the search was carried out in the compressed feasible space, so as to improve the searching ability of the individuals in the limited space and reduce the searching time cost. Based on five high-dimensional benchmark Travel Salesmen Problems (TSP) and two Vehicle Routing Problems (VRP), the performances of several swarm intelligent algorithms combined with the solution space dynamic compression strategy were tested. The results show that the swarm intelligent algorithms combined with the proposed strategy are superior to the corresponding original algorithms in the search accuracy and stability. It is proved that the solution space dynamic compression strategy can effectively improve the performance of swarm algorithms.

Particle Swarm Optimization (PSO) algorithm is easy to be premature and drop into the local optimum and cannot jump out when solving complex multimodal functions. Related researches show that changing the topological structure among particles and adjusting the updating mechanism are helpful to improve the diversity of the population and the optimization ability of the algorithm. Therefore, a Hybrid PSO with Topological time-varying and Search disturbance (HPSO-TS) was proposed. In the algorithm, a K-medoids clustering algorithm was adapted to cluster the particle swarm dynamically for forming several heterogeneous subgroups, so as to facilitate the information flow among the particles in the subgroups. In the speed updating, by adding the guide of the optimal particle of the swarm and introducing the disturbance of nonlinear changing extreme, the particles were able to search more areas. Then, the transformation probability of the Flower Pollination Algorithm (FPA) was introduced into the position updating process, so the particles were able to transform their states between the global search and the local search. In the global search, a lioness foraging mechanism in the lion swarm optimization algorithm was introduced to update the positions of the particles; while in the local search, a sinusoidal disturbance factor was applied to help particles jump out of the local optimum. The experimental results show that the proposed algorithm is superior to FPA, PSO, Improved PSO (IPSO) algorithm and PSO algorithm with Topology (PSO-T) in the accuracy and robustness. With the increase of testing dimension and times, these advantages are more and more obvious. The topological time-varying strategy and search disturbance mechanism introduced by this algorithm can effectively improve the diversity of population and the activity of particles, so as to improve the optimization ability.

With regard to unreasonable waste collection and considerable environmental pollution due to logistics activities, a two-echelon closed-loop logistics network location-routing optimization model based on customer clustering and product recovery was proposed. Firstly, considering the dynamic nature of actual logistics network, the uncertain characteristics of customer demand and recovery rate were assumed, and location-routing optimization model based on minimum operating cost and minimum environmental impact was established. Secondly, based on improvement of multi-objective evolutionary algorithm, an algorithm for two-echelon closed-loop logistics network location-routing optimization model based on customer clustering and product recovery was proposed. Finally, the performance of the proposed optimization algorithm was analyzed and a practical experimentation of model and algorithm was conducted on the location-routing problem of a company in Chongqing city. Analyses show that the proposed model and algorithm can alleviate the final decision difficulty and improve operational efficiency of the logistics system while the optimization scheme obtained can reduce total cost and environmental impact.

To reduce the running energy consumption of the central air conditioner and stabilize and control the return temperature of chilled water effectively, an optimal control method of return water temperature was proposed, and the actual load demand was judged according to the deviation between the measured value of return water temperature and the set value. Firstly, the inertia weight of Particle Swarm Optimization (PSO) algorithm was made decline exponentially which made updating speed of particles match each stage of optimization process. Then, aiming at uncertain disturbance of parameters of the model, the thoughts of Artificial Immune (AI) algorithm were introduced in Particle Swarm Optimization (PSO) algorithm to form AI-PSO algorithm which could expand the diversity of particles and enforce their ability to get rid of local optimum. Finally, three parameters of Proportional Integral Differential (PID) controller were optimized with AI-PSO algorithm, and through this controller, the frequency of chilled water pump was adjusted to make return water temperature steady near set value. The experimental results show that the proposed strategy can reduce operating frequency of chilled water pump more effectively while meeting indoor load demand, in addition, energy saving effect and control quality are much better.

There is no clear space structure among images, so correlation information of images' space structure could not be utilized effectively. To tackle this problem, a new space relationship feature based image retrieval method was proposed. Firstly, feature vectors were extracted from all images which contain the queried image. And then, all of the every two feature vectors' similarities were calculated to form a similarity matrix. The set of similarity matrix's columns was taken as the new feature vector, namely the new space relationship feature vector, so the former feature vectors could be mapped into a Euclidean space. Finally, similarities were calculated on the new feature space. In this way, the problem of feature vectors' similarity was turned into the new space relationship feature vectors' similarity. The space structure among images was clearer than before on the new feature space, so the accuracy of image retrieval was improved. The experimental results on the Corel database show that the average retrieval precision, recall-precision and visual evaluation metric of the proposed method have advantage over the color histogram in the image retrieval task. The proposed image retrieval method based on the new space relationship feature sufficiently utilizes correlation information of images' space structure and it has better retrieval effect.

The existing signature schemes based on bi-linear pairings were proved to be insecure in quantum computing environment. A lattice has the features of simple computational operations and difficult problems on which are hard to solve. In order to resist the quantum attack, an identity based ring signature scheme was presented based on the assumption of the hardness of lattice problem-Small Integer Solution (SIS), and it was provably secure in the standard model by using the Ducas' ideal lattice technology (DUCAS L, MICCIANCIO D. Improved short lattice signatures in the standard model. Proceedings of the 34th Annual Cryptology Conference on Advances in Cryptology. Berlin:Springer, 2014:335-352). The scheme was mainly divided in to four steps:master key generation algorithm, the signature private key generation algorithm, signature algorithm and validation algorithm. The signature was output as a single vector. Compared to the same type signature schemes, to some extent, the proposed scheme shortens the length of private key, public key and the signature, improves the operation efficiency, in addition, it is also suitable for lightweight authentication, and the security of electronic commerce and cloud computing are indirectly ensured by the security of the signature algorithm.

Parameter-free locality preserving projection does not need to set parameters and has stable performance, but the algorithm cannot effectively maintain the local structure of the sample and ignores the role of non-local samples. Moreover, this method exists the Small Size Sample (SSS) problem. A complete parameter-free local neighborhood preserving and non-local maximization algorithm was proposed. In order to make full use of the nearest neighbor samples and non-nearest neighbor samples, which were divided by whether the distance between two samples is no more than 0.5 or not, the neighbor scatter matrix and non-nearest neighbor scatter matrix were constructed. Then, the objective function of the algorithm was to seek a set of projection vectors such that the neighbor scatter matrix was maximized and non-nearest neighbor scatter matrix was minimized simultaneously. As to solve the objective function, the high dimensional samples were projected to a low dimensional subspace by Principal Component Analysis (PCA) algorithm, which was proved without lossing any effective discriminant information according to two theorems. In order to solve the SSS problem, the objective function was converted to differential form. The experimental results on face database and palmprint database illustrate that the proposed method outperforms Parameter-free locality preserving projection with average recognition rate, which proves the effectiveness of the proposed algorithm.

To solve the security problems between the reader and the server of mobile Radio Frequency IDentification (RFID) caused by wireless transmission, a two-way authentication protocol based on pseudo-random function was provided. It satisfied the EPC Class-1 Generation-2 industry standards, and mutual certifications between tags, readers and servers were achieved. The security of this protocol was also proved by using GNY logic. It can effectively resist track, replay and synchronization attack etc.; simultaneously, its main calculations are transferred to the server, thereby reducing the calculations and cost of the tag.

Concerning gender tendency hidden in microblog messages posted by microblog users, a novel approach based on rough set theory was proposed to identify microblog user gender. In the proposed approach, a new Representation Model based on Tolerance Rough Set (TRSRM) was devised, which can effectively represent gender characteristics of microblog messages. The experimental results show that the accuracy rate of the proposed approach is 7% higher than frequency model approach by testing messages of 1000 real microblog users, and so the TRSRM achieves better recognition performance.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

The image quality assessment is to give a reasonable assessment for the quality of image processing algorithm, and No-Reference (NR) quality evaluation method is applied in a lot of situations of being unable to get the original reference image. The result of structure analysis of the infrared image shows that the uncertainty of the image is fuzzy, but not random. Therefore, the concept of fuzzy entropy was introduced into the quality assessment of infrared image. A method of no-reference quality assessment for blurred infrared image was proposed, comparisons and analysis on performance of the algorithm were given from the following aspects: efficiency, consistency and accuracy. The simulation results show that this method has the characteristics of low computation complexity, fast operation speed and consistence of subjective and objective evaluations. And the general performance is better than the assessment based on Mean Squared Error (MSE) and Peak Singal-to-Noise Ratio (PSNR).

The identity-based ring signcryptions have both high efficiency of identity-based cryptosystem and signcryption technology and the ambiguity of ring signature. In the fair exchange protocol based on signcryption proposed by Luo, et al (Luo M, Zou C H, Hu J, 〖WTBX〗et al.〖WTBZ〗Signcryption-based fair exchange protocol. Journal of Communications, 2010, 31(8A): 146-150), the fairness and efficiency are not good enough. The new scheme introduced a more efficient identity-based ring signcryption and dealt with the decryption and the signature-and-identity binding separately. A new fair exchange protocol was proposed based on this new scheme. The analysis shows that this scheme overcomes the fairness defect of the original scheme and has a better efficiency, which makes it a good application in electronic payment and contract signing.

Concerning the flaw that it needs a fully credible third party in ID-based strong designated verifier signature and the low efficiency of existing schemes, taking the advantage of Certificate Authority (CA)'s low trust level in certificate-based public key cryptography, a new strong designated verifier signature scheme was proposed in this paper. Furthermore, the formal security analysis under assumed Bilinear Diffie-Hellman (BDH) problem in the random oracle model was presented. Performance analysis shows the scheme meets all properties of strong designated verifier signature schemes and enjoys higher correspondence efficiency used in bandwidth limited environment since the signature length is just one of the group elements.

To improve the efficiency of Particle Swarm Optimization (PSO), a quantum particle swarm optimization algorithm combined with quantum theory on the basis of Bloch sphere was proposed. In Bloch spherical coordinates, the particle automatically updated rotation angle and particle position, without setting the rotation angle in the form of look-up table (or setting fixed value of the interval), making up for the deficiency of quantum evolutionary algorithm and quantum genetic algorithm on the basis of Bloch sphere, and the algorithm is more generalizable. Using quantum Hadamard gate to realize the variation of particle enhanced the diversity of population, and prompted particle jump out of local extreme value. The simulation results of the typical function optimization problem show that the algorithm is stable with high precision and fast convergence rate, and it is practical.

By analyzing the certificateless verifiable ring signature scheme (LUO DAWEN, HE MINGXING, LI XIAO. Certificateless verifiable ring signature scheme. Computer Engineering,2009, 35(15): 135-137) and the verifiable proxy ring signature scheme (LUO DAWEN, HE MINGXING, LI XIAO.A verifiable proxy ring signature scheme.Journal of Southwest University for Nationalities:Natural Science Edition, 2009, 35(3):608-611), it was found that these convertible ring signature schemes were susceptible to non-repudiation attack, i.e., any member in the ring can impersonate others identity to sign the message and the verifier believed the signature was signed by the latter. To address the above problems, improved schemes were proposed by using the private key of the signer to have a secret value. The security analysis proves that the improved schemes overcome the security defect of the original scheme and satisfy all security requirements of verifiable ring signature.

To improve the survivability of networks, a strategy selection model based on stochastic game was proposed. According to the damage impact on system made by the attacker behavior, the game process was divided into the resistance phase, the recognition phase, and the recovery phase. The relationship between the transition of system state and strategy selection was depicted from a specific view, based on it, a strategy selection analytical method was presented. A representative network example was provided to verify the feasibility and validity of the proposed method in the prediction of attacker behavior and decision of optimal strategy.

Div + CSS is popular in Web page layout. In this layout, a lot of data records of Web pages gather in a layer in the form of repetition structure. To mine data from Web well, this paper proposed a new kind of Web data mining algorithm, computed tree edit distance through string edit distance, improved string edit distance algorithm,used string edit distance to access similarity between one tree and another, and then found repeated patterns in Web pages and mined data. By testing pages of different features of repeated patterns, this algorithm is proved to extract Web data successfully with the feature whether the root and upper layer nodes are the same or the lowest layer nodes are the same.

Efficient Graph-Based algorithm is a novel image segmentation method based on graph theory and it can segment an image at an extraordinary speed. However, it is easily influenced by the threshold value and the segmentation result is imprecise when dealing with the border and texture. Here, an improved algorithm is proposed, which has three main contributions: 1) RGB color space is replaced by Lab color space; 2) Laplacian operator is used to divide the edges of weighted graph into border edges and non-border edges, and those non-border edges are given priority; 3) the optimum threshold is evaluated based on uniformity measurement. Experimental results show that the improved algorithm is more accurate and adaptive than traditional Graph-based algorithms, and segmentation results are closer to human vision property.

In order to overcome the shortcomings of Covariance Matrix Adaptation Evolution Strategy (CMAES), such as premature convergence and low precision, when it is used in high-dimensional multimodal optimization, a hybrid algorithm combined CMAES with Orthogonal Design with Quantization (OD/Q) was proposed. Firstly, the small population CMAES was used to realize a fast searching. When orthogonal CMAES algorithm trapped in local extremum, base vectors for OD/Q were selected dynamically based on the position of current best solution. Then the entire solution space, including the field around extreme value, was explored by trial vectors generated by OD/Q. The proposed algorithm was guided by this process jumping out of the local optimum. The new approach was tested on six high-dimensional multimodal benchmark functions. Compared with other algorithms, the new algorithm has better searching precision, convergence speed and capacity of global search.

The security of E-commerce determines its development; however, traditional means of security certification cannot meet the demand on reliability. To ensure the security in E-commerce communication, a new method of security certification was proposed based on rolled fingerprint reconstruction and digital signature. Firstly, the image sequence of rolling fingerprints was reconstructed into a high-quality complete fingerprint image, and then the features were extracted as the key of digital signature. Thirdly, the message embedded with digital signature was transferred. At the same time, the correctness and completeness of the message were checked, and the validity of the sender was also identified. The experimental results show that the proposed algorithm can be used for rolling fingerprint reconstruction in any direction to get a high-quality fingerprint image. And it can run in real-time because of low complexity. The rolling fingerprint, as the security certification medium, ensures the security of E-commerce.

Deploying sensor nodes’ has to meet certain coverage and connectivity in the energy constrained wireless sensor networks. Concerning this, a new square regionbased coverage and connectivity probability computational method was proposed in this paper. The new method can not only depict the relations among coverage rate, connectivity rate, the number of nodes, the sensing (communication) range of nodes and the size of network, but also calculate the number of sensor nodes scattered for maintaining certain coverage rate and connectivity rate. The simulation results show that the errorrate of deployment is less than the value obtained from the theoretical analysis.

In order to overcome the problems of poor local search and premature convergence on Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization approach based on Dynamic Random Search Technique (DRST) and good-point set was proposed in this paper. DRST was introduced to optimize the current best position of the swarm. On the other hand, reinitialization with a good-point set manner was employed for the swarm falling into premature convergence to go out of the local optimum. Linear search in the negative gradient direction was also applied to accelerate the optimization. In the end, an experiment was given and the results show that the improved algorithm has rapid convergence, great ability of preventing premature convergence and better performance than Standard Particle Swarm Optimization (SPSO) and Dissipative Particle Swarm Optimization (DPSO).

A new method of facial expression recognition is proposed, which is based on Independent Component Analysis (ICA) and Support Vector Clustering (SVC), which aims at solving the problem of features extraction of facial expression and auto-clustering. First, the facial expression features were extracted by ICA, and then the radius of Locally Constrained Support Vector Clustering (LCSVC) could be acquired according to the adjustment of inter-parameter Mixture of Factor Analysis (MFA) method. This clustering method effectively restrained the disturber of the clustering boundary region and also was better than the one that only uses SVC. The test sample is classified by a comparison with the difference between new and old radius, and the experimental results show that the proposed method is effective and successful.