Deep reinforcement learning algorithms are difficult to learn optimal policy through interaction with environment in reward sparsity environments， so that the intrinsic reward needs to be built to guide the update of algorithms. However， there are still some problems in this way： 1） statistical inaccuracy of state classification will misjudge reward value， thereby causing the agent to learn wrong behavior； 2） due to the strong ability of the prediction network to identify state information， the state freshness generated by the intrinsic reward decreases， which affects the learning effect of the optimal policy； 3） due to the random state transition， the information of the teacher strategies is not effectively utilized， which reduces the agent’s ability to explore the environment. To solve the above problems， a reward construction mechanism combining prediction error of stochastic generative network with hash discretization statistics， namely RGNP-HCE （Randomly Generated Network Prediction and Hash Count Exploration）， was proposed， and the knowledge of multi-teacher policy was transferred to student policy through distillation. In RGNP-HCE mechanism， the fusion reward was constructed through the idea of curiosity classification. In specific， the global curiosity reward was constructed by stochastic generative network’s prediction error between multiple episodes， and the local curiosity reward was constructed by hash discretization statistics in one episode， which guaranteed the rationality of intrinsic rewards and the correctness of policy gradient updates. In addition， multi-teacher policy distillation provides students with multiple reference directions for exploration， which improved environmental exploration ability of the student policy effectively. Finally， in the test environments of Montezuma’s Revenge and Breakout， experiment of comparing the proposed mechanism with four current mainstream deep reinforcement learning algorithms was carried out， and policy distillation was performed. The results show that compared with the average performance of current high-performance deep reinforcement learning algorithms， the average performance of RGNP-HCE mechanism in both test environments is improved， and the distilled student policy is further improved in average performance， indicating that RGNP-HCE mechanism and policy distillation are effective in improving the exploration ability of agent.

In order to construct a transparent and trustworthy recommendation mechanism， relevant research works mainly provide reasonable explanations for personalized recommendation through explainable recommendation mechanisms. However， there are three major limitations of the existing explainable recommendation mechanism： 1） using correlations only can provide rational explanations rather than causal explanations， and using paths to provide explanations will bring privacy leakage； 2） the problem of sparse user feedback is ignored， so it is difficult to guarantee the fidelity of explanations； 3） the granularities of explanations are relatively coarse， and users’ personalized preferences are not considered. To solve the above problems， an explainable recommendation mechanism ERCKCI based on Collaborative Knowledge Graph （CKG） and counterfactual inference was proposed. Firstly， based on the user’s own behavior sequence， the counterfactual inference was used to achieve high-sparsity causal decorrelation by using the casual relations， and the counterfactual explanations were derived iteratively. Secondly， in order to improve the fidelity of explanations， not only the CKG and the neighborhood propagation mechanism of the Graph Neural Network （GNN） were used to learn users’ and items’ representations based on single time slice； but also the user long-short term preference were captured to enhance user preference representation through self-attention mechanism on multiple time slices. Finally， via a higher-order connected subgraph of the counterfactual set， the multi-granularity personalized preferences of user was captured to enhance counterfactual explanations. To verify the effectiveness of ERCKCI mechanism， comparison experiments were performed on the public datasets MovieLens（100k）， Book-crossing and MovieLens（1M）. The obtained results show that compared with the Explainable recommendation based on Counterfactual Inference （ECI） algorithm under the Relational Collaborative Filtering （RCF） recommendation model on the first two datasets， the proposed mechanism has the explanation fidelity improved by 4.89 and 3.38 percentage points respectively， the size of CF set reduced by 63.26% and 66.24% respectively， and the sparsity index improved by 1.10 and 1.66 percentage points respectively； so the explainability is improved effectively by the proposed mechanism.

In order to minimize the expected logistics cost of the supplier alliance, the Inventory Routing Problem (IRP) of multiple suppliers and multiple products under random fluctuations of demand was studied. Based on the horizontal collaboration strategy, a reasonable share method of vehicle distribution costs among the members of the supplier alliance was designed. By considering the retailer's distribution soft and hard time windows and inventory service level requirements, a heterogeneous vehicle inventory routing mixed-integer stochastic programming model of multiple suppliers and multiple products was established, and the inverse function of demand cumulative distribution was employed to transform this model into a deterministic programming model. Then an improved genetic algorithm was designed to solve the programming model. The results of example analysis show that the use of heterogeneous vehicles for distribution can reduce the total cost of supplier alliance by 8.3% and 11.92% respectively and increase the loading rate of distribution vehicles by 24% and 17% respectively, compared with the use of homogeneous heavy-duty and light-duty vehicles. The sensitivity analysis results indicate that no matter how the proportion of suppliers' supply to the total supply of the alliance and the variation coefficient of retailers' commodity demand change, the total cost of the supplier alliance can be effectively reduced by using heterogeneous vehicles for distribution; and the greater the demand variation coefficient is, the more obvious the advantage of using heterogeneous vehicles for distribution has.

It is a precondition of achieving code reuse to search open source code accurately. The current methods based on keyword search only concern matching function signatures. Considering the source code comments on the semantic description of the method's function, a method based on keyword search was proposed, which took into account code comments. The features of code, such as function signatures and different types of comments, were identified from the generated abstract syntax tree of source code; the code features and query statements were transformed into vectors respectively, and then based on the cosine similarity between the vectors, the scoring mechanism of multi-feature weight assignment to the results was created. According to the scores, an ordered list of relevant functions was obtained that reflects the associations between code features in the functions and a query. The experimental results demonstrate that the accuracy of search results can be improved by using multiple code features with different weights.

Considering the shortcomings of existing research on the problem of user reliability in crowdsourcing systems, it was assumed that each user had different reliability for different type of tasks, and on this basis, a task allocation mechanism for crowdsourcing system was designed based on the reliability of users. Firstly, an efficient task allocation mechanism was designed by using the greedy technology to maximize the profit of task publishers, and the task allocation scheme with the maximum benefit was chosen every time. Secondly, a mechanism of user reliability updating based on historical information was designed and determined by user historical reliability and the quality of the current task, and the final payment paid to the user was linked with the reliability of the user, so as to motivate the user to finish tasks with high quality continuously. Finally, the effectiveness of the designed mechanisms was analyzed in three ways:the total profit of task publishers, the task completion rate and the user reliability. The simulation results show that compared with ProMoT (Profit Maximizing Truthful auction mechanism), the proposed method is more effective and feasible, and the rate of the total benefit of task publishers is 16% higher. At the same time, it can solve the problem of user unreliability in the existing methods, and increase the reliability of crowdsourcing systems and the total revenue of task publishers.

Dimensionality reduction plays an important role in big data analysis and visualization. Many dimensionality reduction techniques with probabilistic distribution models rely on the optimizaition of cost function between low-dimensional model distribution and high-dimensional real distribution. The key issue of this type of technology is to efficiently construct the probabilistic distribution model representing the feature of original high-dimensional dataset most. In this paper, Wasserstein distance was introduced to dimensionality reduction, and a novel method named Wasserstein Embedded Map (W-map) was presented for high-dimensional data reduction and visualization. W-map converts dimensionality reduction problem into optimal transportation problem by constructing the similar Wasserstein flow in the high-dimensional dataset and its corresponding low-dimensional representation, and then the best matched low-dimensional visualization was found by solving the optimal transportation problem of Wasserstein distance. Experimental results demonstrate that the presented method performs well in dimensionality reduction and visualization for high-dimensional data.

Outsourcing data owner’s data to Database Services Provider (DSP) securely provides XML database service for companies and organizations, which is an important data service form in cloud computing. This paper proposed an improved polyphonic splitting scheme for XML database service(IPSS-XML). IPSS-XML overcame the drawback of low verifying efficiency in other existing schemes by adding an Assistant Verifying Data (AVD) to each non-leaf node at low cost. The improvement enhances query executing efficiency without breaking the confidentiality constraints.

To solve the problem that software implementation cannot meet real-time requirements, a hardware scheme based on Field-Programmable Gate Array (FPGA) was presented. By analyzing the digital watermark extraction system, a watermark-embedding algorithm suitable for FPGA implementation was designed and its structure is applicable to 5/3 wavelet transform. Moreover, a new watermark extraction structure that corresponded to the embedding algorithm was also proposed. The pipeline and highly parallel structure has the features of high computation efficiency, small size, low-power and real-time process. The simulation results demonstrate the system's correctness and the algorithm's abroad applicability.

To solve the problem of inference on mass ontology, a new algorithm was proposed based on cloud computing platform. Ontology schema was transformed into graph, inference strategy was designed accordingly. Inference algorithm was designed based on the computing model of Map/Reduce. After one time iteration, mass ontology could be inferred in the course of Map. Later, duplicated triples were eliminated in the course of Reduce. The experimental results show that inference of one hundred million triples costs less than four minutes. The algorithm is effective.

A novel decoding algorithm of turbo codes was proposed in this paper. Based on the MAX-Log-MAP algorithm, the novel algorithm not only used the segment linearity function but also took the statistic of the decoding data into account. Experimental results demonstrate that it performs better than the MAX-Log-MAP algorithm using traditional segment linearity function.