To solve the problem of inconsistent data standards, uneven data quality, and compromised data security privacy in current data governance process, a new blockchain-based data governance collaboration method which integrates the characteristics of multi-party cooperation, security and reliability of blockchain was proposed and applied to the construction of data standards, the protection of data security, and the control of data sharing processes. Based on data governance requirements and blockchain characteristics, a collaborative data governance model was formed. A multi-collaborative data standard process, an efficient data standard construction and update mechanism, and secure data sharing and access control were developed afterwards. So this blockchain-based data governance collaboration method could be implemented to improve the efficiency and security of data standardization work. The experimental and analysis results show that the proposed method has a significant improvement in the efficiency of the standard term application time than the traditional data standard construction method. Especially in the big data environment, the application of the smart contract improves the time efficiency. The distributed storage of the blockchain provides powerful basis and guarantee for system security, user behavior traceability and auditing. Methods mentioned above provide a good application demonstration for data governance and a reference for the industry's metadata management, data standards sharing and application.

Since the tourism text for destinations contains semantic noise and different scenic spots, which can not be displayed intuitively, a new scenic spots-topic model based on the probabilistic topic model was proposed. The model assumed that one document included several scenic spots with correlation, and a special scenic spot named "global scenic spot" was introduced to filter the semantic noise. Then Gibbs sampling algorithm was employed to learn the maximum a posteriori estimates of the model and get a topic distribution vector for each scenic spot. A clustering experiment was conducted to indirectly evaluate the effects of the model and analyze the impact of "global scenic spot" on the model. The result shows that the proposed model has better effect than baseline model such as TF-IDF (Term Frequency-Inverse Document Frequency) and Latent Dirichlet Allocation (LDA), and the "global scenic spot" can improve the modeling effect significantly. Finally, scenic spots association graph was employed to display the result visually.

In order to effectively balance the global and local search performance of Particle Swarm Optimization (PSO) algorithm, an improved PSO algorithm based on Gaussian disturbance and natural selection (GDNSPSO) was proposed. Based on the simple PSO algorithm, the improved algorithm took into account the mutual influence among all individual best particles and replaced the individual best value of each particle with the mean value of them which contained Gaussian disturbance. And the evolution mechanism of survival of the fittest in natural selection was employed to improve the performance of algorithm. At the same time, the nonlinear adjustment of the inertia weight was adjusted by the cosine function with adaptive adjustment of the threshold of inertia weight and the adjustment strategy of the asynchronous change was used to improve the learning ability of the particles. The simulation results show that the GDNSPSO algorithm can improve the convergence speed and precision, and it is better than some recently proposed improved PSO algorithms.

Since the single-criterion-based tracker can not adapt to the complex environment, a tracking approach based on adaptive fusion of dual-criteria was proposed. In the method, the second-order spatiogram was employed to represent the target, the similarity between the target candidate and the target model as well as the contrast between the target candidate and its neighboring background were used to evaluate its reliability, and the objective function (or likelihood function) was established by weighted fusion of the two criteria. The particle filter procedure was used to search the target, and the fuzzy logic was applied to adaptively adjust the weights of the similarity and contrast. Experiments were carried out on several challenging sequences such as person, animal, and the results show that, compared with other trackers such as incremental visual tracker, ι₁ tracker, the proposed algorithm obtains better comprehensive performance in handling occlusion, deformation, rotation, and appearance change, and its success rate and average overlap ratio are respectively more than 80% and 0.76.