To address the result instability caused by subjective visual estimation dependence in traditional ceramic tile color difference detection methods， a method integrating texture and color features was proposed for quantitation and grading of chromatic aberration in ceramic tiles. A large-scale dataset named TILE-TCR （TILE Texture and Color Recognition）， containing both texture and color labels， was constructed to enhance the model’s ability to represent texture and color features. At the same time， a color difference grading dataset named TILE-CAG （TILE Chromatic Aberration Grade） was established to support the color difference classification task. Based on these datasets， the network structure of Fractal Encoding Network （FENet） was improved by introducing Spatial Pyramid Pooling （SPP） and SE （Squeeze-and-Excitation） modules， thereby extracting multi-task features and focusing on critical regions. Then， a clustering algorithm was employed to determine the thresholds for color difference grading adaptively， thereby implementing objective quantification of color difference grading. Experimental results show that the proposed improved method achieves an accuracy of 92.82% in the ceramic tile texture classification task， representing a 1.84 percentage point improvement compared to the FENet； in the color difference grading task， the accuracy， precision， recall and F1 score of the proposed method exceed 90%. Furthermore， a simulated production line was built for industrial deployment and real-time performance test of the model. On commonly used ceramic tiles， the average detection time of the proposed method is under 3 seconds， meeting the real-time requirements for online inspection of industrial conveyor belts.

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.

Traditional information extraction method based on Conditional Random Field (CRF) requires large-scale labeled corpus, it is expensive to label corpus manually and the extraction precision is low in processing agriculture-related product name extraction and category labeling. In order to solve this problem, a method of agriculture-related product name extraction and category labeling based on agricultural ontology and CRF was proposed, automatic extraction and classification of agriculture-related product names was regarded as sequence labeling. Firstly, original data was processed, word, part of speech, geographical attributes and ontology concept features were selected. Then, parameters of the CRF model were trained by the improved quasi-Newton algorithm and decoding was implemented by Viterbi algorithm. A total of four groups of comparative experiments were completed and seven categories were identified. CRF, Hidden Markov Model (HMM) and Maximum Entropy Markov Model (MEMM) were compared through experiments. Finally, the supply and demand trend analysis of agriculture produce was accomplished. The experimental results show that the overall precision, recall and F-score of the open test were increased by 10.20%, 59.78% and 37.17% respectively by adding ontology concepts with appropriate CRF features; it also proves the feasibility, effectiveness and practical significance of the method in promoting automatic supply and demand docking of agricultural products.

Reverse parsing unknown network protocol is of great significance in many network security applications. Most of the existing protocol reverse parsing methods can not handle the encryption protocol or get the semantic information of the protocol field. To solve this problem, a network protocol parsing technique based on dataflow analysis was proposed. According to the data flow recording tool developed on Pin platform, it could parse the network protocol with the aid of the dependence analysis based data flow tracking technology, as well as obtain the protocol format and semantic information of each protocol field. The experimental results show that the technique can parse out the protocol format correctly, especially for the encryption protocol, and extract the program behavior semantics of each protocol field.

A modified Normalized cut (Ncut) method considering the texture weight was proposed to effectively segment feather quill. The weight including texture information enhanced association on each edge of similar texture and reduced interference of the similar region. Narrow unidirectional expansion method with a region-scalable fitting term was used to optimize the initial boundary for the final result and eliminated boundary leakage of the feather quill. The experimental results show that the proposed method can realize boundary extraction of feather quill efficiently and pave the way for the next step research of crease detection.