To address challenges of detecting multi-scale objects under adverse weather conditions such as fog weather， including severe occlusion， low image visibility， leading to frequent false positives and missed detections， a object detection algorithm based on improved YOLOv11 in foggy weather， named MFA-YOLO （Multi-scale frequency-aware Feature Alignment YOLO）， was proposed. First， the multi-scale edge feature enhancement module C3k2_MSEFE （C3k2_Multi-Scale Edge Feature Enhancement） was designed to replace the C3k2 module in YOLOv11 network， so as to enhance network’s ability to extract features from multi-scale objects. Second， the Lightweight Frequency-Aware Feature Pyramid Network （LFFPN） was designed to address the issue of inaccurate bounding box localization caused by low contrast in foggy images， so as to improve both accuracy and robustness of detection. Finally， the Dynamic Feature Alignment Detection Head （DFADHead） was designed to strengthen capability to detect overlapping and occluded objects in dense scenarios through mechanisms such as dynamic convolution and task decomposition. Experimental results demonstrate that on the RTTS dataset， the proposed algorithm achieves an mAP@50 of 76.5% and an mAP@50：95 of 52.6%， representing improvements of 2.7 and 1.9 percentage points compared to the baseline model， respectively. Meanwhile， the number of parameters and model size are reduced by 15.3% and 23.4%， respectively. Furthermore， verification results on two types of foggy images with different fog concentrations synthesized from Cityscapes dataset show that the proposed algorithm achieves the mAP@50 of 48.5% and 46.7%， respectively， which are 2.4 and 3.5 percentage points higher than those of YOLOv11s， respectively， further validating the adaptability and effectiveness of the proposed algorithm in various foggy weather scenarios.

Private Set Intersection （PSI） is an important solution for privacy information sharing. A fair multi-party PSI protocol based on cloud server was proposed for the unfairness caused by the existing protocols in which the parties involved do not have simultaneous access to the calculation results. Firstly， the storage of a sub-share of the private information in Garbled Bloom Filter （GBF） was accomplished by using hash mapping. Secondly， in order to avoid the leakage of the index value of each party’s set element during the interaction， combined with Oblivious Transfer （OT） technique， the share replacement of the stored information was realized. Finally， the bit-by-bit calculation was performed by the cloud server， and the results were returned to each party at the same time to ensure the fairness of each party’s access to the results. The correctness and security analysis of the protocol shows that the proposed protocol can achieve the fairness of the parties in obtaining the intersection results， and can resist the collusion of parties with the cloud server. The performance analysis shows that both of the computational complexity and the communication complexity of the proposed protocol are independent of the total number of elements contained in the set of participants. Under the same conditions， compared with Multi-party PSI protocol （MPSI）， practical multiparty maliciously-secure PSI protocol （PSImple） and Private Intersection Sum algorithm （PI-Sum）， the proposed protocol has less storage overhead， communication overhead and running time.

Concerning low solution accuracy and slow convergence of traditional Artificial Cooperative Search （ACS） algorithm， a Quasi opposition Artificial Cooperative Search algorithm based on Sigmoid function （SQACS） algorithm was proposed to solve Traveling Salesman Problem （TSP）. Firstly， the Sigmoid function was used to construct the scale factor to enhance the global search ability of the algorithm. Then， in the mutation stage， the mutation strategy DE/rand/1 of Differential Evolution （DE） algorithm was introduced into the current population for secondary mutation， thereby improving the calculation accuracy of the algorithm and the diversity of the population. Finally， in the later development stage， the quasi opposition learning strategy was introduced to further improve the quality of the solution. Four instances in TSP test library TSPLIB were used to perform simulation experiments， and the results show that SQACS algorithm is superior to seven comparison algorithms such as Sparrow Search Algorithm （SSA）， DE and Archimedes Optimization Algorithm （AOA） in the shortest path and time consumption， and has good robustness； and compared with other improved algorithms for solving TSP comprehensively， SQACS algorithm also shows good performance. Experimental results prove that the SQACS algorithm is effective in solving small-scale TSPs.

The traditional feature selection algorithms are limited to single-label data. Concerning this problem, multi-label ReliefF algorithm was proposed for multi-label feature selection. For multi-label data, based on label co-occurrence, this algorithm assumed the label contribution value was equal. Combined with three new methods calculating the label contribution, the updating formula of feature weights was improved. Finally a distinguishable feature subset was selected from original features. Classification experiments demonstrate that, with the same number of features, classification accuracy of the proposed algorithm is obviously higher than the traditional approaches.

To solve the category-deficiency and the tendency of selecting low-frequency words in feature selection process based on Mutual Information (MI), the method named LDA-σ was presented. Firstly, the latent topics were extracted by the Latent Dirichlet Allocation (LDA) model, and then the standard deviation of "Word-Topic" MI was calculated as the feature evaluation function. When conducting feature selection and categorization in Reuters-21578, the micro average F1 of LDA-σ reached up to 0.9096, and the highest macro average F1 of LDA-σ was 0.7823, which were higher than that of other algorithms. The experimental results indicate that LDA-σ can be applied to feature selection in text sets.

Chinese segmentation is a special and important issue in Chinese texts processing.The traditional segmentation methods based on an existing dictionary have an obvious defect when they are used to segment texts which may contain words unknown to the dictionary.And the probabilistic methods those consider the probabilistic model of the training set only also do a bad job on the texts of a specific domain.In this paper,a probabilistic segmentation method based on extracting context information was proposed,which adds the context information of the segmenting text into the segmentation probabilistic model so as to guide the processing.The method combining n-gram model and EM algorithm achieves a good effect in the close and opening test.