To address the challenges of insufficient label semantic understanding， vague relationship modeling， and high computational costs of Large Language Models （LLMs） in zero-shot re-ranking tasks， a hierarchical filtering and label semantic extension method named HFLS （Hierarchical Filtering and Label Semantics） was proposed. In the method， by constructing a multi-level label semantic extension path， a progressive prompting strategy “keyword matching → semantic association → domain knowledge integration” was designed to guide LLMs in deep relational reasoning. At the same time， a hierarchical filtering mechanism was introduced to reduce computational complexity while retaining high-potential candidate documents. Experimental results indicate that on seven benchmark datasets such as TREC-DL2019， HFLS achieves average gains of 21.92%， 13.43% and 8.59%， respectively， in NDCG（Normalized Discounted Cumulative Gain）@10 compared to Pointwise methods like Pointwise.qg， Pointwise.yes_no， and Pointwise.3Label. In terms of reasoning efficiency， HFLS has the processing latency per query reduced by 91.06%， 68.87% and 33.54% compared to Listwise， Pairwise， and Setwise methods， respectively.

Aspect-Based Sentiment Analysis （ABSA） tasks aim to determine the sentiment polarity of specific aspect words in comments. In the field of ABSA， dual-channel models that extract both grammar and semantic information have achieved certain results. However， the existing models fail to consider the different degrees of importance among grammar nodes， the additional noise introduced by global attention mechanism， and the existence of correlations between similar features comprehensively. To address these issues， a dual-channel graph convolutional model with dependency type and distance enhancements was proposed. Firstly， dependency types were introduced in the grammar module to measure the importance of neighborhood nodes. Secondly， mask matrices based on the dependency tree distance were constructed to filter out grammar unrelated noise. Finally， a supervised contrastive loss was introduced to facilitate the model to learn correlations between similar features. Experimental results show that on SemEval-2014 Restaurant， SemEval-2014 Laptop and Twitter datasets， compared to the second-best model DGNN （Dual Graph Neural Network）， the proposed model achieves accuracy improvements of 0.11， 0.94， 1.01 percentage points， respectively， and Macro-F1 improvements of 0.63， 1.66， 0.83 percentage points， respectively， verifying the effectiveness of the proposed model.

To effectively capture the dynamic information of the gait and accelerate the authentication and identification, a novel gait recognition algorithm was presented in this paper, which employed the row mass vector of the Frame Difference Energy Image (FDEI) as the gait features. The gait contour images were extracted through the object detection, binarization, morphological process and connectivity analysis of the original images. Using the width of the contour images sequence, the quasi-periodicity analysis and the row mass vector of the frame difference image were obtained, then the Continuous Hidden Markov Model (CHMM) was employed to train and recognize the parameters of model. The proposed algorithm was applied to Central Asia Student International Academic (CASIA) gait database. The experimental results show that it can easily extract the features of the gait with low dimension, achieving fast recognition speed and high recognition rate, so it can be used for real-time gait recognition.

In order to forecast the number of equipment maintenance people more easily and validly, a common approach of selecting the features of input vector in Support Vector Machine (SVM) named Main Factor Method (MFM) was proposed. The relevant terms of "main factor", "driving factor", "voluntary action" and "actions' carrier" were defined, based on which the theoretical MFM was constructed. Firstly, the predicting vector's main factor of voluntary actions was setup by "infinitely related principle" and "action purpose" method. Then the driving factors which can be looked as the characteristics of SVM input vector were refined through the selected main factor and "selecting principles of driving factors". The experimental results and comparison with other congeneric methods show that the proposed method can select the more accurate prediction with the value of relative average error 0.0109.

In order to effectively extract nonlinear features of data set, the paper proposed a new method, called Kernel Semi-supervised Locality Preserving Projection (KSSLPP). It redefined the between-class similarity and within-class similarity using rich labeled and unlabeled samples that contain valuable information, which was used to maximize the between-class separability and minimize the within-class separability in a high dimensional kernel space. The proposed method preserves the global and local structures of unlabeled samples in addition to separating labeled samples in different classes. Contrast experiments in the Olivetti face database and UCI database verify the effectiveness of the proposed algorithm.

The performance test of software is to examine efficiency、resource occupation and stability etc，in order to verify system capacity，find defects in early and provides support for system performance tuning. This paper studies the architecture and performance characteristics of Web application system then proposed a general performance testing process model. Basing on the model we use HP’s automatic test tool LoadRunner implement testing on Evaluation Subsystem of City Management System.

One query expansion method, which was based on concept graph, was proposed to solve the low recall and precision rates in the traditional information retrieval methods based on matching keywords. On one hand, words and phrases, which are retrieved by users, can be expanded based on HowNet .Meanwhile, user queries and documents will be transformed into concept graphs. On the other hand, partial matching and semantic similarity based on concept graphs is adopted to acquire similarity between concept graphs, which will optimize the whole retrieval process. This method is proved to be more effective by experiment.