Face detection has been well studied for many years. However, the problem of face detection in complex environments is still being studied. In complex environments, faces is often blocked and blurred. This article proposes applying YOLOv3 to face detection problems in complex environments. First, we will re-cluster the data set in order to find the most suitable a priori box. Then we set multiple score values to make it possible to predict the results of multiple sets of images and find the optimal score value. Experimental results show that after adjustment, the model has more advantages in face detection than the original model in complex environments. The average accuracy is more than 10% higher than that of aggregate channel feature (ACF), Towstage convolutional neural network (CNN) and multi-scale Cascade CNN in face detection benchmarks WIDER FACE. Our code

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There is an urgent need into the study on how to help correct and explain ill-formed sentences related to "Chinese verb and 'zai plus locative' structure". The theoretical foundation of this paper is based on semantic feature analysis proposed by Qi (1994). With reference to the two different situational classifications of Chinese verbs by Ma (1981) and by Chen (1988), this paper, combined with the Generalized Valence Mode by Zhan (1999), elaborates on a large number of linguistic facts related to "Chinese verb and 'zai plus locative' structure" and figures out the constraints on constructional transformations. Research results show that each observed linguistic fact can be categorized into and explained by one of the 13 substructures derived from two basic structures, in addition, in spite of the syntactic and semantic complexity of the "Chinese verb and 'zai plus locative' structure", each observed linguistic fact and the corresponding constructional transformation can be explained through the semantic situation of the verb and the subcategorization of the verb. Constraints on transformation for sentences with locatives such as theme locative, agent locative, event locative, or entity locative are different, and constraints on transformation for sentences with verbs/VP such as action verb, state verb, V-zhe, or V-O vary greatly. The semantic feature analysis in this paper shed light on producing grammatical Chinese sentences of "zai plus locative" structure in teaching and learning Chinese as a first and second language.