Moving object detection aims to separate the background and foreground of the video， however， the commonly used low-rank factorization methods are often difficult to comprehensively deal with the problems of dynamic background and intermittent motion. Considering that the skewed noise distribution after background subtraction has potential background correction effect， a moving object detection model based on the reliability low-rank factorization and generalized diversity difference was proposed. There were three steps in the model. Firstly， the peak position and the nature of skewed distribution of the pixel distribution in the time dimension were used to select a sub-sequence without outlier pixels， and the median of this sub-sequence was calculated to form the static background. Secondly， the noise after static background subtraction was modeled by asymmetric Laplace distribution， and the modeling results based on spatial smoothing were used as reliability weights to participate in low-rank factorization to model comprehensive background （including dynamic background）. Finally， the temporal and spatial continuous constraints were adopted in proper order to extract the foreground. Among them， for the temporal continuity， the generalized diversity difference constraint was proposed， and the expansion of the foreground edge was suppressed by the difference information of adjacent video frames. Experimental results show that， compared with six models such as PCP（Principal Component Pursuit）， DECOLOR（DEtecting Contiguous Outliers in the Low-Rank Representation）， LSD（Low-rank and structured Sparse Decomposition）， TVRPCA（Total Variation regularized Robust Principal Component Analysis）， E-LSD（Extended LSD） and GSTO（Generalized Shrinkage Thresholding Operator）， the proposed model has the highest F-measure. It can be seen that this model can effectively improve the detection accuracy of foreground in complex scenes such as dynamic background and intermittent motion.

Abductive reasoning is an important task in Natural Language Inference （NLI）， which aims to infer reasonable process events （hypotheses） between the given initial observation event and final observation event. Earlier studies independently trained the inference model from each training sample； recently， mainstream studies have considered the semantic correlation between similar training samples and fitted the reasonableness of the hypotheses with the frequency of these hypotheses in the training set， so as to describe the reasonableness of the hypotheses in different environments more accurately. On this basis， while describing the reasonableness of the hypotheses， the difference and relativity constraints between reasonable hypotheses and unreasonable hypotheses were added， thereby achieving the purpose of two-way characterization of the reasonableness and unreasonableness of the hypotheses， and the overall relativity was modeled through many-to-many training. In addition， considering the difference of the word importance in the process of event expression， an attention module was constructed for different words in the samples. Finally， an abductive reasoning model based on attention balance list was formed. Experimental results show that compared with the L2R² （Learning to Rank for Reasoning） model， the proposed model has the accuracy and AUC improved by about 0.46 and 1.36 percentage points respectively on the mainstream abductive inference dataset Abductive Reasoning in narrative Text （ART） ， which prove the effectiveness of the proposed model.

In order to solve the problem that the algorithms of publication venue recommendation always consider the text topics or the author’s history of publications separately， which leads to the low accuracy of publication venue recommendation results， a contribution recommendation algorithm of academic journal based on author preferences was proposed. In this algorithm， not only the text topics and the author’s history of publications were used together， but also the potential relationship between the academic focuses of publication venues and time were explored. Firstly， the Latent Dirichlet Allocation （LDA） topic model was used to extract the topic information of the paper title. Then， the topic-journal and time-journal model diagrams were established， and the Large-scale Information Network Embedding （LINE） model was used to learn the embedding of graph nodes. Finally， the author’s subject preferences and history of publication records were fused to calculate the journal composite scores， and the publication venue recommendation for author to contribute was realized. Experimental results on two public datasets， DBLP and PubMed， show that the proposed algorithm has better recall under different list lengths of recommended publication venues compared to six algorithms such as Singular Value Decomposition （SVD）， DeepWalk and Non-negative Matrix Factorization （NMF）. The proposed algorithm maintains high accuracy while requiring less information from papers and knowledge bases， and can effectively improve the robustness of publication venue recommendation algorithm.