The purpose of disentangled representation learning is to model the key factors that affect the form of data， so that the change of a key factor only causes the change of data on a certain feature， while the other features are not affected. It is conducive to face the challenge of machine learning in model interpretability， object generation and operation， zero-shot learning and other issues. Therefore， disentangled representation learning always be a research hotspot in the field of machine learning. Starting from the history and motives of disentangled representation learning， the research status and applications of disentangled representation learning were summarized， the invariance， reusability and other characteristics of disentangled representation learning were analyzed， and the research on the factors of variation via generative entangling， the research on the factors of variation with manifold interaction， and the research on the factors of variation using adversarial training were introduced， as well as the latest research trends such as a Variational Auto-Encoder （VAE） named β-VAE were introduced. At the same time， the typical applications of disentangled representation learning were shown， and the future research directions were prospected.
Symbolic music generation is still an unsolved problem in the field of artificial intelligence and faces many challenges. It has been found that the existing methods for generating polyphonic music fail to meet the marke requirements in terms of melody， rhythm and harmony， and most of the generated music does not conform to basic music theory knowledge. In order to solve the above problems， a new Transformer-based multi-track music Generative Adversarial Network （Transformer-GAN） was proposed to generate music with high musicality under the guidance of music rules. Firstly， the decoding part of Transformer and the Cross-Track Transformer （CT-Transformer） adapted on the basis of Transformer were used to learn the information within a single track and between multiple tracks respectively. Then， a combination of music rules and cross-entropy loss was employed to guide the training of the generative network， and the well-designed objective loss function was optimized while training the discriminative network. Finally， multi-track music works with melody， rhythm and harmony were generated. Experimental results show that compared with other multi-instrument music generation models， for piano， guitar and bass tracks， Transformer-GAN improves Prediction Accuracy （PA） by a minimum of 12%， 11% and 22%， improves Sequence Similarity （SS） by a minimum of 13%， 6% and 10%， and improves the rest index by a minimum of 8%， 4% and 17%. It can be seen that Transformer -GAN can effectively improve the indicators including PA and SS of music after adding CT-Transformer and music rule reward module， which leads to a relatively high overall improvement of the generated music.
Social media highly facilitates people’s daily communication and disseminating information， but it is also a breeding ground for rumors. Therefore， how to automatically monitor rumor dissemination in the early stage is of great practical significance， but the existing detection methods fail to take full advantage of the semantic information of the microblog information propagation graph. To solve this problem， based on Heterogeneous graph Attention Network （HAN）， a rumor monitoring model was built， namely MicroBlog-HAN. In the model， a hierarchical attention mechanism including node-level attention and semantic-level attention was adopted. First， the neighbors of microblog nodes were combined by the node-level attention to generate two groups of node embeddings with specific semantics. After that， different semantics were fused by the semantic-level attention to obtain the final node embeddings of microblog， which were then treated as the classifier’s input to perform the binary classification task. In the end， the classification result of whether the input microblog is rumor or not was given. Experimental results on two real-world microblog rumor datasets convincingly prove that MicroBlog-HAN model can accurately identify microblog rumors with an accuracy over 87%.
Traffic flow forecasting is an important research topic for the intelligent transportation system， however， this research is very challenging because of the complex local spatio-temporal relationships among traffic objects such as stations and sensors. Although some previous studies have made great progress by transforming the traffic flow forecasting problem into a spatio-temporal graph forecasting problem， in which the direct correlations across spatio-temporal dimensions among traffic objects are ignored. At present， there is still lack of a comprehensive modeling approach for the local spatio-temporal relationships. A novel spatio-temporal hypergraph modeling scheme was first proposed to address this problem by constructing a kind of spatio-temporal hyper-relationships to comprehensively model the complex local spatio-temporal relationships. Then， a Spatio-Temporal Hyper-Relationship Graph Convolutional Network （STHGCN） forecasting model was proposed to capture these relationships for traffic flow forecasting. Extensive comparative experiments were conducted on four public traffic datasets. Experimental results show that compared with the spatio-temporal forecasting models such as Attention based Spatial-Temporal Graph Convolutional Network （ASTGCN） and Spatial-Temporal Synchronous Graph Convolutional Network （STSGCN）， STHGCN achieves better results in Root Mean Square Error （RMSE）， Mean Absolute Error （MAE） and Mean Absolute Percentage Error （MAPE）； and the comparison of the running time of different models also shows that STHGCN has higher inference speed.
In view of the small target of cigarette butts in surveillance videos of public places and the easy divergence of smoke generated by smoking， it is difficult to determine the smoking behavior only by target detection algorithm. Considering that the algorithm of posture estimation using skeleton key points is becoming more and more mature， a smoking behavior detection algorithm was proposed by using the relationship between human skeleton key points and smoking behavior. Firstly， AlphaPose and RetinaFace were used to detect the key points of human skeleton and face respectively. According to the ratio of distance between wrist and middle point of two corners of mouth and between wrist and the eye on the same side， a method for calculating whether the Smoking Action Ratio （SAR） in humans falls within the Golden Ratio of Smoking Actions （GRSA） to distinguish smoking from non-smoking behaviors was proposed. Then， YOLOv4 was used to detect whether cigarette butts existed in the video. The results of GRSA determination and YOLOv4 were combined to determine the possibility of smoking behavior in the video and make a determination of whether smoking behavior was present. The self-recorded dataset test shows that the proposed algorithm can accurately detect smoking behavior with the accuracy reached 92%.
Because multiple kernel learning can avoid selection of kernel functions and parameters effectively， and graph clustering can fully mine complex structural information between samples， Multiple Kernel Graph Clustering （MKGC） has received widespread attention in recent years. However， the existing MKGC methods suffer from the following problems： graph learning technique complicates the model， the high rank of graph Laplacian matrix cannot ensure the learned affinity graph to contain accurate c connected components （block diagonal property）， and most of the methods ignore the high-order structural information among the candidate affinity graphs， making it difficult to fully utilize the multiple kernel information. To tackle these problems， a novel MKGC method was proposed. First， a new graph learning method based on capped simplex projection was proposed to directly project the kernel matrices onto graph simplex， which reduced the computational complexity. Meanwhile， a new block diagonal constraint was introduced to keep the accurate block diagonal property of the learned affinity graphs. Moreover， the low-rank tensor learning was introduced in capped simplex projection space to fully mine the high-order structural information of multiple candidate affinity graphs. Compared with the existing MKGC methods on multiple datasets， the proposed method has less computational cost and high stability， and has great advantages in Accuracy （ACC） and Normalized Mutual Information （NMI）.
In reality， recommender systems usually suffer from various bias problems， such as exposure bias， position bias and selection bias. A recommendation model that ignores the bias problems cannot reflect the real performance of the recommender system， and may be untrustworthy for users. Previous works show that a recommendation model based on propensity score estimation can effectively alleviate the exposure bias problem of implicit feedback data in recommender systems， but only item information is usually considered to estimate propensity scores， which may lead to inaccurate estimation of propensity scores. To improve the accuracy of propensity score estimation， a Match Propensity Estimator （MPE） method was proposed. Specifically， a concept of users’ popularity preference was introduced at first， and then more accurate modeling of the sample exposure rate was achieved by calculating the matching degree of the user’s popularity preference and the item’s popularity. The proposed estimation method was integrated with a traditional recommendation model and an unbiased recommendation model， and the integrated models were compared to three baseline models including the above two models. Experimental results on a public dataset show that the models combining MPE method achieve significant improvement on three evaluation metrics such as recall， Discounted Cumulative Gain （DCG） and Mean Average Precision （MAP） compared with the corresponding baseline models respectively. In addition， experimental results demonstrate that a large part of the performance gain comes from long-tail items， showing that the proposed method is helpful to improve the diversity and coverage of recommended items.
Concerning that the traditional saliency detection algorithm has low segmentation accuracy and the deep learning-based saliency detection algorithm has strong dependence on pixel-level manual annotation data， an unsupervised saliency object detection algorithm based on graph cut refinement and differentiable clustering was proposed. In the algorithm， the idea of “coarse” to “fine” was adopted to achieve accurate salient object detection by only using the characteristics of a single image. Firstly， Frequency-tuned algorithm was used to obtain the salient coarse image according to the color and brightness of the image itself. Then， the candidate regions of the salient object were obtained by binarization according to the image’s statistical characteristics and combination of the central priority hypothesis. After that， GrabCut algorithm based on single image for graph cut was used for segmenting the salient object finely. Finally， in order to overcome the difficulty of imprecise detection when the background was very similar to the object， the unsupervised differentiable clustering algorithm with good boundary segmentation effect was introduced to further optimize the saliency map. Experimental results show that compared with the existing seven algorithms， the optimized saliency map obtained by the proposed algorithm is closer to the ground truth， achieving an Mean Absolute Error （MAE） of 14.3% and 23.4% on ECSSD and SOD datasets， respectively.
Bayesian networks can represent uncertain knowledge and perform inferential computational expressions， but due to the noise and size limitations of actual sample data and the complexity of network space search， Bayesian network structure learning will always have certain errors. To improve the accuracy of Bayesian network structure learning， a Bayesian network structure learning algorithm with the results of maximum frequent itemset and association rule analysis as the prior knowledge was proposed， namely BNSL-FIM （Bayesian Network Structure Learning algorithm based on Frequent Item Mining）. Firstly， the maximum frequent itemset was mined from data and the structure learning was performed on the itemset， then the association rule analysis results were used to correct it， thereby determining the prior knowledge based on frequent item mining and association rule analysis. Secondly， a Bayesian Dirichlet equivalent uniform （BDeu） scoring algorithm was proposed combining with prior knowledge for Bayesian network structure learning. Finally， experiments were carried out on 6 public standard datasets to compare the Hamming distance between the structure with/without prior and the original network structure. The results show that the proposed algorithm can effectively improve the structure learning accuracy of Bayesian network compared to the original BDue scoring algorithm.
Concerning that the multi-view data analysis is susceptible to the noise of the original dataset and requires additional steps to calculate the clustering results， a Robust Multi-view subspace clustering based on Consistency Graph Learning （RMCGL） algorithm was proposed. Firstly， the potential robust representation of data in the subspace was learned in each view， and the similarity matrix of each view was obtained based on these representations. Then， a unified similarity graph was learned based on the obtained multiple similarity matrices. Finally， by adding rank constraints to the Laplacian matrix corresponding to the similarity graph， the obtained similarity graph had the optimal clustering structure， and the final clustering results were able to be obtained directly by using this similarity graph. The process was completed in a unified optimization framework， in which potential robust representations， similarity matrices and consistency graphs could be learned simultaneously. The clustering Accuracy （ACC） of RMCGL algorithm is 3.36 percentage points， 5.82 percentage points and 5.71 percentage points higher than that of Graph-based Multi-view Clustering （GMC） algorithm on BBC， 100leaves and MSRC datasets， respectively. Experimental results show that the proposed algorithm has a good clustering effect.
The Non-negative Matrix Factorization （NMF） algorithm based on graph regularization makes full use of the assumption that high-dimensional data are usually located in a low-dimensional manifold space to construct the Laplacian matrix. The disadvantage of this algorithm is that the constructed Laplacian matrix is calculated in advance and will not be iterated during the multiplicative update process. In order to solve this problem， the self-representation method in subspace learning was combined to generate the representation coefficient， and the similarity matrix was further calculated to obtain the Laplacian matrix， and the Laplacian matrix was iterated during the update process. In addition， the label information of the training set was used to construct the class indicator matrix， and two different regularization items were introduced to reconstruct the category indicator matrix respectively. This algorithm was called Graph Learning Regularized Discriminative Non-negative Matrix Factorization （GLDNMF）， and the corresponding multiplicative update rules and the convergence proof of the objective function were given. Face recognition experimental results on two standard datasets show that the accuracy of the proposed algorithm for face recognition is increased by 1% - 5% compared to the existing classic algorithms， verifying the effectiveness of the proposed method.
In the framework of traditional knowledge distillation， the teacher network transfers all of its own knowledge to the student network， and there are almost no researches on the transfer of partial knowledge or specific knowledge. Considering that the industrial field has the characteristics of single scene and small number of classifications， the evaluation of recognition performance of neural network models in specific categories need to be focused on. Based on the attention feature transfer distillation algorithm， three specific knowledge learning algorithms were proposed to improve the classification performance of student networks in specific categories. Firstly， the training dataset was filtered for specific classes to exclude other non-specific classes of training data. On this basis， other non-specific classes were treated as background and the background knowledge was suppressed in the distillation process， so as to further reduce the impact of other irrelevant knowledge on specific classes of knowledge. Finally， the network structure was changed， that is the background knowledge was suppressed only at the high-level of the network， and the learning of basic graphic features was retained at the bottom of the network. Experimental results show that the student network trained by a specific knowledge learning algorithm can be as good as or even has better classification performance than a teacher network whose parameter scale is six times of that of the student network in specific category classification.
Deep learning methods are widely used in bearing fault diagnosis， but in actual engineering applications， real service fault data during bearing service are not easily collected and lack of data labels， which is difficult to train adequately. Focused on the difficulty of bearing service fault diagnosis， a transfer learning model based on Graph Convolutional Network （GCN） in bearing service fault diagnosis was proposed. In the model， the fault knowledge was learned from artificially simulated damage fault data with sufficient data and transferred to real service faults， so as to improve the diagnostic accuracy of service faults. Specifically， the original vibration signals of artificially simulated damage fault data and service fault data were converted into the time-frequency maps with both time and frequency information through wavelet transform， and the obtained maps were input into graph convolutional layers for learning， so as to effectively extract the fault feature representations in the source and target domains. Then the Wasserstein distance between the data distributions of source domain and target domain was calculated to measure the difference between two data distributions， and a fault diagnosis model that can diagnose bearing service faults was constructed by minimizing the difference in data distribution. A variety of different tasks were designed for experiments with different bearing failure data sets and different operating conditions. Experimental results show that the proposed model has the ability to diagnose bearing service faults and also can be transferred from one working condition to another， and perform fault diagnosis between different component types and different working conditions.
All market activities of stock market participants combine to affect stock market changes， making stock market volatility fraught with complexity and making accurate prediction of stock prices a challenge. Among these activities that affect stock market changes， financial disclosure is an attractive and potentially financially rewarding means of predicting stock indexe changes. In order to deal with the complex changes in the stock market， a method of stock index prediction was proposed that incorporates data from financial statements disclosed by corporates. Firstly， the stock index historical data and corporate financial statement data were preprocessed， and the main task is dimension reduction of the high-dimensional matrix generated from corporate financial statement data， and then the dual-channel Long Short-Term Memory （LSTM） network was used to forecast and research the normalized data. Experimental results on SSE 50 and CSI 300 Index datasets show that the prediction effect of the proposed method is better than that using only historical data of stock indexes.
Focused on the issues that the single deep distance metric based on triplet loss has poor adaptability to the diversified datasets and easily leads to overfitting， a deep distance metric learning method based on optimized triplet loss was proposed. Firstly， by thresholding the relative distance of triplet training samples mapped by neural network， and a piecewise linear function was used as the evaluation function of relative distance. Secondly， the evaluation function was added to the Boosting algorithm as a weak classifier to generate a strong classifier. Finally， an alternating optimization method was used to learn the parameters of the weak classifier and neural network. Through the evaluation of various deep distance metric learning methods in the image retrieval task， it can be seen that the Recall@1 of the proposed method is 4.2， 3.2 and 0.6 higher than that of the previous best score on CUB-200-2011， Cars-196 and SOP datasets respectively. Experimental results show that the proposed method outperforms the comparison methods， while avoiding overfitting to a certain extent.
Most of the existing directed graph clustering algorithms are based on the assumption of approximate linear relationship between nodes in vector space， ignoring the existence of non-linear correlation between nodes. To address this problem， a directed graph clustering algorithm based on Kernel Nonnegative Matrix Factorization （KNMF） was proposed. First， the adjacency matrix of a directed graph was projected to the kernel space by using a kernel learning method， and the node similarity in both the original and kernel spaces was constrained by a specific regularization term. Second， the objective function of graph regularization kernel asymmetric NMF algorithm was proposed， and a clustering algorithm was derived by gradient descent method under the non-negative constraints. The algorithm accurately reveals the potential structural information in the directed graph by modeling the non-linear relationship between nodes using kernel learning method， as well as considering the directivity of the links of nodes. Finally， experimental results on the Patent Citation Network （PCN） dataset show that compared with the comparison algorithm， when the number of clusters is 2， the proposed algorithm improves the Davies-Bouldin （DB） and Distance-based Quality Function （DQF） by about 0.25 and 8% respectively， achieving better clustering quality.
Learning the latent vector representations of nodes in the graph is an important and ubiquitous task， which aims to capture various attributes of the nodes in the graph. A lot of work demonstrates that static graph representation learning can learn part of the node information； however， real-world graphs evolve over time. In order to solve the problem that most dynamic network algorithms cannot effectively retain node neighborhood structure and temporal information， a dynamic network representation learning method based on Deep Neural Network （DNN） and Gated Recurrent Unit （GRU）， namely DynAEGRU， was proposed. With Auto-Encoder （AE） as the framework of the DynAEGRU， the neighborhood information was aggregated by encoder with a DNN to obtain low-dimensional feature vectors， then the node temporal information was extracted by a GRU network，finally， the adjacency matrix was reconstructed by the decoder and compared with the real graph to construct the loss. Experimental results on three real-word datasets show that DynAEGRU method has better performance gain compared to several static and dynamic graph representation learning algorithms.
The reasonable exploration of the infeasible region in constrained multi-objective evolutionary algorithms for solving optimization problems with large infeasible domains not only helps the population to converge quickly to the optimal solution in the feasible region， but also reduces the impact of unpromising infeasible region on the performance of the algorithm. Based on this， a Constrained Multi-Objective Evolutionary Algorithm based on Space Shrinking Technique （CMOEA-SST） was proposed. Firstly， an adaptive elite retention strategy was proposed to improve the initial population in the Pull phase of Push and Pull Search for solving constrained multi-objective optimization problems （PPS）， so as to increase the diversity and feasibility of the initial population in the Pull phase. Then， the space shrinking technique was used to gradually reduce the search space during the evolution process， which reduced the impact of unpromising infeasible regions on the algorithm performance. Therefore， the algorithm was able to improve the convergence accuracy while taking account of both convergence and diversity. In order to verify the performance of the proposed algorithm， it was simulated and compared with four representative algorithms including C-MOEA/D （adaptive Constraint handling approach embedded MOEA/D）， ToP （handling constrained multi-objective optimization problems with constraints in both the decision and objective spaces）， C-TAEA （Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization） and PPS on the test problems of LIRCMOP series. Experimental results show that CMOEA-SST has better convergence and diversity when dealing with constrained optimization problems with large infeasible regions.
In recommender system field， most of the existing works mainly focus on the One-Class Collaborative Filtering （OCCF） problem with only one type of users’ feedback， e.g.， purchasing feedback. However， users’ feedback is usually heterogeneous in real applications， so it has become a new challenge to model the users’ heterogeneous feedback to capture their true preferences. Focusing on the Heterogeneous One-Class Collaborative Filtering （HOCCF） problem （including users’ purchasing feedback and browsing feedback）， a transfer learning solution named Staged Variational AutoEncoder （SVAE） model was proposed. Firstly， the latent feature vectors were generated via the Multinomial Variational AutoEncoder （Multi-VAE） with users’ browsing feedback auxiliary data. Then， the obtained latent feature vectors were transferred to another Multi-VAE to assist the modeling of users’ target data， i.e.， purchasing feedback by this Multi-VAE. Experimental results on three real-world datasets show that the performance of SVAE model on the important metrics such as Precision@5 and Normalized Discounted Cumulative Gain@5 （NDCG@5） is significantly better than the performance of the state-of-the-art recommendation algorithms in most cases， demonstrating the effectiveness of the proposed model.
In view of the problems of ant colony algorithm in global path planning under static environment， such as being unable to find the shortest path， slow convergence speed， great blindness of path search and many inflection points， an improved ant colony algorithm was proposed. Taking the grid map as the running environment of the robot， the initial pheromones were distributed unevenly， so that the path search tended to be near the line between the starting point and the target point； the information of the current node， the next node and the target point was added into the heuristic function， and the dynamic adjustment factor was introduced at the same time， so as to achieve the purpose of strong guidance of the heuristic function in the early stage and strengthening the guidance of pheromone in the later stage； the pseudo-random transfer strategy was introduced to reduce the blindness of path selection and speed up finding the shortest path； the volatilization coefficient was adjusted dynamically to make the volatilization coefficient larger in the early stage and smaller in the later stage， avoiding premature convergence of the algorithm； based on the optimal solution， B-spline curve smoothing strategy was introduced to further optimize the optimal solution， resulting in shorter and smoother path. The sensitivity analysis of the main parameters of the improved algorithm was conducted， the feasibility and effectiveness of each improved step of the algorithm were tested， the simulations compared with the traditional ant colony algorithm and other improved ant colony algorithms under 20×20 and 50×50 environments were given， and the experimental results verified the feasibility， effectiveness and superiority of the improved algorithm.
Current research on Person Re-Identification （Re-ID） mainly concentrates on short-term situations with person’s clothing usually unchanged. However， more common practical cases are long-term situations， in which a person has higher possibility to change his clothes， which should be considered by Re-ID models. Therefore， a method of person re-identification with cloth changing based on joint loss capsule network was proposed. The proposed method was based on ReIDCaps， a capsule network for cloth-changing person re-identification. In the method， vector-neuron capsules that contain more information than traditional scalar neurons were used. The length of the vector-neuron capsule was used to represent the identity information of the person， and the direction of the capsule was used to represent the clothing information of the person. Soft Embedding Attention （SEA） was used to avoid the model over-fitting. Feature Sparse Representation （FSR） mechanism was adopted to extract discriminative features. The joint loss of label smoothing regularization cross-entropy loss and Circle Loss was added to improve the generalization ability and robustness of the model. Experimental results on three datasets including Celeb-reID， Celeb-reID-light and NKUP prove that the proposed method has certain advantages compared with the existing person re-identification methods.
Under the constrains of very short message content on social media platforms， a large number of empty forwards in the transmission structure， and the mismatch between user roles and contents， a rumor detection model based on user attribute information and message content in the propagation network， namely GMB_GMU， was proposed. Firstly， user propagation network was constructed with user attributes as nodes and propagation chains as edges， and Graph Attention neTwork （GAT） was introduced to obtain an enhanced representation of user attributes； meanwhile， based on this user propagation network， the structural representation of users was obtained by using node2vec， and it was enhanced by using mutual attention mechanism. In addition， BERT （Bidirectional Encoder Representations from Transformers） was introduced to establish the source post content representation of the source post. Finally， to obtain the final message representation， Gated Multimodal Unit （GMU） was used to integrate the user attribute representation， structural representation and source post content representation. Experimental results show that the GMB_GMU model achieves an accuracy of 0.952 on publicly available Weibo data and can effectively identify rumor events， which is significantly better than the propagation algorithms based on Recurrent Neural Network （RNN） and other neural network benchmark models.
K-anonymous algorithm makes the data reached the condition of K-anonymity by generalizing and suppressing the data. It can be seen as a special feature selection method named K-anonymous feature selection which considers both data privacy and classification performance. In K-anonymous feature selection method， the characteristics of K-anonymity and feature selection are combined to use multiple evaluation criteria to select the subset of K-anonymous features. It is difficult for the filtered K-anonymous feature selection method to search all the candidate feature subsets satisfying the K-anonymous condition， and the classification performance of the obtained feature subset cannot be guaranteed to be optimal， and the wrapper feature selection method has very high-cost calculation. Therefore， a hybrid K-anonymous feature selection method was designed by combining the characteristics of filtered feature sorting and wrapper feature selection by improving the forward search strategy in the existing methods and thereby using classification performance as the evaluation criterion to select the K-anonymous feature subset with the best classification performance. Experiments were carried out on multiple public datasets， and the results show that the proposed algorithm can outperform the existing algorithms in classification performance and has less information loss.
As a hot issue in natural language processing， summarization generation has important research significance. The abstractive method based on Seq2Seq （Sequence-to-Sequence） model has achieved good results， however， the extractive method has the potential of mining effective features and extracting important sentences of articles， so it is a good research direction to improve the abstractive method by using extractive method. In view of this， a fusion model of abstractive method and extractive method was proposed. Firstly， incorporated with topic similarity， TextRank algorithm was used to extract significant sentences from the article. Then， an abstractive framework based on the Seq2Seq model integrating the semantics of extracted information was designed to implement the summarization task； at the same time， pointer-generator network was introduced to solve the problem of Out-Of-Vocabulary （OOV）. Based on the above steps， the final summary was obtained and verified on the CNN/Daily Mail dataset. The results show that on all the three indexes ROUGE-1， ROUGE-2 and ROUGE-L， the proposed model is better than the traditional TextRank algorithm； meanwhile， the effectiveness of fusing extractive method and abstractive method in the field of summarization is also verified.
Making full use of the spatio-temporal context information in the video can significantly improve the performance of object tracking， but most of the current object tracking algorithms based on deep learning only use the feature information of the current frame to locate the object， without using the spatio-temporal context information of the same object in the video frames before and after the current frame， which leads to the tracking object being susceptible to the interference from the similar object nearby， so a potential cumulative error will be introduced during tracking and locating. In order to retain spatio-temporal context information， a short-term memory storage pool was introduced based on SiamMask algorithm to store features of the historical frames； meanwhile， an Appearance Saliency Boosting Module （ASBM） was proposed， which not only enhanced the saliency features of the tracking object， but also suppressed the interference from similar object around the tracking object. On the basis of the above， an object tracking algorithm based on spatio-temporal context information enhancement was proposed. To verify the performance of the proposed algorithm， experiments were carried out on four datasets， including VOT2016， VOT2018， DAVIS-2016 and DAVIS-2017. Experimental results show that compared with SiamMask algorithm， the proposed algorithm has the accuracy and Expected Average Overlap rate （EAO） increased by 4 percentage points and 2 percentage points respectively on VOT2016 dataset， and has the accuracy， robustness and EAO improved by 3.7 percentage points， 2.8 percentage points and 1 percentage point respectively on VOT2018 dataset， and has the decay of the regional similarity and contour accuracy indicators on DAVIS-2016 datasets both reduced by 0.2 percentage points， and has the decay of the regional similarity and contour progress indicators on DAVIS-2017 datasets reduced by 1.3 and 0.9 percentage points respectively.
Whether the learned features have better invariance in the significant intra-class changes will determine the upper limit of performance of the Person Re-identification （ReID） model. Environmental light， image resolution change， motion blur and other factors may cause color deviation of pedestrian images， and these problems will cause overfitting of the model to color information of the data， thus limiting the performance of the model. By simulating the color information loss of the data samples and highlighting the structural information of the samples， the model was helped to learn more robust features. Specifically， during model training， the training batch was randomly selected according to the set probability， and then a rectangular area of the image or the entire image was randomly selected for each RGB image sample in the selected batch， and the pixels of the selected area was replaced with the pixels of the same rectangular area in the corresponding grayscale image， thus generating a training image with different grayscale areas. Experimental results demonstrate that compared with the benchmark model， the proposed method achieves a significant performance improvement of 3.3 percentage points at most on the evaluation index mean Average Precision （mAP）， and performs well on multiple datasets.
The traditional social collaborative filtering algorithms based on rating prediction have the inherent deficiency in which the prediction value does not match the real sort， and social collaborative ranking algorithms based on ranking prediction are more suitable to practical application scenarios. However， most existing social collaborative ranking algorithms focus on explicit feedback data only or implicit feedback data only， and not make full use of the information in the dataset. In order to fully exploit both the explicit and implicit scoring information of users’ social networks and recommendation objects， and to overcome the inherent deficiency of traditional social collaborative filtering algorithms based on rating prediction， a new social collaborative ranking model based on the newest xCLiMF model and TrustSVD model， namely SPR_SVD++， was proposed. In the algorithm， both the explicit and implicit information of user scoring matrix and social network matrix were exploited simultaneously and the learning to rank’s evaluation metric Expected Reciprocal Rank （ERR） was optimized. Experimental results on real datasets show that SPR_SVD++ algorithm outperforms the existing state-of-the-art algorithms TrustSVD， MERR_SVD++ and SVD++ over two different evaluation metrics Normalized Discounted Cumulative Gain （NDCG） and ERR. Due to its good performance and high expansibility， SPR_SVD++ algorithm has a good application prospect in the Internet information recommendation field.
Multi-kernel learning method is an important type of kernel learning method， but most of multi-kernel learning methods have the following problems： most of the basis kernel functions in multi-kernel learning methods are traditional kernel functions with shallow structure， which have weak representation ability when dealing with the problems of large data scale and uneven distribution； the generalization error convergence rates of the existing multi-kernel learning methods are mostly O 1 / n ， and the convergence speeds are slow. Therefore， a multi-kernel learning method based on Neural Tangent Kernel （NTK） was proposed. Firstly， the NTK with deep structure was used as the basis kernel function of the multi-kernel learning method， so as to enhance the representation ability of the multi-kernel learning method. Then， a generalization error bound with a convergence rate of O 1 / n was proved based on the measure of principal eigenvalue ratio. On this basis， a new multi-kernel learning algorithm was designed in combination with the kernel alignment measure. Finally， experiments were carried out on several datasets. Experimental results show that compared with classification algorithms such as Adaboost and K-Nearest Neighbor （KNN）， the newly proposed multi-kernel learning algorithm has higher accuracy and better representation ability， which also verifies the feasibility and effectiveness of the proposed method.
Damage to sensors in Industrial Internet of Things （IIoT） system due to continuous use and normal wear leads to hidden anomalies in the collected and recorded sensing data. To solve this problem， an anomaly detection algorithm based on Local Sensitive Bloom Filter （LSBF） model was proposed， namely LSBFAD. Firstly， the Spatial Partition based Fast Johnson-Lindenstrauss Transform （SP-FJLT） was used to perform hash mapping to the data， then the Mutual Competition （MC） strategy was used to reduce noise， and finally the Bloom filter was constructed by 0-1 coding. In simulation experiments conducted on three benchmark datasets including SIFT， MNIST and FMA， the false detection rate of LSBFAD algorithm is less than 10%. Experimental results show that compared with the current mainstream anomaly detection algorithms， the proposed anomaly detection algorithm based on LSBF has higher Detection Rate （DR） and lower False Alarm Rate （FAR） and can be effectively applied to anomaly detection of IIoT data.
In machine learning， data quality has a far-reaching impact on the accuracy of system prediction. Due to the difficulty of obtaining information and the subjective and limited cognition of human， experts cannot accurately mark all samples. And some probability sampling methods proposed in resent years fail to avoid the problem of unreasonable and subjective sample division by human. To solve this problem， a label noise filtering method based on Dynamic Probability Sampling （DPS） was proposed， which fully considered the differences between samples of each dataset. By counting the frequency of built-in confidence distribution in each interval and analyzing the trend of information entropy of built-in confidence distribution in each interval， the reasonable threshold was determined. Fourteen datasets were selected from UCI classic datasets， and the proposed algorithm was compared with Random Forest （RF）， High Agreement Random Forest Filter （HARF）， Majority Vote Filter （MVF） and Local Probability Sampling （LPS） methods. Experimental results show that the proposed method shows high ability on both label noise recognition and classification generalization.
