Many of the existing multi-view clustering algorithms lack the ability to estimate the reliability of different views and thus weight the views accordingly， and some multi-view clustering algorithms with view-weighting ability generally rely on the iterative optimization of specific objective function， whose real-world applications may be significantly influenced by the practicality of the objective function and the rationality of tuning some sensitive hyperparameters. To address these problems， a Multi-view Ensemble Clustering algorithm based on View-wise Mutual Information Weighting （MEC-VMIW） was proposed， whose overall process consists of two phases： the view-wise mutual weighting phase and the multi-view ensemble clustering phase. In the view-wise mutual weighting phase， multiple random down-samplings were performed to the dataset， so as to reduce the problem size in the evaluating and weighting process. After that， a set of down-sampled clusterings of multiple views was constructed. And， based on multiple runs of mutual evaluation among the clustering results of different views， the view-wise reliability was estimated and used for view weighting. In the multi-view ensemble clustering phase， the ensemble of base clusterings was constructed for each view， and multiple base clustering sets were weighted to model a bipartite graph structure. By performing efficient bipartite graph partitioning， the final multi-view clustering results were obtained. Experiments on several multi-view datasets confirm the robust clustering performance of the proposed multi-view ensemble clustering algorithm.
In recent years， more and more schools tend to use online education widely. However， learners are hard to search for their needs from the massive learning resources in the Internet. Therefore， it is very important to research the online learning resource recommendation and perform personalized recommendations for learners， so as to help learners obtain the high-quality learning resources they need quickly. The research status of online learning resource recommendation was analyzed and summarized from the following five aspects. Firstly， the current work of domestic and international online education platforms in learning resource recommendation was summed up. Secondly， four types of algorithms were analyzed and discussed： using knowledge point exercises， learning paths， learning videos and learning courses as learning resource recommendation targets respectively. Thirdly， from the perspectives of learners and learning resources， using the specific algorithms as examples， three learning resource recommendation algorithms based on learners’ portraits， learners’ behaviors and learning resource ontologies were introduced in detail respectively. Moreover， the public online learning resource datasets were listed. Finally， the current challenges and future research directions were analyzed.
Aiming at the classification problems of Three-Dimensional （3D） models， a method of few-shot recognition of 3D models based on Transformer was proposed. Firstly， the 3D point cloud models of the support and query samples were fed into the feature extraction module to obtain feature vectors. Then， the attention features of the support samples were calculated in the Transformer module. Finally， the cosine similarity network was used to calculate the relation scores between the query samples and the support samples. On ModelNet 40 dataset， compared with the Dual-Long Short-Term Memory （Dual-LSTM） method， the proposed method has the recognition accuracy of 5-way 1-shot and 5-way 5-shot increased by 34.54 and 21.00 percentage points， respectively. At the same time， the proposed method also obtains high accuracy on ShapeNet Core dataset. Experimental results show that the proposed method can recognize new categories of 3D models more accurately.
Aiming at the problems that there are few researches on the combination of single users and group users in group buying recommendation methods， and the context-related information such as time interval and social relationship is not fully utilized， a group buying recommendation method based on social relationship and time series information was proposed. When recommending for single users， the Gated Recurrent Unit （GRU） of Recurrent Neural Network （RNN） do not consider the influence of time series information， and the irrelevant commodity data in the user-commodity interaction sequence will generate noise. Therefore， a group buying Recommendation model integrating Time-series aware GRU and Self-Attention （RTSA） was proposed. Firstly， a Time-series aware GRU （TGRU） model was constructed by calculating the personalized time interval between any two commodities purchased by the user. Then， the influence of the commodity locations and the personalized time intervals was studied by using a self-attention network. Finally， experimental results show that on Amazon Beauty dataset， compared with the optimal baseline model of recommending for single users — Time interval aware Self-Attention for Sequential Recommendation （TiSASRec）， RTSA has the hit rate for top-10 commodities increased by 11.73%. When recommending for group users， the pre-defined fusion strategy in group buying group recommendation cannot dynamically obtain group user weights， and there is sparseness in group-item interaction data. Therefore， a Group buying Recommendation model integrating Social network and hierarchical Self-Attention （SSAGR） was proposed. Firstly， an RNN was employed to capture the complex potential interests of users in group buying changing over time. Secondly， a hierarchical self-attention network was used to integrate social network information into user representations， and a group preference aggregation strategy was implemented under different weights. Thirdly， the group-item interactions were mined through Neural Collaborative Filtering （NCF） to complete group buying recommendations. Finally， experimental results show that on MaFengWo dataset， compared with the optimal baseline model of recommending for group users — AGREE （Attentive Group REcommEndation）， SSAGR has the hit rate for top-5 commodities improved by 3.53%.
Aiming at the problem of inaccurate prediction of edges and the farthest region in monocular image depth estimation， a monocular depth estimation method based on Pyramid Split attention Network （PS-Net） was proposed. Firstly， based on Boundary-induced and Scene-aggregated Network （BS-Net）， Pyramid Split Attention （PSA） module was introduced in PS-Net to process the spatial information of multi-scale features and effectively establish the long-term dependence between multi-scale channel attentions， thereby extracting the boundary with sharp change depth gradient and the farthest region. Then， the Mish function was used as the activation function in the decoder to further improve the performance of the network. Finally， training and evaluation were performed on NYUD v2 （New York University Depth dataset v2） and iBims-1 （independent Benchmark images and matched scans v1） datasets. Experimental results on iBims-1 dataset show that the proposed network reduced 1.42 percentage points compared with BS-Net in measuring Directed Depth Error （DDE）， and has the proportion of correctly predicted depth pixels reached 81.69%. The above proves that the proposed network has high accuracy in depth prediction.
To solve the problem that information hiding algorithms based on neural style transfer do not solve the embedding problem of color images， a color image information hiding algorithm based on style transfer process was proposed. Firstly， the advantages of feature extraction of Convolutional Neural Network （CNN） were utilized to extract the semantic information of the carrier image， the style information of the style image and the feature information of the color image， respectively. Then， the semantic content of images and different styles were fused together. Finally the embedding of color image was completed while performing the style transfer of the carrier image through the decoder. Experimental results show that the proposed algorithm can integrate the secret image into the generated stylized image effectively， making the secret information embedding behavior indistinguishable from the style change behavior. Under the premise of maintaining the security of the algorithm， the proposed algorithm has the hiding capacity increased to 24 bpp， and the average values of Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） reached 25.29 dB and 0.85 respectively， thereby solving the color image embedding problem effectively.
Focused on the existing diagnosis methods of Parkinson's Disease （PD）， the auxiliary diagnosis methods of PD based on gait analysis was reviewed. In clinical practice， the common diagnosis method of gait assessment for PD is based on scales， which is simple and convenient， but is highly subjective and requires well-experienced clinical doctors. With the development of computer technology， more methods of gait analysis are provided. Firstly， PD and its abnormal manifestations in gait were summarized. Then， the common methods of auxiliary diagnosis for PD based on gait analysis were reviewed. These methods were able to be roughly divided into two types： methods based on wearable or non-wearable devices. Wearable devices are small and have high accuracy for diagnosis， and with the use of them， the gait status of patients can be monitored for a long time. With the use of non-wearable devices， human gait data is captured through video sensors such as Microsoft Kinect， without wearing related devices and restricting patients' movements. Finally， the deficiencies in the existing gait analysis methods were pointed out， and the possible development trends in the future were discussed.
The algorithm platform， as the implementation way of automatic machine learning， has attracted the wide attention in recent years. However， the business processes of these platforms need to be built manually， and these platforms are faced with inflexible model calling and the incapability of customized automatic algorithm construction for specific business requirements. To address these problems， an algorithm path self-assembling model for business requirements was proposed. Firstly， the sequence features and structural features of code were modeled simultaneously based on Graph Convolutional Network （GCN） and word2vec representation. Secondly， functions in the algorithm set were further discovered through a clustering model， and the obtained function subsets were used for the preparation of the path discovery of algorithm components between subsets. Finally， based on the relationship discovery model and ranking model trained with prior knowledge， the self-assembled paths of candidate code components were mined， thus realizing the algorithm code self-assembling. Using the proposed evaluation indicators for comparison and analysis， the best result of the proposed algorithm path self-assembling model is 0.8， while that of the baseline model Okapi BM25+word2vec is 0.21. To a certain extent， the proposed model solves the problem of missing code structure and semantic information in traditional code representation methods and lays the foundation for the research of refinement of algorithm process self-assembling and automatic construction of algorithm pipelines.
To address the problems of Web crawler code failure and high manual maintenance cost caused by webpage source code changes led by frequent webpage redesigns， especially changes in element structures or attribute identifiers of target entities such as article dates， main body of text or source organizations， a self-adaptive Web crawler code generation method based on webpage source code structure comprehension was proposed. Firstly， the corresponding Web crawler code was extracted by analyzing the change patterns of webpage structural characteristics. Secondly， the changes in the webpage source code and code were represented by the Encoder-Decoder model. By fusing the semantic features of the webpage source code structure， the features of webpage source code changes and the features of webpage code changes， an adaptive code generation model was obtained. Finally， the perception， generation and activation mechanisms of the adaptive system were improved to form a Web crawler system with adaptive processing capability. Compared with TF-IDF+Seq2Seq and TriDNR+Seq2Seq models， the proposed adaptive code generation model was experimentally verified to show the superiority in the representation of webpage source code changes and the effectiveness of code generation with a final accuracy of 78.5%. With the proposed method， the Web crawler code operation problems caused by the webpage source code changes could be solved， and a new idea for the adaptive processing capability of Web resource acquisition — Web crawler technique was provided.
Image segmentation is a key step from image processing to image analysis. For the limitation that cluster partitioning has a large dependence on the initial cluster center, an image segmentation model PSOM-K (Particle Swarm Optimization Mutations-K-means) based on improved Particle Swarm Optimization (PSO) algorithm and genetic mutation was proposed. Firstly, the PSO formula was improved by increasing the influence of random neighbor particle positions on its own position, and expanding the search space of the algorithm, so that the algorithm was able to find out the global optimal solution quickly. Secondly, mutation operation of genetic algorithm was combined to improve the generalization ability of the model. Thirdly, the positions of the k-means cluster centers were initialized with the improved PSO algorithm from the three channels: Red (R), Green (G) and Blue (B). Finally, k-means was used to perform the image segmentation from the three channels: R, G, and B, and the images of the three channels were merged. Experimental results on Berkeley Segmentation Dataset (BSDS500) show that the improvement of Feature Similarity Index Measure (FSIM) at k=4 is 7.7% to 12.69% compared to CEFO (Chaotic Electromagnetic Field Optimization) method and 5.05% to 19.02% compared to WOA-DE (Whale Optimization Algorithm-Differential Evolution) method.Compared with the fine-grained segmentation algorithm HWOA (Hybrid Whale Optimization Algorithm), PSOM-K decreases at most 0.45% in FSIM but improves 7.59% to 13.58% in Peak Signal-to-Noise Ratio （PSNR） at k=40. Therefore, three independent channels, increasing the position influence of random neighbor particles in the particle swarm and genetic mutation are three effective strategies to find the better positions of k-means cluster centers, and they can improve the performance of image segmentation greatly.
Constructing the transition probability matrix for outlier detection by using traditional graph-based methods requires the use of the overall distribution of the data， and the local information of the data is easily ignored， resulting in the problem of low detection accuracy， and using the local information of the data may lead to “suspended link” problem. Aiming at these problems， an Outlier Detection algorithm based on Hologram Stationary Distribution Factor （HSDFOD） was proposed. Firstly， a local information graph was constructed by adaptively obtaining the set of neighbors of each data point through the similarity matrix. Then， a global information graph was constructed by the minimum spanning tree. Finally， the local information graph and the global information graph were integrated into a hologram to construct a transition probability matrix for Markov random walk， and the outliers were detected through the generated stationary distribution. On the synthetic datasets A1 to A4， HDFSOD has higher precision than SOD （Outlier Detection in axis-parallel Subspaces of high dimensional data）， SUOD （accelerating large-Scale Unsupervised heterogeneous Outlier Detection）， IForest （Isolation Forest） and HBOS （Histogram-Based Outlier Score）； and AUC （Area Under Curve） also better than the four comparison algorithms generally. On the real datasets， the precision of HSDFOD is higher than 80%， and the AUC of HSDFOD is higher than those of SOD， SUOD， IForest and HBOS. It can be seen that the proposed algorithm has a good application prospect in outlier detection.
The optimization process of Remora Optimization Algorithm （ROA） includes three modes： attaching to host， empirical attack and host foraging， and the exploration ability and exploitation ability of this algorithm are relatively strong. However， because the original algorithm switches the host through empirical attack， it will lead to the poor balance between exploration and exploitation， slow convergence and being easy to fall into local optimum. Aiming at the above problems， a Modified ROA （MROA） based on chaotic host switching mechanism was proposed. Firstly， a new host switching mechanism was designed to better balance the abilities of exploration and exploitation. Then， in order to diversify the initial hosts of remora， Tent chaotic mapping was introduced for population initialization to further optimize the performance of the algorithm. Finally， MROA was compared with six algorithms such as the original ROA and Reptile Search Algorithm （RSA） in the CEC2020 test functions. Through the analysis of the experimental results， it can be seen that the best fitness value， average fitness value and fitness value standard deviation obtained by MROA are better than those obtained by ROA， RSA， Whale Optimization Algorithm （WOA）， Harris Hawks Optimization （HHO） algorithm， Sperm Swarm Optimization （SSO） algorithm， Sine Cosine Algorithm （SCA）， and Sooty Tern Optimization Algorithm （STOA） by 28%， 33%， and 12% averagely and respectively. The test results based on CEC2020 show that MROA has good optimization ability， convergence ability and robustness. At the same time， the effectiveness of MROA in engineering problems was further verified by solving the design problems of welded beam and multi-plate clutch brake.
The traditional classifiers are difficult to cope with the challenges of complex types of data streams with concept drift， and the obtained classification results are often unsatisfactory. Aiming at the methods of dealing with concept drift in different types of data streams， classification methods for complex data streams with concept drift were summarized from four aspects： imbalance， concept evolution， multi-label and noise-containing. Firstly， classification methods of four aspects were introduced and analyzed： block-based and online-based learning approaches for classifying imbalanced concept drift data streams， clustering-based and model-based learning approaches for classifying concept evolution concept drift data streams， problem transformation-based and algorithm adaptation-based learning approaches for classifying multi-label concept drift data streams and noisy concept drift data streams. Then， the experimental results and performance metrics of the mentioned concept drift complex data stream classification methods were compared and analyzed in detail. Finally， the shortcomings of the existing methods and the next research directions were given.
As the uncertainty of user requirements in the cloud-edge environment causes the microservice composition logic to be dynamically adjusted with the changes of user needs， a Dynamic Evolution method for Microservice Composition system （DE4MC） in the cloud-edge environment was proposed. Firstly， the user's operation was automatically recognized to implement the corresponding algorithm strategy. Secondly， in the deployment stage， the better node was selected by the system for deployment through the deployment algorithm in the proposed method after the user submitting the business process. Finally， in the dynamic adjustment stage， the dynamic evolution was performed by the system through the dynamic adjustment algorithm in the proposed method after the user adjusting the business process instances. In both algorithms in the proposed method， the migration cost of microservice instances， the data communication cost between microservices and users， and the data flow transmission cost between microservices were comprehensively considered to select better nodes for deployment， which shortened the running time and reduced the evolution cost. In the simulation experiment， in the deployment stage， the deployment algorithm in the proposed method has average running time of all scales 9.7% lower and total evolution cost 16.8% lower than those of the combination algorithm of Heuristic Algorithm （HA） with Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ）； in the dynamic adjustment stage， compared with the combination algorithm of HA and NSGA-Ⅱ， the dynamic adjustment algorithm in the proposed method has the average running time of all scales 6.3% lower， and the total evolution cost 21.7% lower. Experimental results show that the proposed method ensures timely evolution of the microservice composition system in the cloud-edge environment with low evolution cost and short business process time， and provides users with satisfactory quality of service.
High Utility Itemsets Mining （HUIM） is able to mine the items with high significance from transaction database， thus helping users to make better decisions. In view of the fact that the application of intelligent optimization algorithms can significantly improve the mining efficiency of high utility itemsets in massive data， a survey of intelligent optimization algorithm-based HUIM methods was presented. Firstly， detailed analysis and summary of the intelligent optimization algorithm-based HUIM methods were performed from three aspects： swarm intelligence optimization-based， evolution-based and other intelligent optimization algorithms-based methods. Meanwhile， the Particle Swarm Optimization （PSO）-based HUIM methods were sorted out in detail from the aspect of particle update methods， including traditional update strategy-based， sigmoid function-based， greedy-based， roulette-based and ensemble-based methods. Additionally， the swarm intelligence optimization algorithm-based HUIM methods were compared and analyzed from the perspectives of population update methods， comparison algorithms， parameter settings， advantages and disadvantages， etc. Next， the evolution-based HUIM methods were summarized and outlined in terms of both genetic and bionic aspects. Finally， the next research directions were proposed for the problems of the existing intelligent optimization algorithm-based HUIM methods.
