Similarity Calculation Method Research Articles

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.

MDADroid: A novel malware detection method by constructing functionality-API mapping

As the Android ecosystem develops, malware also evolves to adapt to the changes. Consequently, malware remains a significant threat, posing a challenge in developing a low-resource consumption malware detection method that can adjust to updates in the Android API versions. We propose a novel method called MDADroid, which detects malware based on self-built Functionality-API mapping. We start by building a set of permission-related APIs using open-source knowledge. Then, we construct a Functionality-App-API heterogeneous graph based on collected data and establish a Functionality-API mapping from it. Finally, MDADroid transforms app features from the API level to the functionality level for malware detection, ensuring model resilience to API changes. We also design an API similarity calculation method that updates the Functionality-API mapping at a low cost. We evaluate MDADroid on multiple datasets, and the results show that MDADroid achieves an accuracy of 95.22%, 96.23%, 98.77%, and 99.56% on the AndroZoo, CICAndMal 2017, CICMalDroid 2020, and Drebin datasets, respectively, with training and testing times of 2 s, 0.6188 s, 1.34 s, and 1.02 s. Moreover, our method demonstrates excellent performance in the tests for resilience capabilities.

A trajectory similarity computation method based on GAT-based transformer and CNN model

Trajectory similarity computation is very important for trajectory data mining. It is applied into many trajectory mining tasks, including trajectory clustering, trajectory classification and trajectory search etc. So efficient trajectory similarity computation method is very useful for improving trajectory mining result. Nowadays many trajectory similarity computation methods have been proposed. But most of them can not be applied into long trajectories similarity calculation efficiently. So a new algorithm called TrajGAT is proposed. This algorithm can calculate similarity for long trajectories. It treats long trajectory as a long sequence. By doing so, long-term dependency of long trajectory is considered by this algorithm while computing similarity value. But, the spatial feature of long trajectories is not considered. As long trajectory can be presented in many different shapes, if two long trajectories are judged as similar trajectories, the outline shape of these two trajectories should be similar as well. To solve this problem, a new trajectory similarity computation method is proposed in this paper. This method not only takes the long-term dependence feature into consideration, but also considers the outline feature of long trajectory. The proposed method employs GAT-based transformer to extract long-term dependence feature from long trajectory. And it applies Convolutional Neural Network to extract outline feature.

P-685 An artificial intelligence based medication regimen recommendation model for optimizing the treatment outcomes of controlled ovarian stimulation

Abstract Study question Can a machine learning (ML) algorithm suggest an optimal controlled ovarian stimulation (COS) protocol for maximizing the number of retrieved and mature (MII) oocytes? Summary answer Based on historical electronic medical record patient data, using ML algorithm based medication regimen recommendation model may increase the number of retrieved oocytes. What is known already The application of artificial intelligence in the field of reproductive medicine is mainly focused on the image analysis of gamete and embryo, and prediction of treatment outcomes. The research on the optimization of COS process is limited. Previous studies mainly focused on one or several types of COS protocols such as GnRH-a protocol and GnRH-A protocol. And most of them aimed to assist single-node decision-making during COS process such as optimal gonadotropins starting dose and trigger day. Study design, size, duration The data used for developing this model consists of 38,355 COS cycles from women who underwent their first IVF/ICSI treatment in a large fertility center between January 2008 to December 2022, of which 33,853 were used as the model training set and 4,502 were used as the test set. This dataset contains a subset of 8,382 cycles with information about MII oocytes. Participants/materials, setting, methods The goal of model was to recommend the optimal COS protocol based on optimizing COS outcomes. The similarity of patient’s characteristic was used as model input. XGBoost algorithm was constructed to calculate the similarity of COS outcomes. The accuracy was compared with the patient similarity calculation method of KNN+Euclidean distance and KNN+cosine distance using mean absolute error (MAE). The performance of different models in improving the average number of retrieved and MII oocytes was compared. Main results and the role of chance The patient similarity calculation method based on XGBoost had lowest MAE when predicting the number of retrieved oocytes (XGBoost vs KNN+Euclidean distance+k=500 vs KNN+Euclidean distance+k=1000 vs KNN+cosine distance+k=500 vs KNN+cosine distance+k=1000: 3.729 vs 3.810 vs 3.815 vs 3.785 vs 3.788). Including AMH could improve the accuracy of all recommendation models in predicting protocols and the number of retrieved oocytes. When the optimal number of MII oocytes is used as the protocol recommended target, the patient similarity calculation method based on XGBoost had lowest MAE (XGBoost vs KNN+Euclidean distance+k=500 vs KNN+Euclidean distance+k=1000 vs KNN+cosine distance+k=500 vs KNN+cosine distance+k=1000: 2.868 vs 3.397 vs 3.452 vs 3.356 vs 3.395). The COS medication regimen recommended by the patient similarity calculation method based on COS treatment outcomes has resulted in higher numbers of retrieved oocytes in patients compared to those who did not adopt recommendation (P < 0.01). The medication regimen recommended model based on KNN+Euclidean distance algorithm (k = 500) has the best performance in improving the average number of retrieved oocytes（adopt recommendation vs not adopt recommendation: 13.01±6.61 vs 10.09±6.67, P < 0.01）There was no statistical difference in the number of MII oocytes between patients receiving the recommendation and those not adopt recommendation. Limitations, reasons for caution The medication regimen recommendation model is based solely on the predicted number of retrieved and MII oocytes, not including adverse treatment outcomes such as ovarian hyperstimulation syndrome. Moreover, the database used to train and validate the model is from a single fertility center. Wider implications of the findings The recommendation model is helpful to improve the number of retrieved oocytes. Based on the recommended COS protocol, further mining the medication content, medication time distribution and medication sequence transformation from the similar patient set can assist clinicians to develop individualized COS medication regimen for infertile patients. Trial registration number not applicable

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

The design of advertising text keyword recommendation for internet search engines

As the growth of internet technology, human life is full of various advertisements. It is possible for individuals to obtain the advertising information they require, whether in an online or offline context. A research proposal is presented with the objective of enhancing the precision of online advertising recommendations. The proposal is based on the design of internet search engine advertising text keyword recommendation models, which integrate entity naming recognition models to facilitate tasks such as text classification and feature extraction. A recommendation algorithm based on content similarity is used to achieve keyword recommendation. Under the similarity calculation method of continuous bag-of-words model, when K is 100, the model weighted precision of the feature extraction method based on graph sorting and inverse text frequency index is 0.88, the weighted recall is 0.76, and the weighted F1-score is 0.82. In offline simulation testing, 85 % of the keyword recommendation model's recommendation time is less than 1 s, 99 % of the recommendation time is less than 2 s, and the recommendation cost can be significantly reduced by 75 %. In practical applications, the recommendation efficiency of this method can reach 96.3 %, and the recommendation precision can reach 95.8 %. The recommended satisfaction rate can reach 99.5 %. The results demonstrate that this method can provide highly accurate keyword recommendations and reduce the cost of advertising placement. Furthermore, it has been recognized and praised by users.

Collaborative filtering based talent development algorithm in sustainable modern logistics management project

ABSTRACT Logistics is crucial to the global economy, but traditional logistics management models have issues such as low informationization, single structures, low efficiency, and insufficient recommendation accuracy. This study aims to improve personalized recommendation in logistics management using the collaborative filtering algorithm and comparing it with Logistic Regression (LR) and Factorization Machine (FM) algorithms. The modified cosine similarity calculation method had the lowest absolute error at 0.91. After improving the algorithm, the PR value increased to 0.9952, showing a better balance between accuracy and recall. Combining the improved collaborative filtering recommendation algorithm with the actual logistics management model resulted in higher accuracy in personalized recommendations to users. The accuracy also increased with the number of recommended companies. Overall, the improved algorithm showed promising effectiveness and feasibility in the context of logistics management.

ColorNetVis: An Interactive Color Network Analysis System for Exploring the Color Composition of Traditional Chinese Painting.

In the field of digital humanities, color research aims to discover explanations for painting history and color usage habits. However, researchers analyzing color relationships is challenging and time-consuming, as it requires color extraction and a detailed review of many painting images for reference and comparison of color relationships. In our work, we propose ColorNetVis, an interactive color network analysis tool that enables researchers to explore color relationships through color networks. The core of ColorNetVis is a bipartite network model that establishes a bipartite relationship between colors and Chinese painting within a scope based on color difference measurement. It constructs a one-mode color network through projection algorithms and similarity calculation methods to discover the relationship between colors. We propose a coordinated set of views to demonstrate the combination of determined color networks with painting types and real-world attributes. We use color space view, color attribute distribution view, and single color query components to assist researchers in conducting detailed color analysis and validation. Through case studies, researcher reviews, and user studies, we demonstrate that ColorNetVis can effectively help researchers discover knowledge of color relationships and potential color research directions.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding

Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning similarity computation methods offer better efficiency and accuracy than non-learning solutions, they are still immature in trajectory embedding and suffer from poor generality and heavy preprocessing for training. Targeting these limitations, we propose a novel framework named KGTS based on knowledge graph grid embedding, prompt trajectory embedding, and unsupervised contrastive learning for improved trajectory similarity computation. Specifically, we first embed map grids with a GRot embedding method to vigorously grasp the neighbouring relations of grids. Then, a prompt trajectory embedding network incorporates the resulting grid embedding and extracts trajectory structure and point order information. It is trained by unsupervised contrastive learning, which not only alleviates the heavy preprocessing burden but also provides exceptional generality with creatively designed strategies for positive sample generation. The prompt trajectory embedding adopts a customized prompt paradigm to mitigate the gap between the grid embedding and the trajectory embedding. Extensive experiments on two real-world trajectory datasets demonstrate the superior performance of KGTS over state-of-the-art methods.

Gut microbiota predicts the diagnosis of ulcerative colitis in Saudi children.

Ulcerative colitis (UC) is an immune-mediated chronic inflammatory condition with a worldwide distribution. Although the etiology of this disease is still unknown, the understanding of the role of the microbiota is becoming increasingly strong. To investigate the predictive power of the gut microbiota for the diagnosis of UC in a cohort of newly diagnosed treatment-naïve Saudi children with UC. The study population included 20 children with a confirmed diagnosis of UC and 20 healthy controls. Microbial DNA was extracted and sequenced, and shotgun metagenomic analysis was performed for bacteria and bacteriophages. Biostatistics and bioinformatics demonstrated significant dysbiosis in the form of reduced alpha diversity, beta diversity, and significant difference of abundance of taxa between children with UC and control groups. The receiver operating characteristic curve, a probability curve, was used to determine the difference between the UC and control groups. The area under the curve (AUC) represents the degree of separability between the UC group and the control group. The AUC was calculated for all identified bacterial species and for bacterial species identified by the random forest classification algorithm as important potential biomarkers of UC. A similar method of AUC calculation for all bacteriophages and important species was used. The median age and range were 14 (0.5-21) and 12.9 (6.8-16.3) years for children with UC and controls, respectively, and 40% and 35% were male for children with UC and controls, respectively. The AUC for all identified bacterial species was 89.5%. However, when using the bacterial species identified as important by random forest classification algorithm analysis, the accuracy increased to 97.6%. Similarly, the AUC for all the identified bacteriophages was 87.4%, but this value increased to 94.5% when the important bacteriophage biomarkers were used. The very high to excellent AUCs of fecal bacterial and viral species suggest the potential use of noninvasive microbiota-based tests for the diagnosis of unusual cases of UC in children. In addition, the identification of important bacteria and bacteriophages whose abundance is reduced in children with UC suggests the potential of preventive and adjuvant microbial therapy for UC.

Comprehensive evaluation of intrinsic safety of railway facilities and equipment based on improved cloud model-fuzzy Petri net

In order to achieve the goal of transforming China into a modernized railway powerhouse, a comprehensive evaluation model of intrinsic safety is constructed in this paper by focussing on the intrinsic safety of railway facilities and equipment. Firstly, the index system for evaluating the intrinsic safety of railway facilities and equipment is divided. Secondly, the fuzzy Petri net (FPN) model for intrinsic safety evaluation is constructed according to the fuzzy generative rules, and the similarity calculation method of the cloud model based on shape and distance is applied to derive the initial state value of the FPN model and carry out the calculation of fuzzy reasoning of the intrinsic safety. Finally, the comprehensive evaluation of intrinsic safety is carried out using the example of railroad facilities and equipment at one China Railway Group bureau’s station section. The results indicate that the intrinsic safety level of the station section falls into Class IV intrinsic safety, exhibiting a few vulnerabilities. However, the risk level is low, aligning with the findings of the actual research. Moreover, the evaluation results based on the FPN, the cloud model’s similarity calculation method and the suggested method are compared and analysed with the findings of the actual research. The comparative results demonstrate the accuracy and scientificity of the proposed method, providing a reference for enhancing the intrinsic safety of railway facilities and equipment.

STI: A self-evolutive traffic identification system for unknown applications based on improved random forest

Artificial intelligence technology has proven potential and effective in traffic identification for network management and security. However, the accuracy of its identification is easily influenced by the massive unknown traffic. Given the fact of numerous unknown applications in real networks and even more as time goes by, a promising traffic identification system should have the ability to discover unknown applications and recognize their traffic. In this paper, an innovative and comprehensive traffic identification system, called STI, is proposed, which can achieve fulfilling high precision both on known and unknown traffic identification. More importantly, STI can self-evolve to identify incoming unknown applications and corresponding training samples based on a novel clustering process with minimal manual involvement. In addition, an improved random forest and a novel similarity calculation method are proposed and applied to STI to enhance the classification performance. Experiments on real network traffic demonstrate the core advantages of the proposed system.

Comprehensive Similarity Algorithm and Molecular Dynamics Simulation-Assisted Terahertz Spectroscopy for Intelligent Matching Identification of Quorum Signal Molecules (N-Acyl-Homoserine Lactones).

To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.

Target-aware transformer tracking with hard occlusion instance generation.

Visual tracking is a crucial task in computer vision that has been applied in diverse fields. Recently, transformer architecture has been widely applied in visual tracking and has become a mainstream framework instead of the Siamese structure. Although transformer-based trackers have demonstrated remarkable accuracy in general circumstances, their performance in occluded scenes remains unsatisfactory. This is primarily due to their inability to recognize incomplete target appearance information when the target is occluded. To address this issue, we propose a novel transformer tracking approach referred to as TATT, which integrates a target-aware transformer network and a hard occlusion instance generation module. The target-aware transformer network utilizes an encoder-decoder structure to facilitate interaction between template and search features, extracting target information in the template feature to enhance the unoccluded parts of the target in the search features. It can directly predict the boundary between the target region and the background to generate tracking results. The hard occlusion instance generation module employs multiple image similarity calculation methods to select an image pitch in video sequences that is most similar to the target and generate an occlusion instance mimicking real scenes without adding an extra network. Experiments on five benchmarks, including LaSOT, TrackingNet, Got10k, OTB100, and UAV123, demonstrate that our tracker achieves promising performance while running at approximately 41 fps on GPU. Specifically, our tracker achieves the highest AUC scores of 65.5 and 61.2% in partial and full occlusion evaluations on LaSOT, respectively.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

An Exploration of Ethnic Dance Teaching Methods in Dance Performance Teaching in Colleges and Universities in the Context of Big Data

Abstract In this paper, based on the practical framework of teaching folk dance in colleges and universities under the background of big data, the spatial and temporal characteristics of folk dance are sorted out, and the DTW algorithm is proposed to calculate the similarity of folk dance movements, and a model for estimating the gesture of normative movements of folk dance is constructed by combining multiscale feature fusion. Six kinds of folk dance movements were collected to establish the data samples of folk dance movements in order to verify the effectiveness of the calculation method of dance movement similarity and to obtain the results of the teaching of folk dance normative movements. Analyze the content of dance movements and the dance normative movement gesture estimation method to explore the expressiveness of folk dance movements. To research the effectiveness of emotional dance movement teaching in teaching folk dance in two dimensions, three segments of folk dance were chosen. The data show that the second segment of the dance, M=9.45, SD=2.15, was improved to M=11.55, SD=3.16. Affective dance movement teaching has a significant impact on the teaching of folk dance in colleges and universities, with improvements in both dance movement and dance expression.

Chinese Natural Language Processing: From Text Categorization to Machine Translation

Abstract The level and volume of automatic computerized processing of linguistic information has become one of the most important criteria for measuring whether a country has entered the information society. The study begins with statistical linguistics and aims to process complicated Chinese information. In this paper, after establishing the word database of the Chinese language, the language model is smoothed and compressed, the Chinese character information and Chinese language information are extracted, and the processing of Chinese grammar and Chinese semantic information is emphasized. Among them, Chinese grammar processing includes Chinese word analysis and basic phrase analysis based on the maximum entropy model, and Chinese semantic processing includes Bayesian-based word sense disambiguation, semantic role labeling based on the conditional random field model, and thesaurus-based semantic similarity calculation method. In addition, SECTILE-based Chinese text categorization and statistical linguistics-based machine translation methods are explored and tested for their effectiveness in Chinese natural language processing. The results show that the total average check accuracy and check the completeness of Chinese text are 78.65% and 72.24%, respectively, and the BLEU values of the translation methods are improved by [1.62,3.73] and [0.93,5.01] compared with the Baseline method, which is able to process the Chinese information accurately. The research plays an important role in the process of information processing based on Chinese language processing.