Reviewerly/exHarmony huggingface dataset:
Research on path planning of self-driving vehicles based on improved DWA algorithm

Abstract With the increasing degree of vehicle intelligence, unmanned vehicles have been widely used in civilian and military fields, which is of research value. In this paper, considering the vehicle dynamics constraints and the efficiency of the algorithm calculation to improve the DWA algorithm for the path of the self-driving vehicle, taking the intelligent vehicle node as the center of the circle and the sensor detection distance as the radius to extract the local map, combining the improved A* algorithm to fuse the DWA algorithm to carry out the local path planning at the key points, and through the design of the simulation experiments in the static and dynamic environments, the results show that, in the static environment of the simulation experiments The results show that in the static environment, the changes of the swing angle of the three vehicles fluctuate between -3 and 4, and the changes of the lateral speed of the vehicles fluctuate between -0.06 and 0.08, which are within a reasonable range of changes and satisfy the safety requirements. In the dynamic environment simulation experiments, the curve amplitude of the curvature similarity evaluation function in the improved algorithm is 40% less than that in the traditional algorithm, the number of iterations is 240 times less, and the car can reach the end point faster. This research can improve path planning accuracy, dynamic obstacle avoidance ability, and better path planning effect, which can be applied in the field of intelligent vehicles.
Innovation and Application of English Teaching Mode in Colleges and Universities by Integrating Entropy Power-Cloud Modeling

Abstract This study divides eight indexes from three dimensions—the outer layer, middle layer, and inner layer—in order to evaluate the English teaching modes in colleges and universities. The evaluation is focused on the traditional English teaching method, the flipped classroom, and the blended English teaching method. An English teaching model assessment framework based on the entropy weight-cloud model is created by applying the cloud model to the English teaching model and using the entropy weight technique to establish the weights of each evaluation index. The comprehensive grade of each sample is computed using the subordination degree of each assessment index. A new teaching model is suggested based on the evaluation results, and the model is examined in terms of learners’ attitudes and learning effects in order to confirm the flexibility and benefits of the cloud model for the assessment of English teaching models. According to the data analysis, the three teaching modes’ values in the excellent grade are 0.001, 0.1502, and 0.3020. The comprehensive grade in the traditional teaching mode is poor, the comprehensive grade in the flipped teaching mode is medium, and the comprehensive grade in the blended teaching mode is good.
Research on Talent Cultivation Mode of Vocational Education in the Context of Information Technology

Abstract This paper mainly analyzes the educational level of vocational education in the context of information technology from the perspective of matching degrees of vocational education specialty settings and talent demand. Combined with the framework of vocational education service supply under information technology, the SARIMA-BP model is utilized to predict the demand for talent training, and then the match between the professional setting and the education level is explored in a quantitative description. The PSO algorithm is used to optimize the linear combination prediction model of the SARIMA-BP neural network, and the prediction accuracy is compared. The degree of vocational education development and talent cultivation mode are analyzed from four aspects. According to the division of secondary vocational school graduates according to the three industries, the proportion of graduates from Guangdong’s primary industry decreased by 0.1053, and the proportion of graduates from the secondary industry decreased by 0.0123. At the same time, the proportion of graduates from the tertiary industry increased by 0.0243. It can be seen that, to cope with the change of industrial structure in the context of information technology, secondary vocational education should adjust the relevant specialties offered to upgrade the industry’s development and the market’s need for the Talent cultivation mode.
Application of Electronic Communication Signal Modulation Recognition System Based on Fourier Transform

Abstract In this paper, the weighted class fractional Fourier transform is redefined by means of the weighted superposition of state functions, and the Fourier transform is applied to dissect the intrinsic relationship between the M-WFRFT and the 4-WFRFT and to obtain the weighting coefficients of the electronic communication signals. On the basis of the WFRFT discrete algorithm, the electronic communication signal is expanded to include M a term after the M -WFRFT and the hybrid carrier modulation method based on the WFRFT is given. According to the design idea of the electronic communication system, the hardware part and software part of the electronic communication system are designed, and the electronic communication signal modulation recognition system is experimentally analyzed by using the simulation experiment method. The results show that when the signal-to-noise ratio is -8dB and the optimization algorithms are the Sgd algorithm, Sgdm algorithm, and Adam algorithm, the modulation identification accuracy of OFDM signal is 39.3%, 57.5% and 43.5%, respectively, which indicates that the Sgdm optimization algorithm is the most suitable one compared with other algorithms, and it is more effective in the modulation identification of OFDM signals at low signal-to-noise ratio. The number of scans of the three-layer multi-parameter WFRFT signal by the non-cooperative party is 6.4 × 1013 times, and the increase in the number of scans leads to an increase in the cost paid by the non-cooperative party, which confirms that the secure communication system based on three-layer weighted fractional-order Fourier transform has good resistance to parameter scanning characteristics and high confidentiality. This study further improves the a priori parameter setting of the electronic communication system and enhances its complexity, resulting in better anti-interception identification features.
An Introduction to the Innovative Path and Exploration of the Teaching Reform of Ethnic Vocal Music

Abstract With the continuous development of literature and art, vocal education, as an important part of aesthetic education, has also been developed unprecedentedly. This paper constructs a music emotion recognition model based on deep learning and the Lstm network. The preprocessing of music is accomplished through the methods of pre-emphasis, frame-splitting, and windowing so as to improve the purity of the music signal. Using the Mel frequency cepstrum coefficient and cochlear frequency, the music analog signal is converted into frequency features so as to better distinguish the acoustic features and combined with Word2vec to realize the extraction of music emotion features. By comparing the Lstm music emotion recognition model with other models, the performance of this paper’s model is verified in terms of classification accuracy. To understand the role of its embodiment, the music recognition model is applied to the teaching of ethnic music. The results show that the average fitness of Lstm music recognition is 75%-90% with the increase of the number of evolutions, and the average fitness of Lstm objective function reaches the peak at the number of iterations of 40, with a fitness of 95%. Under the music recognition model, the students’ spectrum is elevated to 0db above the reference line, and the amplitude mostly floats in the interval in (-3, -15), and the teacher can formulate appropriate ethnic vocal music teaching for the students’ spectrum.
Design and Application of SPOC Hybrid Teaching for College Basketball Teaching Based on Artificial Intelligence Technology

Abstract Students love basketball, but traditional teaching approaches are no longer able to suit their demands. In this article, artificial intelligence is combined with innovative teaching methods to create a new type of learning. Firstly, under the SPOC teaching mode, intelligent sensors are used to collect basketball sports data, and Kalman filtering is used for processing and segmentation. Then, through the definition of basketball sports gesture, the data division of basketball sports gesture, designing the basketball sports gesture feature extraction method based on unit division, and using the SVM method to analyze the basketball sports gesture features, designing intelligent SPOC hybrid basketball teaching. Finally, the experiments on data collection and motion attitude resolution in basketball are designed to investigate the effects of intelligent SPOC hybrid basketball teaching. The results show that the deviations of the collected acceleration and angular velocity are within 10 −2 orders of magnitude and 1.8°/s deviation, respectively, and the average recognition effect of various basketball postures is 0.925, which is able to effectively carry out basketball motion recognition. The experimental class 1’s physical quality improved by roughly seven after the teaching application was implemented, and the P-value for the improvement of different basketball sports was less than 0.05, indicating that the planned instruction can greatly enhance students’ physical quality and basketball abilities.
Exploring the Development, Dilemmas and Countermeasures of College English Teaching by Combining the Hierarchical Analysis Approach

Abstract College English teaching has formed a long-term historical persistent problem in the process of higher education expansion, and it is urgent to explore its development dilemma and corresponding countermeasures. This study uses hierarchical analysis to analyze the connection between relevant factors in English teaching activities, constructs a judgment matrix for teaching evaluation, establishes the index ordering through a consistency test, and applies intuitive fuzzy logic to establish the index weights. At the same time, a genetic algorithm was applied to the weight optimization process to iterate the better matrix indicator weights. After verifying the optimization effect through experiments, the comprehensive assessment method was applied to specific teaching cases and analyzed. The comprehensive evaluation results of students, teachers, and experts were obtained respectively, the latter being [0.3459 0.2961 0.1848 0.1086 0.0636]. The final evaluation result of the university English teaching case is [0.3358 0.2790 0.1874 0.1272 0.0695], which corresponds to the grade of “general”. Through the evaluation of university English teaching, the difficulties of current English teaching are fed back, which lays the foundation for the countermeasures to be put forward.
The construction of fine management system of library library materials under the background of informationization

Abstract The growth in the number of books leads to the increasing inefficiency and cost of traditional library data management methods for book verification and classification. To solve the problems of book classification, this paper proposes an automatic classification model, ERBERT-HMATT, for book data features. Firstly, the pre-training of BERT is improved by adding masking at the word and entity level. Then, the network structure of the model is designed based on HMCN. Finally, a multi-label attention mechanism is introduced in the initial feature extraction module, which gives different weights to the words of the input text to increase the attention to the text features, and the model is subjected to recurrent learning, which enhances the robustness of the model by adding fine-grained knowledge. Finally, the classification performance of the three algorithms, KNN, SVM, and ERBERT-HMATT, is tested on the same dataset. The accuracy of the ERBERT-HMATT classification algorithm is 0.1% higher than that of the KNN according to the results. Classifying 300 book information takes less than 100ms, which is significantly less than the processing time for SVM and KNN. This paper also found that the subject word field in the book information has a large positive effect on the model classification, which can improve the classification accuracy by 0.09 compared with the model with only the title field. The test results indicate that the method enhances the classification of book data to a certain extent.
Research and Practice of Digital Training Model Based on Artificial Intelligence Technology

Abstract There are fatal defects in both the simulation practical training model and the actual combat practical training model. This paper firstly takes artificial intelligence technology as the basis for the construction of digital wisdom training classrooms and constructs the artificial intelligence digital wisdom training model. Secondly, it combines multi-factor reasoning and integrated clustering ideas to portray the vocational ability portrait of the learner group in the model and realizes the recommendation of students’ personalized training teaching resources with the learner’s portrait. Finally, the effectiveness is verified through the application practice of the digital training model for AI in higher vocational colleges. The results show that the use of multi-factor reasoning and integrated clustering can realize the accurate portrayal of the learner portrait of the digital intelligent training students and the difference between the loss value of the training and validation of the personalized recommendation model based on the learner portrait is about 0.01, and the average time consumed is about 1.05s. The digital training model supported by artificial intelligence technology can help students establish higher-order scientific thinking, promote students’ mastery of skills, and fully compensate for the shortcomings of the existing practical training model.
Exploring the Emotional Characteristics of Chinese Language Literature Based on the Thinking Operation Model

Abstract In this paper, each text is mapped as a point in a multidimensional vector space in a thought operation model to represent the emotion feature vocabulary in Chinese language literary works in the form of word embedding. The bootstrapping technique is utilized to collect initial seed set vocabulary and target vocabulary, and a corpus of literary works with six basic emotion categories is established. Based on the emotional features of Chinese language literary works, the memory network model for textual emotion analysis based on multiple attention is constructed, and the textual emotion features of Chinese language literary works are empirically analyzed. The results show that the precision is 82.19%, 93.12%, and 94.1%, and the F1Score is 81.93%, 93.4%, and 93.76%, respectively, and the precision and F1Score are better than the effect of the control group model, i.e., it indicates that the effect of sentiment analysis is more excellent. The ratio values of positive emotion, negative emotion, and neutral emotion are, respectively, 63.16%, 15.49%, and 21.35%, i.e., the expression of emotion in Chinese language literary works is more rational. This study provides new perspectives and theoretical references for the study of traditional literary works and has the potential to expand the study in depth.
Innovation of Administrative Management System of Universities Based on Cloud Computing

Abstract In the traditional administrative management mode of higher education institutions, different departments work separately, and it is difficult to realize the sharing of information. This paper follows the hierarchical design of J2EE multilayer architecture to build an administrative management platform for colleges and universities. Through cloud computing technology college management information through Hibernate mapping to the object table to achieve data storage and sharing. Simulated annealing algorithms and genetic algorithms are introduced into the system to realize the optimal scheduling of administrative tasks and promote the overall collaborative efficiency of the task process. Finally, the workflow analysis is carried out on the practical cases of administrative management in colleges and universities, and the optimal scheduling of administrative tasks t 1 ~ t 9 in colleges and universities is completed. The results show that the platform 400Vuser under the integrated response time of 2.833 seconds less than the expected 3 seconds, the maximum number of concurrency in the maximum response time constraints can reach 4500 or so, to meet the performance requirements of university administrative management. And task t 1 ~ t 9 completes the optimal scheduling in the experiment, saving a total of 2 resource types. This system is of great practical significance in improving the efficiency of administrative offices in universities, reducing human errors and saving labor costs.
Research on the Impact of Elective Subjects of College Entrance Examination on Talent Cultivation of Higher Vocational Colleges Based on Students’ Cognitive Behavioral Characteristics

Abstract The reform of the optional subjects of the college entrance examination under the new situation puts forward higher challenges to the talent cultivation of higher vocational colleges. In this paper, for students entering higher vocational colleges and universities after the new college entrance examination, the random forest framework is improved by mining students’ homework and quiz data with PCA dimensionality reduction. Behavioral feature extraction using Random Forest and coding definitions are proposed after feature screening. Based on general feature coding, the behavioral group differentiation extraction of higher education students was constructed, which finally provided label classification of students’ cognitive-behavioral features. After analyzing the impact of the optional subjects on the quality of higher vocational students, the learning behaviors of higher vocational students were dissected. The probability density distribution plots of students’ entry rates show that the overall probability density distribution curves of entry for different students are significantly different. The wave heights of the first, second, third and fourth academic years were 10.25, 16.48, 13.24 and 12.61, respectively, and the study behavior of senior students after the selection of subjects in the college entrance examination showed a unique pattern. In response to the behavioral performance of higher vocational students, institutions need to scientifically use assessment reports to launch talent development work.
Application of AI Interaction Design in the Teaching Model of Landscape Topics Course

Abstract In this paper, we first define the limb node coordinates through the established limb node coordinate system and evaluate the behavior of the human body through the constructed posture sequence to analyze the human body’s body characteristics, such as the area of concern and the emotional level. Then, a new model of landscape theme course teaching was designed based on AI interaction design technology, the human-computer interaction learning needs were analyzed, and a human-computer interaction landscape course learning system was established. Finally, the differences in students’ cognitive ability, learning motivation, and learning emotion under the AI interactive teaching environment and traditional teaching methods were compared, and the advantages of AI interactive teaching were analyzed. The results show that the SD value of students’ behavioral attitudes in the teaching mode based on interactive technology is 27.78±2.34, which is more than the SD value of the ordinary teaching mode.2 In terms of goal attitudes, the SD value of the experimental class is about 4.05 more than that of the control class, and the difference is significant. The interactive teaching mode of AI effectively promotes students’ learning of the landscape topic course, and this study provides positive reference value.
Research on Multiple Evaluation of English Teaching in Higher Vocational School-Enterprise Cooperation in the Era of Multimedia Informatization

Abstract With the progress of the information age, the evaluation of students in higher vocational colleges and universities is not single, and more and more colleges and universities pay more attention to the overall development of students and use diversified evaluation of students’ abilities. This paper constructs a multivariate evaluation model for English teaching in higher vocational school-enterprise cooperation based on deep learning networks and forest stochastic algorithms. By analyzing the application of convolutional neural networks in teaching quality evaluation, Sigmoid and Tanh functions are used to improve the efficiency of English teaching quality evaluation. The bagging method in integrated learning involves averaging the model prediction results of each subset to arrive at the final evaluation results. The multivariate evaluation model is utilized in English teaching evaluation to examine the impact of English teaching under multivariate evaluation. The results show that the evaluation accuracy of the pluralistic evaluation model for English teaching ideas, teaching objectives, teaching contents and teaching effects are 0.981, 0.893, 0.904, and 0.924, respectively, and the students’ liking degree of English is increased from 0.459 to 0.793 under the pluralistic evaluation, which is conducive to the improvement of the teaching effect of English in higher vocational colleges and universities, and provides a new reference for the teaching of English. Perspective.
An Innovative Model of Higher Mathematics Curriculum Education Incorporating Artificial Intelligence Technology

Abstract This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner portrait of the learning outcome data according to the subject knowledge graph. An improved ant colony optimization algorithm is used to search for the optimal learning path, which is then combined with an improved convolutional neural network to generate a personalized learning path. The Trans R method is used to quantify the relationship between learners and learning resources and a semi-supervised learning conditional random field method based on K-NN is proposed to label learning resources and generate learning accurate evaluation for smart teaching. The smart teaching model of advanced mathematics courses is applied and analyzed in terms of students’ advanced mathematics pre and post-test scores, students’ satisfaction, teachers’ teaching methods and teaching resources in four directions. The analysis obtained that the posttest scores of the learners in the experimental group were 75.631, and the posttest scores of the learners in the control group were 66.314, with a difference of 9.317. The significance level of the variance chi-square test was 0.000&lt;0.05, which shows that there is a significant difference between the posttest scores of the experimental group and those of the control group, and it indicates that the wisdom teaching of higher mathematics significantly enhances the learning performance of the learners.