Reviewerly/exHarmony huggingface dataset:
Interactive Multimedia Vocal Music Teaching System Based on Virtual Reality Technology

Abstract Virtual teaching with the help of virtual reality technology to realize two-way interactive thinking and innovative teaching to create a good virtual learning environment for vocal learners. This paper combines the activity form and learning content of the virtual learning environment, designs the virtual scene, realizes the virtual display and virtual operation with the help of virtual reality technology, and completes the physical modeling of the virtual reality teaching environment. Combined with the objectives of vocal music teaching and the teaching needs of teachers and students, respectively, the teacher’s end, the student’s end of the functional design, the virtual simulation teaching and interactive multimedia vocal music teaching combined, using B/S architecture as the basis, follow the MVC design mode, virtual interactive multimedia vocal music teaching system design. The database design utilizes a top-down approach to demand analysis, using the teaching-centered E-R model and focusing on the three elements of the actual teaching curriculum. The vocal music teaching system underwent environment-building analysis, functional and performance testing, and real-world application testing. The practical application test shows that 92.5% of the students think that the virtual interactive multimedia vocal teaching system has a very strong degree of participation, and 90% of the students think that the virtual interactive multimedia vocal teaching system has a better learning efficiency compared with the traditional teaching mode.
The Application of Cognitive Linguistics Theory in Japanese Language Teaching in the Age of Artificial Intelligence

Abstract This paper firstly summarizes the tense expressions in Japanese, Chinese and English according to the cognitive linguistics theory so as to derive the grammatical transformation law in the process of Japanese to Chinese or Japanese to English. Secondly, on the basis of artificial intelligence technology, a hierarchical Japanese phrase model based on maximum entropy tense classification features is proposed, and the extraction of Japanese phrase rules is realized through maximum entropy mathematical modeling to obtain the model feature function and decoding strategy, and the application of the model in Japanese language teaching is explained in the research. Then, the second-year students of Japanese language major in the School of Foreign Studies, Nanjing University of Posts and Telecommunications are selected as the research objects, research tools and experimental process are determined, and simulation analysis and statistical analysis are used to analyze the example of Japanese language teaching with the integration of artificial intelligence. The results show that on the model, compared with the baseline, the model in this paper improves the blue value by 0.64%, the comprehensibility by 4.39%, and the tense accuracy by 4.14% on the whole test set, which verifies the effectiveness of the hierarchical phrase model fused with the tense features proposed in this paper in Japanese-Chinese translation. In terms of Japanese language teaching, the independent samples t-test p-values were less than 0.05, and the students in class A outperformed the students in class B in the four question types: multiple choice, sentence ordering, Chinese-to-Japanese translation and composition. This study improves students’ language proficiency and achieves the objective of developing high-quality talents.
Data Mining Technology Helps Digital Teaching and Learning of English Majors in Colleges and Universities

Abstract The use of information technology in the teaching of English majors in colleges and universities enriches the teaching means, especially the data mining technology that optimizes the digital teaching method. In this paper, starting from English writing, based on the functional requirements of writing training, we constructed a writing training process and utilized the NMF method to mine and decompose the English news text topics. In conjunction with the writing training, the data mining technology centered on the Rete algorithm was used to carry out automatic diagnosis of English composition on the basis of natural language processing. On the basis of mining and analyzing the English news text, the effect of English writing scoring was compared, and its enhancement effect on English composition was verified through controlled tests. The average writing score of the experimental group was 19.62, of which the full score was 25. The mean writing score of the control group is 16.38, which is 3.24 points lower than that of the experimental group. Significance Sig.=0.0004&lt;0.05, P=0.025&lt;0.05, there is a significant difference in the English proficiency of the two groups of students. Data mining technology enhances the language proficiency of students in English writing.
Construction of medical aromatherapy experimental teaching resources based on digital three-dimensional technology

Abstract In this paper, on the basis of the Unity3D engine, image feature points are coarsely extracted, and a quadtree-based division algorithm is applied to filter the optimal FAST corner points. The fast 3D reconstruction technique is constructed through the construction method of nonlinear scale space similar to scale-invariant feature transform (SIFT), which is layer incremented according to the scale level logarithmic layer. The reconstructed 3D virtual teaching laboratory tool is obtained by importing the matched feature points into the model after the diffusion is completed. Finally, the teaching process and teacher-student behavioral data analysis were conducted for TCM and acupuncture students using the medical aromatherapy experimental course “Clinical Experiment of Aromatherapy for Primary Dysmenorrhea” as the teaching objective. The results showed that the maximum value of iFIAS of the experimental group was 100, and the mean value was 79.35, while the maximum value of technical iFIAS of the control group was only 51.32, and the mean value was 21.34, which was a significant difference between the two. It shows that the teaching method improves classroom learning interest and effect. The purpose of this paper is to provide relevant opinions and references for the future development direction of the combination of aromatherapy teaching and 3D virtual reality.
Pengukuran User Experience Platform Otomasi Proses berbasis Low Code Menggunakan UEQ

This study analyzes the user experience of a Low Code-based Process Automation Platform to enhance efficiency, productivity, and accuracy in business processes. In this study, an analysis of user experience was carried out using a modified long version of the User Experience Questionnaire (UEQ), consisting of six scales: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Based on the evaluation of the 26 items from the UEQ, the dependability and novelty scales scored the lowest compared to the other scales. Therefore, it is necessary to improve the aspects related to these two scales in order to enhance the platform's role in improving the holistic user experience of the platform.
The Use of WhatsApp for Speaking Activities: Teacherâ€™s Voices at Islamic Junior High School

Background:&#x0D; The form of information technology development that can be used as a learning medium during Covid 19 Pandemic is WhatsApp. This WhatsApp is widely used to communicate, both interpersonal communication and group communication. Thatâ€™s why teachersâ€™ perceptions on the use of WhatsApp in speaking activities needed to be revealed.&#x0D; Methodology:&#x0D; The qualitative method with a case study research design was used in this study. The participants of this study were two teachers of English taken by using total sampling technique. The data obtained from interview were analysed by using thematic analysis.&#x0D; Findings:&#x0D; It was found that (a) WhatsApp could build the studentsâ€™ interest towards English; (b) WhatsApp made the teachers easier to share speaking materials by using WhatsApp; (c) WhatsApp made the teachers easier to control the class. Meanwhile, the limitations were found that (d) WhatsApp did not build the studentsâ€™ confidence in speaking English; (e) WhatsApp did not promote studentsâ€™ independence in learning speaking; (f) WhatsApp did not create enthusiast atmosphere among students; (g) WhatsApp made the students became less active in learning English; (h) WhatsApp did not make the students pay attention to learning English; and (i) WhatsApp persuaded the students to copy-paste the task.&#x0D; Conclusions:&#x0D; Both benefits and limitations were found on the use WhatsApp in speaking activities of as perceived by the teachers.&#x0D; Originality:&#x0D; This study tries to find out teachersâ€™ perceptions on the use of WhatsApp in speaking activities during COVID-19 Pandemic as never been conducted.&#x0D; Keywords : Teachersâ€™ Perceptions, WhatsApp, Speaking Activities
A Practical Study of Basketball Teaching Reform in Colleges and Universities Based on Big Data

Abstract In this paper, the human body posture estimation algorithm is used to locate the key points of the human body in the RGB screen, and two human body multi-objective algorithms are used to predict the posture trajectory, and they can overcome the influence of the errors contained in the information recorded by the sensors to a certain extent. Secondly, the spatio-temporal graph convolutional neural network is used to identify human behavior and extract behavioral action features, and through the analysis of the action features, we understand the basketball skill level of the students and put forward the reform strategy of college basketball teaching. Sixty students from the basketball minor class at University Q’s College of Physical Education were selected as research subjects for teaching practice. The results show that the average scores of the students in spot-up shooting, half-court folding dribbling and marching one-handed over-the-shoulder shooting after the reform are higher than those before the reform by 1.80, 1.08, and 1.85, which indicates that the reform of basketball teaching based on big data can improve the students’ interest in learning and their training scores, and enhance the students’ basketball skill level.
Research on the construction of evaluation system for high-level scientific and technological talents based on big data analysis

Abstract This paper analyzes the three-level inclusion relationship of high-level innovative talents and combs the structure of high-level scientific and technological talent evaluation models based on big data technology. Aiming at the evaluation problems of high-level scientific and technological talents, a fuzzy neural network model is constructed, and at the same time, the R&amp;D middle school effect is utilized to evaluate the innovation achievements of high-level scientific and technological talents. Construct the evaluation index system of high-level scientific and technological innovative talents by utilizing 6 first-level indexes, 14 second-level indexes and 48 third-level indexes. Create a hierarchical analysis structure model, evaluate the indicator data through a judgment matrix and consistency test, and output the indicator weights. Analyze the relevance of the indicator model for different input layer neurons in fuzzy hierarchical analysis through comparative experiments. Use empirical analysis to analyze the innovative evaluation scores of high-level scientific and technological talents in Group A. The experimental results show that when the input layer contains 48 neurons, the loss value ranges from [0.132,1.765], the loss decreases the fastest, the stronger the indicator correlation, the stronger the generalization ability of the fuzzy neural network regression model. The overall scores of the evaluation of high-level scientific and technological talents of Group A for the first and second-level indicators are 3.54 and 3.869, respectively, and the overall view of Group A’s high-level scientific and technological talent innovative ability is better. Good.
Research on Artificial Intelligence Enabling High-Quality Development of Vocational Education

Abstract This paper studies the use of artificial intelligence technology in the field of education and the way of empowering vocational education and constructs a wisdom teaching model of vocational education based on artificial intelligence. It also applies entropy weight and a fuzzy comprehensive evaluation model to determine evaluation indexes and weights, constructs a fuzzy relationship matrix, and synthesizes a fuzzy comprehensive evaluation model. Based on the model, the teaching effect of vocational education with artificial intelligence is studied, and the advantages of wisdom teaching in the creation of a learning environment and the triggering of students’ interest in learning, creative thinking and problem-solving ability are analyzed by comparing with traditional teaching methods. The results show that there is a significant difference between the effect of AI teaching and traditional teaching, p&lt;0.05. For problem-solving ability, the average score of AI teaching students (M=4.049) is higher than the average score of traditional teaching (M=3.153), where t=14.745, p=0&lt;0.05. The study is crucial for the utilization of artificial intelligence in education and the modernization and reform of teaching.
The Role of English Language and Literature in the Cultivation of Students’ Language Proficiency in the Context of Deep Learning

Abstract In this paper, the PSO-LSTM algorithm is used to construct a model for evaluating students’ language proficiency, and the improvement of the acceleration factor and inertia weights is used to adjust the influence of the individual optimal position and the global optimal position in the speed update. For model performance judgment after optimization of LSTM hyperparameters, the PSO fitness function is replaced by MAPE and MSE functions. Finally, the student’s English composition texts were used as experimental parameters and input into the language proficiency evaluation model to verify the model performance and analyze the influence of English language literature on language proficiency. The results show that English language literature is effective in predicting 18.55% of the variance in English proficiency level for self-efficacy, 8.17% of the variance in English proficiency level for language attitude, and 0.47% of the variance in English proficiency level for learning anxiety, which indicates that English language literature has a significant effect on students’ English proficiency. This paper investigates the role mechanism of the English language and literature in the cultivation of student’s English proficiency, which provides a theoretical reference for the reform of English teaching in colleges and universities.
The Construction of Chinese Language and Literature Resource Base in Colleges and Universities under the Construction of Cognitive Mapping

Abstract Keeping up with the development of the Internet era, it is imperative for colleges and universities to vigorously carry out the construction of a Chinese language and literature resource base to promote the healthy development of Chinese language and literature education. This paper starts with the construction of Chinese language literature resource base related technology, analyzes the basic model of the cognitive map and the construction of the cognitive map of Chinese language literature. The graph database technology is used to transform the data structure of the resource base and load data from the Chinese literature resource base. Based on the cognitive map and graph database, jointly constructed the Chinese language literature resource base and introduced the fuzzy C-mean integration algorithm to integrate the data resources for better access to Chinese language literature resources. To verify the effectiveness of the Chinese language and literature resource base constructed in this paper, it was tested and analyzed through practice. The results show that the overall average response time of the resource library in this paper is 718.50ms, which is 214.78ms lower than that of the online learning data platform, and the resource library developed in this paper can realize the loss in data sharing to be controlled to be less than 0.5MB. Utilizing the resource library to experiment with teaching Chinese language and literature, the average score of the experimental class increased from 88.96 to 95.23, which is an improvement of 6.27 points. The construction of the Chinese language and literature resource base under the cognitive mapping architecture can effectively enhance the common sharing of Chinese language and literature educational resources and prompt teachers to have richer teaching resources.
An Interactive Approach to an Immersive Multimedia-Based Online Course in Vocal Music

Abstract Since the traditional vocal music course cannot meet the learning objectives of current music majors, this paper explores the reform of vocal music courses by combining modern multimedia technology. First, the teaching experience brought by immersive multimedia technology is explored, and based on the spatial and temporal features of the interactive behavior of the vocal music online course are extracted by the convolutional neural network, and multimodal fusion is carried out by combining linguistic features. Then, the GCN-LSTM network is used to model its spatial structure information and long temporal dependency, based on the joint learning of multimodal features, and then identify the interactive behavior. Finally, after constructing the teacher-student features, the extraction effect, recognition effect, and interactivity results of the behavioral features of the vocal online course are analyzed. The results show that the overall teacher-student behavioral feature extraction is above 0.7, except for “writing” with an accuracy of 0.76, “speaking”, and “listening”. The recognition effect of the overall teacher’s behaviors is more than 0.73, and in the range of 100s~250s, students “raise their hands” frequently and interact more. The students “raised their hands” frequently in the 100s~250s and interacted with each other more frequently.
Research on the Comparative Development of Modern Popular Music and Traditional Music Culture in Colleges and Universities in the Age of Artificial Intelligence

Abstract In this paper, the forward neural network multi-feature fusion algorithm is used to extract the emotional features of music culture on artificial intelligence technology, considering the diversity and intermittency of the emotional features of the study, which needs to be parameterized. In the forward neural network architecture, the activation value obtained by using the nonlinear activation function is used, and the results obtained are passed to the next layer of data to realize layer-by-layer forward computation, which leads to the back-propagation activation function. The music culture emotion classification model is constructed based on the propagation mode of the forward neural network to determine the emotion recognition process. The research object is selected, the research process is determined, and in order to ensure the true validity of the research, it is necessary to test the reliability and validity of the research design scheme and to develop an empirical analysis of the comparison between popular music and traditional music culture. The results show that on the model, especially in the recognition of sacred, sad, passionate emotion type of music classification accuracy reached more than 88.2%. This paper’s model can improve the classification accuracy of music emotion to a certain extent. In the ontological knowledge analysis of popular music and traditional music culture, all three editions of textbooks show that general knowledge of music is predominant and has a large proportion, appreciation knowledge and extended knowledge are also considerable, and music knowledge is the least and has a small proportion. This study demonstrates the synergistic development of traditional culture and modern popular music, which is of great significance to the development of music education in colleges and universities.
Exploring English Long Sentence Translation Methods by Applying Natural Language Processing Techniques

Abstract This paper analyzes the “encoder-decoder” framework in neural machine translation and clarifies that the task of natural language processing is sequence learning. Secondly, recurrent neural networks are used to combine the historical hidden layer output information with the current input information, which is specialized in processing sequence data to achieve good translation results. Applying the attention mechanism to the field of natural language processing, a Transformer model based on the full attention mechanism is constructed in order to achieve the purpose of translating the source language while also performing alignment operations on the target language. The evaluation and analysis of the Transformer model based on the full-attention mechanism concludes that the Transformer model has 0.0152 Pearson correlation coefficients higher than the Bilingual Expert model, which is also 2.92% higher than the Bilingual Expert model, with the participation of f feature in both models. This further proves the Transformer model’s ability to correctly and effectively translate English sentences. At the same time, it also shows that the application of natural language processing technology can improve the efficiency of English long-sentence translation and comprehensively improve the quality of long-sentence translation.
John C. Lucking: Phoenix Field Collector

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSI am indebted to John Lucking for the opportunity to prepare this profile, for welcoming a visit to view his collection, for drafting the mineralogical autobiography that I’ve relied on, and for responding to the inevitable follow-up questions. I am also indebted to John S. White for his review that improved the text.NotesAll mineral photos by Jeff ScovilAdditional informationNotes on contributorsCarl A. FrancisDr. Carl A. Francis is curator at the Maine Mineral & Gem Museum and an executive editor of Rocks & Minerals.
Research on Small Target Detection Technology for River Floating Garbage Based on Improved YOLOv7

Traditional manual inspection of floating garbage in artificial river channels is time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and unmanned boats based on computer vision have become the main methods for river channel inspection. In this paper, a dataset of floating garbage in the study area's river channels was constructed based on UAV aerial images. Deep learning methods were employed for garbage classification and recognition. Considering the relatively small proportion of floating garbage in UAV images and the susceptibility to water mist and vapor interference, targeted improvements were made to the YOLOv7 object detection algorithm in terms of multi-scale detection. Experimental results verified that the improved algorithm, compared to the original algorithm, achieved higher detection accuracy for small targets and effectively mitigated water mist interference, with an increase of 2.24% in mean average precision (mAP) for class-balanced accuracy. The study results demonstrate that the integration of deep learning methods and UAV technology enables efficient and accurate identification and classification of garbage, providing decision-making support for the management of floating garbage in river channels.