bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengaruh Penggunaan Alat Peraga Papan Berpasangan pada Materi Operasi Bilangan Bulat terhadap Hasil Belajar Matematika Peserta Didik
Authors: Risman Muhammad , Sriyanti A. , Munirah  , Tayeb Thamrin , Syam Lisa Kurnia 
Published: December 2023

Pemahaman konsep operasi hitung bilangan bulat yang sifatnya abstrak perlu adanya alat peraga seperti benda konkret yang dapat memberikan visualisasi kepada peserta didik. Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan alat peraga papan berpasangan pada materi operasi bilangan bulat terhadap hasil belajar matematika peserta didik. Metode penelitian yang digunakan adalah kuantitatif dengan jenis penelitian pre-eksperimental design dan desain penelitiannya one-group pretest-posttest design. Populasi penelitian ini adalah seluruh peserta didik kelas V SDN 103 Kalimporo yang berjumlah 32 peserta didik. Teknik pengambilan sampel yang digunakan adalah teknik sampling jenuh sehingga sampel dalam penelitian ini adalah kelas V. Instrumen yang digunakan berupa pretest dan posttest. Teknik analisis data yang digunakan adalah analisis statistik deskriptif dan inferensial. Berdasarkan hasil analisis dengan menggunakan analisis regresi sederhana dan uji-t diperoleh persamaan regresi . Dengan nilai uji-t diperoleh nilai yaitu , dengan demikian dapat disimpulkan bahwa terdapat pengaruh penggunaan alat peraga papan berpasangan pada materi operasi bilangan bulat terhadap hasil belajar peserta didik kelas V SDN 103 Kalimporo.
Pengaruh Model Kooperatif Tipe Snowball Throwing terhadap Kemampuan Pemahaman Konsep Matematika ditinjau dari Pengetahuan Awal Peserta Didik
Authors: Aziz Nurjannah , Rasyid Muhammad Rusydi , Baharuddin  , Nur Fitriani , Nur Miftahul Jannah  
Published: December 2023

Penelitian ini bertujuan untuk mengetahui pengaruh model pembelajaran kooperatif tipe Snowball Throwing (ST) ditinjau dari pengetahuan awal peserta didik terhadap kemampuan pemahaman konsep matematika kelas VII SMP Negeri 13 Makassar. Metode yang digunakan adalah kuantitatif dengan jenis penelitian quasi experiment serta desain factorial design 3 x 2. Populasi pada penelitian ini adalah seluruh peserta didik kelas VII SMP Negeri 13 Makassar berjumlah 363 orang. Sampel dalam penelitian ini adalah kelas VII 10 sebagai kelas kontrol sedangkan kelas VII 4 sebagai kelas eksperimen. Teknik analisis data yang digunakan adalah analisis statistik deskriptif dan analisis statistik inferensial. Berdasarkan hasil analisis data, diperoleh nilai F = 0,606 dan nilai Sig. = 0,548 > 0,05 sehingga diterima. Oleh karenanya, dapat disimpulkan bahwa tidak terdapat pengaruh model kooperatif tipe snowball throwing ditinjau dari pengetahuan awal siswa terhadap kemampuan pemahaman konsep matematika kelas VII SMP Negeri 13 Makassar.
MANAJEMEN PELAKSANAAN BIMBINGAN KONSELING BERNILAI ISLAMI
Authors: Arsal Fathur Riyadhi 
Published: December 2023

Pelaksanaan bimbingan konseling yang didapat peserta didik dari pendidik diharapkan meningkatkan kepercayaan dan ketakwaan mereka kepada Allah Swt dengan mengajarkan prinsip-prinsip dan tuntunan yang terdapat dalam al-Qur’an dan hadis. Peneliti menggunakan jenis penelitian literature review. Metode yang digunakan adalah metode tafsir tematik (maudhu’i). Implementasinya adalah meningkatkan manajemen pelaksanaan bimbingan konseling dengan menerapkan nilai Islami dalam pelaksanaan jadwal, ketersediaan prasarana, serta pengadministrasian layanan bimbingan konseling. Hasil penelitian menemukan bahwa nilai-nilai yang dapat diterapkan dalam manajemen pelaksanaan bimbingan konseling adalah nilai muamalah, akhlakul karimah, dan amaliah dalam pelaksanaan jadwal bimbingan konseling di kelas secara tatap muka sebanyak sekali seminggu. Nilai kehormatan dalam ketersediaan prasarana layanan bimbingan konseling. Nilai mu’awanah dan nilai tauhid dalam menemukan gejala-gejala peserta didik yang bermasalah, sebab, serta pemecahannya, serta nilai amanah dalam pengadministrasian layanan bimbingan konseling.
Performance and Yield Stability of Quality Protein Maize (Zea mays L.) Hybrids under Rainfed Condition
Authors: Bankole Folusho , Olajide Olasunkanmi , Olaoye Gbadebo 
Published: December 2023

Abstract
Quality protein maize (QPM) commercialization can combat the food and nutritional insecurity that is common in some developing nations of the world. Evaluation of QPM hybrids under varying rainfed environments provide valuable evidence for the selection of the most productive genotypes for the target environment. The present study was conducted at three locations to assess the agronomic performance and the grain yield stability of 11 QPM and 2 commercial hybrids (checks) over two years. There were significant (p ≥ 0.01) differences among the environments in the expression of all measured traits while genotype as well as genotype × environment interaction also differed significantly for all the traits except for husk cover and Anthesis-silking interval. The tested QPM hybrids outperformed the local and reference checks by 17.28% and 29.47% respectively. The biplot explained 85.3% of the total variation in yield, with 65.4% attributed to principal component 1 and 19.9% to principal component 2. Three vertex hybrids, EWQH-25, EWQH-21 and Local Check, were identified as the most responsive in the environments within which they fall. Hybrid EWQH-21 produced the highest yield across all environments but was unstable. Therefore, hybrids EWQH-22, EWQH-9, and EWQH-13, with similar yield values and more stable performances were recognized as ideal across environments. Lapai 2017 (E2) proves to be both representative and discriminative, making it the ideal test environment for selecting hybrids with broad adaptability. The study concludes that EWQH-9, EWQH-13, and EWQH-22 be recommended for cultivation across the environments while EWQH-21 be recommended for the specific area of adaptation.
Research on the Integration and Development Path of Innovation and Entrepreneurship Education and Mental Health Education in Universities Empowered by the Metaverse
Authors: Chen Xiaolin 
Published: December 2023

Abstract
This paper establishes an integrated education model using hierarchical analysis to divide raw data into a number of sample data based on the magnitude of similarity of data attributes. The centroids of the mental health education model were added to the set, and then the set was updated and looped sequentially. Profile coefficients were used to evaluate the clustering results. The evaluation criteria were precision, which is the degree of accuracy in dividing data categories. The maximum value of the assignment profile of mental health education is 96.04, and the maximum score of mental health education popularization excellence is 71.7. It shows that the integrated education model can optimize the content of mental health education, strengthen the integration of innovative entrepreneurship education and mental health education, and achieve the development of the cultivation path of mental and physical health entrepreneurs.
A reform path for English learning based on data mining algorithms
Authors: Zhou Haiping , Zhang Qian 
Published: December 2023

Abstract
This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data quality, redundant data in the dataset is eliminated. To provide better learning recommendations and personalized services, prediction accuracy is assessed by calculating the deviation between predicted and actual ratings. Teachers and students can use the analysis results of feature weights, correct word cut scores, and other indicators obtained from data mining as key learning references. In the data mining analysis of students’ English learning data, the feature weight is 0.921, which helps to assess students’ knowledge mastery and learning effect more accurately.
Overseas Communication Strategy and Development Direction of Chinese Film and Television Works in the Internet Era
Authors: Li Jinlu , Ma Wangping 
Published: December 2023

Abstract
In this paper, we first improve the influence propagation model by combining sentiment analysis and propose a new algorithm for node activation probability. Sentiment shift is the dynamic change of node attributes during the propagation of movie and television works. The algorithm for node activation probability has been further improved, and a model for influence propagation applicable to dynamic networks has been proposed. Finally, based on the data of the YouTube Internet platform, this paper analyzes the awareness of overseas audiences on Chinese film and television types, popular elements, publicity methods and domestic and foreign box office from 2010 to 2019. The findings indicate that 0.7379, 0.7031, and 0.5596 foreign viewers believe that three Chinese film and television genres, palace fighting, romance, and youth, are the most popular. Although there are still more than 0.42 million overseas viewers who prefer martial arts genres, they are in a downward stage compared to before. In terms of publicity, more than 0.91 percent of them believe that online social networking areas and chat groups for movie and TV enthusiasts are the most effective forms of publicity. Therefore, this study is an important reference for the development of communication strategies.
Modelling of Interactive Experience and User Satisfaction in Interior Design Based on Virtual Reality Technology
Authors: Wang Qin 
Published: December 2023

Abstract
This paper firstly analyzes the essential features of virtual reality technology, constructs the framework of the interior design system based on virtual reality technology, and designs the interaction content of the system as well as the interaction process architecture. Secondly, the interior design system is technically analyzed, and the system’s moving trajectory relative to the environment is determined by using specific point calculation and camera position estimation algorithms, and specific target detection algorithms extract specific target HOC features. Finally, the platform is tested for performance, and the data is quantitatively analyzed using relevant data collected from users in the system as a research sample. The results show that 43 users are satisfied with being able to visit freely in a continuous space, 45 users are all very satisfied with switching the elements in the scene, and they think that replacing the items in the scene is conducive to participating in the design of the program and selecting the decorations they are satisfied with. It shows that the interior design system based on VR technology can directly interact with virtual information in real time, which improves the user’s interactive experience and makes the design “human-oriented”, starting from human needs and feelings.
Fault diagnosis of analogue circuits based on artificial intelligence algorithms
Authors: Wu Wenxian 
Published: December 2023

Abstract
The fault diagnosis problem of analog circuits has been paid more and more attention, and the realization of circuit fault feature extraction and pattern classification are two of the key problems. This paper first combines wavelet packet and energy entropy to design a fault feature extraction method based on wavelet packet entropy to solve the problem of analog circuit fault feature extraction. Secondly, the principle of SVM and particle swarm optimization algorithm are combined to design the fault diagnosis process of analog circuits based on an artificial intelligence algorithm. Finally, take the sallen-key circuit as an example to analyze the effectiveness of the wavelet packet entropy algorithm for fault feature extraction and analyze the effectiveness of this paper’s method based on the analog circuit of Wen’s bridge oscillation circuit. The results show that after wavelet packet entropy extraction of the feature vector values is less than 0.001, and the accuracy of the extraction is more than 0.98, the best parameters of the optimization is (182.4, 0.05), the false alarm rate of the fault diagnostic method is 0, the misdiagnosis rate is 0.08, the omission rate is 0, and the correctness rate of the diagnosis of the fault is 0.92. Based on this research is able to carry out the fault diagnosis of the analog circuit.
A linear discriminant analysis-based algorithm for identifying anomalous traffic in large-scale networks
Authors: Wu Qiubing , Zhao Xiaofeng 
Published: December 2023

Abstract
To protect network security, this paper develops a large-scale network anomalous traffic identification algorithm that utilizes the linear discriminant analysis method to intercept network anomalous traffic. Firstly, the classification of large-scale network anomalous traffic is explored, and the SSAE algorithm is combined with the feature selection of large-scale network traffic on the basis of network flow feature extraction. Secondly, data dimensionality reduction of network anomalous traffic using linear discriminant analysis and feature selection of large-scale network traffic based on SSAE to identify network anomalous traffic. Finally, the CICIDS2017 dataset and the NSL-KDD dataset are used to experimentally analyze the effect and performance of feature selection and anomaly identification algorithms. The results show that the classification accuracy of the feature selection algorithm is 0.989, the 10-dimensional optimal features selected are (12,6,5,38,29,3,33,35,36,40), and the recognition result is 0.803 for normal network traffic and 0.197 for anomalous traffic, with an overall recognition error of 0.003, and a performance of more than 0.988.
Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching
Authors: Chen Chanjuan 
Published: December 2023

Abstract
The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and F
0.5-value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.
Vibrational modal-based characterization and pitch measurement of Pipa
Authors: Cao Lijuan 
Published: December 2023

Abstract
Pipa fretting and pitch measurement are crucial aspects of playing. In this paper, Pipa’s string vibration is simplified to bounded string vibration, and a simplified structural model of Pipa is built. Transverse free vibration is used to establish the vibration equations of Pipa strings, and differential analysis is used to analyze the standing waves. Meanwhile, the vibration of the resonance panel of the Pipa was abstracted into a dynamic model, and the modal analysis was performed using coordinate transformation. In the actual test, the sound signal generated by the vibration was also processed with the help of the Fourier transform, and the pitch of the Pipa was measured by analyzing the time and frequency domains. The vibration of the Pipa panel is complex but regular, with the vibration pitch line at 32.5 hz being a central axis and the vibration pitch line at the frequency of 85 hz being on the upper side, while the vibration corresponding to the frequencies of 32.5 hz and 85 hz can be regarded as a synthesis of the vibration corresponding to the frequency of 175.5 hz. Under the viewpoint of vibration modal analysis, the performance effect of the Pipa has a more refined expression.
Intelligent Teaching of Reading and Writing Skills in Higher Education Languages by Integrating Data Mining Technology
Authors: Gao Yongyan 
Published: December 2023

Abstract
In this paper, we first construct a reading comprehension model based on BERT and EDA strategies and, at the same time, propose sliding-window dynamic cropping to reduce the chapter material to a range that BERT can adapt. Secondly, the TF-IDF model for writing material sentences is proposed, and the algorithm is further improved using TFC. A material sentence quality evaluation mechanism is proposed to extract the most useful sentences in writing based on Bi-Attention. Then, the intelligent teaching system is built and analyzed functionally. Finally, a controlled experiment is established to evaluate the intelligent teaching model. According to the study, the experimental class’s reading comprehension scores increased from 23.83 to 33.25, while the control class’s scores did not improve significantly. As for writing, the five aspects of writing improved in the experimental class, and the posttest percentages were 1.0, 0.9, 0.8, 0.8, 0, and 0.98, respectively. Intellectualized teaching can improve both reading comprehension and writing ability.
Perceptual and cognitive infiltration of piano lessons for college students under the concept of digital health
Authors: Liu Jie 
Published: December 2023

Abstract
In this paper, firstly, according to mental health education, the research on the perception penetration of the piano course is illustrated from the two directions of body perception and mind perception. Secondly, it researches the music feature extraction in the piano course, obtains the amplitude, wavelength and frequency of the music feature according to the piano string vibration equation, then applies the short-time Fourier analysis to identify the music feature and reconstructs the piano timbre by using the identification result, which provides technical support for the design and realization of the piano course teaching system. Then, the overall design direction of the piano course teaching system is determined according to the teaching needs of the piano course in colleges and universities, and then the core module of the piano course system is realized. At the same time, the teaching of piano courses is empirically analyzed through statistical analysis. The results show that there is a significant difference between piano course teaching and the degree of perception and cognition in piano course teaching (F=4.194, p<0.05), there is a significant difference between piano course teaching and the degree of cognition (F=4.243, p<0.05), and there is a significant difference between piano course teaching and interpersonal relationship (F=5.391, p<0.05). This study not only has a promoting effect on piano teaching in colleges and universities but also improves the guiding value of piano curriculum reform in colleges and universities.
English Translation Stylistic Features and Syntax Translation with Application of Knowledge Mapping
Authors: Zhang Limin 
Published: December 2023

Abstract
Aiming at the problems of excessive instantiation complexity, poor interpretability and low generalization ability of knowledge reasoning techniques, this paper unites inductive logic programming ILP with HET neural network to construct a hybrid logic rule and neural network knowledge graph reasoning model - HETIL model. The ILP is utilized to quantify the first-order logic rules, and the multi-layer rule space is constructed by arranging and combining the rules through logic symbols. The rules are instantiated and fed into the HET network, and the attention coefficients aggregate the node features to complete the end-to-end training and generate the rule learning model. Finally, the validity of the model is verified by machine translation experiments, and the results show that the accuracy of the HETIL model in syntactic structure types is more than 0.8 overall. The accuracy in terms of phrase structure reaches 0.879. The average BLEU value of the HETIL model can reach 29.24, which is 1.51 BLEU points higher than the benchmark model. Therefore, the effect of English translation by applying a knowledge graph is better than traditional machine translation.
Problems of Adult Higher Education Development in China Based on Multiple Intelligences Theory and Related Countermeasures
Authors: Zhao Hui 
Published: December 2023

Abstract
This paper first focuses on the problem of quality evaluation of adult education, proposes a data pre-processing method suitable for quality evaluation of adult education - the efficacy coefficient method, and introduces the subjective and objective evaluation factors and the time sequence factors through a variety of weighting methods, and designs the comprehensive evaluation algorithms based on the “pulling the grades method” and the “time sequence weighting method”. Lastly, evaluation algorithms were utilized to analyze teaching satisfaction, student motivation, and internal and external influences on adult education. The results showed that the students’ satisfaction index for all levels of adult education ranged from 0.4 to 0.6, which is between unsatisfactory and average. The main external influences are lack of flexibility in the mechanism, insufficient teaching staff, lack of funding, no advantage, and irrational curricula, which account for 72%, 53%, 48%, 39%, and 24% of the perceptions, respectively. This study is of great significance for the further deepening of the reform and development of adult higher education in China and the healthy development of lifelong learning for all.
The Construction of a Grammatical System for Japanese Linguistics in the Context of Big Data
Authors: Zhao Lei 
Published: December 2023

Abstract
This paper first outlines the concept of teaching Japanese grammar according to the Japanese language talent cultivation standard and the current dilemma in teaching Japanese language in colleges and universities. Secondly, on the basis of big data technology, considering the effective availability of the initial data of Japanese grammar, it is necessary to preprocess the language grammar learning data and, at the same time, according to the fuzzy comprehensive evaluation, determine the Japanese grammar evaluation affiliation function. Then, for the problem of an incomplete and inaccurate traditional fuzzy comprehensive evaluation, they proposed to construct a comprehensive evaluation model of the Japanese linguistics grammar system based on the bat algorithm and carried out research and analysis on the Japanese linguistics grammar system. The results show that on the evaluation model, the comprehensive evaluation score of the Japanese language grammar system is C=2.65, which belongs to the E2 level. On the statistical analysis, the mean value of all the scores of the concise class was higher than that of the standard Japanese class, and all of them showed significant differences (p=0.002, 0.000, and 0.047, all less than 0.05).
Exploring the Chinese Cultural Aphasia Phenomenon and Countermeasures in College English Teaching Based on Semantic Comprehension Models
Authors: Zhu Ruike 
Published: December 2023

Abstract
The purpose of this paper is to study the phenomenon of Chinese cultural aphasia and analyze the reasons for the aphasia phenomenon. Secondly, the BERT model in the deep learning model is utilized to improve the effect of various natural language processing tasks, and the English dialogue system is set up to improve the students’ English oral expression ability when introducing Chinese culture. Then, a semantic comprehension model is constructed based on the role information, and coping strategies are proposed for the current English cultural aphasia phenomenon. Finally, the results of teaching evaluation in the university English classroom and the effects of learning Chinese culture in AI-assisted English teaching are analyzed, respectively. According to the study, the teaching process stage is where the most noticeable progress is made, with a score 18.66 points higher than the pre-test score. The average score of the learning effect of students who received AI-assisted means to learn Chinese culture was 3.603 points.