bluuebunny/crossref_metadata_2025_split huggingface dataset:
Innovative Application of Digital Audiovisual Language Based on Natural Language Processing Technology in Graffiti Art Education
Authors: Yang Xiaolan 
Published: April 2024

Abstract
In this paper, natural language processing technology is used to establish coupled Hidden Markov Models for speech formalization and features of the two channels. The adaptive weight selection method is used to recognize speech under different signal-to-noise ratios in the recognition stage. The audio-visual recognition system is applied to actual graffiti art education research, and the feasibility and development direction of audio-visual graffiti in the classroom are studied through questionnaires. It can be found that most teachers and students believe that the concept of audiovisual graffiti painting is helpful or more helpful in the cultivation and development of students’ subconscious mind. Most teachers and students believed audiovisual doodling was beneficial or advantageous for developing students’ subconscious. In the acceptance test and learning effect analysis of the audiovisual graffiti course, the mean values of the four dimensions of perceived usefulness (41.87), perceived ease of use (18.11), willingness to use (31.45), and perceived experience of the process (28.31) are basically around the maximum value, which indicates that the audiovisual technology is well accepted and that the mean value of the experimental group’s performance score (83.68) is significantly higher than that of the control group. Control group. The audiovisual course has been found to enable learners to concentrate on the learning situation faster and master the teaching essential contents more thoroughly.
Lexical Characterization of Ancient and Modern Chinese Combined with Semantic Association Network Modeling
Authors: Zhang Lixia 
Published: April 2024

Abstract
This paper explores the linguistic features of ancient and modern Chinese, focuses on capturing the knowledge of the relevant domains of ancient and contemporary Chinese using semantic models, and carries out the corresponding semantic resource extraction and semantic representation through the resource expression mechanism in the framework of the Semantic Association Network Model (SANM), and then analyzes the historical evolution of the lexicalization patterns of the Chinese language and the linguistic features of the Chinese language in different periods. In the historical evolution of Chinese lexicalization patterns, it is found that the distribution of lexicalization patterns of ancient Chinese table-cutting action lexical items is [action+object] > [action+method] > [action result] > [action+tool], and the distribution of lexicalization patterns of modern Chinese table-cutting action lexical items is [action+method] > [action+tool] > [action+result] > [action+object]. The linguistic features of different periods show that all networks have small-world properties and scale-free properties, and the statistical parameters of the networks in each period are relatively close to each other, with the modern network having the most significant C (8.59) and the smallest L (0.92) and γ (0.40). Ancient and modern Chinese words and phrases with relational word collocations in the interval [0,23] have the most extensive and densest distribution of relational words. These data present the similarities and differences between linguistic features between ancient and modern Chinese, which is significant for studying ancient and modern Chinese.
An Intelligent Model of Mixed Teaching of Business English in Colleges and Universities in the Information Age
Authors: Zuo Xiaojuan , Chen Zeyu 
Published: April 2024

Abstract
With the rise of foreign trade, various industries have increased the demand for foreign language talents, and colleges and universities have paid more attention to the intelligent teaching of business English. Based on this, this paper proposes a dynamic time regularization recognition algorithm (DTW). It improves the recognition efficiency of the algorithm, preprocesses the speech signal, and selects the feature parameter MFCC. The improved DTW algorithm is used to construct an intelligent mode of mixed teaching of business English in colleges and universities, and the learning effect of the students majoring in business English is calculated and evaluated. The research object of this paper is students majoring in business English at college C and is conducted by conducting experimental research on their oral learning, article writing ability, and learning attitude. From the results of oral teaching, the coefficient of variation P-values before and after the application of each group are 0.000, 0.002 and 0.000 respectively, which are all less than 0.05, indicating that students with different English fundamentals have a more significant effect of improving their performance after applying the mixed teaching mode. Before the application, the average score for article writing increased from 59.67 to 64.50. In terms of affective attitude and value, the learning interest risen from 2.75 to 2.97, with the most significant improvement Thus, it can be seen that the intelligent hybrid teaching mode constructed in this paper can effectively improve the teaching effect of business English in colleges and universities.
Research on IoT Edge Data Mining Techniques Based on Cloud Computing Models
Authors: Kang Yang , Li Qiang , Liu Yuanyong 
Published: April 2024

Abstract
With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.
Research on the Path of Teaching Reform of Colleges and Universities Courses in the Age of Artificial Intelligence
Authors: Wang Zhuyu 
Published: April 2024

Abstract
This paper proposes a simulation of teaching behavior based on metacellular automata in response to the problems in the teaching reform of college courses on the digital platform. Using the rule grid and binary method, the tuple units in the structure of tuple automata are described and represented, followed by the standard combination of many tuples to form a tuple space. Considering that the definition of neighbors is relatively complex, von Neumann-type, Moore-type, and extended Moore-type represent the neighbors of a two-dimensional tuple automaton, and rules for the evolution of tuple automata are also formulated. Led by the constructivist teaching model, the contextual teaching method is divided into five links, on which the contextual teaching model based on metacellular automata is constructed. Simulation and Analysis are used to analyze the teaching reform of college courses in the era of artificial intelligence, for example. The results show that when the simulation condition is the initial satisfaction rate of the system ξ = 0.425, and the teaching strategy n=2, the teaching quality improves with the increase of the response probability, which is in line with the law of teaching, i.e.The teacher should motivate the enthusiasm of learning, stimulate the interest of learning, and entirely play the role of students in teaching. This study aims to promote the reform of college course teaching in the era of artificial intelligence, which will contribute to the improvement of college teaching quality.
Research on Automatic Annotation Method of Korean Language under Data Driving and Fusion
Authors: Xiang Tianyu , Gao Li , Liu Wenming 
Published: April 2024

Abstract
In the quest to streamline Korean text and speech annotation, this research introduces innovative automatic annotation methods that promise to revolutionize efficiency and technical prowess in constructing Korean annotation datasets. By leveraging the sophisticated Seq2Seq architecture with BERT and bidirectional GRU models, we significantly enhance the model’s ability to grasp contextual nuances, ensuring precise text annotations. The speech annotation frontier benefits from a novel amalgamation of the Hidden Markov Model’s forced alignment and semi-supervised learning, perfected with Seneff auditory features for meticulous phonological consonant boundary detection. Empirical validation across diverse datasets showcases our methodology’s superiority, achieving a remarkable 96.01% accuracy in text annotation and setting a new benchmark for phonological boundary detection at a 14.5ms minimum distance threshold. Our approach outperforms traditional algorithms, marking a pivotal step forward in Korean automatic annotation.
Exploration of the Integration of Traditional Music Cultural Elements in Music Informatization Teaching
Authors: Feng Qiyue 
Published: April 2024

Abstract
With the development of information technology, music teaching methods are getting more affluent and more prosperous. This paper proposes a model for emotion classification and assessment that integrates traditional music culture elements with information technology in music teaching. The research first combines TextCNN and BiLSTM algorithms to establish the emotion classification model of conventional music. Then it combines PYIN and DTW algorithms to establish the evaluation model of traditional music, which completes the auxiliary efficacy of music informationized teaching. In the emotion classification test of the model, the classification accuracy and F1 value of emotions of different music samples are 82.98% and 75.22%, respectively. The model’s recognition accuracy of the four voices is 86.76%, and the overall effective scoring percentage is 81.98% under different playing abnormalities. This study has had an impact on traditional music evaluation. The model in this paper can be used to classify and evaluate emotions in conventional music, providing more intelligent and high-quality technical services for integrating traditional music into music teaching.
Curriculum System Design and Teaching Mode Innovation of English for Specialized Purposes Platform
Authors: Li Na 
Published: April 2024

Abstract
The current status of ESP teaching research is not optimistic, failing to eliminate the limitations and constraints of curriculum design and teaching mode. This study first utilizes the forward and back propagation algorithms in the deep learning network to extract features from the student sample data. The clustering center of the sample data and the weight value of each feature are calculated for the clustering results using the phase dissimilarity calculation of the improved K-prototypes algorithm. Through the analysis of the students’ motivation to learn English in a specific school, it is found that the factor of “achievement motivation” accounts for the most significant proportion, which is 55.80%, and most of the students’ specific motivation to learn English for particular purposes is “to obtain the graduation certificate”, which accounts for 95.3% of the students’ motivation. Most students were motivated to learn English for particular purposes mainly by obtaining a diploma, accounting for 95.3%. Among them, students in category 2 have a higher value of learning characteristics, with an average of 0.77. In contrast, students in category 5 have the lowest value of knowledge mastery, with an average of 0.26. This study lays a data foundation and support for the design and innovation of the English for Specialized Purposes platform. At the same time, the innovative teaching mode of English for Specialized Purposes proposed in this paper based on the CDIO teaching model provides a reference for teaching English for Specialized Purposes in the future.
Cloud Data Resources and Library Subject Information Services
Authors: Zhang Chen 
Published: April 2024

Abstract
In the evolving landscape of library services, propelled by advancements in Internet technology and service paradigms, this study utilizes cloud-based lending data from college libraries to improve user profiling and subject-specific lending. Integrating the K-means algorithm with a Boolean matrix-enhanced Apriori algorithm, we devise a data mining model that fine-tunes detecting patterns in user borrowing behaviors. This approach distinguishes five distinct subject areas: energy, computing, electronic communication, machinery, and environmental chemistry. The outcome reveals a bibliographic association rule mining confidence of up to 79.38%, a 30% increase over conventional methods. Moreover, it generates three notable 2-item sets. Our model introduces a groundbreaking way to offer personalized library services, significantly enriching the user experience with tailored subject information.
Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques
Authors: Liu Miaogu 
Published: April 2024

Abstract
In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.
Application of “Artificial Intelligence + Education” Innovation Model in Higher Education Management and Student Training
Authors: Zhang Ruixin 
Published: April 2024

Abstract
The rapid development of modern science and technology promotes technological innovation in education, and this paper constructs the higher education innovation model of “Artificial Intelligence+Education” to realize the goal of responding to the construction of education informatization in higher education. The intelligent education platform is created using the theoretical foundation of three-layer education to manage data integratedly. The intelligent mode includes face recognition and voice recognition, which are realized using LBP feature extraction and neural network algorithms. Their effects are verified. The model is better than the traditional education model in all dimensions of the impact of student training. The P is less than 0.05, which is a significant difference, and the effect of education management is improved by 0.02~0.09. This paper’s “Artificial Intelligence + Education” innovation model has certain advantages in higher education management and student training, and provides a practical path to the construction of informationization and intelligent development of education. It provides a pragmatic path for constructing information and the thoughtful development of education.
A Kannappan-Cosine Functional Equation on Semigroups
Authors: Jafar Ahmed , Ajebbar Omar , Elqorachi Elhoucien 
Published: April 2024

Abstract
In this paper we determine the complex-valued solutions of the Kannappan-cosine functional equation g(xyz
0) = g(x)g(y) − f(x)f(y), x, y ∈ S, where S is a semigroup and z
0 is a fixed element in S.
Phenotypic Characteristics, Pathogenicity, and Molecular Identification of Hypomyces perniciosus Causing Wet Bubble Disease of Edible Mushrooms
Authors: Szumigaj-Tarnowska Joanna , Szczechura Wojciech 
Published: April 2024

Abstract
The research aimed to determine the phenotypic characteristic, pathogenicity and molecular characterization of Hypomyces perniciosus isolates responsible for wet bubble disease of the white button mushroom Agaricus bisporus. Phenotypic characteristics such as colony appearance, mycelium texture, and pigmentation were studied on four different agar media, i.e., potato dextrose agar (PDA), malt extract agar (MEA), Czapek-Dox yeast agar (CYA), and Sabouraud dextrose agar (SDA), after eight days of incubation. Additionally, the growth rate of the tested isolates was studied depending on the pH of the medium. Fungal isolates showed the highest mycelial growth on MEA and SDA at pH 6.0. However, on CYA at pH 7.0, the mycelium exhibited the worst growth. Isolate identification and genetic relationship analysis were carried out using internal transcribed spacer region sequencing and the random amplified polymorphic DNA method. The research confirmed that all Polish isolates belong to the species H. perniciosus, and the genetic diversity is relatively low. Phylogenetic analyses revealed three subgroups of H. perniciosus isolates. The first group included three genetically distinct isolates with a similarity coefficient in the range of 0.76–0.85 to isolates CBS 815.73 and CBS 322.52. The second group was divided into two subgroups and included 16 isolates with a genetic similarity range of 0.91 to 1.0 to CBS 815.73 and CBS 322.52 isolates. Furthermore, the eight genetically similar isolates exhibited the greatest pathogenicity towards A. bisporus.
Architectural Typologies Appeared by Modernism: Case Study of the Edirne Zeppelin Hangar
Authors: Doğan Huriye Armağan 
Published: April 2024

Summary
When modernism started to be seen in architectural spheres, it did not emerge just with an architectural language for design but also with new architectural typologies. Due to the main discourse of the Modern Movement with the famous quote of Louis Sullivan, “Form follows function”, new building types which were explicitly designed for their functions have appeared. However, over time, while some of these functional buildings kept their functions, some of them either lost their function entirely already, or the technology which created that function is in a downward trend. Zeppelin/airship hangars are amongst those buildings constructed for a specific function in the early 20th century which have lost that function in the present. Therefore, even though they might not reflect any tangible qualities, the function can operate as an intangible cultural reference. The object of this paper is one of those zeppelin hangars, which is located in Edirne, Turkey. The research attempts to categorise the architectural typologies that appeared by modernism, and apply a case study method to the Edirne Zeppelin Hangar to gain insight towards the problem, which is related to the consequences created by the language of the Modern Movement due to the emphasis of the function, and to discuss the possible adaptive reuse strategies regarding these artefacts which totally lost their functions. It is concluded that it is not possible to transform all the building stock that emerged in the built environment into museums, including the Edirne Zeppelin Hangar; however, inconsequential to the designated purposes, it is crucial to leave intangible references to the previous function in its design process.
Jewish Popular Music in Galicia: Karol Rathaus’ Recollections of Chune Wolfsthal
Authors: Guzy-Pasiak Jolanta 
Published: April 2024

Abstract
Eminent Polish composer Karol Rathaus (1895–1954) left behind an essay dedicated to his uncle Chune Wolfsthal (1851–1924). In this text, Rathaus recalls his childhood and adolescent years in (Austria-Hungary’s crown land of ) Galicia. His memories of Wolfsthal, a famous klezmer and operetta composer active in the late nineteenth and early twentieth centuries, provide an opportunity to recollect the now non-existent world of Jewish amateur musicians working in Tarnopol and Lwów before World War II. Those musicians performed in synagogues, at family events and social functions, in cafés and tea gardens. Some of them, including Chune, became involved in the activity of the then emerging Jewish theatre, which was a part-vulgar, part-artistic phenomenon.
Counting the Cells Based on Their Size Using Fuzzy Logic and the Image J System
Authors: Kharofa Shahla Hazim Ahmed 
Published: April 2024

Medical diagnosis is the process of identifying the presence of a specific disease. Medical images play an important and major role in helping the doctor and specialist in the process of diagnosing the patient's condition. As a result of the technological development taking place in the fields of artificial intelligence, which processes data quickly and efficiently by responding to various conditions and situations, providing a great deal of flexibility and accuracy, it is widely used in helping to diagnose diseases. Fuzzy logic was used to find the number of cells in medical images, where the increase or decrease in the number of cells beyond the normal limit indicates the presence of a specific disorder or disease.
This paper aims to use fuzzy logic and the Image J system to determine the appropriate mechanism and method for counting cells according to their size in the medical image, where a set of basic steps are implemented on the image by using the Image J system. This includes converting to mask, outlining, filling holes, watershedding, setting measurements, and analyzing particles. After that, using fuzzy logic to determine the best appropriate methods for counting cells in medical images based on size, enter a single random value for size, and cells smaller than that value are ignored. The area and perimeter of cells are accounted for.