Reviewerly/exHarmony huggingface dataset:
Research on the Synergistic Development Mechanism of Vocational Education and Open Education in the Context of High Quality Development with the Application of Multi-scale Integration Features

Abstract In this paper, firstly, the core literacy and teaching mechanism of the synergistic development of vocational education and open education is studied based on the school education plan under the requirement of the new engineering education goal to improve the quality of vocational school education. Then, the mechanism of collaborative development of vocational education and open education based on multi-scale integration characteristics is studied, and the teaching correction multi-scale integration network in the collaborative model of vocational education and open education is proposed according to the students’ learning state in the classroom. To avoid the problem of over-fitting the loss function in the prediction of the learning state, it is necessary to introduce the regular term to prevent the model from over-fitting and to analyze the learning state and the satisfaction of the collaborative model analysis. The results showed that the learning status of the two students was in a state of concentration, which was a more objective analysis of the distribution of students’ attention in the classroom. In terms of satisfaction with the collaborative model, the satisfaction level of the first-year students with the factors of the collaborative model of vocational education and open education was lower than that of the second-year and third-year students. This study is important for achieving the ambitious goal of achieving high-quality development of the synergistic mechanism between vocational education and open education.
Application of Human-Computer Interaction and Digital Image Processing Technology in Virtual Simulation Game Design

Abstract This paper first analyzes the design process, image imaging, and view reconstruction of virtual simulation game scenes and discusses the key technologies used in virtual simulation games. Then, the human-computer interaction techniques of gesture, pose, voice, and data interaction are combined to design a multi-touch detection algorithm based on human-computer interaction. Finally, the edge detection algorithm is combined with the edge detection algorithm to detect the edge of the image, and after the displacement conversion and extraction, the processing process of the digital image is completed. After the performance test, the usability of pose interaction, the accuracy of speech recognition and the timeliness of data interaction of the virtual simulation game are between 0.8 and 1.0, and the overall human-computer interaction capability is highly evaluated.
Real-time signal detection system and reliability analysis of wireless networks under 5G communication architecture

Abstract This paper first analyzes the network for 5G communication architecture and builds a framework for signal detection systems for wireless networks based on this architecture. Secondly, to better ensure the reliability of the signal detection system, the AE-ELM algorithm is built for wireless network signal detection by introducing an automatic coding machine and an overrun learning mechanism. Finally, an example is used to verify the reliability of the wireless network signal detection system in real-time. The results show that the signal detection rate of the real-time signal detection system with data volume over 300GB is 93% correct, and the maximum rate of missed rejection is about 6%. When SNR=25dB, the BER of the AE-ELM algorithm fluctuates around 10-5 with a variation of only about 0.1%. This shows that the signal detection system of a wireless network under 5G communication architecture has good reliability.
Exploring the structure of micro topics in Chinese language and literature based on the fusion of multiple features

Abstract In this paper, firstly, the topic structure of the Chinese language and literature is explained, and the micro topic structure representation system is constructed by using the main statement bit structure theory and the main bit advancement model. Secondly, the word2vec network is used to extract the features of Chinese language and literature elements, and a multi-feature fusion classification recognition model is constructed based on SVM. Finally, the corpus was used to validate the example. The results show that the percentage of radial primary advancement pattern of micro topic structure is the highest and reaches 94.13%, and the recognition accuracy of SVM-based multi-feature fusion recognition classification model for micro topic structure is improved by 12.04%, 18.29% and 7.45% respectively compared with BTM algorithm, LDA algorithm and K-means algorithm.
Quality assessment of intelligent physical education teaching in universities based on multivariate statistical analysis and regression analysis

Abstract In this paper, we first propose the basic technical support and service and management of university intelligent physical education composition, and on this basis, we propose a teaching quality assessment model based on multivariate statistical analysis and regression analysis. Among them, multivariate statistical analysis is used to calculate the factor loadings A of physical education teaching, and multiple linear regression is used to establish the teaching quality assessment model and test it. Finally, an empirical analysis of teaching quality assessment was conducted with smart physical education as an example. The results show that the comprehensive assessment value of smart physical education teaching quality based on a multiple linear regression model is very close to the actual value, and the error value ranges from [0.003 to 0.027]. The error values are smaller than [0.0135~0.0921] of the ANN model and [0.0635~0.1033] of the neural network model. The research in this paper can provide scientific quality assessment methods and theoretical support for smart physical education in colleges and universities to promote the development of smart physical education and improve teaching quality in colleges and universities.
Information-based theory of psychological education and the penetration of college Civics courses in student teaching

Abstract This paper first elaborates on the relationship between psychological education and Civics courses and proposes a three-dimensional evaluation system to comprehend the teaching invention of psychological education and Civics classrooms in colleges. Then, based on information theory, the quantitative evaluation model of psychological education and Civics course effectiveness based on a cluster analysis algorithm is proposed. The data of indicators with high correlation are fused according to the cluster analysis algorithm, and the categories of evaluation indicators of sub-series are divided by the essential factors after clustering, while the automatic evaluation model is constructed by using the factor score coefficients under different categories to realize the evaluation of psychological education and Civics course, followed by an application analysis of psychological education and Civics based on information theory. The findings demonstrated that accuracy was higher when conducting course evaluation in the automated assessment model of psychological education and civics according to the cluster analysis algorithm, and time consumption was lower. Legal expertise, moral character, political behavior, view on life, values, and patriotism had substantial negative correlations with sadness and anxiety and significant positive correlations with subjective well-being in the correlation study.
A study on the strategy to improve the informatization teaching ability of teachers in higher education institutions based on OBE concept

Abstract This paper focuses on the outcome-oriented OBE concept and the teacher information-based teaching model. Then, a regression model was constructed to study teachers’ ability to teach in information technology, and the correlation between variables was studied using regression equations and significance tests. The influencing factors of teachers’ informatization teaching ability enhancement were studied through the indicators of teacher factor, school factor, and policy training, and the strategies of teachers’ informatization teaching ability enhancement in higher education institutions were proposed. The study showed that the overall mean value of teachers’ factors among the influencing factors was 4.09, and the mean values of motivation and self-efficacy were greater than 4 points. The overall mean value of school factors was 3.9, the overall mean value of policy training was 3.55, and the mean values of training, policy, and competition were all greater than 3. This study provides some references and ideas for improving teachers’ information technology teaching abilities in higher education institutions.
Research on the construction and optimization of the teaching model of kindergarten environment creation practice based on virtual simulation technology

Abstract Virtual simulation technology is used to construct a teaching model for kindergarten ring creation based on this paper. Virtual simulation technology uses the classical K-Means clustering algorithm to confirm information on kindergarten teaching resources. Using a particle swarm optimization algorithm, weights are calculated for kindergarten and preschool education resource categories to determine the similarity of usage frequency. To improve the efficiency of preschool education resources scheduling, to verify the feasibility of the teaching model, the results show that the mean value of student-teacher interaction with virtual simulation technology is 2.180, and the highest mean value of student-teacher interaction is 2.220. It proves that the teaching model constructed using virtual reality technology can effectively improve the quality of teaching conducted in early childhood circumvention practices.
Exploring the Reform and Innovation of College English Teaching Based on the Knowledge Diffusion Model

Abstract This paper analyzes the technical and educational aspects of English teaching informatization, explores the impact of university English teaching reform on traditional English teaching in the informatization era, and designs an information technology-assisted precise English teaching model in three stages: before, during and after class. To realize accurate teaching, the FCE_DFM teaching resource recommendation model is constructed by the fuzzy C-mean clustering method in combination with the recommendation model based on self-encoder and FM. The RMSE and MAE values of this paper were reduced by about 0.12 and 0.2, respectively, compared with traditional recommended teaching, the attendance rate was increased by 22%, and about 2.0 points improved the emotional attitude toward learning through English teaching reform practice.
A study on the influence of teacher certification concept on the construction of art majors in universities based on big data analysis

Abstract The development of the data era has put higher requirements for college art courses, but the imperfect evaluation system of practical teaching of college art courses has become a stumbling block to the flourishing development of college art. Based on the decision tree model, this paper proposes constructing the optimal decision tree using pruning techniques. The optimized decision tree is adopted to explore the differences in the construction of art majors in universities under the condition of normal major certification and feedback on the influence of normal major certification on art majors through the evaluation results. From the analysis of the three index examples, the mean values of cognitive evaluation, pedagogical evaluation, and effectiveness evaluation of art courses were 2.527, 4.756, and 3.267, respectively, indicating the best evaluation in the pedagogical evaluation of art courses, but the cognitive evaluation was slightly lower. Normal major certification significantly influences the construction of art majors in colleges and universities. The decision tree model can also guide the innovation direction of college art courses more intuitively through data so that the evaluation system of college art courses can enjoy the development dividend of the new technology era.
Clustering for the Analysis and Enrichment of Corpus of Images for the Spatio-temporal Monitoring of Restoration Sites

After the fire that took place in the cathedral Notre-Dame de Paris, a Scientific research group composed of 9 Working Groups was set up to study the building. With the aim of creating a digital ecosystem for spatio-temporal monitoring of a restoration site, efforts have been put to develop methodologies for data management and enrichment. In line with this approach, this work focuses on the exploration, analysis and enrichment of a corpus of images and annotations of the cathedral, with regards to their temporal, spatial and semantic aspects, in addition to their visual content. Using prevailing clustering methods focusing on different criteria, we obtained several clusters of images from which new information can be inferred. By exploring these complementary aspects, we identified similarity links between the images. Further work will focus on multimodal analyses of the corpus.
Semi-Automatic Assessment of Vocalization Quality for Children With and Without Angelman Syndrome.

Automated methods for processing of daylong audio recordings are efficient and may be an effective way of assessing developmental stage for typically developing children; however, their utility for children with developmental disabilities may be limited by constraints of algorithms and the scope of variables produced. Here, we present a novel utterance-level processing (ULP) system that 1) extracts utterances from daylong recordings, 2) verifies automated speaker tags using human annotation, and 3) provides vocal maturity metrics unavailable through automated systems. Study 1 examines the reliability and validity of this system in low-risk controls (LRC); Study 2 extends the ULP to children with Angelman syndrome (AS). Results showed that ULP annotations demonstrated high coder agreement across groups. Further, ULP metrics aligned with language assessments for LRC but not AS, perhaps reflecting limitations of language assessments in AS. We argue that ULP increases accuracy, efficiency, and accessibility of detailed vocal analysis for syndromic populations.
A data framework enabling BEM and BMS interoperability based on semantic web technologies and Brick ontology

Heterogeneous data sources are generated during the building life cycle. These data sources contain the information required for commissioning. However, their differences in data semantics, structure, and syntax make them disparate data silos, which hinders data interoperability. In this work, a framework for data interoperability is proposed as a first step toward a service-oriented digital twin enable commissioning including gap verification between the expected and actual energy performance of a building. Based on semantic web technologies and Brick ontology, this framework allows data interoperability of heterogeneous data sources, particularly Building Energy Modeling (BEM) and Building Management Systems (BMS).
Two Interviews with Renown Datasets Researchers

This issue of the Dataset Column provides two interviews with the researchers responsible for novel datasets of recent years. In particular, we first interview Nacho Reimat (https://www.cwi.nl/people/nacho-reimat), the scientific programmer responsible for the CWIPC-SXR, one of the first datasets on dynamic, interactive volumetric media. Second, we interview Pierre-Etienne Martin (https://www.eva.mpg.de/comparative-cultural-psychology/staff/pierre-etienne-martin/), responsible for contributions to datasets in the area of sports and culture.
Consumer Electronics Technologies for Enabling an Immersive Metaverse Experience

The idea of the Metaverse as a virtual reality space where people can interact with computer-generated surroundings and each other in real time is gaining attention as this technology is roaring. Consumer electronics (CE) technology plays a crucial role in developing and using the metaverse. It offers the metaverse various devices like virtual reality headsets, augmented reality glasses, smartphones, and haptic feedback devices. These devices give users immersive experiences, improve communication and collaboration, and explore virtual worlds. In this paper, we review several applications of consumer electronics technology in the metaverse, two case studies of consumer electronics in the metaverse, several challenges in the existing CE technology in adapting it to the metaverse, and some future directions for research. As a part of case studies, we analyze how CE technology impacts education and healthcare in the metaverse. In metaverse education and healthcare, CE provides opportunities for immersive learning experiences, virtual simulations, remote training, and improved patient care. Numerous challenges CE faces in the context of metaverse related to structure development, standardization efforts, privacy concerns, and ensuring security that must be addressed for its successful implementation have been identified. Despite its limitations, ongoing efforts are being made to enhance these devices and optimize the overall experience for the metaverse.
Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases
A Model of Reusable Assets in AIE Software Systems

Nowadays, due to the increasing presence of artificial intelligence in software systems, development teams face the challenge of working together to integrate tasks, resources, and roles in a new field, named AI Engineering. Proposals, in the way of models, highlight the needs of integrating two different perspectives – the software and the decision-making support (big data, machine learning, and so on) systems. But there is something more – both systems must achieve high quality levels for different properties; and this is not a straightforward task. Quality properties, such as reusability, traditionally evaluated and reinforced through modeling in software systems, do not exactly apply similarly in decision-making support systems. In this paper, we propose a model for managing reusable assets in AI engineered systems by linking software product line modeling and variety identification. The proposal is exemplified through a case study in the agriculture domain.
Pengembangan Media Evaluasi Pembelajaran Bahasa Indonesia dengan Memanfaatkan Aplikasi Daring Wordwall pada Siswa Kelas VIII A MTs Annur Al Huda Tajinan Malang

The aim of this research is to determine the development of Indonesian language learning evaluation media by utilizing the online wordwall application for class VIII A students at MTs Annur Al Huda Tajinan Malang. The subjects of this research were students at MTs Annur Al Huda Tajinan class VIII A. By using the wordwall application, it is hoped that students will not get bored during teaching and learning activities. The instrument used to collect quantitative data in this research was through a validation process. There are six steps used in this research, namely (1) needs analysis; (2) planning; (3) production; (4) product validation; (5) product evaluation; (6) limited testing. The results of the research using the wordwall application obtained an average score from material experts of 4.4 or very good category and an average score of media experts of 4.5 or very good category, while the average score of language experts was 4.6 or very good category and the results of the respondents' questionnaire calculations with an average of 4.6 or very good category. Thus, it can be concluded according to material, media, language experts and from the results of student questionnaire calculations that research into developing evaluation media using the wordwall application is suitable for use in class VIII A MTs Annur Al Huda Tajinan Malang.&#x0D; &#x0D; Keywords: media, apps, wordwall
Analysis of the Methods for Selecting Children's English Materials

As globalization continues to accelerate and international exchanges become more frequent, English has become an important medium for cross-cultural communication. Reading is an integral part of English learning, especially for children. By reading English materials, they can get access to a rich vocabulary, grammar and sentence structure, which helps to improve their expressive skills. In order to expand children's knowledge, understand the differences between different countries and cultures and develop their open-mindedness and global awareness, this paper aims to provide an in-depth analysis of the selection of children's English materials, using the literature research method to analyse the principles of children's English materials. Firstly, the characteristics of children's English reading are analyzed, and it is found that children have a limited vocabulary, a high reliance on visual elements and different reading levels in English reading. Secondly, the paper classifies children's English materials into 3 genres such as picture books, novels and children's poetry, and summaries the characteristics of the corresponding genres. Finally, a detailed analysis of the principles of material selection for children's English materials is made using relevant literature, and four principles of material selection for children's English materials are proposed: the principle of moderate difficulty, the principle of attractiveness, the principle of diversity and the principle of cultural relevance.
The New Network Fraud of Pig Butchering from the Perspective of Criminal Law

With the continuous development of the network, many new types of fraud also came into being, and pig butchering is a representative crime. This type of crime takes advantage of the weakness of human nature, that is, the lack of emotional life. In the face of this crime, the purpose of this article is to take an easy look at the new cyber crime of pig butchering from the perspective of criminal law. The purpose of this article is to show how to prevent and control this type of cybercrime and what countermeasures people should adopt in the face of these problems. It is argued that to solve the problem it requires the police to cooperate with the work of the judiciary while reducing errors in the investigation and collection of evidence. For another aspect, this new form of network fraud crime could be a challenge for the judical system. The difficulties in identifying accomplices in the crime is also worthy of attention.