Reviewerly/exHarmony huggingface dataset:
The Application of Online-Offline Mixed Teaching Mode in Computer Teaching in Colleges and Universities in the Context of Internet+

Abstract This paper uses a deep learning algorithm to extract training samples for hybrid teaching resources in the context of Internet+. The support vector machine establishes a linear regression function, and a nonlinear function is used to map the samples to a high-dimensional feature space for solving. To optimize the training problem, a deep neural network is employed to construct a neuronal structure model and calculate the loss function. The text vectorization method was selected as the input layer processing of the convolutional neural network, and the index scores of the hybrid teaching model in the context of Internet+ were all 6 and above, with the highest score reaching 10. Therefore, online and offline hybrid teaching modes can provide a more flexible and optimized teaching method and promote resource sharing and optimization.
Optimisation of Master’s Degree Programmes in Translation Based on Multi-Attribute Decision-Making Approach

Abstract In this paper, first of all, in the quality assessment of the teaching quality of the translation course, it is proposed to use a multi-attribute decision model to assess the quality of the set of alternatives for all the courses and to use the existing decision ideal points to rank and select the best of a set of optimized solutions for the translation course through the hesitant fuzzy number and objective optimization model. Finally, to explore the optimization problem of the curriculum, a research analysis was conducted on MTI master’s students who recently graduated from the institution. The results show that by optimizing the MTI course specifically, the students’ positive interest ( p 1 = 0.03) and independent inquiry interest ( p 2 = 0.01) are less than 0.05, showing significant differences ( p &lt;0.05). The special optimization scheme of the MTI course proposed in this paper can reflect some problems worth pondering in the curriculum and provide a guiding reference for optimizing the MTI course in schools.
Application of EFL pedagogy based on cloud computing platform in online interactive teaching of foreign languages

Abstract Internet information technology has greatly aided the improvement of EFL teaching. Aiming at the weaknesses of traditional EFL teaching, this paper proposes a hybrid EFL teaching model based on the cloud computing platform, which is designed in terms of both object model and platform architecture. In the task assignment of EFL teaching, the CRP algorithm is improved based on fuzzy control, and the FCRP algorithm for teaching task assignments is constructed. Meanwhile, from the needs of professional training, the recommendation optimization of teaching resources was realized through keyword parsing, and the resource recommendation effect and learning input were analyzed based on the performance test. The final results of the platform test in this paper show that the average response time for login and query is 1.621 seconds and 0.935 seconds, respectively, and both meet the EFL interactive teaching requirements.
Applying Fuzzy Cluster Analysis to Study the Efficiency of Sports Resource Allocation in Urban Communities

Abstract In the context of sports development, the demand for sports resources is increasing daily, and improving the efficiency of sports resource allocation is especially important. This paper first explores the principle of sports resource allocation, selects indicators according to input and output, and constructs an efficient evaluation system for sports resource allocation. Then, we use the fuzzy cluster analysis method to optimize the objective function and the FCM algorithm to select the cluster center and the number of clusters. To study the efficiency difference between urban and rural sports resource allocation, we analyzed the effectiveness and reliability of sports resource allocation in urban communities. The study shows that the average classification accuracy of the FCM algorithm is 97.87%, and the mean value of sports resource allocation efficiency in towns is 0.1348 higher than that in villages.
The application of multicluster structural model in English teaching model innovation and effect analysis

Abstract This paper analyzes the English teaching model’s structure, function, and characteristics and constructs the basic structure of the classroom innovation teaching model. Then, the English teaching model is constructed, and the English teaching evaluation index system is established based on the algorithms of structural equation model, multigroup structural equation and multigroup parameter estimation. Finally, the quantitative analysis of index weights and satisfaction was conducted with the example of college A. The results showed that the weights of the primary indicators were 0.2873, 0.1294, 0.2546, and 0.3287 for personalized needs, perceived quality, perceived value, and satisfaction, respectively, which indicated that the multigroup structural model could help universities to realize the innovation of English teaching mode and to adopt diversified teaching methods to meet student’s diverse needs in the teaching process. Diversified the needs of students and improved their satisfaction.
Employment Education and Psychological Work of Students in Lower Colleges and Universities Based on the Hierarchical Analysis Method

Abstract This paper establishes a platform interaction model in the context of Internet+ to enhance the motivation of college students to take the initiative to receive employment and entrepreneurship guidance and training. A psychological intervention model regarding hope, optimism, self-efficacy, and resilience is constructed. A new method of fuzzy number ordering determined by the limit coefficient can be obtained by defining the limit coefficient of triangular fuzzy numbers. Hierarchical analysis was used to analyze the employment choice dilemma faced by fresh university graduates and to guide them in making the correct employment choices. The results show that the total mean score of positive psychological capital is 119.99, which indicates that the level of positive psychological capital among college students is high. Employers are very satisfied with the graduates’ operational application ability, which reaches 35%.
Prospects for the Application of Data Analysis Methods in the Evaluation of English Teaching in Colleges and Universities

Abstract This paper studies the specific application of association rule analysis in teaching evaluation in colleges and universities and analyzes the existing cases of the Apriori algorithm applied to college management under association rules. Aiming at the performance bottleneck of the Apriori algorithm in dealing with complex databases, the AMC algorithm based on matrix compression is proposed, and the data storage form is optimized through transaction matrix mapping. The improved AMC algorithm is applied to correlation analysis of English teaching evaluation data in colleges and universities, focusing on the correlation relationship between teaching characteristics, teaching-related factors, and evaluation grades based on confidence level. A confidence level of 69% will be achieved when the teaching effect is good, and the evaluation grade reaches basic satisfaction. A confidence level of 71% can be achieved when the teaching management is excellent and the evaluation grade is satisfactory.
Integrative Development of Modern Literary Works and Traditional Culture Combined with Semantic Association Network Modeling

Abstract The coupling framework of modern literary works and traditional culture is first discussed in this paper, and the intrinsic connection between them is examined. Secondly, a semantic-associated information extraction network model is constructed using LSTM and attention mechanism, and the target semantic fusion is achieved through semantic space conversion and semantic-associated information extraction. Finally, the dataset and empirical analysis confirm the SAIEDMMA model’s effectiveness. The results show that the F1 value of the SAIE-DMMA model on the Total-Text dataset and ICDAR2015 dataset is 85.59% and 87.72%, respectively. The traditional culture of folk culture has the highest degree of integration in modern literature, and the growth of its literature from 2017 to 2021 is 1,273 books. This shows that the semantic correlation information extraction network can analyze the semantics of the integration of modern literary works and traditional culture, and it can also be used to promote the effective inheritance and development of traditional culture so that modern literary works are closer to public life.
Characteristics of Kindergarten’s Village Education Community Curriculum Operation: Focusing on the case of public kindergartens in ‘A’ City

The purpose of this study is to examine the perception of kindergarten teachers on village education community education and how the village education community curriculum is operated in kindergartens by subdividing it into innovative kindergartens and general kindergartens. To achieve this goal, focusing on the case of ‘A’ city, the current study investigated perceptions of public kindergarten teachers of village education community education, curriculum operation status, difficulties, and improvement requirements. The followings are the summary of the main findings from this study. First, kindergarten teachers in general positively recognized the concept of the village education community, and teachers working in innovative kindergartens were not only more aware of the concept of the village education community than general kindergarten teachers, but also recognized a higher degree of usefulness of village education community on early childhood education. Second, kindergarten teachers perceived greater difficulty in operating the curriculum that are operated through consultation with the community(village), rather than operating curriculum based on the topic of the community and curriculum that utilize the community resources. Third, kindergarten teachers pointed out the lack of 'promotion’ and ‘resources and infrastructure as the major hindrance factors, and there was a high demand for improvement on the ‘increase in the participation of the local community and ‘diversification of the programs’ operated by village education community curriculum.
The Effectiveness on the Core Competency Scale for Primary Pre-service Teachers by Using Latent Profile Analysis: Focusing on D University of Education

The purpose of this paper was to develop the core competency scale for primary pre-service teachers based on the concept of talent and key competencies constructed by D university of Education, and also to describe its psychometric evaluation. After implementing the core competency scale developed for 544 primary pre-service teachers, average analysis and potential profile analysis were performed according to gender and grade. The analysis results are as follows. First, There was no statistically significant difference in the mean by gender and grade in all competencies except for the teacher’s characteristics competency. Second, the latent profile analysis was applied based on the developed the core competency scale, and as a result, six latent groups were derived. In detail, latent group 1(5%) showed all core competencies were low, and in particular, teacher’s characteristics competency was found to be extremely low. In group 2(12%), teacher’s characteristics competency showed a value close to the average. Group 3(15%) is a group with a lower-than-average teacher’s characteristics competency. Group 4(30%) showed a slightly higher level of teacher’s characteristics competency than average, and group 5(23%) had all core competencies slightly higher than the average. Group 6(16%) showed the overall core competency was higher than the average. Finally, the effectiveness of customized competency training was discussed.
Sistem Pengering Daun Kelor Berbasis Internet of Things dan Artificial Intteligence

Drying Moringa leaves is needed to reduce the water content so that the Moringa leaves become fresh and can be used for the following process. Drying Moringa leaves to change the water content from 80% to 9.2% requires ideal heating conditions because the heating speed must not damage the nutritional content in the leaves. Developing an existing drying system using IoT to monitor humidity and temperature to increase the drought stability of the Moringa leaves produced. By using IoT, it is hoped that drying conditions can be watched from anywhere and recorded so that if undesirable things happen, it will be easier to track the history of the drying process that has taken place. This system is also connected to a recommendation system using an Artificial Neural Network (ANN). This system will provide recommendations for the best conditions for Moringa flour production because various external factors influence the drying of Moringa leaves. Utilization of the ANN model can recognize data patterns in seasonal time series. The results of implementing the Moringa leaf drying machine can reduce the time by 120 minutes faster than the previous tool
Rakshak: Post-Disaster Management through an Android Mobile Application

In the aftermath of a natural or man-made disaster, the most important thing is rescue and reconstruction. Large-scale disasters cause significant damage to lives, livelihood, and infrastructure. The breakdown of existing infrastructure is particularly detrimental since it also hampers rescue efforts and emergency services. From an administrative standpoint, effective logistical management and allocation of resources, as well as getting real-time reports, is highly imperative. Key Words: Disaster Management, Application, GIS, SOS
Current Situation and Mode Innovation of Physical Education Teaching in Colleges and Universities under the Perspective of Deep Learning

Abstract This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.
Analyzing Reading Education Reforms in Chinese Schools Using an Ecosystem Model: Insights from China's 2018 PISA Reading Test

This paper explores the imperative of enhancing reading proficiency among Chinese students, particularly for studying abroad. Drawing on the 2018 PISA test, it comprehensively analyzes macro and micro factors in the educational ecosystem. It aims to optimize reading literacy education in China using the PISA and Chinese regional tests. It provides recommendations at systemic and individual learner levels. At the macro level, the study examines national policies, cultural values, and the need for a comprehensive policy framework. Adequate resources, evidence-based practices, and a reading culture are advocated. A comprehensive national policy framework, resource allocation, evidence-based practices, and a reading culture are crucial. At the micro level, the significance of individual factors, curriculum design, and parental involvement are emphasized. An effective curriculum, critical literacy skills, and parental support are essential. In conclusion, this paper acknowledges the need to enhance reading skills among Chinese students studying abroad. By examining macro and micro factors, it offers practical recommendations to optimize reading literacy education. This multifaceted approach considers national policies, cultural values, curriculum design, and parental engagement. By addressing these aspects, China can equip its students with the necessary reading skills to thrive in a globalized world.
Few-shot learning-based lesser-known POI category estimation based on syntactic and semantic information

The estimation of points of interest (POI) categories is essential in several contexts, such as land use estimation, POI and itinerary recommendation in the tourism sector, and so on. Most of these approaches are based on well-known POIs and use information such as people’s mobility or check-in data. This information is not or rarely available for lesser-known POIs. However, these lesser-known POIs cannot be ignored because of this lack of information, as they may be important to local people in terms of their culture and history and worth discovering by tourists or local authorities. To address this challenge, we propose an approach based on the techniques of coupling the syntactic and semantic analysis of data via a knowledge graph using Few-shot learning (FSL) and Light Graph Convolution Network (LightGCN). The FSL technique allows us to work with very little data, which not only works with lesser-known POIs but also reduces the complexity in terms of tasks and execution time. The results show that our approach outperforms the baseline approaches and that considering the semantic aspect of the data via Linked Open Data (LOD) significantly improves the results of the approach based on the syntactic analysis alone.
Value-added tax in the GCC countries: evidence from UAE

This paper aims to examine the determinants of VAT in the UAE and the readiness of the UAE. It also provides a brief history of VAT, differentiating between the original terms of Sales Tax, VAT, and Excise Duty, which can be confusing for many people around the globe. The paper examines significant determinants of VAT revenue, how it is calculated, and reviews some international VAT implementation experiences. Moreover, it clarifies why the GCC countries introduced VAT as an alternative national income, the current situation, and the expected VAT implementation date. The study concludes that most businesses should be prepared for VAT and VAT registration and achieves the government's requirements to comply with VAT laws and regulations. Additionally, the paper highlights that UAE and other GCC countries are taking giant steps in economic income diversification. The GCC governments follow a complete restructuring finance strategy supported by the economic alliance to identify and face their challenges. The introduction of VAT was one of the significant decisions, and they carefully planned every step to ensure the success of introducing the tax to their countries.
Proposition of Rubustness Indicators for Immersive Content Filtering

With the full-fledged service of mobile carrier 5G networks, it is possible to use large-capacity, immersive content at high speed anytime, anywhere. It can be illegally distributed in web-hard and torrents through DRM dismantling and various transformation attacks; however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. Since applying existing 2D filtering techniques to immersive content directly is not possible, in this paper we propose a set of robustness indicators for immersive content. The proposed indicators modify and enlarge the existing 2D video robustness indicators to consider the projection and reproduction method, which are the characteristics of immersive content. A performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more is achieved in about 3 s of execution time.
Prototypical Examples of Hate Speech

Chapter 2 identifies prototypical examples of hate speech and seeks to explain what makes them such. Section 2.2 lists the original examples of hate speech cited in Mari Matsuda’s seminal article on the legal concept. We then explain how, even though the ordinary and legal concepts of hate speech share paradigmatic examples, the ordinary concept now has its own extended body of exemplars. Section 2.3 attempts to plot the complex pattern of overlapping and criss-crossing similarities among these exemplars. Section 2.4 looks in more depth at one of the paradigmatic examples of hate speech, namely racial slurs such as ‘nigger’. We highlight similarities it shares with other prototypical examples of hate speech. Finally, Section 2.5 defends a particular account of what it means for a new example to have enough similarities with exemplars to count as hate speech. If there are enough similarities across at least four out of five of the distinguishing qualities of target, style, message, act, and effect, then this conceptually justifies applying the phrase ‘x is also hate speech’ to the new example. We dub this the global resemblance test.
Simulation of Cognitive Electronic Warfare System With Sine and Square Waves

Today’s Electronic Warfare (EW) receivers need advanced technology to achieve real-time surveillance operations. Dynamic and intelligent systems are required for UAVs and other airborne applications. The airborne Electronic Warfare systems must be knowledge-based systems, learning from the threat scenario with highly integrated capabilities to detect, react, and adapt to radar threats in real-time. Artificial intelligence is a machine-dependent process, by adapting certain rules and logic supported by human intelligence, AI can be used for cognitive processing. Cognitive signal processing is required for making the system autonomous and dynamic in nature. Military action on radar signatures requires a set of commands to be executed dynamically with the help of the proposed EW system. It is proposed to design and develop a cognitive EW architecture and simulation of machine learning that combines neural network architecture with the help of sine and square waves as input. This paper presents the Cognitive signal processing for EW systems with Neural Network, Recurrent Neural Network (RNN), Machine learning (ML), and Deep learning (DL) techniques with their simulation with sine and square waves.
A Decision-Making Platform for Educational Content Assessment Within a Stakeholder-Driven Digital Educational Ecosystem

The paper discusses the features of the implementation of a digital decision-making platformfor evaluating educational content as part of a stakeholder-driven digital educationalecosystem. The authors propose a methodology of business process digitalization that considersa three-stage quality evaluation of educational resources regarding their content, as intraditional textbooks, digital educational resources, e-learning content, curricula, etc. The digitaldecision-making platform (DDMP) is at the core of the examination of educational contentand resources through interactive voting by experts, which allows multi-criteria evaluation.DDMP is designed to provide experts with digitally intelligent services for expert assessmentand objective decision-making, as well as monitoring the assessment process. The developeddigital platform is integrated into any learning management system (LMS) by contributing tothe improvement of a digital educational ecosystem.