Guomin Chen

The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data. This paper explores data and knowledge condensation methods—such as coreset selection, data compression, knowledge distillation, and dataset distillation—within the context of traffic classification tasks. It clarifies the relationship between these techniques and network traffic classification, introducing each method and its typical applications. This paper also outlines potential scenarios for applying each condensation technique, highlighting the associated challenges and open research issues. To the best of our knowledge, this is the first comprehensive summary of condensation techniques specifically tailored for network traffic classification tasks.

This research investigates the factors and configurations influencing the intention of primary and secondary school students to use online education. Employing the Technology Acceptance Model (TAM), the research framework incorporates considerations of external environmental factors, extending the model. Methodologically, Structural Equation Modeling (SEM) is utilized for path analysis, supplemented by fuzzy-set qualitative comparative analysis to comprehensively explore the influencing mechanism of primary and secondary school students' intentions to use online education. This study collected 423 valid questionnaires through stratified random sampling from urban and rural schools, with the sample comprising 250 secondary school students and 173 primary school students. Firstly, the SEM analysis reveals significant positive relationships between perceived usefulness, external support, information and communication technology atmosphere, satisfaction, and intention to use. Secondly, further configuration analysis explains the impact of different condition combinations on primary and secondary school students' intention to use online education, pinpointing perceived usefulness and satisfaction as core conditions influencing intention. The results indicate that enhancements in usefulness, satisfaction, and the external environment can stimulate students' active participation in and acceptance of online education.

This article presents a comprehensive analysis of the application and effect of ChatGPT, an advanced AI chatbot model, on global information management across various industries such as healthcare, industry, education, and more. Leveraging a dataset obtained from the Scopus database encompassing research papers from 2022 to 2023, this study investigates the influence of ChatGPT by examining publisher impact, authorship patterns based on Lotka's Law, country-specific scientific production, and keyword distribution. The analysis sheds light on prominent publishers, prolific authors, geographic distribution of research contributions, and prevailing research themes. By understanding the impact of ChatGPT in these sectors, this research contributes to the advancement of knowledge and facilitates informed decision-making regarding the responsible and effective utilization of artificial intelligence (AI) chatbot technologies in global information management.

The Smart Education of China, a large education data platform for the aggregation and application of high-quality educational resources, is vital for China to stimulate the balanced development of education and to reduce students’ learning burden. This study focuses on the long-term mechanism of the Smart Education of China, adopting TOE framework and qualitative comparative analysis research method to investigate the technological conditions, environmental conditions, organizational conditions and content conditions of the Smart Education of China before summarizing four usage paths of the Smart Education of China. With regard for the existing issues, the study concludes that for the sake of a long-term mechanism the Smart Education of China needs to solve four problems, including unfinished construction, content-insufficient platform, popular but ineffective application, and substandard quality.

Based on the role separation scenario in which students need parental support, this paper explores the effect of parents’ attitude on secondary school students’ online learning. Through structural equation model analysis and regression analysis of 745 valid responses to a questionnaire, the data results show that parents’ subjective dimension includes perceived gain and perceived loss, and social factor dimension includes teachers’ influence and online comments. Perceived value is the key influencing factor of parents’ attitude towards secondary school students in online learning platform. Perceived usefulness and platform information influence parents’ attitude positively and significantly, while perceived risk influences parents’ attitude negatively and significantly. In the dimension of social factors, teachers’ influence positively influences parents’ attitude, and online comments modulate the influence of perceived value on parents’ attitude.

The purpose of this study is to conduct a mixed research from the perspective of customers perceived value and objective situational factors. The online learning platform for middle school students has a special situation of users (students) using and customers (parents) paying. When it studies the influencing factors of customers (parents) willingness to pay, it puts aside the interference of users’ using influencing factors and conducts a separate study. Firstly, the exploratory research based on the grounded theory carries out category extraction and model construction. Secondly, through empirical research to identify the specific relationship between the variables, we finally get the specific influencing factors of perceived value that affect customers’ willingness to pay. In objective situations, social influence directly affects customers’ willingness to pay. Online comments play a positive moderating role in the impact of perceived value on willingness to pay.

People’s learning style is constantly changing together with the rapid development of information technology. Nowadays, online learning has become a common learning style, but online learning resource providers are also faced with the charging issue. Besides, a large number of studies have been made by scholars at home and abroad in this field. In this paper, the influence of middle school students’ self-efficacy on online learning resource purchase intention is analyzed, and the study comprises two stages. To be specific, in the first stage, the interview method is adopted to acquire related materials, and the grounded theory is also followed to reconstruct the theoretical research framework; in the second stage, the theoretical framework is empirically verified by the questionnaire. Then, it is found that middle school students’ self-efficacy affects the identity about online learning resources’ perceived quality and their purchase intention for online learning resources. At the same time, the teacher-student relation may also reinforce students’ purchase intention for online learning resources. Thus, it can be seen that online learning resources’ perceived quality and the intimate teacher-student relation have significant influence on middle school students’ purchase intention.