The Influence of Online Learning Behavior on Learning Performance

Online education is a significant part of information education. It is an effective way to uncover online learning mechanisms and improve the quality of online teaching by exploiting the behavioral data of online learning platforms for learning performance prediction and analysis. In this paper, we focus on the learner’s learning behavior in an online teaching scenario and explore the predictive effectiveness and impact mechanism of each behavioral feature by building a predictive model based on a machine learning algorithm. Experimental results show that three behavioral characteristics, namely the number of visits to course materials, lecture review time, and assignment, intensively influence learning performance. By comparing various machine learning algorithms, it is found that the random forest algorithm has better prediction results

Double spike Dirichlet priors for structured weighting

Assigning weights to a large pool of objects is a fundamental task in a wide variety of applications. In this article, we introduce a concept of structured high-dimensional probability simplexes, whose most components are zero or near zero and the remaining ones are close to each other. Such structure is well motivated by 1) high-dimensional weights that are common in modern applications, and 2) ubiquitous examples in which equal weights -- despite their simplicity -- often achieve favorable or even state-of-the-art predictive performances. This particular structure, however, presents unique challenges both computationally and statistically. To address these challenges, we propose a new class of double spike Dirichlet priors to shrink a probability simplex to one with the desired structure. When applied to ensemble learning, such priors lead to a Bayesian method for structured high-dimensional ensembles that is useful for forecast combination and improving random forests, while enabling uncertainty quantification. We design efficient Markov chain Monte Carlo algorithms for easy implementation. Posterior contraction rates are established to provide theoretical support. We demonstrate the wide applicability and competitive performance of the proposed methods through simulations and two real data applications using the European Central Bank Survey of Professional Forecasters dataset and a UCI dataset

Research on Poverty Alleviation of County E-Commerce -A Case Study of Luotian County

Since the introduction of the e-commerce poverty alleviation, e-commerce poverty alleviation has been carried out in the counties. Luotian County is a demonstration county in e-commerce poverty alleviation, which is at the forefront of poverty reduction counties in Hubei Province. Playing the leading role of the county level government, Luotian County established the e-commerce industrial park and the village-level service station, developed the e-commerce industry and opened up the rural logistics. Giving full play to the main role of the market, the county also focused on developing agricultural industrialization enterprises, expanded sales channels with e-commerce, drove the development of featured industries, promoted the growth of the industrial chain, and shared the benefits of the poor households. Besides, it also promoted e-commerce tourism and helped poverty-stricken families get rid of poverty. The paper mainly analyzes the main practice and achievements of e-commerce in Luotian County and summarizes the experience of Luotian e-commerce in poverty alleviation, with a view to providing reference for other counties and regions

Study on Social Network for College Students\u27 Job Hunting

SNSs recruitment has caused the change of the traditional recruitment mode due to its advantages such as broad audience, quick information transmission, low recruitment cost and good interpersonal interaction. Through questionnaire survey, the paper implements on a study on the situation that the college students use social networks to find jobs. It is found that social network platforms are popular with college students, but it is not widely used in social job hunting platforms. What is more, the job hunting effect is not obvious. Meanwhile, the user information is prone to leakage, and the job hunting information provided is in poor quality. The paper proposes that the social network job hunting platforms should take measures to perfect the service content, strengthen the technical level and improve the information security for the college students. Additionally, the college students should also make reasonable choices and correctly utilize the social recruitment platform to protect the personal information security and improve the job hunting efficiency

Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection

Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider. Previous research on secure reasoning using multi-party computation (MPC) has proven to be impractical for LLM applications due to its time-consuming and communication-intensive nature. While lightweight anonymization techniques can protect private information in prompts through substitution or masking, they fail to recover sensitive data replaced in the LLM-generated results. In this paper, we expand the application scenarios of anonymization techniques by training a small local model to de-anonymize the LLM's returned results with minimal computational overhead. We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization, respectively. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models. Furthermore, we conduct experiments to evaluate HaS's usability in translation and classification tasks. The experimental findings demonstrate that the HaS framework achieves an optimal balance between privacy protection and utility

Tree-ring stable carbon isotope-based June-September maximum temperature reconstruction since AD 1788, north-west Thailand

The first study of tree-ring stable carbon isotopes in Thailand has demonstrated that stable carbon isotope in northwestern Thailand represents a promising proxy for the temperature reconstruction of core-monsoon periods. A tree-ring delta C-13 chronology was constructed based on four cores covering the period of 1788-2013. After removing the long-term decreasing trend reflecting atmospheric CO2 concentrations, the Delta C-13 chronology was able to capture both temperature and hydro-climate signals Delta C-13 chronology showed particularly strong and significant negative correlation (r = -0.62, p < 0.0001) with June-September maximum temperature (CRU TS 3.24). The maximum temperature was reconstructed, which explained 37.8% of the variance in the instrumental maximum temperatures over the period of 1901-2013. The maximum temperature reconstruction revealed that four cooler and three warmer periods, as well as a slightly increasing temperature trend, occurred during the late seventeenth to mid-eighteenth centuries, which were followed by severe temperature fluctuations during the twentieth century century. While the sea surface temperature anomaly in the Indian Ocean might not affect the maximum temperature, its unstable relationship with the El Nino-Southern Oscillation (ENSO) was detected. In addition, a close relationship was observed between the maximum temperature and ENSO during the negative phase of the Pacific Decadal Oscillation (PDO), but this relationship was lost during the positive phase of the PDO