文本分类中Prompt Learning方法研究综述

文本分类是自然语言处理中的一项基础任务,在情感分析、新闻分类等领域具有重要应用。相较于传统的机器学习和深度学习模型,提示学习可以在数据不足的情况下通过构建提示来进行文本分类。近年来,GPT-3的出现推动了提示学习方法的发展,并且在文本分类领域取得了显著的进展。对以往的文本分类方法进行简要梳理,分析其存在的问题与不足;阐述了提示学习的发展进程,以及构建提示模板的方法,并对用于文本分类的提示学习方法研究及成果进行了介绍和总结。最后,对提示学习在文本分类领域的发展趋势和有待进一步研究的难点进行了总结和展望。

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Analysis of the Exploration of Hematology Integration Education Model Guided by“Social Learning Theory”

Diseases of the blood system are highly complicated and involve many aspects.This article aimed to put forward the necessity of integrating quality and professional education,the necessity of learning logic,and the importance of establishing an integrated medical education model when teaching about blood system diseases.According to the requirements of the new medical science,this article puts forward the integration of an“online+offline+clinical”medical education mode based on the social learning theory and a learning evaluation mode based on medical literacy.The fundamental task of cultivating human beings into clinical medical talents with professional ethics,independent learning abilities,interpersonal communication abilities,complex problem-solving abilities,innovative consciousness,and critical thinking was implemented.This article aims to propose an improved construction plan to create a new and improved teaching system in medicine.

Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Learning Bayesian network parameters under new monotonic constraints

When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.

Monitoring and Early Warning of New Cyber-Telecom Crime Platform Based on BERT Migration Learning

The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.

A Study on the Instructional Design of Interdisciplinary Thematic Learning in Middle School Physical Education Based on the CASES-T Model

With the deepening of educational reform,interdisciplinary thematic learning,as an emerging educational model,has become a focus of attention in the field of educational research.Based on the STEM(science,technology,engineering,and mathematics)education concept and CASES-T(Content,Activity,Situation,Evaluation,Strategy-Target)model,this study provides a theoretical basis for the teaching design and implementation of interdisciplinary thematic learning in middle school physical education.Through the analysis of specific interdisciplinary thematic learning cases,it aims to provide theoretical support and practical guidance for the reform of middle school physical education through the CASES-T model-based interdisciplinary thematic teaching design research in middle school physical education,in order to enhance students’learning effects,cultivate core literacy in physical education,and promote students’all-round development.

Comparison of Higher Education between China and New Zealand in Learning

At present,many students finish high school and enroll the university education.University learning is very important education part in students'life,they are thinking about themselves,improve their identity and values,change their aims,produce knowledge and take skills into the future life and working.University offer different courses to learn,they interest in Arts,Commence and Science and satisfy all students'needs.There is an increasing number of Chinese students choose to study in New Zealand because they believe it is a safe place,it is cheaper than the other countries,and because New Zealand is an English speaking country.Most of the New Zealand universities offer foundation courses which give international students the opportunity to bring their English language skills and academic performance up to university entrance standard.This study is to find out Chinese and New Zealand higher education with some different aspects from curriculum,learning environment and evaluation.

Preparing EFL Learners for Native English-Speaking Context Through Vocabulary Learning： Two Chinese Cases in New Zealand

While numerous studies in English as a second/foreign language （EFL） have examined vocabulary learning and teaching in the perspective of theories and practical tips, there is a paucity of research on the impact of high-frequency words learning on preparing new EFL residents for the life in English-speaking countries. In order to fill this gap, this study draws on the experience of two EFL learners in New Zealand （NZ）, so as to explore the effectiveness of a 16-week daily-English-focused vocabulary learning program, which might generate useful implications about the effective adaption of new EFL residents to their target countries.

A new strategy of integrating American culture with English teaching ＆ learning by using VOA Special English feature programs

The paper mainly studies a new way of combining American culture with English language teaching and learning by means of the VOA Special English feature programs. It is based on a 5-year period of teaching practice, experience and reflection. This interactive, innovative, productive and student-centered approach is a far cry from the rigid, old, unproductive and teacher-centered approach of teaching British and American culture without combining language learning and practice as is commonly used here in Chinese colleges. The author also throws some light on how to use on-line teaching resources and how to cultivate independent learning on the part of the students he has worked with.

Pathways to Enhance New Quality Productivity in New Liberal Arts Education Through Artificial Intelligence

This paper explores the pathways through which artificial intelligence(AI)enhances new quality productivity in new liberal arts education.By analyzing the role of AI in personalized learning,interdisciplinary integration,and the application of virtual reality/augmented reality technologies,it reveals how AI technology promotes the development of students’innovative capabilities and productivity in the context of new liberal arts education.The study shows that AI is not only a technical tool but also a driving force for transforming educational models and fostering knowledge innovation.Further exploration of the deep integration of AI and new liberal arts education is necessary to promote comprehensive social progress.

The Impact of Online Courses towards Students in Autonomous Learning Based on New Media Technologies

In the 21st century,the rapid development of online technology has dramatically transformed people’s way of lives.The emergence of high-tech products has also boosted modern education to embrace informationization and virtualization.With the promotion and development of online courses,autonomous learning is now emerging among students in colleges and universities.If they want to learn relevant professional knowledge,they could use networking and information technology with relevant devices.This learning method could not only impact traditional education but also facilitate students to explore new ways to learn autonomously.This paper is to discuss the impact of online courses towards students in autonomous learning by analyzing its current learning situation,the feature of this new form and its effects towards students.

Research on the Optimization Path of College Counselor Team Construction in the New Era

In the new era,the team of college counselors,as an indispensable and important component of the higher education system,bears multiple responsibilities such as ideological and political education,daily student management,mental health counseling,and employment and entrepreneurship guidance.With the rapid development of society and the diversified transformation of student groups,the construction of the counselor team is facing new challenges and opportunities.In-depth research on the optimization path of the construction of the college counselor team is not only significant for improving the overall quality and work efficiency of the counselor team but also directly related to the completion effect of the fundamental task of cultivating morality and talents in colleges and universities,as well as the achievement of the goal of cultivating more high-quality talents for the country.Based on this and the author’s work experience,this paper first elaborates on the necessity of the construction of the college counselor team,and then conducts a deep analysis of its optimization path and strategy,aiming to provide a useful reference for the professionalization and professional development of the college counselor team,thereby promoting the sustainable development of higher education and the comprehensive growth of students.

LEARNING NEW THINGS

Scene:in the classroom(Baby Alien sits on the desk.He sees some thing on the desk.He doesn’tknow them.So he asks his new friends.)

利用E-learning创建研究型大学教学新体系——以“北大教学网”为例

文章研究了北京大学E-learning的发展状况,将其划分为自主探索、多平台共用和全面推进三个阶段。北大E-learning建设在不同阶段各有特点,但总体上展示了研究型大学在教学改革上向世界一流大学跟进的发展轨迹,文章总结了北大教学网建设的特点,包括部门协作、队伍建设、培训体系、项目案例和宣传推广等。最后,提出了利用ICT构建研究型大学E-learning的若干设想与建议。

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

E-Learning与英语教师新素养

E-Learning作为一种新的教学与学习模式给英语教学带来了深刻的变革,英语教师要适应这种变革,就必须了解E-Learning的特征,明确自己在E-Learning教育中应具备的新素养。

Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.

E-Learning环境下的高校体育教学新模式探讨

互联网已经给许多领域带来了深刻的变革。基于互联网发展起来的E-Learning是教育领域利用Internet技术、多媒体技术的全新教育学习手段,在我国的教育行业正在推广与普及。传统高校体育教学无论在教学理论、教学手段以及教学效果等方面都存在较多问题,本文从新视角探讨高校体育教学面对新形势的改革,并提出了有价值的教学新模式。