Integrating knowledge modeling and multiagent systems
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Integrating Arts and Sciences inEducationIntegrating arts and sciences in education has been a topic of debate for many years. While some argue that the two disciplines should be kept separate, others believe that integrating them can lead to a more well-rounded education. In this response, we will explore the benefits of integrating arts and sciences in education from multiple perspectives, including the impact on student learning, the development of critical thinking skills, and the potential forinterdisciplinary collaboration. From the perspective of student learning, integrating arts and sciences in education can provide a more holistic approach to learning. By incorporating both disciplines, students can develop a deeper understanding of the world around them. For example, studying the science behind the colors of the sunset can be enhanced by creating a piece of art that reflects those colors. This hands-on approach to learning can engage students in a way that traditional classroom instruction may not. Furthermore, integrating arts and sciences can help students develop critical thinking skills. By engaging in both disciplines, students are encouraged to think creatively and analytically. This can help them develop problem-solving skills that are essential in both the arts and sciences. For example, a student studying the physics of sound may also be challenged to create a piece of music that demonstrates their understanding of those principles. This type of interdisciplinary approach can help students develop a more well-rounded set of skills that can be applied in a variety of contexts. In addition, integrating arts and sciences in education can also foster interdisciplinary collaboration. By bringing together students and educators from different disciplines, schools can create a more dynamic learning environment. For example, a science teacher and an art teacher may collaborate on a project that explores the intersection of their respective disciplines. This type of collaboration can help students see the connections between different areas of study and can help them develop a more holistic understanding of the world. However, there are also challenges to integrating arts and sciences in education. One of the main challenges is the need for educators to have a deep understandingof both disciplines. This can be difficult for some educators who may have specialized in one area or the other. Additionally, there may be logistical challenges in terms of scheduling and resources. For example, schools may need to invest in materials and equipment that are suitable for both arts and sciences, which can be costly. Despite these challenges, the benefits of integrating arts and sciences in education are clear. By providing a more holistic approach to learning, fostering critical thinking skills, and promoting interdisciplinary collaboration, schools can create a more dynamic and engaging learning environment for students. As we continue to explore the best practices for integrating arts and sciences in education, it is important to consider the potential impact on student learning and the development of well-rounded individuals.。
Integrating STEM Education into the CurriculumIntroductionSTEM education, which stands for Science, Technology, Engineering, and Mathematics, has gained increasing importance in modern education. It emphasizes the integration of these subjects to develop critical thinking, problem-solving skills, and innovation among students. In this document, we will explore the benefits of integrating STEM education into the curriculum and discuss practical strategies for implementation.Benefits of Integrating STEM Education1.Promoting Critical Thinking: STEM education encourages studentsto question, analyze, and evaluate information critically. It fosterslogical reasoning skills and helps students become active learners.2.Enhancing Problem-Solving Skills: The interdisciplinary nature ofSTEM education teaches students to approach problems holistically.They learn to apply scientific and mathematical concepts to real-world challenges.3.Encouraging Creativity and Innovation: STEM education nurturescreativity by providing opportunities for open-ended exploration and experimentation. Students are encouraged to think outside the box and develop innovative solutions.4.Preparing Students for Future Careers: By integrating STEMsubjects into the curriculum, students gain valuable knowledge and skills that are highly sought after in today's job market. Theydevelop a strong foundation for pursuing careers in science,technology, engineering, or mathematics.5.Fostering Collaboration: STEM education promotes collaborativelearning as students engage in group projects and solve complex problems together. This prepares them for teamwork in futureprofessional settings.Strategies for Implementing STEM Education1.Cross-Curricular Integration: Integrate STEM concepts acrossdifferent subjects rather than teaching them in isolation. Forexample, teach math through scientific experiments or usetechnology to enhance hands-on learning experiences in physics or biology classes.2.Project-Based Learning: Assign projects that require students toapply their knowledge from various STEM disciplines to solve real-world problems. This approach encourages critical thinking andproblem-solving skills development.3.Experiential Learning: Provide students with hands-on experiencesand opportunities to engage with STEM concepts in real-worldsituations. Field trips, guest speakers, and interactive workshops can bring STEM education to life.4.Teacher Professional Development: Offer professionaldevelopment programs for teachers to enhance their knowledgeand skills in delivering effective STEM education. This includestraining on integrating technology tools, designing inquiry-based lessons, and fostering a growth mindset among students.munity Partnerships: Collaborate with local businesses,universities, or research institutions to organize mentorshipprograms or guest lectures by professionals working in STEM fields.These partnerships can provide students with valuable insights into potential career paths.ConclusionIntegrating STEM education into the curriculum has numerous benefits for students. It develops critical thinking, problem-solving skills, creativity, and collaboration abilities while preparing them for future careers in science, technology, engineering, or mathematics. Byimplementing strategies such as cross-curricular integration, project-based learning, experiential learning, teacher professional development, and community partnerships, schools can create a comprehensive STEM educational experience that empowers students for success in the 21st century.。
knowledge distillation of large language model1. 引言1.1 概述在当前的人工智能领域中,大型语言模型的发展和应用日益受到关注。
这些模型在自然语言处理任务中取得了巨大的成功,如机器翻译、文本生成、问答系统等。
然而,由于这些模型庞大且参数众多,训练和应用过程需要消耗大量的计算资源和时间。
为了解决这一挑战,知识蒸馏成为了应对大型语言模型优化的一种有效方法。
该技术旨在通过将复杂庞大的语言模型的知识转移给更小、更高效的模型,以达到加速推理和降低资源消耗的目的。
1.2 文章结构本文将围绕知识蒸馏技术与大型语言模型进行优化展开论述。
文章首先介绍了知识蒸馏概念及其相关方法,并探讨了知识蒸馏在不同领域中的应用情况。
接下来,本文详细介绍了大型语言模型的基本背景以及其训练过程和实际应用案例。
在此基础上,文章将重点探讨基于知识蒸馏的大型语言模型优化方法。
我们将综述各种模型压缩技术,并深入剖析知识蒸馏与大型语言模型优化之间的关系。
同时,为了更好地阐述这一主题,本文还会通过实践案例分析,详细讲解如何利用知识蒸馏技术来优化大型语言模型。
最后,文章将总结所得要点,并对未来研究方向给出展望和建议。
1.3 目的本文的目标是全面探讨知识蒸馏技术在优化大型语言模型中的应用。
通过论述知识蒸馏方法和大型语言模型的基本概念、训练过程以及相关应用案例,旨在帮助读者深入理解知识蒸馏与大型语言模型优化之间的关联和意义。
同时,结合实践案例分析,为读者提供一些有益的实施指导和启示。
最终,我们希望能够为进一步探索和发展知识蒸馏技术以及优化大型语言模型提供有价值且具体可行的建议。
2. 知识蒸馏的概念:2.1 知识蒸馏定义知识蒸馏是一种模型压缩技术,旨在将较大、复杂的模型从教师模型转化为更小、更轻量级的学生模型。
通过这种方式,知识和经验可以被传递给学生模型,从而提高其性能和泛化能力。
这个概念最初由Hinton等人于2015年提出,并已在许多领域中得到广泛应用。
黑龙江科学HEILONGJIANG SCIENCE第12卷第5期2021年3月Vol. 12Mar. 2021环境类专业“三融合"培养模式创新与实践潘法康,张瑾,唐建设,伍昌年(安徽建筑大学环境与能源工程学院,合肥230601)摘要:为了培养创新型、复合型、应用型环境类专业人才,对环境类专业"三融合”培养模式的创新与实践展开探讨。
针对高等教育同质化倾向严重、结构性矛盾突出等问题,结合近年来对环境类专业人才培养模式的实践探索,提出了学科交叉融合、产教融合、科 研教学融合的"三融合”人才培养模式,提升了人才培养质量。
环境类专业"三融合”人才培养模式的探索和实践,取得了良好的教 学成果,提升了人才培养质量,为应用型高校人才培养和教育教学改革提供了参考。
关键词:环境类专业;三融合;培养模式中图分类号:G642.0文献标志码:A 文章编号:1674-8646(2021)05 -0038 -03Innovation and Practice of Three Integration Training Mode of Environmental SpecialtiesPan Fakang, Zhang Jin , Tang Jianshe , Wu Changnian(School of Environment and Energy Engineering , Anhui Jianzhu University , Hefei 230601 , China)Abstract : In order to cultivate innovative , compound and applied environmental talents as the goal orientation , thepaper explores the “ three integration " talent training mode of environmental specialty ・ According to the problems of severe homogenization and structural conflicts of the higher education , and through combining with the practicalexploration of talent training mode of environmental specialty in recent years , the research proposes the talent trainingmode of u three integration n , including integration of interdisciplinary, industry and teaching, scientific research andteaching , in order to improve talent training quality ・ The exploration and practice of u three integration n talent training mode of environmental specialty has achieved good teaching effect , improved talent training quality , and providedreferences for talent cultivation and teaching reform of applied colleges ・Key words : Environmental specialty ; Three integration ;高校要着重培养创新型、复合型、应用型人才,为高等教育人才培养指明方向⑴。
大模型知识蒸馏视觉模型英文文档:Title: Distilling Visual Models with Large ModelsIn recent years, the advancement of large models in the field of artificial intelligence has been remarkable.These models, with their massive scale and profound learning capabilities, have pushed the boundaries of what is possible in various domains, including computer vision.However, despite their remarkable performance, these large models are often computationally expensive, requiring significant resources for both training and inference.To address this issue, researchers have turned to knowledge distillation, a technique that allows the transfer of knowledge from a large, expensive model to a smaller, more efficient one.In the context of visual models, knowledge distillation has shown great potential in reducing the model size and improving inference speed without compromising too much on performance.One approach to knowledge distillation in visual models involves using a teacher-student framework.The teacher model, which is usually a large and powerful model, is trained on a large dataset to learn the underlying patterns and representations of the visual data.The student model, on the other hand, is a smaller and less resource-intensive modelthat is trained to mimic the teacher model"s behavior.Another technique that can be used in knowledge distillation is the use of attention mechanisms.These mechanisms help the student model focus on the most important parts of the input data, thereby reducing the amount of information that needs to be processed and improving efficiency.In conclusion, knowledge distillation is a promising technique for distilling visual models with large models.By transferring knowledge from a large and powerful model to a smaller and more efficient one, researchers can create visual models that are more suitable for deployment in real-world applications, where resources may be limited.中文文档:标题:利用大模型进行视觉模型知识蒸馏近年来,人工智能领域的大型模型取得了显著的进步。
简述howto模型和知识效应库解题流程英文版A Brief Introduction to the Problem-Solving Process of the HowTo Model and Knowledge Effect LibraryIn the realm of educational technology, the HowTo model and Knowledge Effect Library have emerged as powerful tools for enhancing learning outcomes. This article aims to provide a concise overview of the problem-solving process associated with these two innovative methodologies.The HowTo model is an instructional framework that guides learners through the process of achieving a specific goal or task. It emphasizes a step-by-step approach, breaking down complex tasks into manageable chunks. This model encourages active engagement and hands-on experience, allowing learners to apply their knowledge in practical settings.The Knowledge Effect Library, on the other hand, is a repository of educational resources designed to complementthe HowTo model. It provides a wealth of information, examples, and case studies that illustrate the application of knowledge in real-world scenarios. The library encourages learners to explore, experiment, and reflect, fostering a deeper understanding of the subject matter.When combined, the HowTo model and Knowledge Effect Library form a powerful problem-solving framework. Here's a step-by-step breakdown of the process:Identification of the Problem: The learner identifies a specific problem or challenge that requires solving. This could be an academic question, a real-world issue, or a personal goal.Formulation of a Plan: Using the HowTo model, the learner formulates a step-by-step plan to address the problem. This plan outlines the necessary steps, tools, and resources required for successful completion.Execution of the Plan: The learner proceeds to execute the plan, applying the knowledge and skills learned through theHowTo model. This phase involves hands-on experience and active engagement with the material.Analysis and Reflection: After executing the plan, the learner analyzes the results and reflects on the process. This allows them to identify any areas of improvement and gain insights into the effectiveness of their approach.Utilization of the Knowledge Effect Library: Throughout the process, the learner can refer to the Knowledge Effect Library for additional information, examples, and case studies. This resource acts as a valuable reference, enhancing the learner's understanding and ability to apply knowledge effectively.By leveraging the HowTo model and Knowledge Effect Library, learners can approach problems with a structured and informed mindset. This integrated approach not only improves learning outcomes but also fosters a culture of continuous learning and improvement.中文版简述howto模型和知识效应库解题流程在教育技术领域,howto模型和知识效应库已成为提高学习成果的有力工具。
Learning Models for Multi-Source Integration* Sheila Tejada Craig A. Knoblock Steven MintonUniversity of Southern California/ISI4676 Admiralty WayMarina del Rey, California 90292{tejada,knoblock,minton}@AbstractOne issue involved in accessing multiple heterogeneous information sources is how to integrate the retrieved data. SIMS, an information mediator, handles this problem by mapping the data in each information source into a common information or domain model. This model defines the relationships between the data in the different sources, so that the data can be integrated properly. Human experts who are familiar with the contents of the information sources are required to generate the model and perform the mapping of the sources. Automating this process of constructing the model of the information sources would be more convenient and efficient. Assuming that the structure of the information sources is a set of tables, the first step in this process is mining the tables to discover relationships in the data. These relationships are then used to further develop the model by helping to determine the necessary classes, superclass/subclass relationships, and associations.IntroductionBecause of the growing number of information sources available through the internet there are now many cases in which information needed to solve a problem or answer a question is spread across several information sources. In order to obtain the desired information, multiple sources need to be accessed, and the retrieved data then has to be integrated. An example to illustrate this problem of multi-source integration is when given two information sources, one source containing information about comic books and the other about super heroes, and you want to ask the question "Does Spiderman appear in a Marvel comic book?" To answer this question both of the information *This research reported here was suppoerted in part by Rome Laboratory of the Air Force Systems Command and the Advanced Research Projects Agency under Contract Number F30602-94-C-0210, and in part by an Augmentation Award for Science and Engineering Research Training funded by the Advanced Research Projects Agency under Contract Number F49620-93-1-0594. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing the officail opinion or policy of RL, ARPA, the U.S. Governments, or any person or agency connected with them.sources need to be accessed.In this situation it is necessary to have information about the relationships of the data within each source, as well as between sources, to properly access and integrate the data retrieved. The SIMS (Ambite et. al. 1995) information broker captures this type of information in the form of a model, called a domain model. Because all the information sources map into the domain model, the domain model serves as an interface which can provide the user access to multiple sources. The user specifies transactions to be performed using the terms of the domain model, and is not required to have knowledge about specific sources.In the next section we will describe the domain model in more detail, and then discuss our basic approach to address this problem of learning models for multi-source integration. Next, we will present our preliminary work that we have conducted on creating abstract descriptions of the data in order to mine for existing relationships. We will then provide a discussion of related work, and conclude by describing the future directions of our work.Model DescriptionThe diagram shown in Figure 1 is a partial model of both the Comic Book and Super Hero information sources.This model defines the relationships of the data within the two sources and also shows their interaction. The ovals in the diagram represent classes and the lines describe the relationships between the classes. There are two types of classes -- a basic class and a composite class. Members of a basic class are single-valued elements, such as strings or numbers; while the members of a composite class are objects which can have several attributes.In this example Comic Book Illustrator and Super Hero Creator are both basic classes which contain artists' names. Comic Book, Marvel Comic Book, and Super Hero are all represented as composite classes. The arrows represent the attributes of the objects, e.g. Artist, Title, Name. The Artist arrow between Super Hero and Super Hero Creator means that the values of that attribute belong to the set of names of Super Hero Creator. The thick lines in the model represent superclass/subclass relationships,e.g the names of Super Hero Creator are a subset of Comic Book Illustrator. The Contains relationship between Comic Book and Super Hero is an association link, which links a Comic Book object with a Super Hero object.This is like an attribute link, except that it connects two composite classes.Learning the ModelPresently, domain models are manually generated by human experts who are familiar with the data stored in the sources. Automation of this task would improve efficiency and the quality of the model. We have conducted preliminary work in automating the process of model construction. The basic idea for our approach is that we examine the data in the information sources to extract the relationships needed to create the domain model. Learning the model is an iterative process which involves user interaction. The user can browse the generated model to make corrections or specify an information source to be added or deleted. The model proposed to the user is generated by heuristics we have developed to derive the necessary relationships from the data. The user's input is incorporated in generating this model. The heuristics that we have applied involve creating abstract descriptions of the data which is described further in the next section.Abstract DescriptionsWe have assumed that the data is stored as tables, and that values within a column of a table are related to each other. An abstract description of a column of data defines the basic class for that collection of data. Properties or features of the data, such as type and range, are contained in the abstract descriptions. These descriptions are then used to help determine the superclass/ subclass relationships that exist between the basic classes. The descriptions can constrain the number of possible related classes. Potentially, all classes would need to be checkedfor a superclass/subclass relationship, but now only those classes that have similar descriptions are tested. One of the main reasons to generate these abstract descriptions of the data are so that they can be used to help determine composite classes and their relationships.Generating Abstract DescriptionsAn example of a data table is shown in Figure 2. A abstract description is determined for each column or attribute of the table. In this table the set of attributes are Name, Artist, and Super Power. A set of features is computed for each of these attributes which characterizes the basic classes to which each attribute belongs. The set of features that describes the basic classes also helps in discovering the relationships between basic classes. The relationships between basic classes should reflect the relationships that exist between the attributes.Name Artist Super PowerSpiderman Stan Lee Spider SenseSpawn Todd McFarlane ImmortalityCyclops Stan Lee X-ray Power ......Super Hero TableFigure 2Shown in Figure 3 is the table that contains the computed set of features for the attributes given in Figure 2. The set of features used to characterize each attribute in this example is Unique, Type, Subtype, Length, and Set of Values.Unique Type Subtype Length Set ofValues Name Yes String Alpha-betic6<=L<=9{Spawn,. . .} Artist No String Alpha-betic8<=L<=14{StanLee, . . .} SuperPowerYes String Alpha,Other11<=L<=12{SpiderSense.. .}Figure 3The Unique feature determines whether there are duplicate elements, e.g. the Name attribute does not contain duplicate values. The Type of an attribute is either string or number. The values for the feature Subtype are subtypes of string and number. The subtypes for number are integer and real. For string there are four basic subtypes: alphabetic, alphanumeric, numeric, and other. The value other refers to a string that has a least one character which is not a letter, a space or a number. The collection of the values of an attribute may be of one subtype or any combination of the four, as shown by the Super Power attribute.The Length or Range feature determines the range of the length of the set of strings or the range of the set of numbers in the attribute. For each of the attributes a set of their values are calculated by the Set of Values feature; therefore, duplicate values are removed, as is done for the Artist attribute. There are many other features which can be used to find regularities in the data, such as determining if the alphabetic strings of an attribute are words. These features represent only a subset.Relationships between Basic ClassesThe set of features shown in Figure 3 are the abstract descriptions of the attributes. These descriptions define the basic class of the attribute. The Set of Values feature provides all of the instances belonging to that basic class. The Type and Length features can be used to restrict the set of attributes that are related to a given attribute. For the attribute Name, the value for Type is string, and the value for Subtype is Alphabetic; therefore, Name can only be related to other attributes whose basic classes also have Type as string and a Subtype which includes Alphabetic. Based on the table in Figure 3 the set of related attributes whose basic classes overlap with Name's Type feature is {Artist, Super Power}. The same is now done for the Length feature of Name. There are no basic classes whose Length feature contains or is contained in the Name's Length feature, as shown by the comparisons of Length features depicted in Figure 4; therefore, the set of related attributes is empty.Fi g ure 4Next, the Set of Values feature is evaluated using the set of attributes produced by the intersection of the two previous sets of related attributes. This feature will decide the type of relationship that exists between the basic classes. The possible types of relationships are superclass/subclass, overlapping classes, and non-overlapping related classes. The Set of Values feature of Name is evaluated by testing the overlap of its values with those of an attribute in the related set.When the set of values of an attribute is a subset (or superset) of another attribute, then a superclass/subclass relationship exists between their basic classes. An example of this type of relationship can be seen in Figure 1 between the basic class Super Hero Creator of the Super Hero.Artis t attribute and the Comic Book Illustrator class of the Comic Book.Artist. The attributes are mapped into the model using this process which is repeated for each attribute in the table. For the attributes that have an overlapping or non-overlapping class relationship, a superclass of the two attributes needs to be created to properly represent their interaction. Further information needs to be computed in order to show that the set of these types of proposed relationships are valid.Related WorkThere has been similar work conducted in this area (Perkowitz & Etzioni 1995), but it has focused on learning only the attributes of the information sources. Their approach also assumes that it has instances as well as an initial model of the information to be learned from perspective information sources. Related work using mining techniques to learn a model of the information sources has been researched by Kero et al (Kero et al. 1995). Their work uses data mining to determine which attributes are related, while our work described in this paper is primarily interested in the types of the relationships that exist between the attributes.DiscussionThis is preliminary work on the automation of modeling information sources, which has focused mainly on discovering the relationships between basic classes. Further work still needs to be conducted to devise mechanisms for deciding when to generate a primitive superclass for the overlapping and the non-overlapping related basic classes. We are planning to apply statistical methods to assist in addressing these cases, as well as integrating a natural language knowledge base into this process to help determine whether classes are semantically related. The relationships between the basic classes will be useful in the later steps of the modeling process which are to determine the associations and superclass/subclass relationships between composite class.ReferencesKero, B., Russell, L., Tsur, S., & Shen W. An Overview of Database Mining Techniques. The KDD workshop in the 4th International Conference on Deductive and Object-Oriented Databases, Singapore, 1995.Ambite, J., Y. Arens, N. Ashish, C. Chee, C. Hsu, C. Knoblock, and S. Tejada. The SIMS Manual, Technical Report ISI/TM-95-428,1995.Perkowitz, M. & Etzioni, O. Category Translation: Learning to Understand Information on the Internet. The International Joint Conference on Artificial Intelligence, Montreal, 1995.。
第 21 卷 第 7 期2023 年 7 月太赫兹科学与电子信息学报Journal of Terahertz Science and Electronic Information TechnologyVol.21,No.7Jul.,2023高效无源互调建模仿真中的统计方法陈雄1,2,贺永宁3,于明1(1.南方科技大学电子与电气工程系,广东深圳518055;2.香港中文大学电子工程系,香港沙田999077;3.西安交通大学电子信息学部微电子学院,陕西西安710049)摘要:在现代大功率通信干扰问题中,无源器件的非线性杂散干扰机理最为复杂,在现代大功率高密度的通信系统中越来越受到重视。
面对无源器件的典型非线性效应之无源互调(PIM)问题,本文从PIM产生机理、建模角度梳理了近年来国内外普遍的PIM建模方法及关键步骤。
针对几种典型微观界面等效、PIM数值转换方法以及面向工况条件的微波部件建模问题,总结了现有的可行方法。
进一步提出了基于统计方法分析动态PIM区间预测分析方法的思路,该方法同样基于微观统计方法,对接触界面的不可具体量化的界面问题进行建模,最终获得工况条件下接触PIM的统计分布区间及其产物概率,为实际工况条件下微波部件PIM预测提供了一种大样本分析方法,为进一步提高实际微波部件的PIM稳定性提供了参考。
关键词:无源互调;非线性;微观界面分析;PIM统计预测中图分类号:TN98文献标志码:A doi:10.11805/TKYDA2022196Statistics methodology in efficient modeling and simulation ofPassive IntermodulationCHEN Xiong1,2,HE Yongning3,YU Ming1(1.Department of Electronic Electrical and Engineering,Southern University of Science and Technology,Shenzhen Guangdong 518055,China;2.Department of Electronic Engineering,The Chinese University of Hong Kong,Shatin Hong Kong 999077,China;3.School of Microelectronics,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an Shaanxi 710049,China)AbstractAbstract::In modern high power communication distortion problems, the nonlinear mixing distortion effect in passive devices is always the most difficult to investigate, and it has attracted great interests inthe modern high power and high density wireless communication system. Facing the typical nonlineareffect on passive device, Passive Intermodulation(PIM), this work concludes the common modelingmethods and the important steps in PIM mechanism researches in recent years. In particular, targeting tothe typical microscopic contact equivalence, PIM numerical transformation, and PIM prediction method inthe engineered cases, the available methods are concluded for present, while a statistics based PIMprediction method for engineered device with dynamic range is proposed for the first time. This methodcan model the specific contact sources by using statistic strategy. The PIM prediction can be expressed asa statistical interval with the probability of each PIM value. This method can provide a big sample data forthe engineered PIM prediction, and can guide the optimization of the PIM stability by controlling thedetailed parameters.KeywordsKeywords::Passive Intermodulation;nonlinearity;microscopic interface analysis;statistical PIM prediction无源互调(PIM)是指当2个或多个大功率信号通过无源器件时,无源器件自身非线性特性在载波激励下产生新的互调频谱成分,一旦互调成分落入接收通带,尤其以距离信号频点最近的三阶交调信号为主,引起接收通文章编号:2095-4980(2023)07-0864-08收稿日期:2022-09-30;修回日期:2023-04-24基金项目:国家自然科学基金面上资助项目(62271240);国家重点实验室基金2021-JCJQ-LB-006资助项目(6142411122115)第 7 期陈雄等:高效无源互调建模仿真中的统计方法道信号失真的一种现象[1]。
knowledge fusion of large languagemodelsKnowledge Fusion of Large Language ModelsLarge language models (LLMs) have emerged as a transformative technology in the field of artificial intelligence. These models, such as GPT-3 from OpenAI or BERT from Google, are capable of generating human-like text and understanding complex language patterns. However, one of the key challenges in their development is the integration and fusion of knowledge from various sources.Knowledge fusion, in the context of LLMs, refers to the process of combining and整合不同来源的知识 into a single, coherent representation. This involves integrating information from structured databases, unstructured text documents, and even expert knowledge. The goal is to create a model that can draw upon a rich and diverse knowledge base to generate more accurate and informative responses.To achieve this, LLMs rely on a range of techniques and methods. One approach is the use of transfer learning, where a model trained on a large corpus of text is fine-tuned on a specific task or domain. This allows the model to acquire knowledge from a general-purpose dataset and then adapt it to a more focused context.Another approach is the integration of external knowledge sources, such as knowledge graphs or ontologies. These structured representations of knowledge can provide valuable information to the model, enabling it to understand relationships and connections between different concepts and entities.The fusion of knowledge in LLMs is also aided by the use of advanced architectures and algorithms. For example, transformer-based models like GPT-3 employ self-attention mechanisms that allow them to capture complex dependencies between words and phrases. This enables the model to understand context more effectively and generate more coherent and informative text.In summary, knowledge fusion is a crucial aspect of large language modeldevelopment. By integrating diverse sources of knowledge and leveraging advanced techniques and algorithms, we can create models that are not only capable of generating human-like text but also possess a rich and comprehensive understanding of the world. This has the potential to transform a wide range of applications, from language understanding and generation to question answering and knowledge reasoning.。
Education reform is a topic of significant importance in todays world,as it directly impacts the future of society and the development of individuals.Here are some key points to consider when discussing educational reform:1.Curriculum Modernization:One of the primary aspects of educational reform is updating the curriculum to reflect current knowledge and skills required in the modern world.This includes integrating technology,critical thinking,and problemsolving skills into the learning process.2.Teacher Training and Development:Teachers are the backbone of the educational system.Reforms should focus on continuous professional development for teachers, providing them with the necessary tools and training to adapt to new teaching methodologies and technologies.3.Assessment and Evaluation:Traditional assessment methods often focus on rote memorization and standardized testing.Reforms should encourage a more holistic approach to evaluation,considering creativity,collaboration,and practical application of knowledge.4.Inclusivity and Accessibility:Education should be accessible to all,regardless of socioeconomic status,physical abilities,or learning differences.Reforms should aim to remove barriers and provide equal opportunities for all students to succeed.5.StudentCentered Learning:Shifting the focus from teachercentered to studentcentered learning can empower students to take charge of their own education.This involves personalized learning plans,projectbased learning,and fostering a sense of curiosity and selfmotivation.6.Technology Integration:With the rapid advancement of technology,it is crucial to integrate digital tools and resources into the classroom.This can enhance learning experiences,provide access to a wealth of information,and prepare students for the digital age.7.Lifelong Learning:Education reform should promote the concept of lifelong learning, encouraging individuals to continue their education and skill development beyond formal schooling.This can be achieved through community programs,online courses,and flexible learning opportunities.8.Global Perspectives:In an increasingly interconnected world,it is important for educational reform to include a global perspective.This involves teaching about differentcultures,global issues,and fostering international understanding.9.Funding and Resources:Adequate funding is essential for implementing educational reforms.This includes investing in infrastructure,technology,and teacher salaries to ensure quality education for all.munity and Parental Involvement:The success of educational reform often depends on the support and involvement of the community and parents.Engaging stakeholders in the decisionmaking process can lead to more effective and sustainable reforms.By addressing these areas,educational reform can lead to a more equitable,effective,and forwardthinking education system that prepares students for the challenges and opportunities of the21st century.。
可编辑修改精选全文完整版绪论单元测试1【判断题】 (2分)英语学科教学论,是介绍英语知识的一门课。
A.对B.错2【判断题】 (2分)英语学科教学论,是一门介绍如何教英语的课。
A.对B.错3【判断题】 (2分)关于教英语,需要了解相关的语言和语言学习的理论。
A.错B.对4【判断题】 (2分)关于教英语,还需要了解相关的教学方法、了解课堂管理的内容、学习教学设计的理念等。
A.错B.对5【判断题】 (2分)听说读写看的教学,是关于语言技能的教学。
A.错B.对第一章测试1【单选题】 (2分)Which is not the view of language? ( )A.Interactional ViewB.Structural ViewC.Functional ViewD.Constructive View2【单选题】 (2分)Which is not the view of language Learning? ( )A.Behaviorist TheoryB.Cognitive TheoryC.Schema TheoryD.Socio-constructive Theory3【单选题】 (2分)Which is from social-constructivist theory?A.stimulusB.ZPDC.reinforcementD.automatic response4【多选题】 (2分)For new language learning, the aspects that students need to do with are ( )A.Understand the formB.Understand the meaningC.Go abroad for studyingD.Practice the language5【多选题】 (2分)Which belong to process-oriented theory? ( )A.Socio-constructivist TheoryB.Schema TheoryC.Behaviorist TheoryD.Cognitive Theory6【多选题】 (2分)Which belong to condition-oriented theory? ( )A.Constructivist TheoryB.Behaviorist TheoryC.Socio-constructivist TheoryD.Cognitive Theory7【判断题】 (2分)The structural view of language is that language is a system of structurally related ele ments for the transmission of meaning. ()A.错B.对8【判断题】 (2分)The functional view only sees language as a means for doing things. ( )A.对B.错9【判断题】 (2分)For behaviorist theory, mistakes should be immediately corrected, and the correction s hould be immediately praised. ( )A.对B.错10【判断题】 (2分)Learning should be achieved via the dynamic interaction between the teacher and th e learner and between learners. ( )A.错B.对第三章测试1【判断题】 (2分)Teacher Talk Time means teacher should talk more in class and do not leave silence g ap in class. ( )A.对B.错2【判断题】 (2分)Remembering and understanding belong to the higher order thinking ability. ( )A.对B.错3【单选题】 (2分)Which one does not belong to the Bloom’s Taxonomy (2001)? ( )A.ApplyingB.AnalyzingC.RememberingD.Summarizing4【单选题】 (2分)For cognitive level of evaluating, the teacher may ask students the question like ( ).A.What is the relationship between A and B?B.How would you use this?C.Which is more interesting?D.What is the main idea of this paragraph?5【单选题】 (2分)For cognitive level of analyzing, the teacher may ask students the question like ( ).A.Which is more interesting, A or B?B.Can you compare A and B?C.Is there a better solution to this?D.What is an alternative method for this?6【多选题】 (2分)Which belong to teacher’s role? ( )A.controllerB.assessorC.prompterD.organizer7【多选题】 (2分)Teachers as facilitators means ( ).A.to guide them in planning and assessing their own learningB.to use various strategies to motivate learnersC.to develop their learning strategiesD.to create a positive learning environment8【多选题】 (2分)What are the two things that the teacher does as an assessor? ( )A.organizing feedbackB.controlling the paceC.correcting mistakesD.making research on student’s performance9【判断题】 (2分)Even the clearest instructions can be hard to grasp so, after you've given them, it's wo rth checking that they have been understood. ( )A.对B.错10【判断题】 (2分)Allow learners in class the time and the quiet they need, because they need time to thi nk, to prepare what they are going to say and how they are going to say it. ( )A.对B.错第四章测试1【单选题】 (2分)For WHERETO teaching design principle, W refers to ( ).A.allow students to evaluate their work and its implication’sB.be tailored (personalized) to the different needs, interests, and abilities of learnersC.equip students, help them experience the key ideas and explore the issuesD.help the students know where the unit is going and what is expected2【单选题】 (2分)For WHERETO teaching design principle, R refers to ( ).A.help the students know where the unit is going and what is expectedB.allow students to evaluate their work and its implication’sC.equip students, help them experience the key ideas and explore the issuesD.provide opportunities to rethink and revise their understandings and work3【单选题】 (2分)Which belongs to teaching design principle? ( )A.WHERETOB.ESAC.SMARTD.ABCD4【多选题】 (2分)What does WHERETO teaching design principle refer? ( )A.equip students, help them experience the key ideas and explore the issuesB.help the students know where the unit is going and what is expectedC.allow students to evaluate their work and its implication’sD.hook all students and hold their interest5【多选题】 (2分)What are the frequently applied teaching models? ( )A.WHERETO ModelB.Backward Design ModelC.ASSURE ModelD.ADDIE Model6【多选题】 (2分)What are the four elements of articulating learning objectives? ( )A.ConditionB.AudienceC.DegreeD.Behavior7【判断题】 (2分)Develop student’s reading skills. This learning objective is appropriately presented. ( )A.错B.8【判断题】 (2分)SMART is the method for articulating learning objectives. ( )A.对B.错9【判断题】 (2分)Activation of prior knowledge means activating cognitive structures that relate to the to pics and tasks to be studied and completed. ( )A.错B.对10【判断题】 (2分)The purpose of teaching design is to implement teaching effectively. ( )A.B.对第五章测试1【单选题】 (2分)What is used to express meanings in many subtle ways such as surprise, complaint, s arcasm, delighted, threats, etc.? ( )A.PronunciationB.morphologyC.PhonologyD.Intonation2【单选题】 (2分)What is the best age to start learning to read through phonics? ( )A.4-6B.C.1-2D.2-33【单选题】 (2分)After learning the individual letters’ sounds, it is natural to learn the sounds o f ? ( )A.consonant lettersB.blending lettersC.all of the aboveD.vowel letters4【多选题】 (2分)Which generally should be pronounced as weak form while reading aloud? ( )A.Auxiliary wordB.C.VerbD.Preposition5【多选题】 (2分)Phonics is a method for teaching and of the English language by develo ping learners' phonemic awareness—the ability to hear, identify, and manipulate phon emes—in order to teach the correspondence between these sounds and the spelling p atterns that represent them. ( )A.speakingB.writingC.listeningD.reading6【多选题】 (2分)Sound /k/ can be spelled as ? ( )A.kB.chC.ckD.c7【判断题】 (2分)Research has shown that children who have not developed reading skills by second gr ade, will experience an overall delay in learning throughout their school life. ( )A.对B.错8【判断题】 (2分)Understanding phonics will also help children know which letters to use when they ar e writing words. ( )A.对B.错9【判断题】 (2分)A lot of people start the journey of teaching kids phonics through the traditional ways, l ike teaching them to read, and this is the best way to follow. ( )A.错B.对10【判断题】 (2分)Written language can be compared to a code, so knowing the sounds of individual lett ers and how those letters sound when they’re combined will help children code word s as they read. ( )A.对B.错第六章测试1【单选题】 (2分)What does the first aspect of vocabulary learning involve according to Hedge (200 0)? ( )A.The sense relations among words.B.Connotative meaning.C.Denotative meaning.D.Denotative and connotative meaning.2【单选题】 (2分)Which is not the appropriate way of consolidating vocabulary? ( )A.Play a game.B.Categories.C.Spot the difference.D.Guessing.3【单选题】 (2分)Which is not a collocation? ( )A.See a movie.B.Watch a play.C.Movie.D.Look at a picture.4【多选题】 (2分)According to Hedge (2000), what does the second aspect of vocabulary learning invol ve? ( )A.Spelling and PronunciationB.Collocations.C.Synonyms, antonyms and hyponyms.D.Receptive and productive vocabulary.5【多选题】 (2分)What does knowing a word involve? ( )A.How and when to use it to express the intended meaning.B.Its meaning.C.Its spelling and grammatical properties.D.Its pronunciation and stress.6【多选题】 (2分)Which belong to vocabulary learning strategies? ( )A.Use a dictionary.B.Review regularly.C.Guessing from context.D.Presenting vocabulary.7【判断题】 (2分)Denotative meaning refers to those words that we use to label things as regards real o bjects, such as a name or a sign, etc. in the physical world. ( )A.错B.对8【判断题】 (2分)Antonyms refer to the sameness or close similarity of meaning or we can say that wor ds are close in meaning. ( )A.对B.错9【判断题】 (2分)Receptive/passive vocabulary refers to words that one is able to recognize and compr ehend in reading or listening but unable to use automatically in speaking or writing. ( )A.错B.对10【判断题】 (2分)Collocations refer to words that co-occur with high frequency and have been accepte d as ways for the use of words. ( )A.对B.错第七章测试1【单选题】 (2分)Deductive reasoning is essentially a approach which moves from the more gen eral to the more specific. ( )A.gameB.down-topC.traditionalD.top-down2【单选题】 (2分)is an approach that removes you, the teacher, from the main role of “explainer” and ex tends to the students the opportunity to question and discover the target grammar. ( )A.The guided discovery methodB.Mechanical practiceC.Deductive methodD.Inductive method3【单选题】 (2分)usually comes after mechanical practice. ( )A.The guided discovery methodB.Meaningful practiceC.Deductive methodD.Inductive method4【多选题】 (2分)Although a little less effective than inductive teaching, benefits to the deductive approa ch are: ( )A.It encourages faster learning of material and understand the meaningB.Time in the classroom is spent only on the language principle.C.Most material can be easily taught this way.D.Students can have lots of interaction with others in a meaningful context.5【多选题】 (2分)Although inductive teaching takes longer than deductive, many educators agree it i s a very efficient method in the long run. Benefits include: ( )A.Students rely on their critical thinking to figure out the language.B.Students can gain deeper understanding of the language.C.Students can get more interaction and participation among each other.D.No material can be easily taught this way.6【多选题】 (2分)Using prompts has proved to be an effective way of grammar practice. The prompts ca n be: ( )A.picturesB.tablesC.key wordsD.mines7【判断题】 (2分)The deductive and inductive teaching methods can be illustrated in this picture. ( )A.对B.错8【判断题】 (2分)That the students are asked to produce language based on pictures and key phrase s provided by the teacher is using chained phrases for storytelling. ( )A.对B.错9【判断题】 (2分)In mechanical practice the focus is on the production, comprehension or exchange o f meaning through the students “keep an eye on” the way newly learned structures ar e used in the process. ( )A.对B.错10【判断题】 (2分)Grammar practice is usually divided into two categories, mechanical practice and mea ningful practice. ( )A.错B.对第八章测试1【单选题】 (2分)Which is not involved in bottom-up processing while listening? ( )A.Recognizing phrases.B.Referring meaning from background knowledge.C.Recognizing structures.D.Recognizing sounds of words.2【单选题】 (2分)Which is not the stage of listening teaching? ( )A.While-listening.B.Predicting.C.Pre-listening.D.Post-listening.3【单选题】 (2分)Which is not the main listening difficulty of learners? ( )A.Quickly forget what is heard.B.Neglect the next part when thinking about meaning.C.Able to form a mental representation from words heard.D.Do not recognize words they know.4【多选题】 (2分)What are two models that are frequently used to describe different processes of listeni ng? ( )A.Top-up model.B.Top-down model.C.Bottom-up model.D.Bottom-down model.5【多选题】 (2分)Which belong to principles for teaching listening? ( )A.Focus on process.B.Combine listening with other skills.C.Focus on the comprehension of meaning.D.Grade difficulty level appropriately.6【多选题】 (2分)What are three main categories that affect the difficulty level of listening tasks accordin g to Anderson and lynch (1988)? ( )A.Context in which the listening occurs.B.Bottom-up and top-down approaches.C.Type of language used.D.Task or purpose in listening.7【判断题】 (2分)Bottom-up and top-down these two processes are mutually dependent. ( )A.对B.错8【判断题】 (2分)It is important to develop listening skills together with other skills because ordinarily list ening is not an isolated skill. ( )A.错B.对9【判断题】 (2分)Multiple-choice tests play a decisive role in helping students develop good listening ha bits and strategies. ( )A.错B.对10【判断题】 (2分)In the top-down model, listening for gist and making use of the contextual clues and ba ckground knowledge to construct meaning are emphasized. ( )A.对B.错第九章测试1【单选题】 (2分)Which is not the principle for teaching speaking? ( )A.Maximizing meaning interactions.B.Problem-solving activities.C.Personalizing practice.D.Contextualizing practice.2【单选题】 (2分)Which practice is not structure-based with a focus on forms? ( )A.Grammar learning.B.Syntax learning.C.Providing sufficient opportunities for students to develop fluency.D.Vocabulary learning.3【单选题】 (2分)Which of the following activities is often used to develop students’ speaking accurac y? ( )A.Acting out the dialogue in the text.B.Having discussions in groups.C.Describing people in pair.D.Identifying and correcting oral mistakes.4【多选题】 (2分)Like all the other skills, what strategies does speaking involve?( )A.Turn taking.B.Asking for clarification.C.Initiating a conversation.D.Ending a conversation.5【多选题】 (2分)Which belong to common features of spoken language according to Bygate (1987)? ( )A.Using devices such as fillers, hesitation device to give time to think before speaking.B.Taking short cuts, e.g. incomplete sentences.C.Using less complex syntax.D.Using fixed conventional phrases or chunks.6【多选题】 (2分)Which belong to typical speaking tasks? ( )A.Problem-solving activities.B.Dialogues and role-plays.C.Doing translation exercises.D.Information-gap activities.7【判断题】 (2分)Maintaining a balance between accuracy-based and fluency-based practices is essenti al in teaching speaking.( )A.对B.错8【判断题】 (2分)Speaking is the skill that the students will be judged upon most in simulated situation s. ( )A.错B.对9【判断题】 (2分)Problem-solving activities tend to be productive because there is a clear objective to b e reached or problem to be solved and require a higher level of language proficienc y. ( )A.错B.对10【判断题】 (2分)Designing speaking activities that maximize students’ opportunity to speak is one of th e central tasks for language teachers. ( )A.错B.对第十章测试1【单选题】 (2分)Which is not the teaching step of viewing teaching? ( )A.What message does the image transmit?B.What do you feel?C.What can you touch?D.What can you see?2【单选题】 (2分)Which is not the three-dimension paradigm by Serafini (2014)? ( )A.StructuralB.IntellectualC.IdeologicalD.Perceptual3【单选题】 (2分)What is the definition of Visual literacy? ( )A.With technology, images and visual presentations are flourishing more than ever.B.It means student's ability to “use, interpret, analyze, and think critically about visual images and the signi ficance of what they are seeing”.C.This involves exploring how ideas and emotions are expressed and the use of lighting to create an emoti onal or physiological point.D.Visual literacy is based on the idea that can be "read" and that meaning can be through a process of re ading.4【多选题】 (2分)Which are the teaching steps of viewing teaching? ( )A.What can you touch?B.What is the image trying to tell us?C.What can you see?D.What do you feel?5【多选题】 (2分)What are the pedagogical questions that the teacher can use in class to develop learn ers’ visual literacy? ( )A.How could you change/improve this image?B.What more can we find out?C.What does this image say to us?D.Where has this image come from?6【多选题】 (2分)What can be used as visual literacy clues to facilitate identifying the visual products? ( )A.ColorB.ShapeC.GestureD.Lighting7【判断题】 (2分)We need to consider the active viewer as well and engage the students' creative or cur ative responses to the image. ( )A.错B.对8【判断题】 (2分)One of the most effective ways to encourage information to make that important jump f rom the limited short-term memory to the more powerful long-term memory is to pair te xt with images. ( )A.错B.对9【判断题】 (2分)As these students travel on their road to fluency in English, images can provide an eff ective bridge in that learning process. ( )A.错B.对10【判断题】 (2分)Information presented visually is processed extremely quickly by the brain. ( )A.对B.错第十一章测试1【单选题】 (2分)When expectations are set up, what kind of process of reading is ready to begin? ( )A.ForcedB.PassiveC.NegativeD.Active2【单选题】 (2分)What does bottom-up model mean? ( )A.The teacher should teach the background knowledge first, so that students equipped with such knowled ge will be able to guess meaning from the printed page.B.The teacher teaches reading by introducing vocabulary and new words first and then going over the tex t sentence by sentence.C.None of the above.D.Not only linguistic knowledge but also background knowledge is involved in reading.3【单选题】 (2分)Which is not the reading skill that the learners should be developed in reading clas s? ( )A.Making prediction based on vocabulary or titleB.Guessing the word’s meaningC.Cooperating with othersD.Making inference4【多选题】 (2分)What are the principles for reading teaching? ( )A.Prediction is a major factor in reading.B.Students should be encouraged to respond to the content of a reading text, not just to the language.C.Students need to be engaged with what they are reading.D.Good teachers exploit reading texts to the full.5【多选题】 (2分)What are the models for reading teaching? ( )A.Top-down ModelB.Discovery ModelC.Interactive ModelD.Bottom-up Model6【多选题】 (2分)Find out the reading skills that the learners should be cultivated in reading class. ( )A.Making inferenceB.SkimmingC.ScanningD.Guessing the word’s meaning7【判断题】 (2分)As with everything else in lessons, students who are not engaged with the reading tex t will not actively interested in what they are doing. ( )A.错B.对8【判断题】 (2分)In the Top-down Model, the teacher teaches reading by introducing vocabulary and ne w words first and then going over the text sentence by sentence. ( )A.错B.对9【判断题】 (2分)According to the Interactive Model of reading, when one is reading, the brain receive s visual information, and at the same time, interprets or reconstructs the meaning tha t the writer had in mind when he wrote the text. ( )A.错B.对10【判断题】 (2分)Reading comprehension involves extracting the relevant information from the text as e fficiently as possible, connecting the information from the written message with one’s o wn knowledge to arrive at an understanding. ( )A.错B.对第十二章测试1【单选题】 (2分)Which will not help teachers motivate students to write? ( )A.Encourage collaborative group writing as well as individual writing.B.Leave students less room for creativity and imagination.C.Make the topic of writing as close as possible to students' life.D.Provide constructive and positive feedback.2【单选题】 (2分)What product-oriented method of teaching writing mean? ( )A.Writing activities should serve to encourage a process of brainstorming, drafting, writing, feedback, revis ing and editing, which proceeds in a cyclical fashion resembling the writing process of a real writer.B.It pays great attention to the accuracy of the final product but ignores the process, which the students g o through to reach the final goal.C.The process approach to writing does not only pay attention to what students do while they are writing, i t also attaches great importance to what they and the teacher do before they start writing and after they f inish writing.D.What really matter is the help that the teacher provides to guide students through the process that they undergo when they are writing.3【单选题】 (2分)What does process approach to writing mean? ( )A.What really matter is the help that the teacher provides to guide students through the process that they undergo when they are writing.B.Encourage feedback both from themselves.C.Do not give students time to discover what they want to say as they write.D.Teaching writing pays great attention to the accuracy of the final product but ignores the process.4【多选题】 (2分)Which principles can help teachers motivate students to write? ( )A.Leave students enough room for creativity and imagination.B.Provide opportunities for students to share their writings.C.Make the topic of writing as close as possible to students' life.D.Encourage collaborative group writing as well as individual writing.5【多选题】 (2分)Which principles will motivate students to write? ( )A.Give students a sense of achievement from time to time.B.Leave students less room for creativity and imagination.C.。
Cultural education is a vital component of a wellrounded education system.It encompasses a wide range of activities and subjects that aim to foster an appreciation for the diverse cultural heritage of the world.Here are some key aspects to consider when discussing cultural education in an English essay:1.Importance of Cultural Education:Begin by outlining why cultural education is essential.It helps individuals understand and respect different cultures,traditions,and values,promoting tolerance and openmindedness.2.Curriculum Integration:Discuss how cultural education can be integrated into the school curriculum.This might include subjects like history,geography,literature,and the arts,which provide insights into various cultures.nguage Learning:Language is a gateway to understanding a culture.Highlight the role of language learning in cultural education,allowing students to communicate with people from different backgrounds and to access cultural texts in their original language.4.Experiential Learning:Describe the benefits of experiential learning,such as cultural exchange programs,field trips to museums,and participation in cultural festivals.These experiences can provide a deeper understanding of different cultures.5.Cultural Diversity in the Classroom:Talk about the value of having a diverse student body.When students from different cultural backgrounds study together,they learn from each other and develop a broader worldview.6.Role of Technology:Explore how technology can enhance cultural education.Digital resources,such as online courses,virtual museums,and social media,can provide access to global cultures and perspectives.7.Challenges and Solutions:Address potential challenges in implementing cultural education,such as resistance to change,lack of resources,or cultural biases.Propose solutions,such as teacher training,community involvement,and the development of inclusive educational materials.8.Cultural Sensitivity and Awareness:Emphasize the importance of teaching students to be culturally sensitive and aware.This includes understanding and respecting cultural differences and avoiding stereotypes and prejudices.9.Global Citizenship:Connect cultural education to the concept of global citizenship. Educated individuals who understand different cultures are better equipped to contributepositively to a global society.10.Conclusion:Conclude by summarizing the key points and reiterating the importance of cultural education in preparing students for an interconnected world. Remember to use specific examples and evidence to support your arguments,and to structure your essay with a clear introduction,body paragraphs,and conclusion.。
In the realm of creative inspiration,diversity plays a pivotal role in fostering a rich tapestry of ideas and perspectives.Here are several aspects that illustrate how diversity can inspire creativity in English compositions:1.Cultural Perspectives:Drawing from various cultural backgrounds can add depth and authenticity to an English essay.By incorporating elements of different cultures,such as folklore,traditions,or historical events,a writer can create a narrative that resonates with a global audience.2.Linguistic Variations:The English language itself is diverse,with different dialects, slang,and expressions from around the ing these variations can add a unique flavor to the writing and make it more engaging.3.Ideological Diversity:Including a range of ideologies and belief systems in an essay can lead to thoughtprovoking discussions and debates.This can be particularly effective in argumentative essays where presenting multiple viewpoints can lead to a more nuanced and balanced argument.4.Diverse Characters:Creating characters from different walks of life can make a story more relatable and interesting.By giving each character a distinct voice and background, a writer can explore a variety of themes and issues.5.Inclusive Language:Using inclusive language that respects and acknowledges all individuals,regardless of their race,gender,or social status,can make an essay more appealing and inclusive.6.Global Issues:Writing about global issues such as climate change,poverty,or social justice can inspire creativity by encouraging writers to think beyond their immediate surroundings and consider the broader implications of their topics.7.Historical and Contemporary Blends:Combining historical contexts with contemporary issues can create a unique narrative that bridges the past and the present, offering fresh insights into timeless themes.8.Personal Experiences:Sharing personal experiences that reflect diversity can add a layer of authenticity to an essay.These experiences can be related to travel,living in different cultures,or interacting with people from various backgrounds.9.Interdisciplinary Approaches:Integrating knowledge from different fields such as science,art,and philosophy can provide a multifaceted view of a topic,encouragingcreative thinking and innovative arguments.10.Unconventional Formats:Experimenting with different formats,such as a dialogue,a diary entry,or a series of letters,can break the mold of traditional essay writing and inspire unique storytelling techniques.By embracing diversity in all its forms,writers can unlock a wealth of creative potential, leading to English compositions that are not only engaging but also reflective of the multifaceted world we live in.。
论⽂总结:DistillingtheKnowledgeinaNeuralNetwork(蒸。
原⽂地址:Abstract:在机器学习领域,ensemble learning是⼀种普遍适⽤的⽤来提升模型表现的⽅法,将通过ensemble learning训练出的模型称为cubersome model, 但在这种情况下,模型通常很⼤(即参数较多,复杂度较⾼),以⾄于在模型部署阶段所需要的计算资源极为昂贵,尤其是对于⼀些以⼤型神经⽹络作为⼦学习器的集成模型。
其中⼀种潜在的解决⽅法是,将cubersome model中的信息(knowledge)压缩到⼀个单独的模型(single model),将此过程称为distilling(蒸馏)。
1 Introduction对于⽬标检测或语⾳识别等任务,模型训练所花费的⼤部分时间及计算资源是可以接受的,但当模型部署到⽣产环境中,对模型所需资源的要求则会严格很多。
可以通过distilling(蒸馏)来提取cubersome model中的knowledge从⽽使模型更易部署。
对于实现distilling(蒸馏)的阻碍之⼀在于,knowledge是⼀个⽐较抽象的概念,可以认为cubersome model的knowledge体现或者隐藏在cubersome model中的模型参数中,以⾄于很难在改变模型结构或参数的同时保留knowledge。
在cubersome model学习处理多分类任务时,普遍使⽤最⼤似然函数(最⼤化对数概率log probability)来作为⽬标函数, 这么做的⼀个副作⽤是,当使⽤softmax作为神经⽹络的输出层时,模型会赋值给⼀些⾮正确的类别(的概率),即使这些值很⼩,但在这些⾮正确分类的类别的概率值中,其中⼀些会相对更显著,这些relative probalities of incorrect answers是很重要的信息,因为它隐含了cubersome model如何泛化generalize的信息(how the cumbersome model tends to generalize)。
Hanfei Bao: The Theory of Biomedical Knowledge Integration(Ⅰ), Chinese Journal of Medical Treatment, Vol(2):No(13), 1-7, 2003The Theory of Biomedical Knowledge Integration(Ⅰ)Hanfei BaoLab of Informationization and Standardization of TCMShanghai Univ. of Traditional Chinese MedicineShanghai, China, bhf2002@The innumerable biological and medical data, information and knowledge have been found in the scientific activities in biomedical fields. All of them are coming from the identical “mystical lands”, ie the lively, energic and sophisticatedly structured and ordered organisms. Therefore these data, information and knowledge are “brothers and sisters”, should not be the strangers to and isolated from one another. How can they communicate, link and be organized or united like they originally did in the organisms? When can they come back to their lovely homelands hand in hand? How possible can we fully use them to reconstruct or integrate logically or physically the models of the organisms? Biomedical Informatics[1,2] now perhaps has been facing these questions and will be duty-bound to undertake the new important mission, the large scale integration of biomedical data, information and knowledge. Let’s first call the new efforts in this direction Biomedical Knowledge Integration(BMKI). Albeit perhaps some body would say it might be a work in the very remote future and may be he is quite right, it is never too early for people to do some exploratory investigation. In this series of papers the term “knowledge”is defined as the joint name of three widely used and polysemous concepts data, information and knowledge.Both the significance and difficulty of this new mission for Biomedical Informatics might be beyond imagination and comparison. It needs much more efforts of Biomedical Informatics, Biology and Medicine in many fields.Ⅰ. Organisms——The Natural Integration MasterIt is the scientific activities of human being that make our organisms being continuously divided into more macro-micro levels, from quantum biology, molecular biology, biophysics, biochemistry, through cytology, histology, anatomy, physiology, immunology, genetics, to clinical sciences. Whereas the organisms themselves are doing contrarily. They arrange thousands of elements and me chani sms orderly, hierarchically, and jointly in the very limited spaces in the physical bodies. This magical work of nature is some kind of the physical integration and MBKI would be contrastedly an artificial intelligent integration. So I would say that MBKI, which is trying to integrate or link all of the biomedical knowledge we have acquired, obeysthe will of nature.We can’t help admiring so much the fascinating abilities of the organisms to carry out the task of natural or physical integration. In order to show the readers a bit of integration talent of them, the author has summarized the dynamical circles, which perform orderly and jointly to complete a whole blood pump, from molecular level to integral level of heart[3,4]. These circles are drived by the histological structures and physiological mechanisms, such as ion channel, ion pump, membrane electronic potential, action electronic potential, endoplasmic reticulum and calcium ion, A TP enzyme, myofilament protein(thick myofilament and thin myofilament), neurotransmitter-receptor, synapse, myocardial cell, fluid mechanics, stop-cock principle, etc.(see Fig. 1) A normal blood pump relies on the precisely and serially linking up of those circles and any phase mismatching between the circles might lead to malfunction of the heartbeat.What a perfect and miraculous physical integration!Fig.1 The linking topology of the heart blood pump circles from molecular level to organ-integral level:1.membrane potential circle; 2.circle of Ca++ between sarcoplasm and sarcoplasmic reticulum(by Ca++ pump); 3.troponin and Ca++ combining–releasing cirlce;4.troponin molecular configuration change circle;5.tropomyosin molecular configuration change circle;6.cross bridge and actin combining-bending-releasing circle;7.thick myofilament and thin myofilament sliding circle;8.sarcomere contraction-expansion circle;9.muscle fiber contraction-expansion circle;10. muscle contraction-expansion circle;11.blood pressure circle in ventricle;12.open-close circle of atrio-ventricular valve;13. blood pressure circle in atrium;14. open-close circle of aortic valve;15. blood pressure circle in aorta;16.blood pump of heart.Ⅱ. The Revised Story of Blind Men and ElephantA well-known story of Blind Men and Elephant told that one day five blind men was feeling an elephant with the hands and trying to say what the animal is like. One was touching the elephant’s nose and declared “Elephant is like pipe”. Another blind man, who was handling its tail, thought it was similar to rope. And the other three considered the animal was something like “post”, “wall” or “fan”, when they felt the elephant’s leg, trunk and ear, respectively(see Fig.2 A). This story has been historically a funny material to tell people how nonsense to use only the local knowledge to explain the whole.Whereas the things have been changed. The blind men have realized that “knowledge integration” is important and they started to find a way to link their knowledge together and have at last made great progress. The result from the integration is: “A wall is the centre of the elephant, a pipe and two fans are attached in front, a rope linked to the rear and four posts fasten to the lower part of the wall”(see Fig.2 B). Thus through knowledge integration, the understanding of our blind group has got much closer to the true elephant, although the information own by each person has not changed much.Through this invented story, the author try to explain how significant of BMKI will be for the whole understanding of the biologic systems and human body. We should not only and forever fix our eyes on the more and more detailed details of the life systems. We should also pay our energies on examining those existing numerous, heterogenous and isolated (under physical semantic sense) data, information and knowledge we have got in biomedical fields, to see what we can do in the aspect of reassembling them integrally, what fundamental problems we will meet on that way, what an extent to which we can reach in this direction, where the black abysses cross which people can hardly jump are waiting for us and what points we can begin with. We should always bear in mind that these data, information and knowledge are from the organisms arising from the identical bio-evolutional tree. No matter the purposes, perspectives and methods, under which those data , information and knowledge were obtained, are so different.Fig. 2 A What the blind men imagined about the elephantbefore they doing knowledge integration; B theelephant imagined by them afterwards.Ⅲ. The Four-Incompleteness-Principle in Cognition ProcessesHuman being acquires the knowledge or understanding of the world through the cognition processes. Thus there are inherent relations between the knowledge or knowledge integration and cognition sciences. There are at least four key aspects in the cognition processes: (1)the cognition object, ie the world; (2) the cognition context, condition or tools; (3)the knowledge expression; (4)the awareness of the knowledge in our brain. Unfortunately none of the four aspects could be complete for the cognition processes. This property of the cognition processes of human is called in this paper the Four-Incompleteness-Principle.(1)The incompleteness of our knowledge about the worldThis incompleteness is because that the world, ie the cognition object, always tells us parts of its story. The world, including human being himself and the cognition process itself, is a “shy girl”. She always “hides her half face behind Pipa”(a kind of ancient Chinese music instrument), keeping the other half of her face out of our sight. Forever, the world can be partially observed and understood by us. That is the reason why the scientists, generation by generation, have dedicated themselves to its secrets.(2)The incompleteness of awareness of the background of knowledge acquirementThe background or context means here the conditions or methods through which we have obtained the knowledge. As we known, many units of knowledge have been achieved byobservation or measurement under very special or particular clinical or experimental conditions and our brains usually logically or even at will omit them and use those knowledges under the general sense.(3)The incompleteness of the knowledge expressionThat is because the media usually express the knowledge also partially. Suppose there is a very traditional Chinese who knows nothing about the cultural backgrounds of Europe, he might get many troubles when he reads a translation of an Europe novel. Because according to the generalized economic principle which plays a role everywhere, the author of the novel didn’t need to write down everything especially those “understandable without saying”. Otherwise his work would not be considered as succinct. Those left out and “understandable without saying” portions of the story are the potential parts of things for the European, but would be problematic for this traditional Chinese. That is to say there are two attributes, ie the manifestation and potentiality, in knowledge expression. That causes the incompleteness of the knowledge expression. In the scientific data, information and knowledge, in fact, a large quantity of special contexts is left out, because they are “understandable without saying”for the domain exerts, but usually not for the experts in other domains, especially not for computers. Here is another most simple example of physiological knowledge. A textbook of physiology says “the means of the adult heart rate and the duration of adult heart cycle are 75 times/m and 0.8 sec respectively . ”It implies the proposition is based on the physical context: general adult (no patient, no athlete etc), during rest(not exercise, not being excited), etc.Any knowledge has its (obvious or hidden) context or condition which guarantees its validity. Famous Newton’s three laws are valid in world of general size and general speed, but invalid in both quantum and light-speed worlds. Additionally, we could not talk about what the world is like beyond the scientific levels, methods and conditions at that time. As the cognition subject, our brain gets the data from the original world always through certain means or tools of observation or measurement, which could be auditory, optic and contact organs or receptors of human and the electronic, magnetic, electro-magnetic, chemical, etc advices as well. Those means or tools in turns determine the natures of data observed or measured. And, in fact, the means or tools themselves are the components of the generalized context of knowledge.(4)The incompleteness of awareness of the knowledge in brainOnce a baby came into the world, it starts the cognition processes immediately in a way which is utterly different from those of other kinds of animal. So the special, basic abilities and patterns of cognition exist congenitally.Similar to that the world keeps its portions from our sight stated above, the knowledge we actually own hides its portions, especially those gained congenitally, from our feeling, that is some of our knowledge exists in our brain subconsciously. We are not ware of their existence in our mind. It means that as a matter of fact we know more than what we can tell out clearly about our knowledge. That makes many differences between the knowledge structures of domain expert and expert system. To make it clear that how much inborn and acquired knowledge has been stored in our brain is by no means an easy job. We can hardly thoroughly list all the pieces of knowledge which take part in our mind work. There always the substantial parts of it are subconscious. In one word, the knowledge of which we are clearly aware of is never complete. That is the fourth incompleteness of the cognition processes the author would like to point out here. That makes many differences between the knowledge structures of domain expert and expert system.Fig.3 shows two attributes of manifestation and potentiality at the significant links of people ’s cognition processes. It to a certain extent indicates the high complexities in biomedical artificial intelligence and biomedical knowledge integrations.In the case the cognition subject is man, sometimes the four incompletenesses mentioned above may be not a problem at all, whereas for the computer in AL or MBKI, the situation might be much more serious and things could become much more complicated.Fig.3 A kind of topologic figure showing the attributes ofmanifestation and potentiality in the human cognitionaspects: the cognition object indicates the real world,cognition context means the particular circumstance,methods whereby the knowledge is obtained, cognitionsubject and cognition result represent human brain andknowledge-recording media, respectively. EXPor RECpts mean the expressed or recorded parts of knowledgeand UNEXP or UNREC pts, the knowledge parts whichare not expressed or recorded.Interferingknowledge recognitionⅣ. The Basic Concepts of the Theory of Biomedical Knowledge IntegrationThe word “integration”is a polysemant. On the different occasions, it might arbitrarily be equivalent of such as “synthesis”, “being concerned together”, “being linked together”, etc. Sometimes it means things physical but in other cases it means conceptually. In this article series, however, the author try to discuss the biomedical knowledge integration under rather rigorous physical or logical senses. It is expected to do some fundamental investigations and explorations on the biomedical knowledge integration problem. The last goal of BMKI is to help making the biomedical models as seamless as possible and being continuously close to original organism.The all parts of a thing is called an whole, which has both philosophical and operational meanings. From the operational rather than philosophical perspective, an whole, contrasted to its parts, depends on our view angle or the purpose of the operations. For examples, in different cases, a person, family, company, country and the earth might be taken as either an whole or a part. Thus the concept whole is a relative one.If two things are not independent of each other, then we say there is/are relation(s) between them. If the relations exist between two things, then we call them binary relations. The binary relations may be considered consisting of typically four essential parts: the generalized relational operator, subject set(τ), object set(ο) and obvious or obscure condition set(γ). Here set means a group things or elements playing jointly a common role in the relation, with or without structure, and an element is a smallest semantic unit. The generalized relational operator is the logical or physical action factor of a relation and reflects its nature, in most cases it corresponds to the verb between the subject and object in a sentence in Linguistics. Therefore the relational operator is the logical or physical “force”which causes the relation to be realized. The type of the logical relational operator or action factor sometimes reflects the view-angle, thereby is with somewhat arbitrary. The things which take the action make up the subject set and those receive the action are called the object set. So called the condition set is the indirect part of the relation, the role of which is to guarantee the bring about of the relation.The Human-Computer Shareable Language(HCSL)[5-7]developed by the author with C language for the Integrateble and Relationized Medical Book(IREMB), where some examples for logical integration of medical knowledge were presented, has summarized ten generalized relations of medical knowledge according to the logical and especially the operational characters. The ten basic relations are pan-create(CREAT), pan-increase(INCRS), pan-decrease(DECRS), pan-contain(CONTN), pan-company(COMPN), pan-transform(TRANS), pan-equal(EQUAL), pan-order(PORDR), pan-pass(PASTO), pan-zero-relation(PNULL).The relations in the semantic network of UMLS(Unified Medical Language System)[8] directed by NLM are divided into ①the subjective types: eg “is_a”, “conceptually_related_to”and ②the objective types: eg “physically_related_to”,“spatially_related_to”,“functionally_related_to”,“temporally_related_to”. More than forty particular relations ofsemantic network of UMLS are published on Internet and they might be reduced into less definitely defined relations based on their properties as the potential AI or BMKI operators. In UMLS the elements which may compose the subject, object and condition sets of the relations are split into entities(rather “hard” things) and events(rather “soft” things). Both entity and event are subdivided further into the more detailed items based on their qualities, such as subjective(mind-producing, conceptual, etc)-objective(real, physical, etc), normal-abnormal, natural-artificial, qualitative-quantitative, structural-functional, macro-micro, embryonic or full-developed, etc. The certainty and operational characteristics of those relational operators and elements in BMKI might be much different from each other, one of the reasons for that is the generalized degrees or the granularities of them being different.“Relation integration” is almost another way saying “Knowledge integration”, meaning the joint, systematical realization of a set of relations in logical or even physical way. Namely relation integration is a kind of multi-to-one transform or operation. Some basic principles of relation integration will be discussed in next article.The operations of relation integration may be mathematical, general logical, biomedical logical, physical,etc. The logical operations follow the laws of thinking sciences which are interested in the behaviour of our thoughts whereas the physical operations mean here the natural or biological ways by which the living system is running on. Here is an example for logical relation integration. If we have relations A,B,C,D,……∈ F,then we have an integration (A, τ,ο,γ)∪(B, τ,ο,γ)∪(C, τ,ο,γ)∪(D, τ,ο,γ)∪……∈(F, τ,ο,γ). The protein-protein interaction network[9] is an example of this type of relation integration. In molecular biology, we use two-hybrid approach to find the protein-protein(p-p) interactions(Λ) and assume that we haveλ1 (cell-cycle control p-p interaction), λ2 (signal transduction p-p interaction), λ3 (vesicular transport p-p interaction), λ4(cell polarity p-p interaction), etc. Because all theλn ,such as the interactions in the aspects of cell-cycle control, signal transduction, vesicular transport, cell polarity, RNA-processing/modification, RNA-splicing, mitosis, protein synthesis, carbohydrate metabolism, cell stress, chromatin/chromosome structure, protein folding, protein degradation, amino acid metabolism, etc, belong to Λ(p-p interaction), ie λ1,λ2,λ3,λ4……∈Λ, then all the smaller relation networks form a total and larger “protein-protein interaction network”. That is (λ1, τ,ο,γ)∪(λ2, τ,ο,γ)∪(λ3, τ,ο,γ)∪(λ4, τ,ο,γ)∪……∈(Λ, τ,ο,γ).Ⅴ. V ery Ambitious Plans of Biomedical IntegrationⅥ. The More Than Ten Years Long DreamAs stated above, the goal of this series of articles is to explore a new research virgin land for Biomedical Informatics, i.e. to see how widely, how precisely, to what a degree, we can integrate the existing biomedical data or knowledge. The original idea has come from not only the high integrality, ie a basic nature of organisms or the objects which Biology and Medicine deal with, butalso from the desire to promote a new scientific spirit for scientists paying more attention to more aspects of biomedical knowledge rather than limiting themselves life-long to their narrow domains only. Furthermore, BMKI is doubly parallel to the current hot area of biomedical knowledge engineering such as various biomedical ontologies and their integration in future, digital or virtual human, etc.The author has devoted himself to the research on the simulation and integration of medical knowledge since 1989[17-20] and advanced in one of his articles the “new research on large-scale integration of biomedicine”in 1991[21]. Its activities since then have gone through several exploratory steps of theoretical discussions on Quantitatively or Qualitatively Medicine Simulating and Operating by Computer(QMSOC) [17-20], Homo-Information Coded Editing(HICE) technique[22], Integrateable Relationized Medical Electronic Book (IRMEB) [23] , Human-Computer Commonly Understandable and Communicatable Medical Language(or Human-Computer Shareable Language, HCSL)[24],and the development of Internet web site for IREMB(having been thoroughly destroyed for three times by the ISP without any explanations). Because of less understanding and supports, for more than ten years, the author has acted as the theory explorer, algorithm designer, data and knowledge organizer and the operative program and AI program developer, and web site master as well. That is why the author laughed at himself as doing certain “single-gun-and-horse huge project”. May be in that case, any one in the world cannot help feeling alone and hopeless.The things, however, have dramatically been changed since the popularization of Internet, where the so many scientists publish their own best works. The Human Genome Project(HGP) which was finished in last year is doubtlessly a great work in the history of life sciences. After HGP,people has realized more clearly that it is hardly possible to understand the true essence by the separate information of genes and proteins(e.g. enzymes). In the coming time, ie so called meta-genomic time, the integration researches such as protein network integration, drug ontology synthesis, biochemistry reaction network, etc are going to be merging one after another.Suddenly BMKI found so many friendly researches and sciences and is getting excited. With best wishes!(to be continued)References1.JH van Bemmel, MA Musen主编,包含飞,郑学侃主译:《医学信息学》,上海:上海科技出版社,20022.郝柏林,张淑誉:《生物信息学手册》,上海:上海科技出版社,20003.张镜如主编,乔健天副主编:《生理学》,北京:人民卫生出版社,86-88,39-45,1996 4.包含飞:续议中医学是复杂性科学——中医标准化预备研究之三(待发表)5.Bao H.F.: HCSL:A Human-Computer Commonly Understandable and Communicatable Medical Language, 《Proceedings of The First China-Japan-Korea Joint Symposium on Medical Informatics(CJKMI'99)》, p177-181,19996.包含飞:创造一种人机共解互通的医学语言(西医学部分), 第八届全国医药信息学大会论文集, CMIA’99(电子工程师增刊), 1999, 98:18-247.刘沁,包含飞:人机共享语言HCSL(医学)的第二版的计算机实现,中外名医杂志,(1):78-80,20018.UMLS Knowledge Sources 14 Edition, January Release, /research/umls/UMLSDOC_2003AA.pdf9.Benno Schwikowski, Peter Uetz and Stanley Fields: A network of protein–protein interactions in yeast,http://itgmv1.fzk.de/www/itg/uetz/publications/Schwikowski2000.pdf 10.Dieter Fensel, Mark A. 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integrative education model -回复integrative education model is an innovative approach to teaching and learning that aims to integrate diverse subject areas and cultivate holistic understanding among students. This model strives to break the traditional silos of education by merging the disciplines of arts, sciences, humanities, and social sciences into a cohesive curriculum.One of the key aspects of the integrative education model is its focus on interdisciplinary learning. Instead of studying subjects in isolation, students are encouraged to explore connections and relationships between different disciplines. This approach helps students develop a broader perspective and understand how various fields of knowledge intersect and impact one another.The integrative education model also emphasizes connections between theory and practice. It promotes experiential learning opportunities such as internships, field trips, and project-based assignments. These hands-on experiences provide students with real-world context, allowing them to apply theoretical knowledge and develop critical thinking and problem-solving skills. By engaging in practical experiences, students gain a deeper understanding of the subject matter and are better prepared for future challenges.Furthermore, the integrative education model promotes collaboration and teamwork. It encourages students to work together on group projects, discussions, and presentations. By collaborating with peers, students learn how to communicate effectively, share ideas, and respect different perspectives. These collaborative efforts not only promote a sense of community among students but also enhance their learning experience by valuing diverse opinions and promoting creativity.Another important feature of the integrative education model is its emphasis on personalized learning. Educational institutions adopting this model recognize that every student has unique strengths, weaknesses, and learning preferences. Therefore, the curriculum is designed to accommodate individual needs and provide personalized learning opportunities. Teachers employ various instructional strategies, such as differentiated instruction, flipped classrooms, and blended learning, to cater to the diverse learning styles and abilities of students.Moreover, the integrative education model highlights the integration of technology in education. It recognizes the importance of digital literacy and equips students with the necessary skills to navigate and utilize technology effectively. Technology integration can enhance the learning experience byproviding access to a vast range of resources, facilitating research, promoting creativity, and fostering global connections.The integrative education model also emphasizes the development of critical thinking, creativity, and problem-solving skills. It encourages students to think critically, analyze information, and come up with innovative solutions to complex problems. By engaging in inquiry-based learning and open-ended projects, students develop essential skills that will serve them well in their future academic endeavors and professional careers.Furthermore, the integrative education model acknowledges the importance of emotional intelligence and social skills in students' overall development. It promotes a positive and inclusive learning environment that fosters empathy, respect, and collaboration. Through activities such as group discussions,role-playing, and community service, students develop emotional intelligence, social awareness, and interpersonal skills, which are crucial for their personal growth and success in the workplace.In conclusion, the integrative education model is an innovative educational approach that aims to provide students with awell-rounded learning experience. By integrating diverse subject areas, emphasizing interdisciplinary learning, promotingconnections between theory and practice, fostering collaboration and teamwork, personalizing learning, integrating technology, and developing critical thinking and social skills, this model equips students with the necessary knowledge, skills, and attitudes to thrive in an ever-changing world. By embracing this integrative approach, educational institutions can create a transformative learning environment that prepares students for future challenges and opportunities.。
Integrating Knowledge Modeling and Multi-Agent SystemsMario G´o mez and Enric PlazaArtificial Intelligence Research Institute-Spanish Scientific Research CouncilCampus UAB08193BellaterraBarcelona,Spain{mario,enric}@iiia.csic.esAbstractThis paper outlines ORCAS,a framework for openMulti-Agent Systems(MAS)that maximizes the reuseof agent capabilities through multiple application do-mains,and supports the automatic,on-demand configu-ration of agent teams according to stated problem re-quirements.Considerable effort has been devoted tothe applicability of the framework,which resulted in theimplementation of an infrastructure to develop and de-ploy cooperative MAS.This infrastructure explores theidea of configuring an electronic institution on-the-flytofit the specific requirements of each problem to besolved by a group of agents.IntroductionOpen MAS require a new way of managing and integrating agent capabilities based on middleware:connectivity soft-ware that enables multiple processes running on one or more machines to interact across a network.When implemented in MAS,the middleware layer is usually provided by mid-dle agents(Decker,Sycara,&Williamson1997),such as matchmakers(Decker,Williamson,&Sycara1996),facil-itators(Erickson1996;Genesereth&Ketchpel1997)and brokers(Nodine,Bohrer,&Ngu1999).Typically,the func-tion of a middle agent is to pair requesters with providers that are suitable for them,a process called matchmaking. Matchmaking is the process of verifying whether the specification of an agent capability“matches”the specifi-cation of a request to do something(a task to achieve): two specifications“match”if certain relation holds between them,usually a capability being able to achieve some task. Since matchmaking compares the specification of requests and advertisements,both providers and requesters must share a common language to describe them.This language is usually called an Agent Capability Description Language (ACDL).Semantic matchmaking,which is based on the use of shared ontologies to annotate agent capabilities(Guar-ino1997),improves the matchmaking process and facilitates interoperation.However,the reuse of existing capabilities over new application domains is still difficult because capa-bilities are usually associated to a specific application do-main.Copyright c 2005,American Association for Artificial Intelli-gence().All rights reserved.Moreover,in addition to capability discovery throughmatchmaking,we want an ACDL to support other activitiesinvolved in MAS interoperation,namely:invocation,com-position(team-design),team-formation and coordination.•Invocation:an agent willing to invoke the capability pro-vided by another agent must provide the input data in anappropriate format and using a shared interaction proto-col.•Composition:refers to the aggregation of several capabil-ities to achieve a global team goal.This process requires a combination of matchmaking,capability selection,and verification of whether the aggregated functionality sat-isfies the specification of the global goal.We call this process Team Design.•Team Formation:is the process of allocating tasks to agents,according to the constrains determined during the Team Design process.Team members can be selected among several candidate agents,either in a distributed or centralized manner,and agents must agree upon the in-teraction protocols to coordinate during the cooperative activity.•Coordination:team members must synchronize their ac-tions so as to avoid deadlocks and effectively cooperate. We want an ACDL to support both requesters and providers through all these activities.Our main goals are to extend matchmaking so as to maximize capability reuse,and to support the automatic composition of capabilities accord-ing to stated problem requirements.In a wide sense of the word,our focus is on the reuse issue,that we define as how to reuse an agent capability for different tasks,across sev-eral application domains,and interacting with other capabil-ities provided by different,probably heterogeneous agents. Therefore,in order to maximize the reuse of agent capa-bilities,we have explored the potential of the knowledge-modelling stance and the ideas brought about by the compo-nential approach to software development.The result of our work is ORCAS a multi-layered framework for the design, development,and deployment of Cooperative MAS in open environments.But instead of going through the ORCAS framework layer by layer,this paper describes the corner-stones of the ORCAS framework transversally.The aspects of the ORCAS framework we focus hereinare the ORCAS model of the Cooperative Problem Solving(CPS)process,and the ORCAS Agent Capability Descrip-tion Language (ACDL).Overview of the ORCAS frameworkThis section provides an overall view of the framework and reviews the most important concepts needed to under-stand the other sections.The reader is referred to (G´o mez 2004)for a complete,detailed description of the framework.ORCAS has three layers,namely:the Knowledge Mod-elling Framework,the Operational Framework and the In-stitutional Framework:1.The Knowledge Modelling Framework (KMF)proposes a conceptual and architectural description of problem-solving systems from a knowledge-level view,abstract-ing the specification of components from implementation details.In addition,the KMF incorporates a bottom-up design process to configure a system out of elementary components,until a system configuration is found that satisfies some global requirements.This process is used to design the organization of a team and the competence required for each team role during the problem-solving process.2.The Operational Framework deals with the link between the specification of components in the KMF,and the oper-ational aspects of Multi-Agent Systems.This framework comprehends an extension of the KMF to obtain a full-fledged Agent Capability Description Language,together with a new model of the Cooperative Problem Solving process that includes a Team Design stage prior to the Team Formation stage.3.The Institutional Framework describes an implemented infrastructure for developing and deploying Multi-Agent Systems configurable on-demand,according to the the re-quirements of the problem athand.Figure 1:The three layers of the ORCAS framework Figure 1shows the three layers as a pyramid made of three blocks.The block at the bottom corresponds to the more abstract layer,while upper blocks corresponds to increas-ingly implementation dependent layers.Therefore,develop-ers and system engineers can decide to use only a portion of the framework,starting from the bottom,and modifying or changing the other frameworks according to its preferences andneeds.Figure 2:The ORCAS Abstract ArchitectureKnowledge Modelling FrameworkThe ORCAS Knowledge Modelling Framework (KMF)proposes a conceptual description of Multi-Agent Systems at the knowledge level (Newell 1982),abstracting the spec-ification of components from implementation details.The purpose of the Knowledge Modelling Framework (KMF)is twofold:on the one hand,the KMF is a conceptual tool to guide developers in the analysis and design of Multi-Agent Systems in a way that maximizes capability reuse across dif-ferent domains;on the other hand,the KMF provides the ba-sis for an Agent Capability Description Language (ACDL)supporting the automatic,on-demand configuration of agent teams according to stated problem requirements.The ORCAS KMF is based on the Task-Method-Domain (TDM)paradigm prevailing in existing Knowledge Mod-elling frameworks.This paradigm distinguishes between three classes of components:tasks ,problem-solving meth-ods (PSM)and domain models .In ORCAS there are tasks and domain models,while PSMs are replaced by agent capa-bilities,playing the same role as PSMs,but including agent specific features concerning communication and coordina-tion.Adopting this KMF we expect the ORCAS Abstract Architecture to provide an effective organization for con-structing libraries with large “horizontal cover”(Breuker &Van de Velde 1994;Valente,Van de Velde,&Breuker 1994;Motta 1999),thus maximizing reusability and avoiding the brittleness of monolithic libraries (Motta et al.1999).A task is a functional description of a type of problem to be solved.A task is functionally characterized by input roles ,output roles ,and the relationship between them,which is specified as a set of preconditions and postconditions .A capability describes a particular method for solving problems with some specific properties.A capability is specified from a functional viewpoint by stating the input roles ,output roles ,preconditions and posconditions .In ad-dition,a capability can specify the type of domain knowl-edge (knowledge-roles )it requires,and some properties that have to be fulfilled by the domain knowledge to sensibly ap-ply that capability (assumptions ).There are two types of capability:task-decomposer and skill .While skills are used to describe primitive,atomic rea-soning steps,task-decomposers are used to describe com-plex reasoning methods that decompose a problem into sev-eral subtasks.Finally,a domain model (DM)specifies the concepts,re-lations and properties characterizing the knowledge from certain application domain.The ORCAS KMF extends matchmaking in two ways (G´o mez &Plaza 2004):first,in addition to provide its own version of a task-capability matching ,ORCAS introduces a capability-domain matching to decide whether a capability is suitable to be applied over certain application domain;and second,ORCAS addresses the composition of capabilities on-demand,according to the requirements of the problem at hand.We regard the composition of capabilities as a “bottom-up design problem”(Hafedh Mili &Mili 1995),which in agent terms would be rewritten as:given a set of requirements,find a set of agent capabilities whose combined competence and available knowledge satisfies those requirements .The main difficulty to solve that problem is how to decompose the requirements in such a way as to yield component spec-ifications.Our approach to this problem is to use a search process over the space of possible configurations.In OR-CAS ,the bottom-up design process is called Team Design,and the result is a task-configuration ,a hierarchical decom-position of a task into subtasks,and capabilities bound to tasks according to matching relations,in such a way that the resulting task-configuration satisfies the global problem re-quirements.Figure 3:Task-configuration exampleFigure 3shows an example of a task-configuration for the Information-Search task,which is used within the WIM application.This task is being decomposed into four tasks by the Meta-search task-decomposer:Elaborate-query ,Customize-query ,Retrieve and Aggregate ,which is fur-ther decomposed by the Aggregation capability in two sub-tasks:Elaborate-items and Aggregate-items .The ex-ample shows some skills requiring domain knowledge,e.g.the Query-expansion-with-thesaurus requires a thesaurus (e.g.MeSH ,a medical thesaurus),and the Retrieval and Query-customization skills require a description of infor-mation sources.Operational FrameworkThe Operational Framework describes a mapping from con-cepts in the Knowledge-Modelling Framework to concepts from Multi-Agent Systems.Specifically,the Operational Framework describes how a composition of capabilities rep-resented at the knowledge-level can be operationalized by a customized team of agents;in other words,how to form a team of agents able to carry on the execution of a particu-lar composition of capabilities,over a particular application domain.The Operational Framework proposes a hierarchical model of teamwork that is straightforwardly derived from the hierarchical decomposition of tasks into subtasks that is the backbone of the TDM paradigm.This model of team-work is embedded within a complete model of the Coopera-tive Problem Solving process that covers all the stages,from the specification of a problem to be solved to the activities carried on by agents willing to solve it.In order to effectively use a KMF in open agent envi-ronments,a capability description language should include some way of specifying the communication and the coor-dination mechanisms required by agents to cooperate.Our approach to describe such aspects of a capability is based on the macro-level (societal)aspects of agent societies,which is focused on the communication and the observable behav-ior of agents,rather than adopting a micro-level (internal)view on individual agents.In particular,we are using con-cepts from the electronic institutions formalism to describe such aspects,as explained in the next section.Institutional FrameworkAn electronic institution (e-Institution),is a “virtual place”designed to support and facilitate certain goals to the human and software agents concurring to that place (Noriega 1997;Rodr´ıguez-Aguilar 1997).Since these goals are achieved by means of the interaction of agents,an e-institution provides the social mediation layer required to achieve a successful interaction:interaction protocols,shared ontologies,com-munication languages and social behavior rules.The ORCAS Institutional Framework devises a institu-tional model covering all the stages of the ORCAS Coop-erative Problem Solving process:the ORCAS e-Institution,a platform for developing and deploying cooperative MAS that supports both providers and requesters of capabilities along the different stages of the ORCAS model of the CPS process.The e-Institutions formalism was originally conceived to deal with static organizations encompassing a fixed role structure and fixed interaction protocols,while the ORCAS institutional framework (both conceptually and in the imple-mented agent platform)is an electronic institution that al-lows other institutions to be configured on-the fly.The point is that in the ORCAS platform,each team formed to solve a problem implies that a new electronic institution is com-posed on demand out of the interaction protocols agents are equipped with,and that institution is used as the social me-diation layer required by team members to effectively com-municate and coordinate during the CPS activity.The integration of the knowledge-modelling stance and the electronic institutions formalism within the ORCAS e-Institution favors the development of highly configurable and reusable MAS in open environments.Remark that in addition to implement an agent infrastruc-ture using the electronic institutions formalism,we are us-ing the concepts proposed by the e-Institutions approach to specify the communication and coordination mechanisms of individual agents.In order to demonstrate the applicability of the ORCAS framework,we have built WIM ,a MAS-based configurable application to search bibliographic information in the In-ternet (G´o mez &Abasolo 2003;G´o mez 2004).WIM is an e-Institution that results of linking a library of informa-tion search and aggregation (ISA)capabilities provided by agents,and domain knowledge from medicine,and more specifically,Evidence-Based Medicine (EBM).Figure 4outlines the architecture of the WIM application.The OR-CAS e-Institution provides the mediation service for agents to communicate and cooperate.The institution provides a yellow pages service (through a librarian agent)where prob-lem solving agents register their capabilities.The capabili-ties in the library are link to some domain models character-izing the application domain (EBM).User requests to per-form search tasks solve problem are received through a Per-sonal Assistant agent that expresses them using the ISA on-tology.This agent acts as mediator between the user and the institutional agents providing the services required to carry on the CPS process:to find a valid task-configuration (Team Design),to allocate tasks to agents (Team Formation),and to coordinate individual agent behaviors to achieve the global task cooperatively(Teamwork).The figure depicts also the existence of wrappers,ad-hoc elements that are used to agentify external information sources,making them acces-sible toagents.Figure 4:Task-configuration exampleThe ORCAS model of the CooperativeProblem-Solving processMost of the research done in the field of cooperative MAS fits into one or more or the stages of the CooperativeProblem-Solving process as presented in (Wooldridge &Jennings 1994),which consists of four stages:recognition ,team formation ,planning and execution .Although the proposers of this model believe many in-stances of the CPS process exhibit these stages in some form (either explicitly or implicitly),they stress that the model is idealized.In other words,there are cases that the model can-not account for (Wooldridge &Jennings 1999).Since team formation is not guided by a preplan to achieve the overall goal,but is just a commitment to joint action,then neither the agents joining a team (committing to carry on joint ac-tion)are guaranteed to play one role in the team once a plan was decided at the subsequent planning stage,nor the result-ing team assures that a global plan can be found.The SharedPlans theory (Grosz &Kraus 1996)and the frameworks based on it,e.g.(Giampapa &Sycara 2002),have emphasized the need for a common,high-level team model that allows agents to understand the requirements to achieve a global team goal and select a plan.Team plans are used by agents to acquire goals,to identify roles and to relate individual goals to team goals.A plan allows the ini-tiator of the team formation process to know which are the subgoals and (optionally)the actions or capabilities required to achieve each subgoal.Therefore,the initiator of a CPS process can use an initial plan to guide the team formation process (Tidhar,Rao,&Sonenberg 1996).However,there is still another limitation of most planning frameworks used in real,implemented MAS:plans are tightly bound to a very specific domain and generic tasks.Consequently,plans are hardly reusable for different domains,and cannot be adapted to fit specific requirements of the problem at hand (e.g.dif-ferent users may prefer different ways of achieving a task,and thus different capabilities may be preferable depending on the user preferences).Finally,most planning-based MAS devise an internal per-spective of agents,whose internal state is used as the basis for evaluating the cooperative behavior.Concerning this is-sue,though we recognize there are well founded reasons to adopt an internal perspective in several contexts,we think a external view has some notable advantages under certain circumstances;in particular,it is more appropriate for open systems because it avoids imposing a model of the internal agent architecture to external agent developers.Summing up,we address some requirements for devel-oping Cooperative MAS in open environments that are not entirely covered by previous models of the CPS process:(1)the need for initial plans to guide the team formation pro-cess;(2)the consideration of the user preferences and spe-cific problem requirements as a constrain over the compe-tence of a team;and (3)an external view centered on ob-servable events,rather than an internal view imposing a par-ticular agent architecture.As a result of our work upon these issues we have con-ceived a new model of the CPS process with four sub-processes (Figure 5),namely Problem Specification,Team Design,Team Formation and Teamwork.The Problem Specification process produces a specifica-tion of problem requirements to be met by a team,includ-ing a description of the application domain (a collection ofFigure5:Overview of the ORCAS Cooperative Problem Solving process.domain models)and the problem data to be used during the Teamwork process.The Team Design process uses the prob-lem requirements to build a task-configuration—a composi-tion of tasks,capabilities and domain models.The resulting task-configuration is used during the Team Formation pro-cess to allocate tasks and subtasks to agents,and instruct agents to ensure that the requirements of the problem are achievable.Finally,during the Teamwork process,team members try to solve the problem cooperatively,following the instructions received during Team Formation,and thus complying with the specific requirements of the problem at hand.The ORCAS model of the CPS process should not be un-derstood as afixed sequence of steps.Actually,we have im-plemented strategies that interleave Team Design and Team Formation with Teamwork,thus enabling distributed con-figuration,lazy configuration and dynamic reconfiguration of teams on runtime(G´o mez2004).The ORCAS Agent Capability DescriptionLanguageThe functional description of a capability as provided in the Knowledge-Modelling Framework enables the automated discovery and the composition of capabilities,without tak-ing into account communication and coordination require-ments.Nonetheless,the invocation of capabilities and the interoperation of multiple agents are not supported by the functional description of a capability.In order to deal with these activities,we have to include information concerning the communication requirements of an agent over the capa-bilities he provides,and the operational description(control and dataflow)of tasks-decomposers.Figure6shows the main concepts specifiable in the ORCAS ACDL.In ORCAS,both the communication and the operational description of a capability are described using concepts from the electronic institutions formalism(Esteva et al.2001), which adopts an external view on agents.Specifically,OR-CAS uses scenes to describe the communication require-ments of an agent over a particular capability,and perfor-mative structures to specify the operational description of a task-decomposer.Since we are using those concepts in a new way,a brief review of the electronic institutions con-cepts used in ORCAS is pertinentnow:Figure6:Main features of the ORCAS ACDL1.Agent roles:Agents are the players in an electronicinstitution,interacting by the exchange of speech acts, whereas roles are standardized patterns of behavior re-quired by agents playing part in given functional relation-ships.2.Dialogic framework:A dialogic framework determinesthe valid illocutions that can be exchanged among the agents participating in an electronic institution,which in-volves a vocabulary(ontology)and some agent commu-nication language(ACL).munication scenes:A scene defines an interactionprotocol among a set of agent roles,and using some di-alogic framework.4.Performative structure:A performative structure is a net-work of connected scenes that captures the relationships among scenes.A performative structure constrains the paths agents can traverse to move from one scene to an-other,depending on the roles they are playing. CommunicationAgent capabilities should be specified independently of other agents in order to maximize their reuse and facilitate their specification by third party agent developers.In the general case,agent developers do not know a priori the tasks that could be achieved by a particular capability,neither the domains they could be applied to.As a consequence,the team roles an agent could play using a capability are not known in advance,thus the scenes used to specify the com-munication requirements of an agent over certain capabil-ity cannot be specified in terms of specific team-roles,but in terms of abstract,generic problem solving roles.Since ORCAS teams are designed in terms of a hierarchical de-composition of tasks into subtasks,then teamwork in OR-CAS is straightforwardly organized as a hierarchy of team-roles.Some positions within a team(team-roles)are bound to a task-decomposer,thus the agents playing those team-roles are responsible of delegating subtasks to other agents, receiving the results,and performing intermediate data pro-cessing between subtasks.In such an scenario,we can estab-lish an abstract communication model with two basic roles: coordinator,which is adopted by an agent willing to decom-pose a task into subtasks,and operator,which is adopted by the agent having to perform a task on demand,using the data provided by another agent that acts as coordinator of a top-level taskFigure 7:Example of a communicationsceneFigure 8:Choosing scenes during Team Formation Figure 7shows an example of a scene depicting the com-munication requirements of an agent over a capability.This example shows a typical request-inform protocol in terms of the ORCAS generic roles:coordinator (requester)and operator (informer).Operational descriptionThe ORCAS approach to specify the operational descrip-tion of a task-decomposer is based on performative struc-tures,with some distinctive features.In ORCAS ,as in the e-institutions formalism,each scene within a performa-tive structure corresponds to an interaction protocol.How-ever,in ORCAS the scenes within a performative structure are not instantiated beforehand.Actually,these scenes are instantiated during the team-formation process using as a source the set of communication scenes shared by the agents willing to interactEach scene corresponds to the communication required to solve a subtask,which implies an agent acting as coordina-tor invoking the capability provided by another agent acting as operator.Both the coordinator and the operator must ad-here to the same scene in order to communicate,and as a consequence,the scene must be chosen out of the scenes supported by both agents (see Figure 8).Since agents are selected dynamically during the Team Formation process,then the scenes used within ORCAS performative structures must also be chosen dynamically,before starting Teamwork.Figure 9shows an example of a performative structure specifying the operational description of the Aggregation task-decomposer,which decomposes a task into two sub-tasks:Elaborate-items and Aggregate-items .Therefore,this performative structure has two scenes (in addition to the Start and End scenes),one for each subtask,and three roles:x,y,z .There is one role of type coordinator (x )tobe played by the agent applying the task-decomposer,and as many operators as subtasks.In the example there are two operators,one (y )participating in the Elaborate-items (EI)scene,and another (z )participating in the Aggregate-Items (AI)scene.Notice that the coordinator (x )is the same in both scenes,it enters first the EI scene,and can enter the AI scene only after finishing the EIscene.Figure 9:Task-decomposer operational description So far,we have addressed the performative structure as-sociated to a single task-decomposer,but what about the op-erational description of an entire team?The ORCAS organization of a team adheres to the hier-archical decomposition of task into subtasks embodied by a task-configuration:starting from a top team-role,each team-role associated to a task-decomposer introduces a set of team-roles subordinated to it.Since each task-decomposer has an operational description described by a performative structure,therefore,the operational description of a team can be modelled as a nested structure of performative structures (Figure 10shows an example).There is one performative structure for each task-decomposer,starting from the team-role associated to the root task of a task-configuration (theteam-leader).Figure 10:Teamwork as a nested structure of performative structuresFigure 10sums up the specification of the teamwork ac-tivity as a nested structure of performative structures.No-tice that there is a performative structure for each task-decomposer in the task-configuration,and there is one scene for each task.Performative structures embody which roles。