Budgeted learning, part I The multi-armed bandit case. submitted

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。

Linear Algebra and its Applications432(2010)2089–2099Contents lists available at ScienceDirect Linear Algebra and its Applications j o u r n a l h o m e p a g e:w w w.e l s e v i e r.c o m/l o c a t e/l aaIntegrating learning theories and application-based modules in teaching linear algebraୋWilliam Martin a,∗,Sergio Loch b,Laurel Cooley c,Scott Dexter d,Draga Vidakovic ea Department of Mathematics and School of Education,210F Family Life Center,NDSU Department#2625,P.O.Box6050,Fargo ND 58105-6050,United Statesb Department of Mathematics,Grand View University,1200Grandview Avenue,Des Moines,IA50316,United Statesc Department of Mathematics,CUNY Graduate Center and Brooklyn College,2900Bedford Avenue,Brooklyn,New York11210, United Statesd Department of Computer and Information Science,CUNY Brooklyn College,2900Bedford Avenue Brooklyn,NY11210,United Statese Department of Mathematics and Statistics,Georgia State University,University Plaza,Atlanta,GA30303,United StatesA R T I C L E I N F O AB S T R AC TArticle history:Received2October2008Accepted29August2009Available online30September2009 Submitted by L.Verde-StarAMS classification:Primary:97H60Secondary:97C30Keywords:Linear algebraLearning theoryCurriculumPedagogyConstructivist theoriesAPOS–Action–Process–Object–Schema Theoretical frameworkEncapsulated process The research team of The Linear Algebra Project developed and implemented a curriculum and a pedagogy for parallel courses in (a)linear algebra and(b)learning theory as applied to the study of mathematics with an emphasis on linear algebra.The purpose of the ongoing research,partially funded by the National Science Foundation,is to investigate how the parallel study of learning theories and advanced mathematics influences the development of thinking of individuals in both domains.The researchers found that the particular synergy afforded by the parallel study of math and learning theory promoted,in some students,a rich understanding of both domains and that had a mutually reinforcing effect.Furthermore,there is evidence that the deeper insights will contribute to more effective instruction by those who become high school math teachers and,consequently,better learning by their students.The courses developed were appropriate for mathematics majors,pre-service secondary mathematics teachers, and practicing mathematics teachers.The learning seminar focused most heavily on constructivist theories,although it also examinedThe work reported in this paper was partially supported by funding from the National Science Foundation(DUE CCLI 0442574).∗Corresponding author.Address:NDSU School of Education,NDSU Department of Mathematics,210F Family Life Center, NDSU Department#2625,P.O.Box6050,Fargo ND58105-6050,United States.Tel.:+17012317104;fax:+17012317416.E-mail addresses:william.martin@(W.Martin),sloch@(S.Loch),LCooley@ (L.Cooley),SDexter@(S.Dexter),dvidakovic@(D.Vidakovic).0024-3795/$-see front matter©2009Elsevier Inc.All rights reserved.doi:10.1016/a.2009.08.0302090W.Martin et al./Linear Algebra and its Applications432(2010)2089–2099Thematicized schema Triad–intraInterTransGenetic decomposition Vector additionMatrixMatrix multiplication Matrix representation BasisColumn spaceRow spaceNull space Eigenspace Transformation socio-cultural and historical perspectives.A particular theory, Action–Process–Object–Schema(APOS)[10],was emphasized and examined through the lens of studying linear algebra.APOS has been used in a variety of studies focusing on student understanding of undergraduate mathematics.The linear algebra courses include the standard set of undergraduate topics.This paper reports the re-sults of the learning theory seminar and its effects on students who were simultaneously enrolled in linear algebra and students who had previously completed linear algebra and outlines how prior research has influenced the future direction of the project.©2009Elsevier Inc.All rights reserved.1.Research rationaleThe research team of the Linear Algebra Project(LAP)developed and implemented a curriculum and a pedagogy for parallel courses in linear algebra and learning theory as applied to the study of math-ematics with an emphasis on linear algebra.The purpose of the research,which was partially funded by the National Science Foundation(DUE CCLI0442574),was to investigate how the parallel study of learning theories and advanced mathematics influences the development of thinking of high school mathematics teachers,in both domains.The researchers found that the particular synergy afforded by the parallel study of math and learning theory promoted,in some teachers,a richer understanding of both domains that had a mutually reinforcing effect and affected their thinking about their identities and practices as teachers.It has been observed that linear algebra courses often are viewed by students as a collection of definitions and procedures to be learned by rote.Scanning the table of contents of many commonly used undergraduate textbooks will provide a common list of terms such as listed here(based on linear algebra texts by Strang[1]and Lang[2]).Vector space Kernel GaussianIndependence Image TriangularLinear combination Inverse Gram–SchmidtSpan Transpose EigenvectorBasis Orthogonal Singular valueSubspace Operator DecompositionProjection Diagonalization LU formMatrix Normal form NormDimension Eignvalue ConditionLinear transformation Similarity IsomorphismRank Diagonalize DeterminantThis is not something unique to linear algebra–a similar situation holds for many undergraduate mathematics courses.Certainly the authors of undergraduate texts do not share this student view of mathematics.In fact,the variety ways in which different authors organize their texts reflects the individual ways in which they have conceptualized introductory linear algebra courses.The wide vari-ability that can be seen in a perusal of the many linear algebra texts that are used is a reflection the many ways that mathematicians think about linear algebra and their beliefs about how students can come to make sense of the content.Instruction in a course is based on considerations of content,pedagogy, resources(texts and other materials),and beliefs about teaching and learning of mathematics.The interplay of these ideas shaped our research project.We deliberately mention two authors with clearly differing perspectives on an undergraduate linear algebra course:Strang’s organization of the material takes an applied or application perspective,while Lang views the material from more of a“pure mathematics”perspective.A review of the wide variety of textbooks to classify and categorize the different views of the subject would reveal a broad variety of perspectives on the teaching of the subject.We have taken a view that seeks to go beyond the mathe-matical content to integrate current theoretical perspectives on the teaching and learning of undergrad-uate mathematics.Our project used integration of mathematical content,applications,and learningW.Martin et al./Linear Algebra and its Applications432(2010)2089–20992091 theories to provide enhanced learning experiences using rich content,student meta cognition,and their own experience and intuition.The project also used co-teaching and collaboration among faculty with expertise in a variety of areas including mathematics,computer science and mathematics education.If one moves beyond the organization of the content of textbooks wefind that at their heart they do cover a common core of the key ideas of linear algebra–all including fundamental concepts such as vector space and linear transformation.These observations lead to our key question“How is one to think about this task of organizing instruction to optimize learning?”In our work we focus on the conception of linear algebra that is developed by the student and its relationship with what we reveal about our own understanding of the subject.It seems that even in cases where researchers consciously study the teaching and learning of linear algebra(or other mathematics topics)the questions are“What does it mean to understand linear algebra?”and“How do I organize instruction so that students develop that conception as fully as possible?”In broadest terms, our work involves(a)simultaneous study of linear algebra and learning theories,(b)having students connect learning theories to their study of linear algebra,and(c)the use of parallel mathematics and education courses and integrated workshops.As students simultaneously study mathematics and learning theory related to the study of mathe-matics,we expect that reflection or meta cognition on their own learning will enable them to construct deeper and more meaningful understanding in both domains.We chose linear algebra for several reasons:It has not been the focus of as much instructional research as calculus,it involves abstraction and proof,and it is taken by many students in different programs for a variety of reasons.It seems to us to involve important mathematical content along with rich applications,with abstraction that builds on experience and intuition.In our pilot study we taught parallel courses:The regular upper division undergraduate linear algebra course and a seminar in learning theories in mathematics education.Early in the project we also organized an intensive three-day workshop for teachers and prospective teachers that included topics in linear algebra and examination of learning theory.In each case(two sets of parallel courses and the workshop)we had students reflect on their learning of linear algebra content and asked them to use their own learning experiences to reflect on the ideas about teaching and learning of mathematics.Students read articles–in the case of the workshop,this reading was in advance of the long weekend session–drawn from mathematics education sources including[3–10].APOS(Action,Process,Object,Schema)is a theoretical framework that has been used by many researchers who study the learning of undergraduate and graduate mathematics[10,11].We include a sketch of the structure of this framework and refer the reader to the literature for more detailed descriptions.More detailed and specific illustrations of its use are widely available[12].The APOS Theoretical Framework involves four levels of understanding that can be described for a wide variety of mathematical concepts such as function,vector space,linear transformation:Action,Process,Object (either an encapsulated process or a thematicized schema),Schema(Intra,inter,trans–triad stages of schema formation).Genetic decomposition is the analysis of a particular concept in which developing understanding is described as a dynamic process of mental constructions that continually develop, abstract,and enrich the structural organization of an individual’s knowledge.We believe that students’simultaneous study of linear algebra along with theoretical examination of teaching and learning–particularly on what it means to develop conceptual understanding in a domain –will promote learning and understanding in both domains.Fundamentally,this reflects our view that conceptual understanding in any domain involves rich mental connections that link important ideas or facts,increasing the individual’s ability to relate new situations and problems to that existing cognitive framework.This view of conceptual understanding of mathematics has been described by various prominent math education researchers such as Hiebert and Carpenter[6]and Hiebert and Lefevre[7].2.Action–Process–Object–Schema theory(APOS)APOS theory is a theoretical perspective of learning based on an interpretation of Piaget’s construc-tivism and poses descriptions of mental constructions that may occur in understanding a mathematical concept.These constructions are called Actions,Processes,Objects,and Schema.2092W.Martin et al./Linear Algebra and its Applications432(2010)2089–2099 An action is a transformation of a mathematical object according to an explicit algorithm seen as externally driven.It may be a manipulation of objects or acting upon a memorized fact.When one reflects upon an action,constructing an internal operation for a transformation,the action begins to be interiorized.A process is this internal transformation of an object.Each step may be described or reflected upon without actually performing it.Processes may be transformed through reversal or coordination with other processes.There are two ways in which an individual may construct an object.A person may reflect on actions applied to a particular process and become aware of the process as a totality.One realizes that transformations(whether actions or processes)can act on the process,and is able to actually construct such transformations.At this point,the individual has reconstructed a process as a cognitive object. In this case we say that the process has been encapsulated into an object.One may also construct a cognitive object by reflecting on a schema,becoming aware of it as a totality.Thus,he or she is able to perform actions on it and we say the individual has thematized the schema into an object.With an object conception one is able to de-encapsulate that object back into the process from which it came, or,in the case of a thematized schema,unpack it into its various components.Piaget and Garcia[13] indicate that thematization has occurred when there is a change from usage or implicit application to consequent use and conceptualization.A schema is a collection of actions,processes,objects,and other previously constructed schemata which are coordinated and synthesized to form mathematical structures utilized in problem situations. Objects may be transformed by higher-level actions,leading to new processes,objects,and schemata. Hence,reconstruction continues in evolving schemata.To illustrate different conceptions of the APOS theory,imagine the following’teaching’scenario.We give students multi-part activities in a technology supported environment.In particular,we assume students are using Maple in the computer lab.The multi-part activities,focusing on vectors and operations,in Maple begin with a given Maple code and drawing.In case of scalar multiplication of the vector,students are asked to substitute one parameter in the Maple code,execute the code and observe what has happened.They are asked to repeat this activity with a different value of the parameter.Then students are asked to predict what will happen in a more general case and to explain their reasoning.Similarly,students may explore addition and subtraction of vectors.In the next part of activity students might be asked to investigate about the commutative property of vector addition.Based on APOS theory,in thefirst part of the activity–in which students are asked to perform certain operation and make observations–our intention is to induce each student’s action conception of that concept.By asking students to imagine what will happen if they make a certain change–but do not physically perform that change–we are hoping to induce a somewhat higher level of students’thinking, the process level.In order to predict what will happen students would have to imagine performing the action based on the actions they performed before(reflective abstraction).Activities designed to explore on vector addition properties require students to encapsulate the process of addition of two vectors into an object on which some other action could be performed.For example,in order for a student to conclude that u+v=v+u,he/she must encapsulate a process of adding two vectors u+v into an object(resulting vector)which can further be compared[action]with another vector representing the addition of v+u.As with all theories of learning,APOS has a limitation that researchers may only observe externally what one produces and discusses.While schemata are viewed as dynamic,the task is to attempt to take a snap shot of understanding at a point in time using a genetic decomposition.A genetic decomposition is a description by the researchers of specific mental constructions one may make in understanding a mathematical concept.As with most theories(economics,physics)that have restrictions,it can still be very useful in describing what is observed.3.Initial researchIn our preliminary study we investigated three research questions:•Do participants make connections between linear algebra content and learning theories?•Do participants reflect upon their own learning in terms of studied learning theories?W.Martin et al./Linear Algebra and its Applications432(2010)2089–20992093•Do participants connect their study of linear algebra and learning theories to the mathematics content or pedagogy for their mathematics teaching?In addition to linear algebra course activities designed to engage students in explorations of concepts and discussions about learning theories and connections between the two domains,we had students construct concept maps and describe how they viewed the connections between the two subjects. We found that some participants saw significant connections and were able to apply APOS theory appropriately to their learning of linear algebra.For example,here is a sketch outline of how one participant described the elements of the APOS framework late in the semester.The student showed a reasonable understanding of the theoretical framework and then was able to provide an example from linear algebra to illustrate the model.The student’s description of the elements of APOS:Action:“Students’approach is to apply‘external’rules tofind solutions.The rules are said to be external because students do not have an internalized understanding of the concept or the procedure tofind a solution.”Process:“At the process level,students are able to solve problems using an internalized understand-ing of the algorithm.They do not need to write out an equation or draw a graph of a function,for example.They can look at a problem and understand what is going on and what the solution might look like.”Object level as performing actions on a process:“At the object level,students have an integrated understanding of the processes used to solve problems relating to a particular concept.They un-derstand how a process can be transformed by different actions.They understand how different processes,with regard to a particular mathematical concept,are related.If a problem does not conform to their particular action-level understanding,they can modify the procedures necessary tofind a solution.”Schema as a‘set’of knowledge that may be modified:“Schema–At the schema level,students possess a set of knowledge related to a particular concept.They are able to modify this set of knowledge as they gain more experience working with the concept and solving different kinds of problems.They see how the concept is related to other concepts and how processes within the concept relate to each other.”She used the ideas of determinant and basis to illustrate her understanding of the framework. (Another student also described how student recognition of the recursive relationship of computations of determinants of different orders corresponded to differing levels of understanding in the APOS framework.)Action conception of determinant:“A student at the action level can use an algorithm to calculate the determinant of a matrix.At this level(at least for me),the formula was complicated enough that I would always check that the determinant was correct byfinding the inverse and multiplying by the original matrix to check the solution.”Process conception of determinant:“The student knows different methods to use to calculate a determinant and can,in some cases,look at a matrix and determine its value without calculations.”Object conception:“At the object level,students see the determinant as a tool for understanding and describing matrices.They understand the implications of the value of the determinant of a matrix as a way to describe a matrix.They can use the determinant of a matrix(equal to or not equal to zero)to describe properties of the elements of a matrix.”Triad development of a schema(intra,inter,trans):“A singular concept–basis.There is a basis for a space.The student can describe a basis without calculation.The student canfind different types of bases(column space,row space,null space,eigenspace)and use these values to describe matrices.”The descriptions of components of APOS along with examples illustrate that this student was able to make valid connections between the theoretical framework and the content of linear algebra.While the2094W.Martin et al./Linear Algebra and its Applications432(2010)2089–2099descriptions may not match those that would be given by scholars using APOS as a research framework, the student does demonstrate a recognition of and ability to provide examples of how understanding of linear algebra can be organized conceptually as more that a collection of facts.As would be expected,not all participants showed gains in either domain.We viewed the results of this study as a proof of concept,since there were some participants who clearly gained from the experience.We also recognized that there were problems associated with the implementation of our plan.To summarize ourfindings in relation to the research questions:•Do participants make connections between linear algebra content and learning theories?Yes,to widely varying degrees and levels of sophistication.•Do participants reflect upon their own learning in terms of studied learning theories?Yes,to the extent possible from their conception of the learning theories and understanding of linear algebra.•Do participants connect their study of linear algebra and learning theories to the mathematics content or pedagogy for their mathematics teaching?Participants describe how their experiences will shape their own teaching,but we did not visit their classes.Of the11students at one site who took the parallel courses,we identified three in our case studies (a detailed report of that study is presently under review)who demonstrated a significant ability to connect learning theories with their own learning of linear algebra.At another site,three teachers pursuing math education graduate studies were able to varying degrees to make these connections –two demonstrated strong ability to relate content to APOS and described important ways that the experience had affected their own thoughts about teaching mathematics.Participants in the workshop produced richer concept maps of linear algebra topics by the end of the weekend.Still,there were participants who showed little ability to connect material from linear algebra and APOS.A common misunderstanding of the APOS framework was that increasing levels cor-responded to increasing difficulty or complexity.For example,a student might suggest that computing the determinant of a2×2matrix was at the action level,while computation of a determinant in the 4×4case was at the object level because of the increased complexity of the computations.(Contrast this with the previously mentioned student who observed that the object conception was necessary to recognize that higher dimension determinants are computed recursively from lower dimension determinants.)We faced more significant problems than the extent to which students developed an understanding of the ideas that were presented.We found it very difficult to get students–especially undergraduates –to agree to take an additional course while studying linear algebra.Most of the participants in our pilot projects were either mathematics teachers or prospective mathematics teachers.Other students simply do not have the time in their schedules to pursue an elective seminar not directly related to their own area of interest.This problem led us to a new project in which we plan to integrate the material on learning theory–perhaps implicitly for the students–in the linear algebra course.Our focus will be on working with faculty teaching the course to ensure that they understand the theory and are able to help ensure that course activities reflect these ideas about learning.4.Continuing researchOur current Linear Algebra in New Environments(LINE)project focuses on having faculty work collaboratively to develop a series of modules that use applications to help students develop conceptual understanding of key linear algebra concepts.The project has three organizing concepts:•Promote enhanced learning of linear algebra through integrated study of mathematical content, applications,and the learning process.•Increase faculty understanding and application of mathematical learning theories in teaching linear algebra.•Promote and support improved instruction through co-teaching and collaboration among faculty with expertise in a variety of areas,such as education and STEM disciplines.W.Martin et al./Linear Algebra and its Applications432(2010)2089–20992095 For example,computer and video graphics involve linear transformations.Students will complete a series of activities that use manipulation of graphical images to illustrate and help them move from action and process conceptions of linear transformations to object conceptions and the development of a linear transformation schema.Some of these ideas were inspired by material in Judith Cederberg’s geometry text[14]and some software developed by David Meel,both using matrix representations of geometric linear transformations.The modules will have these characteristics:•Embed learning theory in linear algebra course for both the instructor and the students.•Use applied modules to illustrate the organization of linear algebra concepts.•Applications draw on student intuitions to aid their mental constructions and organization of knowledge.•Consciously include meta-cognition in the course.To illustrate,we sketch the outline of a possible series of activities in a module on geometric linear transformations.The faculty team–including individuals with expertise in mathematics,education, and computer science–will develop a series of modules to engage students in activities that include reflection and meta cognition about their learning of linear algebra.(The Appendix contains a more detailed description of a module that includes these activities.)Task1:Use Photoshop or GIMP to manipulate images(rotate,scale,flip,shear tools).Describe and reflect on processes.This activity uses an ACTION conception of transformation.Task2:Devise rules to map one vector to another.Describe and reflect on process.This activity involves both ACTION and PROCESS conceptions.Task3:Use a matrix representation to map vectors.This requires both PROCESS and OBJECT conceptions.Task4:Compare transform of sum with sum of transforms for matrices in Task3as compared to other non-linear functions.This involves ACTION,PROCESS,and OBJECT conceptions.Task5:Compare pre-image and transformed image of rectangles in the plane–identify software tool that was used(from Task1)and how it might be represented in matrix form.This requires OBJECT and SCHEMA conceptions.Education,mathematics and computer science faculty participating in this project will work prior to the semester to gain familiarity with the APOS framework and to identify and sketch potential modules for the linear algebra course.During the semester,collaborative teams of faculty continue to develop and refine modules that reflect important concepts,interesting applications,and learning theory:Modules will present activities that help students develop important concepts rather than simply presenting important concepts for students to absorb.The researchers will study the impact of project activities on student learning:We expect that students will be able to describe their knowledge of linear algebra in a more conceptual(structured) way during and after the course.We also will study the impact of the project on faculty thinking about teaching and learning:As a result of this work,we expect that faculty will be able to describe both the important concepts of linear algebra and how those concepts are mentally developed and organized by students.Finally,we will study the impact on instructional practice:Participating faculty should continue to use instructional practices that focus both on important content and how students develop their understanding of that content.5.SummaryOur preliminary study demonstrated that prospective and practicing mathematics teachers were able to make connections between their concurrent study of linear algebra and of learning theories relating to mathematics education,specifically the APOS theoretical framework.In cases where the participants developed understanding in both domains,it was apparent that this connected learning strengthened understanding in both areas.Unfortunately,we were unable to encourage undergraduate students to consider studying both linear algebra and learning theory in separate,parallel courses. Consequently,we developed a new strategy that embeds the learning theory in the linear algebra。

高等教育自学考试自考《英语二》模拟试题及答案指导一、阅读判断（共10分）First Question: Reading Comprehension and JudgementRead the following passage carefully and then decide whether the statements that follow are true or false. Mark your answers as “True (T)” or “False (F)”.Passage:In today’s globalized world, learning a second language has become more important than ever. Not only does it enhance personal development but it also opens up numerous career opportunities. Many experts agree that the best way to learn a new language is through immersion—living in a country where the language is spoken and using it daily. However, not everyone has this luxury. For those who cannot immerse themselves fully in another culture, there are other effective methods such as watching movies in the target language, practicing with native speakers online, or attending language classes at local community centers. Regardless of the method chosen, consistency and practice are key to mastering any language.Questions:1、Learning a second language is consi dered less important in today’s world. Answer: False (F)2、According to the passage, immersion is the most effective way to learna new language.Answer: True (T)3、The passage suggests that living in a foreign country is the only way to learn a new language.Answer: False (F)4、Watching movies in the target language is mentioned as an alternative method to full immersion.Answer: True (T)5、Consistency and practice are crucial for mastering a new language. Answer: True (T)This is a sample set of questions based on the given passage. Please note that actual examination questions would be more varied and challenging. Second Question: Reading Comprehension and JudgmentPassage:In the heart of the city stands an old library, known for its vast collection of rare books and historical manuscripts. Established in the early 19th century, it has been a beacon of learning and culture for generations. The library not only serves as a repository of knowledge but also hosts regular workshops and seminars to encourage literacy and academic research among the young and old. In recent years, due to the increasing use of digital media, the library has adapted by incorporating e-books and online databases into its resources. Despite these changes, the essence of the library remains the same—a place wherepeople can come together to learn, share ideas, and grow intellectually.Questions:1、The old library is primarily known for its modern architecture. Answer: False2、The library was established in the late 20th century.Answer: False3、The library offers workshops and seminars to promote literacy. Answer: True4、Recently, the library has begun to offer digital resources. Answer: True5、The library’s purpose has changed significantly with the introduction of e-books.Answer: False二、阅读理解（共10分）Title: The Digital Age and Its Impact on EducationIn the digital age, education has undergone significant transformations, reshaping the way we learn and access knowledge. With the widespread adoption of the internet, smartphones, and tablets, learning has become more accessible, interactive, and personalized than ever before. This revolution in education, often referred to as e-learning or online education, has opened doors to countless opportunities for learners worldwide.One of the most notable changes is the rise of massive open online courses (MOOCs). These platforms offer a wide range of courses, from introductory subjects to advanced specialties, for free or at a minimal cost. Students can enroll in courses from top universities around the globe, engaging with world-class professors and peers from diverse backgrounds. The flexibility of these courses allows learners to study at their own pace, fitting education into their busy schedules.Moreover, digital tools have enhanced the learning experience by providing interactive resources, such as simulations, videos, and virtual labs. These multimedia elements make abstract concepts more tangible, helping students grasp complex ideas more easily. Additionally, educational apps and software facilitate personalized learning, tailoring content and pace to each student’s needs and abilities.However, the digital age also poses challenges for education. The abundance of information online can be overwhelming, and the authenticity of sources needs to be carefully verified. Moreover, not everyone has access to the technology or the internet, creating a digital divide that exacerbates existing educational inequalities.Despite these challenges, the digital age has undeniably revolutionized education, making it more inclusive, accessible, and engaging. As we continue to navigate this digital landscape, it is crucial to harness its potential while addressing the challenges it presents, ensuring that everyone has theopportunity to benefit from this new era of learning.Questions:1.What is the main topic of the passage?A) The history of online education.B) The benefits and challenges of the digital age in education.C) The evolution of traditional classrooms.D) The role of technology in the workplace.Answer: B2.What are MOOCs?A) A type of online course offered by universities at no cost.B) A software program for creating educational videos.C) A method of assessing students’ understanding through tests.D) A tool for tracking students’ progress in traditional classrooms.Answer: A3.How do digital tools enhance the learning experience?A) By making education less accessible to students.B) By providing interactive resources like simulations and videos.C) By reducing the number of courses available online.D) By creating more opportunities for face-to-face interaction.Answer: B4.What is one challenge of the digital age in education?A) The lack of technology available to students.B) The ease of verifying the authenticity of online sources.C) The abundance of information available online.D) The inability to tailor content to individual students’ needs.Answer: C5.What is the author’s overall attitude towards the digital age in education?A) Negative, as it has created more problems than solutions.B) Neutral, as it has both advantages and disadvantages.C) Positive, as it has revolutionized education for the better.D) Undecided, as the impact is still unclear.Answer: C三、概况段落大意和补全句子（共10分）第一题Passage:The following passage is about the benefits and challenges of online learning. Please read the passage carefully and answer the questions below.Online learning has become increasingly popular in recent years, especially with the advancements in technology. It offers numerous benefits, such as flexibility, convenience, and cost-effectiveness. However, it also presents several challenges, including lack of face-to-face interaction, time management issues, and potential for distraction. Despite these challenges, many students have successfully completed their courses online and achieved academic success.Questions:1、What are some of the benefits of online learning?2、What challenges do students face when taking online courses?3、According to the passage, how have many students responded to the challenges of online learning?4、What is the author’s opinion about online learning?5、Choose the sentence that best summarizes the main idea of the passage.Answers:1、Flexibility, convenience, and cost-effectiveness.2、Lack of face-to-face interaction, time management issues, and potential for distraction.3、Many students have successfully completed their courses online and achieved academic success.4、The author believes that online learning offers numerous benefits but also presents several challenges.5.”Online learning has become increasingly popular due to its benefits, such as flexibility and convenience, but it also comes with challenges like lack of face-to-face interaction and time management issues.”第二题Passage:The history of the Great Wall of China is a testament to the ingenuity and perseverance of ancient Chinese engineers. Constructed over several centuries,the Great Wall was originally built to protect the Chinese Empire from invasions by nomadic tribes from the north. As time passed, the Wall became a symbol of China’s strength and unity. Its construction involved the labor of millions of workers, including soldiers, convicts, and civilians. The Wall stretches over 13,000 miles and is made primarily of stone, brick, wood, and earth, depending on the region. Despite its age, the Great Wall remains a marvel of human achievement and continues to attract tourists from around the world.1、The Great Wall of China was primarily built to __________.a) connect different provincesb) protect the Chinese Empire from northern invasionsc) serve as a road for traded) enhance the beauty of the Chinese landscape2、The construction of the Great Wall spanned over __________.a) a few decadesb) a few centuriesc) a few yearsd) a few months3、The Great Wall is a symbol of __________.a) Chinese agricultureb) Chinese strength and unityc) Chinese religiond) Chinese politics4、The Great Wall was built using various materials such as __________.a) only stone and brickb) only wood and earthc) stone, brick, wood, and earthd) only metal and glass5、Today, the Great Wall attracts __________.a) only local touristsb) a few tourists each yearc) millions of tourists from around the worldd) no tourists at allAnswers:1、b) protect the Chinese Empire from northern invasions2、b) a few centuries3、b) Chinese strength and unity4、c) stone, brick, wood, and earth5、c) millions of tourists from around the world四、填空补文（共10分）Section 4: Fill in the BlanksRead the following passage and choose the best word or phrase to complete each blank.Passage:In the modern world, the importance of learning a second language cannot be overstated. The ability to communicate in multiple languages has become a crucial skill for personal and professional development. One such language that has gained immense popularity is English. The demand for English proficiency is evident in various sectors, such as business, technology, and entertainment. To meet this demand, many individuals opt for self-study programs to improve their English skills. One of the most popular self-study programs is the Self-Taught Higher Education Self-Exam (Zhíxíng Zhíshì Gàokǎo, 简称自考) for English Level Two (Yīngyǔ Èr, 简称英语二).1.The ability to communicate in multiple languages has become a crucial skillfor_____________________and professional development.A. personalB. personalizationC. personallyD. personalizes2.The demand for English proficiency is evident in various sectors, such as______________ _______.A. businessB. businessesC. the businessD. businesses’3.One such language that has gained immense popularity is______________ _______.A. EnglishB. EnglishesC. EnglishsD. Englished4.To meet this demand, many individuals optfor_____________________programs to improve their English skills.A. self-studyB. self-studyingC. self-studiedD. self-studyingly5.The most popular self-study program is the Self-Taught Higher Education Self-Exam for English Level Two, also known as______________ _______.A. English Level TwoB. Yīngyǔ ÈrC. Zhíxíng Zhíshì GàokǎoD. Self-Taught Higher Education Self-ExamAnswers:1.A2.A3.A4.A5.B五、填词补文（共15分）First QuestionPlease read the following passage and choose the best word or phrase to complete each sentence. There is only one correct answer for each question.Passage:The world of technology has seen tremendous growth over the past few decades. With advancements in computing power and connectivity, the Internet has become an integral part of our daily lives, facilitating communication, commerce, education, and entertainment. From smartphones that keep us connected on the go, to software that makes our work more efficient, technology has transformed how we interact with the world around us.1、The rapid development in technology over the last few decades has been ________.A)astoundingB)decliningC)stagnantD)puzzlingAnswer: A) astounding2、With the increase in computing power and connectivity, the Internet has become ________.A)less relevantB)optionalC)essentialD)obsoleteAnswer: C) essential3、Smartphones allow us to stay connected while we are ________.A)stationaryB)disconnectedC)at homeD)on the moveAnswer: D) on the move4、Technology has________how we communicate and conduct business.A)hinderedB)complicatedC)transformedD)isolatedAnswer: C) transformed5、Software applications have made our work ________.A)less productiveB)more cumbersomeC)more efficientD)outdatedAnswer: C) more efficientThis completes the “First Question” section of the cloze test designed for the Higher Education Self-Study Examination’s English Course B. Eachquestion requires choosing the most appropriate word or phrase to fit into the context of the paragraph provided.第二题阅读下面的短文，并从每个空格后的四个选项中选择最佳答案填入空白处。

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

第45卷 第5期 包 装 工 程2024年3月PACKAGING ENGINEERING ·263·收稿日期：2023-09-28基金项目：国家自然科学基金（72371044，71871035）；重庆市教委科学技术研究重大项目(KJZD-M202300704)；重庆市基于时间窗和多仓温控的生鲜商品配送车辆路径优化问题王勇，王静媛，苟梦圆，罗思妤（重庆交通大学，重庆 400074）摘要：目的 针对当前生鲜商品配送效率低和成本高等问题，采用车仓温度可控的多仓车辆作为配送装备，并结合时间窗等约束，研究基于时间窗和多仓温控的生鲜商品配送车辆路径优化问题。

方法 建立最小化物流运营成本和车辆使用数量的双目标模型，然后设计基于Clarke-Wright 节约算法的非支配排序遗传算法（CW-NSGA-Ⅱ）求解该模型。

利用CW 节约算法生成初始配送路径，以提高初始解的质量，并设计精英迭代策略，以提高算法的寻优性能。

结果 基于改进的Solomon 算例，将文中所提算法与多目标粒子群算法、多目标蚁群算法、多目标遗传算法进行了对比，验证了CW-NSGA-Ⅱ算法的求解性能。

结合实例，对多仓车辆使用数量、温控成本和运营成本等指标进行对比分析，结果表明，经优化后多仓车辆使用数量减少了35.7%，温控成本减少了39.2%，物流运营总成本减少了47.7%。

结论 文中所提模型和算法能够有效优化配送路径，降低运营成本，为构建高效率、低成本的生鲜配送网络提供了理论支持和决策参考。

关键词：生鲜商品配送；多仓温控；时间窗；CW-NSGA-Ⅱ中图分类号：F570；TB485.3 文献标志码：A 文章编号：1001-3563(2024)05-0263-13 DOI ：10.19554/ki.1001-3563.2024.05.032Fresh Commodity Distribution Vehicle Routing Optimization Based on TimeWindows and Multi-compartment Temperature ControlWANG Yong , WANG Jingyuan , GOU Mengyuan , LUO Siyu(Chongqing Jiaotong University, Chongqing 400074, China)ABSTRACT: Aiming at the inefficient and high-cost fresh commodity distribution, the work aims to study the fresh commodity distribution route optimization based on time windows and multi-compartment temperature control by adopting multi-compartment vehicles with temperature controlled compartments as distribution equipment and applying time windows and other constraints. Firstly, the bi-objective model was established to minimize logistics operating cost and the number of vehicles. Then, the non-dominated sorting genetic algorithm based on the Clarke-Wright saving algorithm (CW-NSGA-Ⅱ) was designed to solve the model. Among them, the initial population was generated by the Clarke-Wright saving algorithm, which improved the quality of the initial solution, and an elite iterative strategy was designed to improve the optimization performance. Based on the improved Solomon example, the proposed algorithm was compared with the multi-objective particle swarm algorithm, multi-target ant colony algorithm and multi-target genetic algorithm, verifying the solution performance of the CW-NSGA-Ⅱ. Combined with a casestudy, the indicators such as the number of multi-compartment vehicles, temperature control costs and operating costs·264·包装工程2024年3月were compared and analyzed. The results showed that the number of optimized multi-compartment vehicles was reduced by 35.7%, the temperature control cost was reduced by 39.2%, and the total operating cost was reduced by47.7%. The proposed model and algorithm can effectively optimize the distribution route, reduce the total operatingcost, and provide theoretical support and decision-making reference for the construction of the efficient and low-cost fresh distribution network.KEY WORDS: fresh commodity distribution; multi-compartment temperature control; time window; CW-NSGA-Ⅱ近年来，随着电子商务的发展和人民生活水平的提高，生鲜商品配送服务逐步向高品质、精细化、个性化方向发展，客户不再局限于单一的商品需求[1]。

常用英语词汇 -andrew Ng课程average firing rate均匀激活率intensity强度average sum-of-squares error均方差Regression回归backpropagation后向流传Loss function损失函数basis 基non-convex非凸函数basis feature vectors特点基向量neural network神经网络batch gradient ascent批量梯度上涨法supervised learning监察学习Bayesian regularization method贝叶斯规则化方法regression problem回归问题办理的是连续的问题Bernoulli random variable伯努利随机变量classification problem分类问题bias term偏置项discreet value失散值binary classfication二元分类support vector machines支持向量机class labels种类标记learning theory学习理论concatenation级联learning algorithms学习算法conjugate gradient共轭梯度unsupervised learning无监察学习contiguous groups联通地区gradient descent梯度降落convex optimization software凸优化软件linear regression线性回归convolution卷积Neural Network神经网络cost function代价函数gradient descent梯度降落covariance matrix协方差矩阵normal equations DC component直流重量linear algebra线性代数decorrelation去有关superscript上标degeneracy退化exponentiation指数demensionality reduction降维training set训练会合derivative导函数training example训练样本diagonal对角线hypothesis假定，用来表示学习算法的输出diffusion of gradients梯度的弥散LMS algorithm “least mean squares最小二乘法算eigenvalue特点值法eigenvector特点向量batch gradient descent批量梯度降落error term残差constantly gradient descent随机梯度降落feature matrix特点矩阵iterative algorithm迭代算法feature standardization特点标准化partial derivative偏导数feedforward architectures前馈构造算法contour等高线feedforward neural network前馈神经网络quadratic function二元函数feedforward pass前馈传导locally weighted regression局部加权回归fine-tuned微调underfitting欠拟合first-order feature一阶特点overfitting过拟合forward pass前向传导non-parametric learning algorithms无参数学习算forward propagation前向流传法Gaussian prior高斯先验概率parametric learning algorithm参数学习算法generative model生成模型activation激活值gradient descent梯度降落activation function激活函数Greedy layer-wise training逐层贪心训练方法additive noise加性噪声grouping matrix分组矩阵autoencoder自编码器Hadamard product阿达马乘积Autoencoders自编码算法Hessian matrix Hessian矩阵hidden layer隐含层hidden units隐蔽神经元Hierarchical grouping层次型分组higher-order features更高阶特点highly non-convex optimization problem高度非凸的优化问题histogram直方图hyperbolic tangent双曲正切函数hypothesis估值，假定identity activation function恒等激励函数IID 独立同散布illumination照明inactive克制independent component analysis独立成份剖析input domains输入域input layer输入层intensity亮度/灰度intercept term截距KL divergence相对熵KL divergence KL分别度k-Means K-均值learning rate学习速率least squares最小二乘法linear correspondence线性响应linear superposition线性叠加line-search algorithm线搜寻算法local mean subtraction局部均值消减local optima局部最优解logistic regression逻辑回归loss function损失函数low-pass filtering低通滤波magnitude幅值MAP 极大后验预计maximum likelihood estimation极大似然预计mean 均匀值MFCC Mel 倒频系数multi-class classification多元分类neural networks神经网络neuron 神经元Newton’s method牛顿法non-convex function非凸函数non-linear feature非线性特点norm 范式norm bounded有界范数norm constrained范数拘束normalization归一化numerical roundoff errors数值舍入偏差numerically checking数值查验numerically reliable数值计算上稳固object detection物体检测objective function目标函数off-by-one error缺位错误orthogonalization正交化output layer输出层overall cost function整体代价函数over-complete basis超齐备基over-fitting过拟合parts of objects目标的零件part-whole decompostion部分-整体分解PCA 主元剖析penalty term处罚因子per-example mean subtraction逐样本均值消减pooling池化pretrain预训练principal components analysis主成份剖析quadratic constraints二次拘束RBMs 受限 Boltzman 机reconstruction based models鉴于重构的模型reconstruction cost重修代价reconstruction term重构项redundant冗余reflection matrix反射矩阵regularization正则化regularization term正则化项rescaling缩放robust 鲁棒性run 行程second-order feature二阶特点sigmoid activation function S型激励函数significant digits有效数字singular value奇怪值singular vector奇怪向量smoothed L1 penalty光滑的L1 范数处罚Smoothed topographic L1 sparsity penalty光滑地形L1 稀少处罚函数smoothing光滑Softmax Regresson Softmax回归sorted in decreasing order降序摆列source features源特点Adversarial Networks抗衡网络sparse autoencoder消减归一化Affine Layer仿射层Sparsity稀少性Affinity matrix亲和矩阵sparsity parameter稀少性参数Agent 代理 /智能体sparsity penalty稀少处罚Algorithm 算法square function平方函数Alpha- beta pruningα - β剪枝squared-error方差Anomaly detection异样检测stationary安稳性（不变性）Approximation近似stationary stochastic process安稳随机过程Area Under ROC Curve／ AUC Roc 曲线下边积step-size步长值Artificial General Intelligence/AGI通用人工智supervised learning监察学习能symmetric positive semi-definite matrix Artificial Intelligence/AI人工智能对称半正定矩阵Association analysis关系剖析symmetry breaking对称无效Attention mechanism注意力体制tanh function双曲正切函数Attribute conditional independence assumptionthe average activation均匀活跃度属性条件独立性假定the derivative checking method梯度考证方法Attribute space属性空间the empirical distribution经验散布函数Attribute value属性值the energy function能量函数Autoencoder自编码器the Lagrange dual拉格朗日对偶函数Automatic speech recognition自动语音辨别the log likelihood对数似然函数Automatic summarization自动纲要the pixel intensity value像素灰度值Average gradient均匀梯度the rate of convergence收敛速度Average-Pooling均匀池化topographic cost term拓扑代价项Backpropagation Through Time经过时间的反向流传topographic ordered拓扑次序Backpropagation/BP反向流传transformation变换Base learner基学习器translation invariant平移不变性Base learning algorithm基学习算法trivial answer平庸解Batch Normalization/BN批量归一化under-complete basis不齐备基Bayes decision rule贝叶斯判断准则unrolling组合扩展Bayes Model Averaging／ BMA 贝叶斯模型均匀unsupervised learning无监察学习Bayes optimal classifier贝叶斯最优分类器variance 方差Bayesian decision theory贝叶斯决议论vecotrized implementation向量化实现Bayesian network贝叶斯网络vectorization矢量化Between-class scatter matrix类间散度矩阵visual cortex视觉皮层Bias 偏置 /偏差weight decay权重衰减Bias-variance decomposition偏差 - 方差分解weighted average加权均匀值Bias-Variance Dilemma偏差–方差窘境whitening白化Bi-directional Long-Short Term Memory/Bi-LSTMzero-mean均值为零双向长短期记忆Accumulated error backpropagation积累偏差逆传Binary classification二分类播Binomial test二项查验Activation Function激活函数Bi-partition二分法Adaptive Resonance Theory/ART自适应谐振理论Boltzmann machine玻尔兹曼机Addictive model加性学习Bootstrap sampling自助采样法／可重复采样Bootstrapping自助法Break-Event Point／ BEP 均衡点Calibration校准Cascade-Correlation级联有关Categorical attribute失散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类型不均衡Closed -form闭式Cluster簇/ 类/ 集群Cluster analysis聚类剖析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT 国际学习理论会议Committee-based learning鉴于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解说性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift观点漂移Concept Learning System /CLS观点学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／ CPT 条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混杂矩阵Connection weight连结权Connectionism 连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient有关系数Cosine similarity余弦相像度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交错熵Cross validation交错考证Crowdsourcing众包Curse of dimensionality维数灾害Cut point截断点Cutting plane algorithm割平面法Data mining数据发掘Data set数据集Decision Boundary决议界限Decision stump决议树桩Decision tree决议树／判断树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial NetworkDCGAN深度卷积生成抗衡网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度预计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合胸怀Discriminative model鉴别模型Discriminator鉴别器Distance measure距离胸怀Distance metric learning距离胸怀学习Distribution散布Divergence散度Diversity measure多样性胸怀／差别性胸怀Domain adaption领域自适应Downsampling下采样D-separation（ Directed separation）有向分别Dual problem对偶问题Dummy node 哑结点General Problem Solving通用问题求解Dynamic Fusion 动向交融Generalization泛化Dynamic programming动向规划Generalization error泛化偏差Eigenvalue decomposition特点值分解Generalization error bound泛化偏差上界Embedding 嵌入Generalized Lagrange function广义拉格朗日函数Emotional analysis情绪剖析Generalized linear model广义线性模型Empirical conditional entropy经验条件熵Generalized Rayleigh quotient广义瑞利商Empirical entropy经验熵Generative Adversarial Networks/GAN生成抗衡网Empirical error经验偏差络Empirical risk经验风险Generative Model生成模型End-to-End 端到端Generator生成器Energy-based model鉴于能量的模型Genetic Algorithm/GA遗传算法Ensemble learning集成学习Gibbs sampling吉布斯采样Ensemble pruning集成修剪Gini index基尼指数Error Correcting Output Codes／ ECOC纠错输出码Global minimum全局最小Error rate错误率Global Optimization全局优化Error-ambiguity decomposition偏差 - 分歧分解Gradient boosting梯度提高Euclidean distance欧氏距离Gradient Descent梯度降落Evolutionary computation演化计算Graph theory图论Expectation-Maximization希望最大化Ground-truth实情／真切Expected loss希望损失Hard margin硬间隔Exploding Gradient Problem梯度爆炸问题Hard voting硬投票Exponential loss function指数损失函数Harmonic mean 调解均匀Extreme Learning Machine/ELM超限学习机Hesse matrix海塞矩阵Factorization因子分解Hidden dynamic model隐动向模型False negative假负类Hidden layer隐蔽层False positive假正类Hidden Markov Model/HMM 隐马尔可夫模型False Positive Rate/FPR假正例率Hierarchical clustering层次聚类Feature engineering特点工程Hilbert space希尔伯特空间Feature selection特点选择Hinge loss function合页损失函数Feature vector特点向量Hold-out 留出法Featured Learning特点学习Homogeneous 同质Feedforward Neural Networks/FNN前馈神经网络Hybrid computing混杂计算Fine-tuning微调Hyperparameter超参数Flipping output翻转法Hypothesis假定Fluctuation震荡Hypothesis test假定考证Forward stagewise algorithm前向分步算法ICML 国际机器学习会议Frequentist频次主义学派Improved iterative scaling/IIS改良的迭代尺度法Full-rank matrix满秩矩阵Incremental learning增量学习Functional neuron功能神经元Independent and identically distributed/独Gain ratio增益率立同散布Game theory博弈论Independent Component Analysis/ICA独立成分剖析Gaussian kernel function高斯核函数Indicator function指示函数Gaussian Mixture Model高斯混杂模型Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相像度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相像度Intrinsic value固有值Isometric Mapping/Isomap等胸怀映照Isotonic regression平分回归Iterative Dichotomiser迭代二分器Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性鉴别剖析K-fold cross validation k折交错考证／k 倍交错考证K-Means Clustering K–均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation知识表征Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯光滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷散布Latent semantic analysis潜伏语义剖析Latent variable隐变量Lazy learning懒散学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／ LDA 线性鉴别剖析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／ logit对数几率Logistic Regression Logistic回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function损失函数Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多半投票法Manifold assumption流形假定Manifold learning流形学习Margin theory间隔理论Marginal distribution边沿散布Marginal independence边沿独立性Marginalization边沿化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然预计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling 最大池化Mean squared error均方偏差Meta-learner元学习器Metric learning胸怀学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描绘长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混杂专家Momentum 动量Moral graph道德图／正直图Multi-class classification多分类Multi-document summarization多文档纲要One shot learning一次性学习Multi-layer feedforward neural networks One-Dependent Estimator／ ODE 独依靠预计多层前馈神经网络On-Policy在策略Multilayer Perceptron/MLP多层感知器Ordinal attribute有序属性Multimodal learning多模态学习Out-of-bag estimate包外预计Multiple Dimensional Scaling多维缩放Output layer输出层Multiple linear regression多元线性回归Output smearing输出调制法Multi-response Linear Regression／ MLR Overfitting过拟合／过配多响应线性回归Oversampling 过采样Mutual information互信息Paired t-test成对 t查验Naive bayes 朴实贝叶斯Pairwise 成对型Naive Bayes Classifier朴实贝叶斯分类器Pairwise Markov property成对马尔可夫性Named entity recognition命名实体辨别Parameter参数Nash equilibrium纳什均衡Parameter estimation参数预计Natural language generation/NLG自然语言生成Parameter tuning调参Natural language processing自然语言办理Parse tree分析树Negative class负类Particle Swarm Optimization/PSO粒子群优化算法Negative correlation负有关法Part-of-speech tagging词性标明Negative Log Likelihood负对数似然Perceptron感知机Neighbourhood Component Analysis/NCA Performance measure性能胸怀近邻成分剖析Plug and Play Generative Network即插即用生成网Neural Machine Translation神经机器翻译络Neural Turing Machine神经图灵机Plurality voting相对多半投票法Newton method牛顿法Polarity detection极性检测NIPS 国际神经信息办理系统会议Polynomial kernel function多项式核函数No Free Lunch Theorem／ NFL 没有免费的午饭定理Pooling池化Noise-contrastive estimation噪音对照预计Positive class正类Nominal attribute列名属性Positive definite matrix正定矩阵Non-convex optimization非凸优化Post-hoc test后续查验Nonlinear model非线性模型Post-pruning后剪枝Non-metric distance非胸怀距离potential function势函数Non-negative matrix factorization非负矩阵分解Precision查准率／正确率Non-ordinal attribute无序属性Prepruning 预剪枝Non-Saturating Game非饱和博弈Principal component analysis/PCA主成分剖析Norm 范数Principle of multiple explanations多释原则Normalization归一化Prior 先验Nuclear norm核范数Probability Graphical Model概率图模型Numerical attribute数值属性Proximal Gradient Descent/PGD近端梯度降落Letter O Pruning剪枝Objective function目标函数Pseudo-label伪标记Oblique decision tree斜决议树Quantized Neural Network量子化神经网络Occam’s razor奥卡姆剃刀Quantum computer 量子计算机Odds 几率Quantum Computing量子计算Off-Policy离策略Quasi Newton method拟牛顿法Radial Basis Function／ RBF 径向基函数Random Forest Algorithm随机丛林算法Random walk随机闲步Recall 查全率／召回率Receiver Operating Characteristic/ROC受试者工作特点Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model 参照模型Regression回归Regularization正则化Reinforcement learning/RL加强学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS重生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映照Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限制等距性Re-weighting重赋权法Robustness稳重性 / 鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／ SOM自组织映照Semi-naive Bayes classifiers半朴实贝叶斯分类器Semi-Supervised Learning半监察学习semi-Supervised Support Vector Machine半监察支持向量机Sentiment analysis感情剖析Separating hyperplane分别超平面Sigmoid function Sigmoid函数Similarity measure相像度胸怀Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图建立Singular Value Decomposition奇怪值分解Slack variables废弛变量Smoothing光滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀少表征Sparsity稀少性Specialization特化Spectral Clustering谱聚类Speech Recognition语音辨别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性 - 稳固性窘境Statistical learning统计学习Status feature function状态特点函Stochastic gradient descent随机梯度降落Stratified sampling分层采样Structural risk构造风险Structural risk minimization/SRM构造风险最小化Subspace子空间Supervised learning监察学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss代替损失Surrogate function代替函数Symbolic learning符号学习Symbolism符号主义Synset同义词集T-Distribution Stochastic Neighbour Embeddingt-SNE T–散布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU张量办理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值挪动Time Step时间步骤Tokenization标记化Training error训练偏差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁徙学习Treebank树库algebra线性代数Tria-by-error试错法asymptotically无症状的True negative真负类appropriate适合的True positive真切类bias 偏差True Positive Rate/TPR真切例率brevity简洁，简洁；短暂Turing Machine图灵机[800 ] broader宽泛Twice-learning二次学习briefly简洁的Underfitting欠拟合／欠配batch 批量Undersampling欠采样convergence收敛，集中到一点Understandability可理解性convex凸的Unequal cost非均等代价contours轮廓Unit-step function单位阶跃函数constraint拘束Univariate decision tree单变量决议树constant常理Unsupervised learning无监察学习／无导师学习commercial商务的Unsupervised layer-wise training无监察逐层训练complementarity增补Upsampling上采样coordinate ascent同样级上涨Vanishing Gradient Problem梯度消逝问题clipping剪下物；剪报；修剪Variational inference变分推测component重量；零件VC Theory VC维理论continuous连续的Version space版本空间covariance协方差Viterbi algorithm维特比算法canonical正规的，正则的Von Neumann architecture冯· 诺伊曼架构concave非凸的Wasserstein GAN/WGAN Wasserstein生成抗衡网络corresponds相切合；相当；通讯Weak learner弱学习器corollary推论Weight权重concrete详细的事物，实在的东西Weight sharing权共享cross validation交错考证Weighted voting加权投票法correlation互相关系Within-class scatter matrix类内散度矩阵convention商定Word embedding词嵌入cluster一簇Word sense disambiguation词义消歧centroids质心，形心Zero-data learning零数据学习converge收敛Zero-shot learning零次学习computationally计算(机)的approximations近似值calculus计算arbitrary任意的derive获取，获得affine仿射的dual 二元的arbitrary任意的duality二元性；二象性；对偶性amino acid氨基酸derivation求导；获取；发源amenable 经得起查验的denote预示，表示，是的标记；意味着，[逻]指称axiom 公义，原则divergence散度；发散性abstract提取dimension尺度，规格；维数architecture架构，系统构造；建筑业dot 小圆点absolute绝对的distortion变形arsenal军械库density概率密度函数assignment分派discrete失散的人工智能词汇discriminative有辨别能力的indicator指示物，指示器diagonal对角interative重复的，迭代的dispersion分别，散开integral积分determinant决定要素identical相等的；完整同样的disjoint不订交的indicate表示，指出encounter碰到invariance不变性，恒定性ellipses椭圆impose把强加于equality等式intermediate中间的extra 额外的interpretation解说，翻译empirical经验；察看joint distribution结合概率ennmerate例举，计数lieu 代替exceed超出，越出logarithmic对数的，用对数表示的expectation希望latent潜伏的efficient奏效的Leave-one-out cross validation留一法交错考证endow 给予magnitude巨大explicitly清楚的mapping 画图，制图；映照exponential family指数家族matrix矩阵equivalently等价的mutual互相的，共同的feasible可行的monotonically单一的forary首次试试minor较小的，次要的finite有限的，限制的multinomial多项的forgo 摒弃，放弃multi-class classification二分类问题fliter过滤nasty厌烦的frequentist最常发生的notation标记，说明forward search前向式搜寻na?ve 朴实的formalize使定形obtain获取generalized归纳的oscillate摇动generalization归纳，归纳；广泛化；判断（依据不optimization problem最优化问题足）objective function目标函数guarantee保证；抵押品optimal最理想的generate形成，产生orthogonal(矢量，矩阵等 ) 正交的geometric margins几何界限orientation方向gap 裂口ordinary一般的generative生产的；有生产力的occasionally有时的heuristic启迪式的；启迪法；启迪程序partial derivative偏导数hone 怀恋；磨property性质hyperplane超平面proportional成比率的initial最先的primal原始的，最先的implement履行permit同意intuitive凭直觉获知的pseudocode 伪代码incremental增添的permissible可同意的intercept截距polynomial多项式intuitious直觉preliminary预备instantiation例子precision精度人工智能词汇perturbation不安，搅乱theorem定理poist 假定，假想tangent正弦positive semi-definite半正定的unit-length vector单位向量parentheses圆括号valid 有效的，正确的posterior probability后验概率variance方差plementarity增补variable变量；变元pictorially图像的vocabulary 词汇parameterize确立的参数valued经估价的；可贵的poisson distribution柏松散布wrapper 包装pertinent有关的总计 1038 词汇quadratic二次的quantity量，数目；重量query 疑问的regularization使系统化；调整reoptimize从头优化restrict限制；限制；拘束reminiscent回想旧事的；提示的；令人联想的（ of ）remark 注意random variable随机变量respect考虑respectively各自的；分其他redundant过多的；冗余的susceptible敏感的stochastic可能的；随机的symmetric对称的sophisticated复杂的spurious假的；假造的subtract减去；减法器simultaneously同时发生地；同步地suffice知足scarce罕有的，难得的split分解，分别subset子集statistic统计量successive iteratious连续的迭代scale标度sort of有几分的squares 平方trajectory轨迹temporarily临时的terminology专用名词tolerance容忍；公差thumb翻阅threshold阈，临界。

英语流利说的ai学习计划IntroductionArtificial Intelligence (AI) is one of the most exciting and rapidly evolving fields in technology today. With the potential to revolutionize industries and improve countless aspects of our daily lives, AI is a field that offers tremendous opportunities for those who have the skills and expertise to work in it. As someone who is passionate about AI and its potential, I have developed a comprehensive learning plan to help me acquire the knowledge and skills necessary to excel in this field.GoalsMy main goal in pursuing AI learning is to become a proficient and knowledgeable AI professional, capable of understanding the latest technological advancements and applying them to real-world problems. In order to achieve this goal, I have set a number of specific learning objectives, including:1. Acquiring a solid foundational understanding of the principles and techniques of artificial intelligence, including machine learning, deep learning, and neural networks.2. Mastering programming languages and tools commonly used in AI, such as Python, TensorFlow, and Keras.3. Gaining hands-on experience with AI projects and applications, in order to develop practical skills and expertise.4. Keeping abreast of the latest developments in AI through ongoing education and professional development.Learning PlanTo achieve my learning objectives, I have developed a comprehensive learning plan that is divided into several key areas of study. These areas include:1. Basic Concepts and Principles of AIThe first step in my learning plan is to acquire a solid foundation in the basic concepts and principles of AI. This includes understanding the history of AI, its current state of development, and its potential future directions. I will also study the main principles and techniques of AI, including machine learning, deep learning, and neural networks, as well as the ethical and societal implications of AI.To achieve this goal, I will use a variety of resources, including online courses, academic textbooks, and scholarly articles. I will also seek out opportunities to engage with experts in the field, such as attending lectures and conferences, and participating in online forums and discussion groups.2. Programming Languages and ToolsIn order to work effectively in AI, it is essential to have a strong foundation in programming languages and tools commonly used in AI research and development. To this end, I will focus on mastering Python, which is widely considered to be the language of choice for AI due to its simplicity and efficiency. I will also gain proficiency in specific AI-related tools and frameworks, such as TensorFlow, Keras, and scikit-learn.To achieve this goal, I will take online courses and tutorials focused on Python and AI-related tools. I will also work on practical projects and assignments to gain hands-on experience with these tools.3. Hands-on Experience and ProjectsOne of the most important aspects of my learning plan is gaining hands-on experience with AI projects and applications. This will allow me to apply the knowledge and skills I have acquired in a real-world setting, and develop the practical expertise necessary to succeed in the field.To achieve this goal, I will seek out opportunities to work on AI projects, either independently or as part of a team. I will also engage in internships or work placements in organizations that are involved in AI research and development, in order to gain practical experience and mentorship from experts in the field.4. Ongoing Education and Professional DevelopmentFinally, I recognize that AI is a rapidly evolving field, and that in order to stay current and competitive, I will need to engage in ongoing education and professional development. This will involve staying informed about the latest developments in AI, as well as seeking out opportunities for advanced training and certification in specific areas of AI.To achieve this goal, I will regularly participate in conferences, workshops, and seminars focused on AI. I will also seek out opportunities for advanced training and certification in specific areas of AI, such as machine learning, deep learning, or reinforcement learning.Timeline and EvaluationIn order to ensure that my learning plan is effective and on track, I have developed a timeline and evaluation process to monitor my progress and make adjustments as needed. This timeline includes specific milestones and deadlines for each area of study, as well as regular checkpoints to evaluate my progress and make adjustments to my learning plan. For example, I have set a six-month deadline to acquire a foundational understanding of the principles and techniques of AI, and a one-year deadline to gain proficiency in programming languages and tools. I will also regularly evaluate my progress by reviewing my work, seeking feedback from mentors and peers, and reflecting on my learning experience.ConclusionIn conclusion, I am excited to embark on my AI learning plan, and I am confident that with dedication and hard work, I will achieve my learning objectives and become a proficient and knowledgeable AI professional. I am committed to staying current and competitive in the field of AI, and I am eager to contribute to the ongoing advancement of this exciting and rapidly evolving field. I look forward to the challenges and opportunities that lie ahead, and to the fulfillment of my goals as an AI professional.。

Vocabulary collection and idiomatic usage
Introduction collection
Explain collection as the natural combination of words that occurs frequently and sound right when used together Provide examples of common settlements in English
Developing writing skills: Students will be able to write clear, well structured essays that present a coherent argument
Refine speaking skills: Students will be able to express themselves fluently and accurately in English, using appropriate language and promotion
• Variety of text types: The textbooks include a range of text types such as narratives, repository texts, and argumentative essays to expose students to different styles of writing
Emphasis on critical thinking
The current resources students to think critically about the material they are studying, developing their ability to analyze and synthesize information

如何在课堂充分利用人工智能英语作文全文共3篇示例，供读者参考篇1How to Fully Utilize Artificial Intelligence in the ClassroomArtificial Intelligence (AI) is really cool technology that is becoming more and more a part of our lives! It's kind of like having a super smart robot friend that can help you with all sorts of tasks. I think AI has lots of potential to make learning at school way more fun and interactive. Here are some ideas on how we can use AI to its fullest in the classroom:Personal AI TutorsImagine having your own customized AI tutor that could provide one-on-one assistance anytime you need it. This AI tutor would be able to explain concepts in a way that makes sense specifically for you based on your learning style and pace. It could give you extra practice exercises on topics you're struggling with and provide encouragement to keep you motivated.The AI tutor could even use games, stories, and interactive activities to reinforce the lessons in an engaging way. No moregetting bored during lessons or feeling lost! With an AI tutor, you'd always have a friendly guide to ensure you truly understand what's being taught.Immersive Learning ExperiencesRegular classroom lessons can sometimes feel a bit dry and disconnected from the real world. But with AI, we could take virtual field trips to any place or time period we're learning about in vivid detail! Want to visit Ancient Rome or walk alongside dinosaurs? No problem!The AI could generate incredibly realistic simulations with smart AI characters we could interact with. Learning about the American Revolution? We could talk to Benjamin Franklin and Thomas Jefferson like they were really there. This immersive, firsthand experience would make the lessons come alive and stick in our memories far better than just reading about it from a textbook.AI Teacher AssistantsIt can be hard for teachers to give each student all the personalized attention they need, especially in crowded classrooms. But what if they had an AI assistant to help out? The AI could keep track of each student's individual progress andstrugglingpoints, then provide customized materials and activities for extra practice or review.The AI teacher's assistant could automate grading assignments for teachers and give real-time feedback to students on their work. It could even have one-on-one conversations with students to ensure they understand concepts and provide tutoring when needed. With an AI assistant to share the workload, our human teachers could spend more quality time focusing on the creative and interpersonal elements of teaching.AI-Enhanced Lesson PlanningTeachers have to spend so much time researching, preparing materials, and planning creative lessons. An AI could be an incredible tool for streamlining this whole process. The AI could instantly analyze curriculum requirements across different subjects and grade levels. Then it could generate comprehensive lesson plans and create all the worksheets, activities, and multimedia materials needed with just a few prompts from the teacher.The AI could even customize each lesson for different learning abilities and incorporate the latest effective teaching strategies. It could trawl online resources for relevant videos,games, and other engaging content to include. Having an AI handle all the busy work would allow teachers to focus on the high-level planning and adding their own creative spin.AI-Powered research AssistantsWhenever we get assigned research projects or essays to write, an AI assistant could be incredibly helpful for gathering information from trustworthy sources across the internet. The AI could compile all the relevant facts and insights on our topic in an easy-to-understand way. It could even generate outlinesand initial drafts for our paper which we could then edit and put in our own words.The AI could serve as a knowledgeable research buddy we could bounce ideas off of. It could poke holes in our logic and push us to explore different perspectives on our topic. Having this kind of AI assistant would make research projects so much richer and easier to tackle.AI Language TutorsFor students learning another language, having an AI tutor with conversational abilities in that language could be agame-changer. We could practice speaking and listening comprehension by having natural conversations with the AI. Itcould use games to make learning vocabulary more fun and memorable.The AI tutor would be an endless font of patience, never getting frustrated with repeated mistakes. It could provide constructive feedback in a kind way to help improve our skills. Being able to practice a new language anytime with a supportive AI partner could really accelerate the learning process.There are so many possibilities for how AI could supercharge our education! Of course, AI won't completely replace our human teachers. But it could be an incredibly useful aid to enhance lessons and provide personalized learning experiences. With the right AI tools, every student could learn in whatever style works best for them at their own pace.I, for one, can't wait for AI to become a bigger part of our daily classroom experience. School would be a lot more fun, hands-on, and productive with the assistance of these super smart AI technologies. They could open up amazing new worlds of learning for us that just isn't possible with traditional classroom methods. The future of AI in education is bright!篇2How to Fully Utilize Artificial Intelligence in the ClassroomHey there! I'm sure you've heard about artificial intelligence (AI) and how it's changing the world. Well, get ready because AI is also making learning way more fun and engaging in our classrooms!What exactly is AI? It's like having a super smart robot helper that can understand our language, see pictures and videos, and even create things like stories, artwork, and computer code. Pretty neat, right? With AI assistants becoming more advanced each year, there are so many awesome ways we can use them to learn better.One of the coolest things about AI is how it can explain complex topics in simple terms that make sense to us kids. You know how sometimes the lessons can get really confusing with all those big fancy words? AI tutors can break it down into easy-to-understand language and even provide fun examples to help the concepts click. No more getting lost or zoning out during class!Plus, AI is a whiz at answering our questions on any subject you can imagine. Have a burning question about dinosaurs, outer space, or why the sky is blue? Just ask the AI assistant and it'll give you all the details in a way that's super engaging and interactive. No more waiting for the teacher or scramblingthrough books and websites. The knowledge is right at our fingertips!Another stellar way AI can help is by being our personal writing buddy. We all know how tough it can be to come up with ideas for stories, essays, and poems. But AI can actually help spark our creativity by suggesting creative storylines, descriptions, and even funky new words to spice things up. Of course, we still have to put in the hard work of writing. But with AI's help, putting our ideas into words becomes way more fun.And get this – AI can also lend a hand with math, the subject that gives many of us nightmares! Those confusing word problems and multi-step equations suddenly become a lot less scary when you have an AI math tutor walking you through each step. No more feeling like you want to pull your hair out. AI has our backs!But AI's skills go far beyond just academics. It can also teach us super cool tricks like coding, graphic design, music production – you name it! We're the generation that's going to grow up completely fluent in technology. And having an AI assistant to guide us as we explore these future skills? That's an opportunity no kid before us has ever had!Now, I know what you might be thinking: "Doesn't using AI assistants mean we won't have to think for ourselves anymore?" That's a totally fair question! The teachers make sure we don't become too dependent on AI and that we're still exercising our brains. The AI helpers are just there to support us, not do all the work.For example, when we're working on a big research project, the teacher might say we can use AI to gather information and get an overview of the topic. But then it's our job to read through everything, analyze it ourselves, and put it all together in our own words with our own unique perspectives. The AI gives us a head start, but we still have to do all the heavy lifting of processing the info and forming our own ideas.Same goes for getting AI to help with writing assignments. We can use it for brainstorming sessions or to get unstuck if we have writer's block. But at the end of the day, the entire story or essay has to be written by us. The teachers are really strict about making sure we're not just copying and pasting from the AI. That's considered cheating, and it defeats the whole purpose of learning how to express ourselves through writing.There are also a lot of fun AI tools built just for engaging classroom activities and games. My favorite is the AI storytellingapp where you start a story, and then the AI continues it with unexpected plot twists and turns. Then you get to respond to what the AI came up with by adding the next part of the story yourself. It's like an incredible writing adventure that never goes the way you'd expect! The teachers use it to get our creative juices flowing before we have to write our own original stories.I could go on and on about the awesome AI tools we're using at school nowadays. But I think you get the main idea – AI is truly revolutionizing how we learn and interact with knowledge. It makes lessons way more fun and motivates us to be curious about any topic we can dream up. While the teachers are still the ones driving our education, AI acts as this incredibly supportive co-pilot that helps us learn in whole new ways.Of course, there's always the possibility that AI could go a little too far and start doing more harm than good if we rely on it too much. That's why our teachers are really wise about setting limits for how we use AI. They make sure we're still building our critical thinking abilities and not just blindly accepting everything the AI tells us. After all, nobody's perfect – not even crazy smart robots!But as long as we use AI responsibly as a supporting tool, I'm confident it will only continue to open more doors for how welearn and grow. Who knows what genius new ways to use AI will pop up next year or five years from now? The possibilities are endless when you combine human creativity with artificial intelligence. I can't wait to see what sort of AI adventures are still in store for us!So, what do you think? Are you as excited as I am to make the most of AI and take our learning experiences to awesome new heights? Let me know! I'm always eager to hear other kids' perspectives on the latest tech innovations happening in our schools. The future of education is looking brighter than ever, and I'm just thrilled to be along for the incredibly coolAI-enhanced ride.篇3How to Fully Utilize Artificial Intelligence in the ClassroomArtificial Intelligence (AI) is really cool technology that can help us learn better in the classroom! AI refers to computer systems that can think and learn kind of like humans. Instead of just following a set of instructions, AI can look at information, figure out patterns, and make decisions on its own. It's almost like having a super smart robot assistant!I think AI has a lot of awesome potential to improve our education experience. We just have to be creative in finding ways to use it. Here are some of my ideas on how we can fully utilize AI in the classroom:AI Tutors and Personalized LearningOne amazing way AI could help is by providing personalized tutoring and learning for each student. Current classroom setups have one teacher trying to teach 20-30 kids at once. But we all learn at different paces and have different strengths and weaknesses.With an AI tutor program, it could look at each student's performance and learning style to customize the lessons perfectly for them. The AI could give extra practice on areas I'm struggling with, skip over concepts I've already mastered, and present the information in the format that works best for how my brain learns. Maybe I'm a visual learner, so the AI gives me more videos, diagrams and animations. Or maybe I'm an auditory learner, so it uses more spoken explanations.The AI tutor could also be available 24/7 to give me unlimited practice problems and explanations whenever I need it outside of class. It would be so awesome to have my own personal tutor always there to help whenever I'm stuck onhomework or need extra lessons. No more waiting for limited office hours or appointments!Interactive AI Learning GamesSpeaking of making learning fun, AI could also create super engaging educational games and activities. Math, spelling, reading, you name it – AI systems could generate unlimited novel games to drill those skills in a really entertaining way.For example, maybe an AI could create an open-world adventure game where I'm a knight going on quests. But to progress through the game, I have to solve math problems or spelling challenges along the way. The AI could customize all the game content to focus on the specific areas I need to practice. As I master concepts, it would automatically level up the difficulty. And it could create endless new worlds, characters, and storylines so I never get bored.The games could also have realistic speech recognition so I could practice language skills just by talking out loud to AI characters. Or it could integrate educational video/TV shows that are interactive, where the AI characters ask me questions and I have to respond correctly to move the story forward.Those kinds of immersive, gamified learning experiences would make practicing skills so much more motivating and less tedious than just doing problem sets over and over. I'd be so engaged that it wouldn't even feel like I was studying! Plus, the AI could use my gameplay data to identify my exact sticking points and provide custom feedback.Augmented Writing AssistanceI think AI could also give us super cool augmented writing capabilities to improve our skills. We could use AI writing assistants to help brainstorm ideas, pick out grammatical mistakes, suggest better vocabulary, and more.For example, if I was writing an essay and got stuck on my introduction paragraph, I could ask the AI for help. It could rapidly generate a bunch of potential intro paragraphs for me to pick from or combine ideas. As I continued writing, it could suggest sentence revisions and better word choices to elevate my writing in real-time.The AI could also help by automatically detecting areas where I tend to make common errors, like run-on sentences or comma splices. Then it could provide focused practice drills generated specifically for improving those weak areas in my writing mechanics.Even cooler, the AI writing aid could be a speech-to-text system. So I could just talk out my essay ideas, and the AI would transcribe it while cleaning up my spoken language into proper written English. For kids like me who struggle with writing and typing, being able to just discuss my thoughts freely while the AI translates it into an essay could be a total game-changer!Research AssistanceAI could also make our research projects so much easier and more efficient. Instead of just searching for basic keyword matches, we could interact with an AI research assistant using natural language queries.For example, if I was doing a report on photosynthesis, I could ask the AI open-ended questions like "What are the key steps involved in photosynthesis and how do they work?" Or "What are the most important factors that impact photosynthetic rates?"The AI could then rapidly scan through all available information sources and provide a nicely synthesized summary responding to my specific questions. It could pull in relevant quotes, data visualizations, videos, and more to create a rich research paper starter pack customized for my needs.As I reviewed the information, I could ask follow-up questions to clarify any confusing areas. The AI could even generate sample report outlines and spot any contradictory or potentially inaccurate information for me.Having an AI research buddy would save me so much time from getting lost down internet rabbit holes or struggling to comprehend complex topics from just static web pages and books. The AI could tailor all the information at an appropriate level for my understanding while filling in knowledge gaps through our back-and-forth dialogue.General Classroom AssistanceFinally, I can also envision AI assistants being helpful for teachers in managing our classroom activities. Maybe there could be an AI monitoring system that keeps track of things like student engagement levels, confusion zones where many students are getting stuck, and instances of potentialbullying/disruption.The AI could alert the teacher when it detects an issue like a student zoning out or looking frustrated for too long. Then the teacher would know to provide that student some extra attention or a learning intervention.For classroom discussions, an AI system could transcribe everything and do real-time sentiment analysis to gauge student reactions and highlight insightful comments to circle back on. Or it could compile all the main points and questions at the end to ensure key takeaways weren't missed.An AI assistant could be really helpful for things like tracking assignments, managing the queue for students to getone-on-one teacher time, and automating routine tasks so the teacher can focus more on hands-on instruction.So those are some of my ideas for using AI to upgrade our classroom experience! Of course, we'd still need awesome teachers too. AI would just be a tool to make their jobs easier while providing us students with powerful customized learning capabilities.By leveraging the power of AI for personalized tutoring, interactive educational activities, writing assistance, research help, and smart classroom orchestration, I think we could transform our education into something much more engaging and effective. We'd enjoy learning, cut down on dull busy work, and ensure no students get left behind or bored.Technology is supposed to make our lives easier – so let's make sure we fully utilize these incredible AI innovations toupgrade our classroom and equip the future generation with the best education possible! I'm really excited about the potential of AI to revolutionize how we learn.。

2024智能版艾斯英语高中英语听力模拟试题精编全文共3篇示例，供读者参考篇12024智能版艾斯英语高中英语听力模拟试题精编Section 1: Listen to the conversation and answer the questions.1. What does the woman want to do this weekend?A. Go campingB. Visit a museumC. Watch a movie2. Why does the man suggest going to the beach?A. It’s a beautiful dayB. They can swim and relaxC. There’s a volleyball tournament3. What time will they meet on Saturday?A. 10:00 amB. 11:00 amC. 12:00 pmSection 2: Listen to the monologue and answer the questions.4. What information does the speaker provide about the history project?A. The due dateB. The topic optionsC. The grading criteria5. What advice does the speaker give for choosing a topic?A. Pick something easyB. Select a topic that interests youC. Avoid controversial subjects6. How long should the presentation be?A. 5 minutesB. 10 minutesC. 15 minutesSection 3: Listen to the dialogue and answer the questions.7. What is the man’s problem?A. He has lost his walletB. He needs a ride homeC. He forgot his homework8. Why can’t the woman give the man a ride?A. She has to study for a testB. She has to meet a friendC. She doesn’t have a car9. Where does the man promise to meet the woman later?A. In front of the libraryB. By the student centerC. At the cafeteriaSection 4: Listen to the passage and answer the questions.10. What does the speaker say about learning a new language?A. It’s easy for everyoneB. It requires time and practiceC. It’s impossible to master11. How many languages does the speaker speak fluently?A. 2B. 3C. 412. What advice does the speaker give for improving language skills?A. Watch movies in the languageB. Take a classC. Travel to the countryNow, check your answers and see how well you did on the simulation test. Good luck on your English language learning journey!篇2Title: 2024 Intelligent Version Eis English High School English Listening Simulation Test CompilationIntroduction:The 2024 Intelligent Version Eis English High School English Listening Simulation Test is designed to assess students' listening skills in a high school English environment. This test includes a variety of listening tasks that require students to listen to conversations, lectures, and other spoken materials and answer questions based on what they have heard.Section 1: Listening ComprehensionIn this section, students will listen to a series of short conversations and answer multiple-choice questions about the content. These conversations will cover topics such as daily routines, school life, and social activities, and will test students' ability to understand spoken English in various contexts.Sample Question:You will hear a conversation between two friends discussing their plans for the weekend. What are the speakers planning to do?A. Go shoppingB. Play soccerC. Study togetherD. Attend a concertSection 2: Listening PracticeIn this section, students will listen to a longer passage, such as a lecture or a radio broadcast, and answer multiple-choice questions or fill in the blanks with the missing information. These tasks will test students' ability to understand more complex spoken English and extract key information from extended listening materials.Sample Question:You will hear a lecture about the history of the English language. According to the speaker, which of the following languages has had the least influence on English?A. FrenchB. LatinC. GermanD. ChineseSection 3: Listening InterpretationIn this section, students will listen to a recorded dialogue or monologue and answer questions that require them to interpret the speakers' intentions, emotions, or opinions. These tasks willtest students' ability to infer meaning from tone of voice, choice of words, and other subtle cues in spoken English.Sample Question:You will hear a monologue in which a student talks about her experience studying abroad. What is the speaker's attitude towards her host family?A. IndifferentB. GratefulC. DisappointedD. AnnoyedConclusion:The 2024 Intelligent Version Eis English High School English Listening Simulation Test is a valuable tool for assessing students' listening skills in a high school English environment. By practicing with this test, students can improve their ability to understand spoken English and succeed in their English studies.篇3Title: 2024 Intelligent Edition AIS English High School English Listening Simulation Test CompilationIntroduction:The 2024 Intelligent Edition AIS English High School English Listening Simulation Test Compilation is designed to help students improve their listening skills and prepare for exams. This compilation includes a variety of listening exercises that cover different topics and difficulty levels, helping students to enhance their listening comprehension and language proficiency.Section 1: Short Dialogues1. You will hear a short dialogue between two people. Listen carefully and then choose the best answer to the question.Example:- Boy: Can you help me with my homework?- Girl: Sorry, I’m busy right now.Question: What does the girl say?A. She can help him with his homework.B. She is not able to help him with his homework.C. She will help him later.Section 2: Long Dialogues2. You will hear a longer dialogue between two people. Listen carefully and then answer the questions that follow.Example:- Woman: How was your trip to Paris?- Man: It was amazing! The Eiffel Tower was breathtaking.Question 1: What did the man think of the Eiffel Tower?Question 2: Where did the man go on his trip?Section 3: News Reports3. You will hear a news report. Listen carefully and then answer the questions that follow.Example:News Report: A new study suggests that drinking tea can improve brain function.Question 1: What does the news report suggest about drinking tea?Question 2: What benefit does tea have on brain function?Conclusion:The 2024 Intelligent Edition AIS English High School English Listening Simulation Test Compilation is a valuable resource for students looking to improve their listening skills in English. By practicing with these listening exercises, students can enhance their comprehension abilities and perform better in listening tests and exams. We encourage students to use this compilation regularly to build their confidence and proficiency in listening to English.。

2025年研究生考试考研英语（一201）自测试卷与参考答案一、完型填空（10分）Section I: Cloze Test (20 points)Directions: Read the following text. Choose the best word(s) for each numbered blank and mark A, B, C, or D on your answer sheet.Passage:In the world of higher education, there is an ongoing debate about the significance of standardized tests, particularly the Graduate Record Examinations (GRE) for those aspiring to pursue graduate studies. The GRE, particularly its English Language Test, known as GRE General Test (Verbal Reasoning), aims to assess a candidate’s ability to comprehend, analyze, and evaluate complex written materials. This section, specifically, the “Verbal Reasoning - Text Completion” segment, exemplifies this objective by presenting a passage with 20 blanks, each requiring a precise word or phrase to maintain the flow and meaning of the text.Text:Academic research is a meticulous process that demands not only a deep understanding of a subject matter but also the (1)_____to questionestablished knowledge and seek new perspectives. Researchers are often(2)_____with vast amounts of data, requiring them to possess excellent(3)_____skills to sift through and organize information effectively. The ability to (4)_____conclusions from such data is crucial, as it forms the basis of scientific discoveries and scholarly contributions.However, the path to research excellence is rarely (5) _____. It is fraught with challenges, including the pressure to publish in high-impact journals, the (6)_____for funding, and the constant need to innovate and stay (7)_____with the latest research trends. Despite these obstacles, researchers persevere, driven by their (8)_____to uncover the truth and make a meaningful impact on their fields.Collaboration is a cornerstone of the research process. Working together, researchers can pool their expertise, share resources, and (9)_____each other’s strengths and weaknesses. This not only accelerates the pace of research but also fosters an environment of (10)_____and mutual respect. In the realm of language and literature, researchers engage in critical analyses of texts, examining their (11)_____meaning, cultural context, and historical significance. The GRE English Test assesses this ability by testing cand idates’ comprehension of complex texts and their capacity to draw (12)_____from them. For instance, candidates may be asked to identify the author’s (13)_____or the tone of a passage, or to infer the implications of a statement made within the text.To excel in this section, candidates must develop a (14)_____vocabulary,enabling them to comprehend a wide range of vocabulary and idiomatic expressions. Additionally, honing their reading comprehension skills, such as identifying the main idea, supporting deta ils, and author’s purpose, is essential. Furthermore, the ability to (15)_____logical connections between ideas and sentences within a text is key to accurate interpretation. While preparing for the GRE, it is important to engage in regular practice, utilizing a variety of resources that mimic the actual test format. This includes working through (16)_____passages, analyzing their structure, and practicing answering questions similar to those found on the test. By doing so, candidates can (17)_____their skills and gain confidence in their abilities.Ultimately, the GRE English Test is a tool that measures a candidate’s readiness for graduate-level studies in the English language and literature. It is not a definitive measure of one’s intellectual capacity bu t rather an indication of one’s ability to navigate and excel in academic environments that prioritize (18)_____and critical thinking. As such, it is important for candidates to approach the test with a mindset focused on demonstrating their strengths and areas of improvement, rather than (19)_____on a single score.In conclusion, the GRE English Test is a challenging yet essential component of the graduate admissions process. By (20)_____a comprehensive preparation strategy that includes regular practice, vocabulary enhancement, and thedevelopment of critical reading skills, candidates can position themselves to succeed on this important milestone in their academic journey. Answers:1.courage2.confronted3.analytical4.draw5.smoothpetition7.current8.passion9.recognize10.collaboration11.literal12.inferences13.perspective14.robust15.establish16.practice17.refine18.research19.fixating20.adopting二、传统阅读理解（本部分有4大题，每大题10分，共40分）Section II: Traditional Reading ComprehensionFirst PassageTitle: The Digital Revolution and Its Impact on EducationIn the past few decades, the world has witnessed an unprecedented digital revolution that has transformed virtually every aspect of our lives. From the way we communicate to the way we access information, technology has played a pivotal role in shaping our societies. This transformation is particularly evident in the field of education, where the integration of digital tools and resources has not only revolutionized teachingmethodologies but also expanded learning opportunities for studentsglobally.The advent of the internet and the subsequent rise of online platforms have made educational resources accessible to millions of people who might not have had access to traditional educational institutions. Online courses, known as massive open online courses (MOOCs), have emerged as a popular mode of learning, offering a wide range of subjects from top universities around the world. These courses are often free or at a minimal cost, making them affordable for students from diverse economic backgrounds.Moreover, the use of digital tools in classrooms has enhanced the learningexperience for students. Interactive whiteboards, tablets, and educational software have made lessons more engaging and dynamic. Teachers can now incorporate multimedia elements such as videos, animations, and simulations into their lessons, making complex concepts easier to understand. Additionally, personalized learning programs, which utilize data analytics to tailor educational co ntent to individual students’ needs and strengths, are becoming increasingly common.However, the digital revolution in education has not been without its challenges. Concerns over digital addiction, privacy issues, and the potential for technological distractions in the classroom have been raised. Furthermore, the digital divide—the unequal distribution of access to technology and the internet—remains a significant barrier to achieving equitable education opportunities for all.Despite these challenges, the benefits of the digital revolution in education are undeniable. It has democratized access to knowledge, improved teaching and learning outcomes, and fostered innovation in educational practices. As technology continues to evolve, it is likely that the future of education will be even more deeply intertwined with digital tools and resources.Questions:1.What is the main topic of the passage?A)The rise of online shoppingB)The impact of the digital revolution on educationC)The history of the internetD)The challenges faced by traditional educational institutions2.Which of the following is NOT mentioned as a benefit of online courses?A)They are accessible to people from diverse economic backgrounds.B)They are taught by the best teachers in the world.C)They offer a wide range of subjects.D)They are often free or at a minimal cost.3.How do digital tools enhance the learning experience for students in classrooms?A)By making lessons less engaging and dynamic.B)By incorporating multimedia elements into lessons.C)By eliminating the need for teachers.D)By making it difficult to understand complex concepts.4.What is a major concern related to the digital revolution in education?A)The high cost of educational software.B)The lack of access to technology and the internet for some students.C)The excessive use of paper in classrooms.D)The decline in the quality of traditional educational institutions.5.What does the author suggest about the future of education in relation to digitaltools and resources?A)They will become less important over time.B)They will continue to play a minor role in educational practices.C)They will be completely replaced by traditional methods.D)They will become even more deeply intertwined with education.Second Section: Traditional Reading ComprehensionPassage:Title: The Impact of Digitalization on the Traditional Book IndustryIn recent years, the digital age has transformed nearly every aspect of our lives, and the book industry is no exception. With the rise of e-books, audiobooks, and digital reading platforms, the traditional paper-based model of book publishing and distribution is facing unprecedented challenges. This transformation has sparked debates among readers, authors, publishers, and librarians alike, as they grapple with the implications of this digital shift.At the heart of the matter lies the convenience offered by digital formats. E-books, for instance, can be accessed instantly on a range of devices, from smartphones to tablets, eliminating the need for physical storage space and allowing for seamless cross-device reading. They are also often cheaper than their physical counterparts, appealing to readers on a budget. Additionally, the advent of cloud storage and online libraries has made it easier than ever to access and share vast collections of books.However, these benefits come at a cost. Many argue that the digitalization of books threatens the cultural significance and physicality of the printed word. Books have traditionally served as tactile objects, conveying a sense of ownership and permanence that cannot be replicated by a screen. Moreover, the disappearance of physical bookstores has had a profound impact on communities, reducing opportunities for social interaction andbrowsing-based discovery.Authors and publishers, too, have been affected. While digital platforms have opened up new avenues for reaching readers worldwide, they have also created a crowded and competitive marketplace where visibility can be difficult to achieve. Furthermore, concerns over piracy and the loss of control over how their work is presented and distributed have led some to question the value of embracing digital formats.Yet, despite these challenges, the book industry is adapting. Publishers are exploring innovative ways to integrate digital elements into physical books, such as augmented reality and interactive features, to enhance the reading experience. Meanwhile, libraries are embracing digital resources while maintaining their physical collections, recognizing the importance of both formats for diverse user needs.Ultimately, the future of the book industry lies in a delicate balance between the traditional and the digital. As readers continue to demand convenience and accessibility, it is essential that the industry evolves to meet these needs while preserving the cultural and physical value of books.Questions:1.What is the main topic of the passage?A)The advantages of e-books over traditional books.B)The impact of digitalization on the book industry.C)The role of libraries in the digital age.D)The future of book publishing.2.What is one benefit of e-books mentioned in the passage?A)They require more physical storage space.B)They are often more expensive than physical books.C)They can be accessed instantly on various devices.D)They cannot be shared easily with others.3.According to the passage, what has been a negative impact of the decline ofphysical bookstores?A)Increased competition among publishers.B)Decreased social interaction opportunities.C)Higher prices for e-books.D)Increased piracy of books.4.How have some publishers responded to the challenges of digitalization?A)By completely abandoning physical books.B)By embracing only digital formats.C)By integrating digital elements into physical books.D)By ignoring the changing market trends.5.What is the author’s overall stance on the future of the book industry?A)It will completely shift to digital formats.B)It will maintain its traditional form without change.C)It requires a balance between the traditional and digital.D)It is impossible to predict its future trajectory.Third Question: Traditional Reading ComprehensionPassage:Title: The Digital Divide: Bridging the Gap in Access to TechnologyIn today’s rapidly evolving digital age, technology has become an integral part of our daily lives, shaping how we communicate, learn, work, and entertain ourselves. However, amidst this technological boom, a significant disparity exists –the digital divide, a term coined to describe the unequal distribution of access to, use of, or impact of information and communication technologies (ICTs) among individuals, households, businesses, and geographic areas.The digital divide manifests itself in various forms, but a primary concern lies in the gap between those who have access to the latest technological advancements and those who are left behind. This disparity can be attributed to several factors, including economic status, education levels, age, gender, and geographical location. In developing countries, the digital divide is often exacerbated by infrastructural limitations and affordability issues, while in developed nations, it may be a result of digital illiteracy or a lack of motivation to adopt new technologies. The consequences of the digital divide are far-reaching and multifaceted. On an individual level, limited access to technology can hinder educational opportunities, limit career prospects, and isolate individuals from social networks. At a societal level, it can exacerbate economic inequalities, widen the achievement gap among students, and stifle innovation and progress.Efforts to bridge the digital divide have been ongoing for years, with governments, non-profit organizations, and private companies working together to provide access to technology for those in need. Initiatives such as e-learning programs, community technology centers, and low-cost devices aim to increase digital literacy and ensure that no one is left behind in the digital age.Despite these efforts, the digital divide remains a persistent challenge. As technology continues to evolve at an unprecedented pace, it is crucial that we remain vigilant in our efforts to ensure equitable access to its benefits. Only by bridging the gap in access to technology can we ensure that everyone has the opportunity to thrive in the digital age. Questions:1.What is the digital divide, and what does it refer to?Answer: The digital divide refers to the unequal distribution of access to, use of, or impact of information and communication technologies (ICTs) among individuals, households, businesses, and geographic areas.2.What are some of the primary factors contributing to the digital divide?Answer: Some of the primary factors contributing to the digital divide include economic status, education levels, age, gender, and geographical location.3.How can limited access to technology hinder educational opportunities?Answer: Limited access to technology can hinder educational opportunities by restricting access to digital resources, such as online courses and educational software, which can be vital for learning and development.4.What are some initiatives aimed at bridging the digital divide?Answer: Some initiatives aimed at bridging the digital divide include e-learning programs, community technology centers, and low-cost devices, which aim to increase digital literacy and ensure equitable access to technology.5.Why is it important to bridge the digital divide?Answer: Bridging the digital divide is important because it ensures that everyone has equal access to the benefits of technology, which can help to reduce economic inequalities, improve educational outcomes, and foster innovation and progress. Section IV: Traditional Reading ComprehensionPassage FourTitle: The Impact of Digital Technology on Reading HabitsIn the digital age, the way we consume information has undergone a profound transformation. From the traditional paper-and-ink books to the sleek electronic screens of tablets and smartphones, the advent of digital technology has reshaped our reading habits in ways that were once unimaginable. This passage delves into the various aspects of how digitalization has influenced our reading experiences, both positively and negatively.The convenience offered by digital devices cannot be overstated. With the tap of a finger, readers can access an endless library of books, articles, and news from anywhere in the world. Gone are the days of trudging to the bookstore or waiting for a book to arrive in the mail. The instant gratification of digital reading appeals to many, especially those with busylifestyles who value time efficiency. Moreover, the ability to customize reading settings such as font size, background color, and brightness levels caters to individual preferences, enhancing the overall reading experience. However, the shift towards digital reading has also raised concerns about its impact on comprehension and retention. Some studies suggest that reading from screens can lead to decreased attention spans and reduced ability to process information deeply. The constant distractions of notifications and social media alerts can further fragment our focus, making it challenging to fully immerse oneself in a book or article. Additionally, the lack of tactile feedback from physical pages and the absence of the traditional smell and feel of a book can diminish the emotional connection readers form with the content.Moreover, the proliferation of digital content has led to an explosion of information, much of which is of questionable quality. The ease of publishing online has democratized access to the written word but has also opened the floodgates to misinformation and clickbait. Navigating through this deluge of content can be overwhelming, and readers must develop critical thinking skills to discern fact from fiction.Despite these challenges, digital technology also presents new opportunities for reading and learning. Interactive e-books, for instance, incorporate multimedia elements like videos, animations, and quizzes that can enrich the learning experience and make complex concepts more accessible.Furthermore, personalized recommendation algorithms can curate tailored reading lists based on an individual’s interests and reading history, fostering a sense of discovery and exploration.In conclusion, the impact of digital technology on reading habits is multifaceted. While it has undeniably brought about convenience and new forms of engagement, it has also raised concerns about comprehension, attention, and the quality of information available. As we continue to navigate this digital landscape, it is essential to strike a balance between embracing the benefits of technology and preserving the essence of reading as a deeply personal and enriching experience.Questions:1.What is the main advantage of digital reading mentioned in the passage?•A) The ability to access an endless library of content instantly. •B) The tactile feedback from physical pages.•C) The lack of distractions from notifications and social media. •D) The improved comprehension and retention of information.2.Which of the following is a concern raised about digital reading?•A) The enhanced emotional connection readers form with the content. •B) The increased attention spans and ability to process information deeply.•C) The decreased attention spans and reduced ability to process information deeply.•D) The limited customization options for reading settings.3.What is the primary issue with the proliferation of digital content mentioned in thepassage?•A) The lack of accessible information for readers.•B) The overabundance of high-quality content.•C) The challenge of navigating through a deluge of information, including misinformation.•D) The ease of publishing traditional books.4.How do interactive e-books contribute to the reading and learning experience?•A) By reducing the emotional connection readers form with the content. •B) By limiting access to multimedia elements like videos and animations. •C) By enriching the learning experience and making complex concepts more accessible.•D) By decreasing the convenience of digital reading.5.What is the overall message of the passage regarding the impact of digitaltechnology on reading habits?•A) Digital technology has only negative impacts on reading habits. •B) Digital technology has completely replaced traditional reading methods.•C) The impact is multifaceted, with both positive and negative aspects that require balance.•D) The benefits of digital technology far outweigh any potential drawbacks.三、阅读理解新题型（10分）Title: The Rise of E-commerce and Its Impact on Traditional RetailIn recent years, the landscape of retail has undergone a dramatictransformation, fueled primarily by the exponential growth of e-commerce.Once a niche market, online shopping has now become an integral part of consu mers’ lives, challenging the dominance of brick-and-mortar stores.This shift has far-reaching implications, reshaping not only the way we shop but also the very fabric of our economic and social structures.The Convenience Factor: At the heart of e-commer ce’s success lies its unparalleled convenience. With just a few clicks, customers can browse through a vast selection of products from the comfort of their homes, compare prices effortlessly, and have their purchases delivered right to their doorsteps. This has not only saved time but also reduced the need for physical travel, making it especially appealing to busy professionals and those living in remote areas.Access to a Global Market: Another significant advantage of e-commerce is its ability to break down geographical barriers. No longer constrained by the limitations of their local markets, businesses can now reach customers worldwide. Similarly, consumers have access to an unprecedented range of products from across the globe, often at more competitive prices than those available locally.Challenges for Traditional Retail: However, this digital revolution has notcome without its challenges for traditional retailers. The rise ofe-commerce has led to a decline in footfall at physical stores, impacting sales and profitability. To stay afloat, many retailers have had to adapt by investing in their online presence, offering click-and-collect services, and enhancing in-store experiences to attract customers.The Future of Retail: The future of retail is likely to be a blend of both online and offline experiences, with retailers leveraging technology to create seamless omnichannel strategies. Augmented reality, virtual try-ons, and personalized recommendations are just a few examples of how technology is reshaping the shopping experience. As consumers continue to embrace digital solutions, retailers must innovate and evolve to meet their changing needs.Questions:1.What is the main driver behind the transformation of the retail landscape in recentyears?A)The increasing popularity of mobile payments.B)The exponential growth of e-commerce.C)The decline of physical infrastructure.D)The introduction of new tax policies.Answer: B2.Which of the following is NOT mentioned as an advantage of e-commerce?A)The ability to compare prices easily.B)The elimination of physical travel for shopping.C)Access to exclusive products not available locally.D)The convenience of shopping from home.Answer: C3.What does the term “omnichannel strategies” refer to in the context of retail?A) A single sales channel used by retailers.B) A blend of online and offline shopping experiences.C) A marketing technique focused on social media.D) A strategy to reduce operating costs.Answer: B4.How has the rise of e-commerce impacted traditional retailers?A)It has led to an increase in their sales and profitability.B)It has made them more competitive in the global market.C)It has caused a decline in footfall at their physical stores.D)It has made them completely obsolete in the retail industry.Answer: C5.Which technology is mentioned as having the potential to reshape the shoppingexperience?A)Artificial Intelligence.B)Augmented Reality.C)Internet of Things.D)Blockchain.Answer: B四、翻译（本大题有5小题，每小题2分，共10分）First QuestionQuestion: Translate the following paragraph into Chinese:The digital era has revolutionized the way we interact with information, making it possible to access vast amounts of knowledge instantly from anywhere in the world. This paradigm shift has not only altered our personal lives but also transformed industries, businesses, and the very fabric of society. As individuals navigate this ever-evolving landscape, it becomes increasingly crucial to develop a strong sense of digital literacy, enabling us to critically evaluate information, protect our privacy, and harness the power of technology for positive outcomes.Answer:数字时代彻底改变了我们与信息互动的方式，使我们能够瞬间从世界任何地方获取大量知识。

正确答案：A、B 你选对了Quizzes for Chapter 11 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 5 多选(1 分)选择下列计算机系统中属于人工智能的实例得分/总分总分A. Web搜索引擎A. 人类思考B.超市条形码扫描器B. 人工智能C.声控电话菜单该题无法得分/1.00C.机器智能 1.00/1.00D.智能个人助理该题无法得分/1.00正确答案：A、D 你错选为C、DD.机器动作正确答案： C 你选对了6 多选(1 分)选择下列哪些是人工智能的研究领域得分/总分2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分A.人脸识别0.33/1.00A. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/1.00C.图像理解C.人工智能将其定义为人类智能体的研究该题无法D.分布式计算得分/1.00正确答案：A、B、C 你错选为A、BD.人工智能是为了开发一类计算机使之能够完成通7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DA.以竞技水平玩德州扑克游戏0.33/1.003 多选(1 分)如下学科哪些是人工智能的基础？得分/总分B.打一场像样的乒乓球比赛A. 经济学0.25/1.00C.在Web 上购买一周的食品杂货0.33/1.00B. 哲学0.25/1.00D.在市场上购买一周的食品杂货C.心理学0.25/1.00正确答案：A、B、C 你错选为A、CD.数学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下正确答案：A、B、C、D 你选对了做正确的事。

得分/总分正确答案：已知4 多选(1 分)下列陈述中哪些是描述强AI (通用AI )的正确答案？得1 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 分/总分总分A. 指的是一种机器，具有将智能应用于任何问题的A.人类思考能力0.50/1.00B.人工智能B. 是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑0.50/1.00C.机器智能 1.00/1.00C.指的是一种机器，仅针对一个具体问题D.机器动作正确答案： C 你选对了D.其定义为无知觉的计算机智能，或专注于一个狭2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分窄任务的AIA. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的C.图像理解智能体程序该题无法得分/1.00D.分布式计算C.人工智能将其定义为人类智能体的研究该题无法正确答案：A、B、C 你错选为A、B得分/1.00 7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分D.人工智能是为了开发一类计算机使之能够完成通A.以竞技水平玩德州扑克游戏0.33/1.00常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DB.打一场像样的乒乓球比赛3 多选(1 分)如下学科哪些是人工智能的基础？得分/总分C.在Web 上购买一周的食品杂货0.33/1.00A. 经济学0.25/1.00D.在市场上购买一周的食品杂货B. 哲学0.25/1.00正确答案：A、B、C 你错选为A、CC.心理学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下D.数学0.25/1.00 做正确的事。

全英交流无障碍学习计划English has become a global lingua franca, and it plays a crucial role in international communication, trade, education, and technology. In today's interconnected world, mastering English is essential for individuals to thrive in various professional and social settings. However, many non-native speakers still face challenges in achieving fluency and proficiency in English, often due to limited access to resources and support. As a result, it is important to develop a comprehensive study plan to break down language barriers and facilitate effective English communication.This study plan is designed for non-native English speakers who are committed to improving their English communication skills. It encompasses various aspects of language learning, including vocabulary building, grammar improvement, speaking and listening practice, reading comprehension, and writing proficiency. The goal of this plan is to provide a structured and accessible framework for learners to enhance their English language abilities and gain confidence in communicating with native speakers and fellow non-native English speakers.I. Vocabulary BuildingA. Daily vocabulary exercises:1. Start each day with a vocabulary exercise to enhance word recognition and retention.2. Use flashcards or mobile apps to memorize new words and their meanings.3. Create a personal dictionary to record and review new vocabulary regularly.B. Reading comprehension with emphasis on vocabulary:1. Read English newspapers, magazines, and online articles to encounter diverse vocabulary in context.2. Make a habit of highlighting or underlining unfamiliar words and looking up their definitions.3. Use the newfound vocabulary in sentences or conversations to solidify understanding and usage.II. Grammar ImprovementA. Mastering basic grammar rules and structures:1. Begin with a review of basic grammar concepts such as tenses, articles, prepositions, and sentence structure.2. Practice grammar exercises and quizzes to reinforce understanding and application of rules.3. Seek assistance from grammar books, online tutorials, or language learning platforms to clarify any doubts or confusion.B. Immersive language exposure:1. Watch English-language movies, TV shows, and documentaries to observe grammar patterns and usage in natural contexts.2. Engage in English conversations, discussions, or language exchange sessions to put grammar knowledge into practice.3. Stay updated with English grammar rules and trends through reputable language learning websites and resources.III. Speaking and Listening PracticeA. Regular speaking exercises:1. Speak in English as much as possible, even in everyday situations and casual conversations.2. Practice speaking fluently and confidently through role-playing, interactive games, or public speaking exercises.3. Seek feedback and correction from fluent English speakers or language instructors to improve pronunciation and intonation.B. Active listening and comprehension:1. Listen to English podcasts, audio books, or radio programs to train the ear for different accents and speech patterns.2. Engage in active listening during English language interactions by focusing on context, keywords, and non-verbal cues.3. Engage in discussions or debates with fellow learners to improve listening comprehension and critical thinking skills.IV. Reading ComprehensionA. Diverse reading materials:1. Read a variety of English texts, including literature, essays, academic papers, and online blogs, to expand comprehension abilities.2. Dedicate time to read and analyze challenging texts, identifying main ideas, supporting details, and underlying themes.3. Discuss reading materials with peers or mentors to gain multiple perspectives and deeper insights.B. Enhancing reading speed and comprehension:1. Practice speed reading techniques to increase reading fluency and efficiency.2. Take regular reading comprehension tests to monitor progress and identify areas for improvement.3. Incorporate reading into daily routines, such as before bedtime or during breaks, to cultivate a habit of sustained reading.V. Writing ProficiencyA. Structured writing exercises:1. Devote time to daily writing practice, focusing on different genres such as essays, letters, reports, and creative writing.2. Set specific writing goals and challenges to expand writing skills and creativity.3. Seek feedback from peers, teachers, or professional editors to identify strengths and areas for improvement in writing.B. Developing a personal writing style:1. Experiment with different writing styles, tones, and voices to find a personal writing style that resonates with the learner.2. Study accomplished English writers and emulate their techniques and strategies to enhance writing proficiency.3. Reflect on past writing pieces and identify recurring errors or weaknesses for targeted improvement.VI. Enhanced Communication SkillsA. Engaging in meaningful conversations:1. Participate in debates, group discussions, or language exchange activities to engage in meaningful and challenging conversations.2. Seek out opportunities to interact with native English speakers through social events, professional networks, or online communities.3. Embrace diversity in language use by interacting with speakers from different English-speaking regions and cultures.B. Overcoming language barriers:1. Embrace mistakes and learn from them by approaching language barriers as opportunities for growth and improvement.2. Consistently expand vocabulary and grammar knowledge to confidently navigate linguistic challenges.3. Demonstrate patience and empathy when communicating with other non-native English speakers to foster a supportive language learning environment.Conclusion:By diligently following this comprehensive study plan, non-native English speakers can confidently break down language barriers and achieve effective communication in English. It is important to approach language learning with dedication, curiosity, and a growth mindset to overcome challenges and reach proficiency. With continuous practice and exposure to various language components, learners can gradually build confidence, expand their linguistic abilities, and thrive in the global English-speaking community. The key to success lies in a consistent commitment to learning, an open-minded approach to language acquisition, and a willingness to adapt to new linguistic challenges and opportunities.。

2025年全国大学英语CET四级考试自测试题与参考答案一、写作（15分）Task 1: Letter of RecommendationWrite a letter to your friend, who is applying for a scholarship in a foreign university. In your letter, you should:1.Express your support for your friend’s application.2.Mention your friend’s academic achievements and extracurricular activities.3.Briefly describe your friend’s personal qualities and how they would benefit from the scholarship.4.End the letter by encouraging your friend to pursue their goals.Writing Tip:Remember to use formal language and a clear structure. Start with a proper introduction, followed by the main body, and conclude with a polite closing.Sample Letter:[Your Name][Your Address][City, State, ZIP][Email Address][Date][Friend’s Name][Friend’s Address][City, State, ZIP]Dear [Friend’s Name],I am writing to express my wholehearted support for your application for the scholarship at [Foreign University]. I believe that you are an exceptional candidate who deserves this opportunity to further your education abroad.Throughout your academic journey at [Your University], you have consistently demonstrated exceptional dedication and commitment to your studies. Your academic achievements are nothing short of remarkable. You have consistently ranked among the top students in your major, earning honors and awards along the way. Moreover, your ability to grasp complex concepts and apply them creatively is truly impressive.In addition to your academic prowess, you have been actively involved in various extracurricular activities. You have served as the president of the Student Council, where you have shown excellent leadership qualities and have been instrumental in organizing successful events. Your ability to work collaboratively and inspire others is a testament to your strong interpersonal skills.On a personal level, you possess a genuine passion for learning and a strongsense of curiosity. Your determination to explore new cultures and ideas will undoubtedly benefit you greatly as you pursue your studies at [Foreign University]. The scholarship would not only provide you with financial support but also allow you to immerse yourself in a diverse and enriching academic environment.I have no doubt that you will excel in your studies and make the most of this opportunity. Please do not hesitate to reach out to me if you need any assistance or guidance during your application process.Wishing you the best of luck with your scholarship application. I am confident that you will achieve your goals.Warm regards,[Your Name]Analysis:This sample letter follows the structure and guidelines provided in the task. It starts with a formal introduction, expressing support for the friend’s application. The body of the letter focuses on the friend’s academic achievements and extracurricular activities, providing specific examples to illustrate their strengths. The writer then briefly describes the friend’s personal qualities and how the scholarship would benefit them. Finally, the letter concludes with a polite closing and well-wishes, encouraging the friend to pursue their goals. The language used is formal and respectful, appropriate for a letter of recommendation.二、听力理解-短篇新闻（选择题，共7分）第一题News Item 1:A new study by the World Health Organization (WHO) has found that the consumption of sugary drinks is linked to an increased risk of type 2 diabetes. The study, which involved nearly 600,000 people from 10 different countries, showed that those who consumed more than one sugary drink per day had a 1.93 times higher risk of developing type 2 diabetes compared to those who did not consume sugary drinks at all. The WHO recommends limiting the intake of sugary drinks to less than 50 grams per day to reduce the risk of diabetes.Questions:1、What is the main finding of the study conducted by the WHO?A. The consumption of sugary drinks does not increase the risk of type 2 diabetes.B. The consumption of sugary drinks is linked to an increased risk of type2 diabetes.C. The consumption of sugary drinks can prevent type 2 diabetes.D. The consumption of sugary drinks has no effect on the risk of type 2 diabetes.2、How many times higher is the risk of developing type 2 diabetes for those who consume more than one sugary drink per day compared to those who do not consume sugary drinks at all?A. 1.5 timesB. 1.93 timesC. 2.5 timesD. 3 times3、According to the WHO, how many grams of sugary drinks should be consumed per day to reduce the risk of diabetes?A. 100 gramsB. 50 gramsC. 25 gramsD. 75 gramsAnswers:1、B2、B3、B第二题News Item 1:The World Health Organization (WHO) has declared a global emergency due to the rapid spread of a new strain of influenza. The virus, known as H5N1, has already infected hundreds of people in Asia and Europe, and there are concerns it could mutate into a form that spreads easily among humans. Governments around the world are taking steps to control the virus and prevent a pandemic.Questions:1、What has the WHO declared due to the spread of the new influenza strain?A) A regional emergencyB) A global emergencyC) A pandemicD) A localized outbreak2、How many people in Asia and Europe have been infected with the new influenza strain?A) DozensB) HundredsC) ThousandsD) Tens of thousands3、What is the main concern regarding the H5N1 virus?A) It has already mutated into a form that spreads easily among humans.B) It is highly resistant to existing vaccines.C) It is causing severe health complications in infected individuals.D) It is primarily affecting animals rather than humans.Answers:1、B) A global emergency2、B) Hundreds3、A) It has already mutated into a form that spreads easily among humans.三、听力理解-长对话（选择题，共8分）第一题听力原文：W: Hi, John. How’s your preparation for the CET-4 exam going?M: Oh, not too bad. I’ve been studying pretty hard, but I’m really worried about the listening section. It’s always been my weakest point.W: I know what you mean. It’s hard to improve your list ening skills in such a short time. But don’t worry too much. Just keep practicing.M: Yeah, but how should I practice? Should I just listen to English news and podcasts every day?W: That’s a good start, but it’s also important to practice with real exam q uestions. That way, you’ll get used to the format and the types of questions.M: Right. So, where can I find some practice materials?W: There are many online resources available. You can check out websites like CET-4 Listening Practice or even download some apps that offer listening exercises.M: Great! I’ll start looking for those resources right away. By the way, do you have any tips for improving listening skills?W: Well, one thing I find helpful is to take notes while listening. It helps me to focus and remember the main points of the conversation. Also, try to predict what the speaker might say next based on the context.M: That’s a good tip. I’ll try that. Thanks for the advice!W: No problem. We’re all in this together. Good luck with your exam!Questions:1、What is the main topic of the conversation?A) Preparing for the CET-4 examB) Improving listening skillsC) Finding practice materials for the CET-4 examD) Taking notes during the exam2、What does the man think about the listening section of the CET-4 exam?A) He is confident about it.B) He is not very worried about it.C) He feels it is his weakest point.D) He thinks it is the easiest section.3、What does the woman suggest the man do to improve his listening skills?A) Listen to English songs and movies.B) Read a lot of English books.C) Take notes while listening.D) Practice speaking English.4、Where does the woman suggest the man find practice materials for the CET-4 listening section?A) At the library.B) On the internet.C) In the bookstore.D) At the school’s language center.Answers:1、B) Improving listening skills2、C) He feels it is his weakest point.3、C) Take notes while listening.4、B) On the internet.第二题听力原文：W: Hey, John. Have you heard about the new English proficiency test t hat’s going to be introduced next year?M: Yeah, I have. It’s called the CET-4 English Proficiency Test. I heard it’s going to be more challenging than the current one.W: That’s true. The test format is going to change a bit. Instead of just having listening, reading, writing, and speaking sections, there will also be a long conversation section.M: Oh, really? That sounds interesting. How will it work?W: Well, the long conversation will be a conversation between two or more people on a specific topic. We’ll have to listen to the conversation and answer questions based on what we hear.M: That sounds like a good way to test our listening skills and understanding of the language in real-life situations.W: Exactly. And the questions will be a mix of multiple-choice andshort-answer questions.M: So, are you planning to take the test?W: Yes, I am. I want to improve my English skills, and this test seems like a good way to do that.M: Good for you! I think I’ll take it too. Let’s study together.Questions:1、What is the new English proficiency test called?A) CET-3B) CET-4C) CET-5D) CET-62、What is one of the main changes in the new test format?A) There will be no speaking section.B) There will be a long conversation section.C) The reading section will be longer.D) The writing section will be more difficult.3、What type of questions will be included in the long conversation section?A) Only multiple-choice questions.B) Only short-answer questions.C) A mix of multiple-choice and short-answer questions.D) Only essay questions.4、What is the main reason the speakers are interested in taking the test?A) To improve their grades.B) To get a job abroad.C) To improve their English skills.D) To meet the requirements for a higher education program.Answers:1、B) CET-42、B) There will be a long conversation section.3、C) A mix of multiple-choice and short-answer questions.4、C) To improve their English skills.四、听力理解-听力篇章（选择题，共20分）第一题Passage OneAfter the terrorist attacks on the United States on September 11, 2001, the world changed in many ways. People’s lives, both in the United States and around the world, were forever altered. The attacks led to increased security measures at airports, government buildings, and public events. They also had a profound impact on the global economy and international relations.1、What was the primary cause of the changes in people’s lives after the September 11 attacks?A)Increased security measures.B)The global economy.C)International relations.D)All of the above.Passage One Questions:2、What immediate effect did the September 11 attacks have on airports?A)They were closed for several days.B)They introduced more stringent security checks.C)They were forced to reduce flights.D)They had no immediate effect.3、How did the September 11 attacks change international relations?A)They led to a strengthening of alliances.B)They caused a breakdown in diplomatic relations.C)They had no immediate impact on international relations.D)They led to a reduction in international trade.Answers:1、D) All of the above.2、B) They introduced more stringent security checks.3、A) They led to a strengthening of alliances.第二题Passage OneIn recent years, there has been a growing interest in the study of sleep and its effects on our health. Researchers have found that sleep plays a crucial role in our physical and mental well-being. Here are some key findings from recent studies:1.Sleep deprivation can lead to serious health issues. Lack of sleep has been linked to an increased risk of heart disease, diabetes, and obesity. It can also weaken the immune system, making us more susceptible to infections.2.Sleep is essential for cognitive functions. Memory, attention, and problem-solving skills are all affected by sleep deprivation. Without adequate sleep, our ability to perform daily tasks can be significantly impaired.3.Sleep helps regulate our hormones. Lack of sleep can disrupt the balance of hormones, leading to weight gain and other health problems.4.Sleep improves our mood. Regular, quality sleep can reduce stress and anxiety, and improve our overall sense of well-being.Now, listen to a conversation between two friends discussing the importance of sleep.Question 1: What is one potential health issue associated with sleep deprivation?A. Improved cognitive functions.B. Increased risk of heart disease.C. Enhanced immune system.D. Decreased stress levels.Answer: BQuestion 2: According to the passage, how does sleep affect our cognitive abilities?A. It enhances memory and attention.B. It impairs problem-solving skills.C. It increases stress levels.D. It weakens the immune system.Answer: AQuestion 3: What is one benefit of regular, quality sleep mentioned in the passage?A. It disrupts hormone balance.B. It increases stress levels.C. It improves our mood.D. It weakens the immune system.Answer: C第三题PassageDr. John Brown, a renowned psychologist, has conducted extensive research on the effects of sleep on cognitive performance. In his latest study, he investigates how sleep deprivation affects problem-solving abilities.Dr. Brown explains that during sleep, the brain processes and consolidates information, which is crucial for memory formation and problem-solving. Sleep deprivation, however, can hinder this process, leading to decreased cognitive performance.In his study, Dr. Brown divided participants into two groups. One group was allowed to sleep for 8 hours, while the other group was kept awake for 24 hours. After the sleep deprivation period, both groups were given a series ofproblem-solving tasks to complete.The results were striking. The group that had slept for 8 hours performed significantly better on the problem-solving tasks than the group that had been kept awake. The sleep-deprived group showed slower reaction times, lower accuracy, and higher levels of stress.Dr. Brown concludes that adequate sleep is essential for optimal cognitive performance. Sleep deprivation not only impairs problem-solving abilities but also has long-term negative effects on mental health and overall well-being.Questions1、What is the main focus of Dr. John Brown’s research?A) The effects of stress on sleepB) The importance of sleep in cognitive performanceC) The role of genetics in sleep patternsD) The impact of caffeine on sleep quality2、How does sleep deprivation affect cognitive performance, according to Dr. Brown?A) It improves reaction times and accuracyB) It enhances memory formation and problem-solving abilitiesC) It hinders the brain’s ability to process and consolidate informationD) It has no significant impact on cognitive performance3、What is the conclusion Dr. Brown draws from his study?A) Sleep deprivation has no negative effects on cognitive performanceB) Adequate sleep is unnecessary for optimal cognitive performanceC) The quality of sleep is more important than the quantity of sleepD) Sleep deprivation can have long-term negative effects on mental health and overall well-beingAnswers1、B) The importance of sleep in cognitive performance2、C) It hinders the brain’s ability to process and consolidate information3、D) Sleep deprivation can have long-term negative effects on mental health and overall well-being五、阅读理解-词汇理解（填空题，共5分）第一题Reading passage:In the digital age, our lives are increasingly intertwined with technology. From the moment we wake up to the time we go to bed, we rely on various devices to help us with our daily tasks. One such device that has become an integral part of our lives is th e smartphone. It’s not just a communication tool anymore; it’s a personal assistant, a fitness tracker, and a source of entertainment.The rise of smartphones has brought about both benefits and challenges. On the one hand, they have made our lives more convenient. We can access information, make purchases, and stay connected with friends and family at any time. On the other hand, excessive use of smartphones can lead to negative consequences suchas sleep disturbances, reduced face-to-face interactions, and even mental health issues.As we continue to integrate technology into our daily routines, it’s important to be mindful of how we use our devices. Here are some tips to help you maintain a healthy balance:1.Set specific times for checking your phone, such as during meals or before bedtime.e apps that limit your screen time to encourage healthier habits.3.Engage in offline activities to reduce your reliance on digital devices.4.Prioritize face-to-face interactions with loved ones.Now, complete the following sentences by choosing the most appropriate word from the word bank for each blank. Write the corresponding letter (A, B, C, orD) in the blank.1.The rise of smartphones has brought about both__________and challenges.A. benefitsB. obstaclesC. confusionD. excitement2.Excessive use of smartphones can lead to__________such as sleep disturbances.A. benefitsB. obstaclesC. confusionD. excitement3.It’s important to be__________of how we use our devices.A. carefulB. lazyC. indifferentD. obsessed4.Set specific times for checking your phone to__________your reliance on digital devices.A. reduceB. increaseC. ignoreD. depend on5.Engage in__________activities to reduce your reliance on digital devices.A. onlineB. offlineC. digitalD. physicalAnswers:1.A2.A3.A4.A5.BSecond PartDirections: There are 5 incomplete statements followed by a list of words or phrases. Choose the most appropriate word or phrase to complete each statement. Mark your answers on the ANSWER SHEET.Passage:In today’s rapidly evolving technological landscape, digital literacy has become an indispensable skill for students. It not only encompasses the ability to use technology efficiently but also involves understanding the ethical implications of its usage. As educators emphasize the importance of integrating technology into the classroom, they must also address the challenges that come with it, such as ensuring equitable access and preventing the digital divide from widening. The goal is to prepare students to be responsible digital citizens who can navigate the online world safely and effectively.Questions:1、The term “digital literacy” refers to the skills required to _______.a)Use technology efficiently and understand its ethical implicationsb)Read books onlinec)Type fast on a computerd)Navigate social media platformsAnswer: a)2、“Ethical implications” in this context means _______.a)The consequences related to right and wrong behavior concerning technology useb)The speed at which one can operate a computerc)The cost of buying new technologyd)The process of learning how to codeAnswer: a)3、“Equitable access” implies that _______.a)All students have fair opportunities to use technologyb)Technology should be equally expensive for everyonec)Students should spend equal time on homeworkd)Teachers should give equal grades to all studentsAnswer: a)4、The “digital divide” refers to the gap between those who have ready access to computers and the internet, and those who do not because of _______.a)Societal inequalitiesb)Personal preferencesc)Technological advancementsd)Educational qualificationsAnswer: a)5、Being a “responsible digital citizen” means that one is able to _______.a)Use technology without any restrictionsb)Contribute positively to the online community while respecting others’ rightsc)Own the latest gadgetsd)Have unlimited data plansAnswer: b)六、阅读理解-长篇阅读（选择题，共10分）第一题Reading PassagesPassage OneIn recent years, the rise of online shopping has revolutionized the way people purchase goods and services. With just a few clicks, consumers can now access a vast array of products from the comfort of their own homes. This shift has had a significant impact on traditional brick-and-mortar retailers, forcing many to adapt or risk becoming obsolete.The convenience of online shopping is undeniable. Consumers can shop at any time of the day or night, compare prices across different platforms, and read reviews from other customers. However, this convenience comes with its own set of challenges. One major concern is the issue of online security, as consumers are vulnerable to identity theft and fraud. Additionally, the lack of physical interaction with products can lead to dissatisfaction with the purchase, especially when it comes to clothing or electronics.Despite these challenges, online shopping continues to grow in popularity. One reason for this is the rise of social media, which has become a powerful tool for promoting products and influencing consumer behavior. Influencers, for example, can showcase products to their followers, who may then be more inclined to make a purchase. Moreover, the rise of mobile shopping has made it even easier for consumers to shop on the go.Questions:1、What is the main idea of the first paragraph?A. The rise of online shopping has changed the way people shop.B. Online shopping is convenient but has its drawbacks.C. Traditional retailers are struggling to compete with online shopping.D. Online shopping is influenced by social media and mobile technology.2、What is one of the main concerns mentioned regarding online shopping?A. The high cost of shipping.B. The lack of physical interaction with products.C. The potential for identity theft and fraud.D. The difficulty in returning items.3、According to the passage, what is a benefit of online shopping?A. The ability to touch and feel products before purchasing.B. The opportunity to interact with sales representatives.C. The ability to read reviews from other customers.D. The reduced need for travel to physical stores.4、How does social media influence online shopping?A. It increases the cost of online transactions.B. It promotes products and influences consumer behavior.C. It makes online shopping less convenient.D. It reduces the need for physical stores.5、What is the author’s attitude towards online shopping?A. NegativeB. PositiveC. IndifferentD. CriticalAnswers:1、A2、C3、C4、B5、BSecond QuestionPassage:The Digital Divide and Its Impact on EducationIn the age of information technology, access to digital resources has become increasingly important for educational success. However, there exists a significant gap in access between those who have ready access to computers and the internet and those who do not. This phenomenon is known as the digital divide. It affects students in various ways, from limiting their ability to complete homework assignments that require internet access to hindering their development of critical digital literacy skills necessa ry for today’s job market.While urban areas tend to have better infrastructure and more resources tosupport digital education, rural communities often lag behind. Schools in these regions may lack the funding needed to provide adequate computer facilities and internet connectivity, leaving students at a disadvantage when it comes to competing with their peers in more technologically advanced settings.Moreover, the digital divide also extends beyond hardware and connectivity issues. There is a disparity in the availability of digital literacy training programs that teach individuals how to effectively use technology. Without such training, even if hardware and connectivity were available, many students would still struggle to make effective use of digital tools for learning.Efforts to bridge the digital divide include initiatives aimed at providing affordable internet access and devices to underprivileged communities as well as programs designed to train educators and students in digital literacy. Despite these efforts, challenges remain, particularly in ensuring equitable access to technology across all socioeconomic groups.Questions:1、What does the term “digital divide” refer to?A. The gap in technology access between different socioeconomic groups.B. The difference in technology preferences among students.C. The physical distance between urban and rural areas.D. The separation of online and offline educational resources.Answer: A2、How does the digital divide affect students’ educational outcomesaccording to the passage?A. By limiting their access to traditional textbooks.B. By preventing them from participating in extracurricular activities.C. By hindering their ability to complete homework that requires internet access.D. By reducing the number of subjects they can study.Answer: C3、Which area is more likely to face challenges due to the digital divide?A. Urban areas with high population density.B. Rural communities with limited resources.C. Suburban neighborhoods with moderate income levels.D. Metropolitan cities with advanced technological infrastructure.Answer: B4、What additional issue besides hardware and connectivity is mentioned regarding the digital divide?A. The cost of software subscriptions.B. The lack of digital literacy training programs.C. The shortage of qualified teachers.D. The absence of government regulations.Answer: B5、What kind of efforts are being made to reduce the impact of the digital divide?A. Increasing the production of printed materials.B. Providing affordable internet access and digital literacy training.C. Encouraging the use of traditional teaching methods.D. Reducing the emphasis on technology in schools.Answer: BThis completes the section on Long Reading Comprehension for the CET Level 4 exam preparation.七、阅读理解-仔细阅读（选择题，共20分）第一题Reading PassagesPassage 1In today’s fast-paced world, the importance of effective communication skills cannot be overstated. Communication is the key to success in both personal and professional settings. One of the most widely recognized tests of English proficiency is the College English Test Band 4 (CET-4). This exam, conducted annually in China, evaluates the English language skills of university students.The CET-4 exam consists of several sections, including listening comprehension, reading comprehension, writing, and translation. In this passage, we will focus on the reading comprehension section, which is often considered the most challenging part of the exam.The reading comprehension section consists of three parts: reading passageA, reading passage B, and reading passage C. Each part contains several questions th at test the reader’s ability to understand the main idea, details, and inference of the text.Reading passage A is a passage that covers a broad range of topics, such as science and technology, education, culture, and society. Reading passage B is a detailed account of a particular event or phenomenon, while reading passage C is a passage that requires the reader to infer the author’s attitude or opinion based on the given information.Questions:1、The CET-4 exam is widely recognized as a test of ________.A. listening skillsB. speaking skillsC. reading skillsD. writing skills2、The reading comprehension section of the CET-4 exam consistsof________parts.A. oneB. twoC. threeD. four3、Which of the following is NOT mentioned as a topic in reading passage A?。

2024-2025学年外研版英语高一上学期期中自测试卷及答案指导一、听力第一节（本大题有5小题，每小题1.5分，共7.5分）1、W: Excuse me, can you tell me how to get to the nearest bank from here?M: Sure, you can take Bus No. 2 and get off at the next stop. Then, walk straight for about 200 meters. The bank will be on your right.Q: Where does the conversation take place?A) At a bus stop.B) In a bank.C) On a bus.D) At a restaurant.Answer: A) At a bus stop.Explanation: The conversation is about directions to a bank, which indicates that they are at a bus stop.2、M: Hi, Lisa, how was your vacation in Greece last month?W: It was amazing! The food was delicious, and the ancient ruins were fascinating. I especially loved the local music and dance performances.Q: What did Lisa enjoy most about her vacation?A) The food.B) The ancient ruins.C) The local music and dance.D) The beautiful beaches.Answer: C) The local music and dance.Explanation: In the conversation, Lisa specifically mentions that she loved the local music and dance performances, indicating that this was her favorite part of the vacation.3、What does the man say about his favorite sport?A)He likes playing it very much.B)It’s a team sport.C)He thinks it’s not too physically demanding.D)He prefers watching it to playing it.Answer: C) He thinks it’s not too physically demanding.Explanation: The man mentions that his favorite sport is something that doesn’t require too much physical effort, which indicates that he believes it’s not too physically demanding.4、Why does the woman decide to go to the art gallery?A)She wants to see the new exhibit.B)She has to drop off her friend there.C)She needs to pick up some art supplies.D)She’s visiting the city for the first time.Answer: A) She wants to see the new exhibit.Explanation: The woman explicitly states that she wants to see the new exhibit at the art gallery, making this the correct answer.5、You will hear a conversation between two students, Jack and Lisa, discussing their plans for the weekend. Listen carefully and choose the best answer to the following question.Question: How many days will Jack spend on his school project this weekend?A. 1 dayB. 2 daysC. 3 daysD. 4 daysAnswer: B. 2 daysExplanation: In the conversation, Lisa asks Jack how long he plans to work on the project. Jack responds, “I think I’ll need about two days to finish it up.” This indicates that he will spend two days on the project.二、听力第二节（本大题有15小题，每小题1.5分，共22.5分）1、Question One:What is the relationship between the two speakers?A) Teacher and studentB) Doctor and patientC) Boss and employeeD) Shop assistant and customerRecording Transcript:M: Good morning, Ms. Green. I’ve brought the reports you asked for.W: Thank you, John. Could you also check on the status of the Johnson account?Answer: C) Boss and employeeExplanation: The conversation indicates that John is bringing reports to Ms. Green and she is asking him to do more work, suggesting asuperior-subordinate relationship.2、Question Two:Where does this conversation most likely take place?A) In a restaurantB) At a bus stopC) In a libraryD) At a travel agencyRecording Transcript:W: Excuse me, I need to return these books before the weekend. Can you tell me where the returns desk is?M: Of course, it’s just down the hall to your left.Answer: C) In a libraryExplanation: The woman is returning books and asking about the returns desk, which strongly suggests that the conversation is taking place in a library.3.You hear a conversation between two students discussing their weekend plans.A. What are they planning to do this weekend?B. What is the weather forecast for the weekend?C. Who is going to join them for the trip?D. What are their favorite activities?Answer: AExplanation: The question asks about their plans for the weekend, and the conversation indicates that they are discussing what activities they will do.4.You listen to a short speech given by a teacher at a school event.A. What is the teacher mainly discussing?B. Who is the guest speaker at the event?C. When is the next school event scheduled?D. Why is the school proud of its students?Answer: AExplanation: The question asks for the main topic of the teacher’s speech, and the speech would typically cover the event or subject being discussed, not specific details about the guest speaker, the date of the next event, or the reason for school pride.5.W: Hey, John, have you been to the new shopping center downtown yet? M: Yeah, I went there last weekend. It’s amazing! There are so many stores, including a big bookstore and a coffee shop.Q: What did John do last weekend?A: He went to the new shopping center downtown.B: He visited a big bookstore.C: He had a coffee with his friends.D: He stayed at home.Answer: AExplanation: The question asks what John did last weekend. In the dialogue, John mentions, “I went there last weekend,” referring to the n ew shopping center downtown. Therefore, the correct answer is option A.6.W: Do you think you’ll have time to go to the museum with me this weekend? M: I’m not sure. I have a couple of projects due next week, so I might be quite busy. Maybe next weekend?Q: What does the man imply about his plans for this weekend?A: He doesn’t want to go to the museum.B: He will definitely go to the museum with the woman.C: He doesn’t have time to go this weekend but will have time next weekend. D: He doesn’t want to go to the museum next weekend either.Answer: CExplanation: The question asks what the man implies about his plans for this weekend. In the dialogue, the man says, “I’m not sure. I have a couple of projects due next week, so I might be quite busy. Maybe next weekend?” This indicates that he doesn’t have time to go to the museum this weekend due to his busy schedule. However, he suggests going next weekend, implying that he will have time then. Therefore, the correct answer is option C.7.Question: What is the speaker’s main concern about the school’s new policy?A)The policy will increase the students’ workload.B)The policy will reduce the quality of education.C)The policy will affect the students’ mental health.D)The policy will disrupt the school’s daily routine.Answer: C) The policy will affect the students’ mental health.Explanation: In the conversation, the speaker mentions that the new policy will lead to increased pressure on students, which could have a negative impact on their mental health. The other options are not mentioned as the main concern.8.Question: Why does the teacher recommend the student to join the school debate team?A)The student has a good command of English.B)The student is a top performer in their class.C)The student has shown interest in public speaking.D)The student needs to improve their communication skills.Answer: C) The student has shown interest in public speaking.Explanation: The teacher mentions that the student has expressed interest in public speaking, and that the debate team would be a great platform for them to develop their skills. The other options are not mentioned as reasons for the recommendation.9.W: Have you decided what to do for your summer vacation, Tom?M: Well, I’m thinking of going to Greece, but my parents suggested I try a volunteer program in a rural area of China.Q: What does Tom’s parents suggest he do?A: [B] Go to Greece.B: [C] Travel to a rural area in China.C: [D] Stay at home.Answer: [C]Explanation: The conversation implies that Tom’s parents are suggesting he consider doing a volunteer program in a rural area in China, which is why option[C] is the correct answer.10.W: I heard you’re planning to study abro ad next year. Which country do you prefer, the US or the UK?M: I’ve been thinking about both, but the UK’s education system is more rigorous, and it’s where I’d like to go.Q: What is the man’s preference for studying abroad?A: [A] The US.B: [B] The UK.C: [C] Both the US and the UK.D: [D] He is not sure.Answer: [B]Explanation: The man explicitly states that he would like to go to the UK due to its rigorous education system, making option [B] the correct answer.11、Questions 11 to 13 are based on the passage you have just heard.11、What is the main topic of the passage?A) The importance of recycling.B) The effects of pollution on marine life.C) Ways to reduce water consumption at home.D) Techniques for conserving energy in industry.Answer: B. The passage discusses the impact of pollution on sea creatures, highlighting how plastic waste affects their habitats and food chains.Explanation: The speaker spends most of the time discussing examples of how pollution harms marine animals and ecosystems, making it clear that this is the central theme.12、According to the passage, what is one way that plastic waste affects marine animals?A) It provides new habitats for small fish.B) It increases the oxygen levels in the water.C) It entangles animals and can be ingested.D) It promotes faster growth in coral reefs.Answer: C. The passage mentions that plastic debris can trap marine life and when ingested, it can cause serious health issues or death.Explanation: During the discussion, there’s a detailed explanation about how plastic items in the ocean pose a threat to marine species by both physically trapping them and being mistakenly consumed as food.13.W: Have you finished your homework yet, Tom?M: Not yet. I’m still working on the math problems. They’re really tough.Q: What subject is Tom finding difficult?A: Math解析：对话中，Tom提到他还在做数学题，并表示它们很难，因此答案是数学。

英语作文手写模板范文英文回答：Topic: The Role of Technology in Education: Empoweringor Enervating?Introduction:Technology has become an indispensable part of our lives, influencing every aspect of our existence. Education, in particular, has witnessed a profound transformation with the advent of digital tools and resources. However, the integration of technology into education has sparked a debate: Is it empowering or enervating?Empowering Effects:Enhanced Accessibility: Technology bridges physicaland temporal barriers, making education accessible to a wider range of learners, including those in remote areas orwith disabilities.Personalized Learning: Digital platforms enable educators to tailor instruction to individual student needs, creating personalized learning experiences that foster academic growth.Interactive Content: Technology provides interactive content, such as simulations, videos, and games, which engage students and make learning more enjoyable.Collaboration and Communication: Technologyfacilitates collaboration among students and teachers, fostering a sense of community and promoting effective communication.Enervating Effects:Digital Distraction: The constant availability of technology can be distracting, leading students to lose focus on academic tasks.Equity Concerns: Access to and proficiency withdigital tools vary widely, creating an opportunity gap between students with and without access to technology.Reduced Social Interaction: Excessive use of technology can reduce face-to-face interaction, which is essential for social and emotional development.Erosion of Critical Thinking Skills: Reliance on technology for information retrieval may diminish students' ability to think critically and develop independent problem-solving skills.Balancing Empowerment and Enervation:To harness the benefits of technology while mitigating its potential drawbacks, a balanced approach is crucial:Integrate Technology Meaningfully: Use technology to enhance student learning, rather than merely as a distraction.Foster Digital Literacy: Provide students with instruction and support in digital literacy to bridge the equity gap.Encourage Human Interaction: Create opportunities for students to interact with teachers and peers face-to-face, fostering social and emotional development.Cultivate Critical Thinking: Encourage students to critically evaluate information obtained through digital sources and develop problem-solving skills.Conclusion:The integration of technology into education has the potential to both empower and enervate students. By understanding the empowering and enervating effects of technology, educators and policymakers can strike a balance that maximizes its benefits while minimizing its negative consequences. Ultimately, technology should be used as a tool to enhance student learning, rather than a replacement for traditional educational practices.中文回答：科技在教育中的作用，赋能还是萎靡？引言：科技已成为我们生活中不可或缺的一部分，影响着我们存在的方方面面。

Design teaching objectives
01
Clear teaching objectives: Based on the theme and the actual situation of students, establish clear teaching objectives, including knowledge, skills, and emotional goals.
Content is closely related to the theme
ensuring that the teaching content is closely related to the theme helps to achieve teaching objectives and highlight the significance of the theme.
The concept of holistic teaching in English units
Holistic teaching refers to an approach in which the various components of a unit of study are integrated and viewed as related parts of a whole In the context of English units, this means that language skills, content knowledge, and learning processes are seamless integrated to achieve a unified learning experience
Content with contemporaneity and novelty