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高级架构师考试题库及答案一、单选题1. 在软件架构中，以下哪一项不是微服务架构的特点？A. 服务独立性B. 服务自治性C. 服务集中管理D. 服务轻量级通信答案：C2. 以下关于分布式系统CAP理论的描述，哪一项是错误的？A. 一致性（Consistency）B. 可用性（Availability）C. 分区容错性（Partition tolerance）D. 所有分布式系统都可以同时满足CAP理论中的所有特性答案：D3. 在云原生架构中，以下哪个不是容器化技术的典型特点？A. 环境一致性B. 资源隔离C. 无需操作系统D. 快速启动答案：C二、多选题1. 以下哪些是微服务架构设计时需要考虑的关键因素？A. 服务拆分B. 服务发现C. 服务编排D. 数据一致性答案：A, B, C, D2. 在构建高可用性系统时，以下哪些措施是有效的？A. 负载均衡B. 冗余设计C. 单点故障D. 定期备份答案：A, B, D三、判断题1. 在分布式系统中，CAP理论告诉我们，一致性、可用性和分区容错性可以同时实现。
（对/错）答案：错2. 微服务架构中，服务之间通过同步调用可以提高系统的响应速度。
（对/错）答案：错四、简答题1. 描述一下在云原生架构中，服务网格（Service Mesh）的主要作用是什么？答案：服务网格的主要作用是管理微服务之间的通信，提供服务发现、负载均衡、故障恢复、度量和监控等功能，同时它还有助于实现服务间的安全通信，如加密和授权。
2. 解释一下在分布式系统中，为什么需要进行服务拆分？答案：服务拆分是为了提高系统的可维护性、可扩展性和容错性。
通过将一个大的单体应用拆分成多个小的、独立的服务，每个服务可以独立部署、升级和扩展，从而减少系统间的耦合，提高系统的灵活性和可维护性。
五、案例分析题1. 假设你是一个高级架构师，你的团队正在设计一个面向全球的在线购物平台。
请描述你会如何设计这个平台的架构，以确保它具有高可用性、可扩展性和良好的用户体验。
Context-Aware Services based on Secure Mobile AgentsUlrich Pinsdorf*,Jan Peters*,Mario Hoffmann and Piklu Gupta*Fraunhofer Institute for Computer GraphicsFraunhoferstraße5,D-64283Darmstadt,Germanyulrich.pinsdorf jpeters@igd.fraunhofer.deFraunhofer Institute for Secure TelecooperationRheinstraße75,D-64295Darmstadt,Germanymario.hoffmann@sit.fraunhofer.deFraunhofer Institute for Integrated Publication and Information SystemsDolivostraße15,D-64293Darmstadt,Germanygupta@ipsi.fraunhofer.deAbstract:In this paper we introduce the concept ofcontext-aware services as an extended form of location-based services.We describe a new architecture for real-izing context-aware services which has a number of keybenefits.This architecture relays on mobile softwareagents as security-centric middleware.Our approach allowsto combine disjunct databases by establishing application-dependant links between remote data sets with a proprietaryaccess.We describe all necessary roles and places to estab-lish a multilateral security concept for all participants.Keywords:Mobile software agents,context-aware ser-vices,location-based services,databases,profiles,security.1MotivationPresently there is a wide range of information avail-able on the Internet stored in disjunct databases whichto date can only be queried individually.Additionallysuch publicly accessible databases are also typicallydesigned for proprietary access which may also only beavailable on a limited number of computing platforms.The challenge therefore remains of being able to opti-mize the querying of these discrete data sources so thatinformation can be combined in order to achieve thethem.The time of a query is a factor in determining the relevance of a query answer;shop opening times, which differ from country to country can therefore be considered in relation to the time of a st of all, services which are aware of what kind of device they are being delivered to can obviate problems of inappro-priate presentation formats;for instance large graphic files will therefore not be sent to a mobile device using a standard GSM connection for data transfer.2ApproachSince it is no longer a problem to store huge amounts of data,the problem has shifted towards the question of how tofind specific information.Moreover,en-abling the querying of disjunct databases with totally incompatible table structures or even different storage concepts is a major problem,which has still not been solved satifactorily.In our opinion the sole way to integrate incompati-ble data sources is by means of proactive components. Proactive components,such as software agents,are able to establish links between data according to the application.We suggest mobile software agents as proactive components realizing interconnections between data sources.Mobile agents offer the optimum solution due to their benefits with respect to aspects of security,flex-ibility and data compression.For each type of query in an application we suggest a specific agent.If a query has to be performed,the according agent will be in-stantiated,migrate to a number of data sources and re-turn with a report which aggregates the results of its queries.Since it is sufficient for each agent to know the structure of the small number of specific databases it is working on to perform the query task,the devel-opment becomes very effective,scalable and therefore manageable on a large scale.The interconnection between two data sources be-comes even more valuable when context specific queries become involved as defined in section1.Be-sides information about current time and the user’s lo-cation usually used to provide location-based services (LBS)[1],information about the user’s current situa-tion,his habits and needs enables us to focus the query on a small subset of a huge amount of available data and thereby enables effective context aware data min-ing[6]as an extension to“common”LBS.In other words:depending on the current user’s con-text a specific agent creates an application-specific link between two or more databases.This link exists just as long as the agent has to be alive to provide the in-formation.Of course,an agent could communicate its ”experience”to other agents before it dies,but since the user’s context[4]is highly dynamic as well,one would usally want to renew these database links for each query.3ArchitectureWe define different roles(see Figure1)appearing in our approach and the dependencies between them.So-called service providers with detailed knowledge about the interfaces to data sources of certain infor-mation providers develop specific services as mobile software agents.Those services will be collected,re-viewed and published by service brokers as pieces of executable code.Besides structuring andfiltering the variety of provided services quality inspection is a ma-jor issue.Through a cryptographic signature on the agent’s code the service broker can be identified un-equivocally as instance of trust,wherever the agent is to be executed.This is one requirement to set up fine-granular security policies on those agent servers on which an agent is executed to fulfill its tasks.Thus it is the job of the service broker to ensure the harmlessness of agents by means of the often addressed malicious agent problem[3],as far as possible.An user who wants to access a service downloads this service as a mobile agent from the service broker and installs it on his mobile device-subsequently he is able to benefit from its features.Two kinds of ser-vices are conceivable here:those which are instructed by the user in direct interaction before they investigate the validity of the information and those which are ac-tivated once and then run as autonomous background processes providing the user with information and as-sisting him in case of special events.To optimize the instruction process of services the personal profile keeps track of the user’s preferences. These include the user’s generic favours,program set-tings,and productive data such as datebook,phone-book,or diary.Agents will query the profile databaseif they require certain information about the user.Anuser will therefore not be inconvenienced by informa-tion requests until a requested piece of information can-not be found in the profile.In that case it is the task ofthe personal profile as active component to request therequired information in communication with the user.Of course the user has the possibilty to alter automaticgenerated settings,add new settings,or delete particu-lar entries from the profile.The underlying infrastructure implicitly defines sev-eral logic places separated by means of different secu-rity domains and is described in the following.Mobile device The mobile device acts as the standardunit of execution for the user’s uallyit has small computing power,but a high con-nectivity.It stores just the information which isactually needed and those which enable the userto continue work even if a network connectionis not available.The mobile device could beequipped with additional(generally plugable)ex-tensions such as GPS receivers for the user’s cur-rent location.User’s homebase The homebase keeps the user’s per-sonal profile.This data should be(authorized)ac-cessible via the Internet as well.The user is al-ways reachable by means of his homebase sincethe home base remembers the mobile device thatwas recently used by the user,e.g.by the device’sIP address.A messenger agent sent by Alice withthe order to meet Bob would migratefirst to Bob’shome base.A stationary personal agent at Bob’shomebase would check the identity and intentionof Alics agents,and occasionally allow him to mi-grate to Bob’s current location in order to handover the message.Service portal The service portal provides servicesfor download.These services are manifested asa group of agents1which interact with the user,migrate back and forth between mobile device,homebase and data sources,and perform the taskaccoring to the users preferences.The serviceprovider usually integrates database access,whichare coordinated with the information providers.Hence he can rely on different but stable APIs2Encryption and signing operations should always performed bya service at the user’s homebase,since it is no good idea to equip amobile agent with a secure communication key.4Secure mobile agent platformAs middleware for data access we need a mobile agent platform which provides the runtime environment for the agents.It is quite clear that this platform should follow a secure aware design concept.For our architecture we used the SeMoA(Secure Mobile Agent)platform[5],whichfits our needs best. The SeMoA project developes an open server for mo-bile agents with a special focus on all aspects of mobile agent security,including protection of mobile agents against malicious hosts.SeMoA builds on JDK1.3 and is a“best effort”to provide adequate security for mobile agent systems,servers as well as agents[2]. The security architecture of the SeMoA server com-pares to an onion:agents have to pass all of several lay-ers of protection before they are admitted to the runtime system(see Figure2)and thefirst class of an agent is loaded into the server’s JVM.Thefirst(outer)security layer is a transport layer se-curity protocol such as TLS or SSL.This layer pro-vides mutual authentication of agent servers,transpar-ent encryption and integrity protection.Connection re-quests of authenticated peers can be accepted or re-jected as specified in a configurable policy.The second layer consists of a pipeline of securityfilters.Sepa-rate pipelines for incoming agents and outgoing agents are supported.Eachfilter inspects and processes in-coming/outgoing agents,and either accepts or rejects them.Subsequent to passing all securityfilters,Se-MoA sets up a sandbox for the accepted agent(which can be regarded as layer three).Each agent gets a sepa-rate thread group and class loader.This class loader supports loading classes that came bundled with the agent,as well as loading classes from remoted code sources specified in the agent.Agents cannot share classes so one agent cannot not load a Trojan Horse class into the name space of any other agent.Agents are separated from all other agents in the system;no references to agent instances are published by default. The only means to share instances between agents is to publish them in a global environment.Each agent gets its own view on this global environment,which tracks the instances registered by that agent.All published objects are wrapped into proxys which are created dy-namically.SeMoA supports a lot of(security)standards,such as JAR archive format,X.509,PKCS,and ASN.5ConclusionsThe advantages of our approach fall into three broad categories:flexibility,feasibility and security.New query agents can be easily integrated in an existing query infrastructure which is a major build-ing block for the highflexibility of the presented ap-proach.Since each agent establishes just application-dependend links between disjunct data sources,the major problem of integration databases is broken in smaller parts,which guarantees its feasability.The se-curity of the undelying mobile agent system allowfine grained access control and secure transport of valuable query results.References[1]Ajay Magnon and Reena Shukla.LBS,the ingre-dients and the alternatives.In The Asian GPS Con-ference2001Proceedings,pages113–117.GISde-velopment,2001.[2]V olker Roth and Mehrdad Jalali.Concepts andArchitecture of a Security-centric Mobile Agent Server.In Proc.Fifth International Symposium on Autonomous Decentralized Systems(ISADS2001), Dallas,Texas,U.S.A.,March2001.IEEE Com-puter Society Press.[3]Tomas Sander and Christian F.Tschudin.Pro-tecting mobile agents against malicious hosts.In Giovanni Vigna,editor,Mobile Agents and Secu-rity,volume1419of Lecture Notes in Computer Science,pages44–60,Berlin Heidelberg,1998.Springer Verlag.[4]J¨u rgen Schirmer and Holger Bach.Context-management within an agent-based approach for service assistance in the domain of consumer elec-tronics.In Proceedings of Intelligent Interac-tive Assistance&Mobile Multimedia Computing (IMC2000),Rostock,Germany,November2000.[5]SeMoA Project.Website.Available at URL/.[6]Christopher Westphal and Teresa Blaxton.DataMining Solutions-Methods and Tools for Solving Real-World Problems.Wiley Computer Publish-ing,1998.ISBN:0-471-25384-7.Information SourcesService Portal(1)Mobile AgentPersonal Agent Basic Service Figure 1:General architeture with roles and places.Content inspection: Dynamic proxy generation, agent encapsulationImplicit names for privacy protection and scalable message routingFigure 2:The SeMoA security architecture.。
先进网络感知和特征管理Now,network is very common all over the field in human life. It is not only asimple media for communication but also roles a very important effect in thedevelopment of our human society. People pay more and more attention to informationand network then before, and with the development of mobilc communication devices,they have higher request for intellectualized and mobile network,so we need tointroduce the context-aware concept to satisfy the demands.Future 3G nctwork will provide services of context-aware which were producedby cooperating with contcxt information and context managcmcnt. It is very importantthat context-aware can improve system cfficiency and support network. We import theconcept of context-aware to a new network-Ambicnt network, which can make somenew applications and scrvices. I believe that this technology will be gradually used inour lives to make them convenient and comfortable in the future.Either Ambient network or context-awarc technology is a new topic for us.soexlensive research and applications have not becn devcloped. Thus,the author of thispaper realized their concepts，frameworks and system processing through studying.Also, it can be an introduction of this field for the rescarch to more and more peoplc,and it will be useful for thc rapid development of the context-aware technology inambient network.Based on the studying composing or corrclative literature by Raffaele Giaffreda,Matthias Baldauf，Schahram Dustdar and Forian Roscnberg etc,the author mainlymakes her own comprehensions and chicfs about context-aware technology in Ambientnetwork.The work l do as follows:1. Importing the concepts of Ambient network and context-aware and introducingcorrelative background knowledge mainly oncontext-aware.2.Analyzing contcxt-aware system and describing the basic context-aware technologyin Ambient network.3.Lucubrating context-aware frameworks and its key technology,and based on theframework, I construct a new simple context model.4. Putting forward new challenging for future scicntific rcsearch of context-aware.。
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。
语义分析的工作原理语义分析（Semantic Analysis）是自然语言处理领域中的重要研究方向，其主要目标是理解自然语言中的语义信息，并对其进行进一步的处理和分析。
本文将介绍语义分析的工作原理，讨论其主要方法和应用领域。
一、概述语义分析是自然语言处理中的核心任务之一，其主要目标是从文本中提取意义，理解語言和信息之間的關聯。
与传统的基于语法的分析方法不同，语义分析注重从文本中获取更深层次的含义。
其应用广泛，包括情感分析、问答系统、机器翻译等。
二、方法和技术1. 词义消歧词义消歧（Word Sense Disambiguation）是语义分析的一个关键步骤。
在自然语言中，一个词可能有多个不同的意义，而词义消歧的任务就是确定在特定上下文中该词的正确含义。
常用的方法包括基于知识库、统计方法和机器学习等。
2. 句法分析句法分析（Syntactic Parsing）是另一个与语义分析密切相关的任务。
它的主要目标是确定一句话中的各个词语之间的句法关系，从而提供给语义分析更准确的输入。
句法分析方法包括依存句法分析和短语结构分析等。
3. 语义角色标注语义角色标注（Semantic Role Labeling）是一项关键任务，它用于识别和标注句子中的谓词与各个论元之间的语义关系。
通过语义角色标注，我们可以更好地理解句子中不同成分之间的作用和关系。
4. 实体识别实体识别（Named Entity Recognition）是一项重要的任务，旨在识别和提取文本中的特定实体，如人名、地名、组织名等。
实体识别在文本理解和信息提取中具有重要意义，为语义分析提供了重要的输入信息。
5. 语义关系抽取语义关系抽取（Semantic Relation Extraction）是指从文本中抽取出不同实体之间的语义关系。
通过语义关系抽取，我们可以获得更深层次的语义信息，从而实现更高级别的语义分析。
三、应用领域1. 情感分析情感分析（Sentiment Analysis）是一种常见的语义分析应用，用于识别和分析文本中的情感倾向，如正面、负面或中性。
domo ai 原理
DOMO AI是一种人工智能系统，它基于深度学习和自然语言处理技术，旨在模拟人类的思维和行为，以便与人类进行自然而流畅的交流。
DOMO AI的原理涉及多个方面：
1. 深度学习，DOMO AI利用深度神经网络来学习和理解大量的数据，包括文本、图像、声音等。
通过深度学习，DOMO AI可以从数据中提取特征，并进行模式识别和预测。
2. 自然语言处理，DOMO AI能够理解和生成自然语言，包括语音识别、语义理解、对话生成等技术。
它可以分析用户输入的文本或语音，并作出相应的回应，实现与用户的自然交流。
3. 知识图谱，DOMO AI可能会利用知识图谱来存储和组织丰富的实体关系信息，以便更好地理解世界和回答用户的问题。
4. 机器学习，DOMO AI可能会利用机器学习算法来不断优化自己的表现，通过与用户的互动和反馈来改进自己的模型和算法。
总的来说，DOMO AI的原理基于对大数据的学习和理解，以及
对自然语言和人类行为的模拟，它的目标是实现智能化的交流和决策，为用户提供更加智能、个性化的服务。
DOMO AI的原理还在不断发展和完善中，随着人工智能技术的进步，它的表现和能力也将不断提升。
与物联网相关的专业词汇1.物联网Internet of Things2.体系结构architecture3.连接模式interconnection model4.通信协议communication protocol5.服务机制service mechanism6.信息模型information model7.物品关联thing association8.信息感知information sensing9.信息交互information interaction10.无线传感器网络wireless sensor network11.传感仪器sensing instrument12.光谱Spectrum13.系统架构architecture of IoT14.协议体系protocol structure15.时空数据spatial-temporal data16.海量数据管理 massive data management17.数据库集群系统database cluster system18.数据挖掘data mining19.电子饲喂站electronic feeding station20.信息采集系统 information acquisition system21.上下文感知context-aware22.服务质量QoS23.语义标注semantic annotation24.可信计算trusted computing25.匿名认证anonymous authentication26.匿名传输anonymous transmission27.无线射频识别 Radio Frequency Identification（RFID）28.农业物联网agriculture Internet of Things29.设施农业facility agriculture30.服务推荐service recommendation二、我感兴趣的一些专业词汇31.大数据big data32.深度学习deep learning33.社会计算social computing34.可视化visualization35.数据可用性data usability36.流式计算stream computing37.内存计算memory computing38.非结构化数据 unstructured data39.社交网络数据 social network data40.云计算技术cloud computing41.自然语言处理 natural language processing42.自然语言理解 natural language understanding43.贝叶斯推理Bayesian inference44.主题模型topic model45.机器翻译machine translation46.聚类分析clustering analysis47.词频分析frequency analysis48.主题词提取topic words extraction49.人工智能artificial intelligence50.混合模型hybrid model。
Semantic Web Service简介第17组组长：过雨佳101499组员：姜哲民101500黄国强101504高四辈101506黎睿知1015071 Web服务1.1 Web服务简介Web服务是一个为Web这样高度分散化，高度自治的分布环境所量身定做的一个模型，它是描述一些操作的接口，通过标准化的XML消息传递机制，可以通过网络访问这些操作。
该接口隐藏了服务实现的细节，允许通过独立于服务实现，独立于硬件或软件平台，独立于编写服务所用的编程语言的方式实现该服务。
其关键技术之一是SOAP。
SOAP (Simple Object Access Protocol)是一种对象间的访问协议，用于构建Web服务和服务请求之间的通讯，是Web服务的核心和主要的通信协议。
Web服务的实质是“共享资源，交互通信，协作研究”。
通过分布在全球各地Web上的各种资源，在某种共同认可的协议下达到共享的目的。
1.2 Web服务所面临的问题然而，Web服务交互所处的环境是分布的，异构的和动态变化的。
这种环境会给Web服务的交互带来许多问题。
例如，由于使用Web服务环境的不同，提供者和请求者之间的语义差异是不可避免的；另外，动态改变的Web和业务需求所带来的问题，也得通过增加服务的语义来解决。
这就要求SOAP具有较强的适应能力，以提供由Web服务交互环境等因素所带来的语义问题的解决机制。
因此通过增加Web服务的语义描述，使SOAP支持语义处理的机制是解决语义问题的一个可行方案。
2 语义Web2.1 语义Web简介1998年Web的发明者，国际W3C主席Tim Berners-Lee首次提出了语义Web的概念。
2001年9月9日国际W3C“技术与社会领域 (Technology and Society Domain)”下的语义Web活动论坛(Semantic Web Activity)正式宣告成立。
语义Web活动论坛在其宣言中指出：“语义Web 是当前Web的一个扩展，其中信息具有形式化定义的语义，更有助于计算机之间以及计算机与人之间的协同工作。
Information professional 信息专业人士Decision making 决策Information management 信息管理Problem solving 解决问题Corporate file 公司档案Information transfer信息传送Draw original conclusion=arrive at a conclusion得出结论Cognitive state 认知状态Dynamic process 动态处理Point of reference 参考点Province of the professional 知识领域Professional information management专业信息管理者Information explosion 信息爆炸Information policy planning 信息政策Information flow 信息流End-user终端用户Up-to-date information最新信息Long-term planning长期规划Computer and telecommunications technology计算机通信技术Market development市场发展Management construct管理框架Business principal经营原理End-user computing终端计算机系统Cybernetics控制论Information retrieval信息检索Artificial intelligence人工智能Modeling techniques建模技术Commercial transaction商业交易Data mining数据挖掘Common stance共同点Professional career职业生涯Job title 工作名称Professional category专业分类Information intermediary人机界面信息咨询人员Paraprofessional专业辅助人员Information operation信息操作Systems paradigm 系统范式National science foundation国家科学基金Information field信息领域Economic value 经济价值Data transmission数据传送Sensory awareness感知认识Data element数据元素Knowledge spectrum知识体系Information processing信息处理The core competency核心能力Decision maker决策者Integrated system集成系统Lead edge technology前沿技术Parent organization 母体组织Management decision-making管理决策Information over-load信息超载Organization perspective组织观点Sub-discipline子学科Office automation办公自动化Systems analysis系统分析Information services信息服务Operation research运筹学Transaction processing事务处理Electric ordering system电子订货系统Data warehousing数据仓库Scientific inquiry科学问题Physical object物理介质Baccalaureate level学士水平Frames of reference参考系Information program信息项目Logistics of handling后台操作Technical publication专利申请Information storage信息存储Subject field学科领域System evaluation系统评估User community用户群体Classification principles分类原理Private collection私人藏书Relative location相对排架发Arithmetic operations算术运算Catalog card编目卡Fluid mechanics流体力学Telephone directory电话薄Fixed location固定排架法Ambiguous 含混不清的Assess 评估Assume 假设Awareness 认识，意识Boundary 界限Clue 线索Cognitive 认知的，认识的Commodity 商品Context 背景，情境上下文Continuum 连续（统一体）Convention 传统习俗，常规Corporate 公司Destination 目的地Discrete 离散的Dynamic 动态的Embed 嵌于，存在于Emit 放射Entity 实体Ethics 伦理道德Extent 程度Extrapolate 推测File 文档Glyph 标志Hierarchical 按等级排列的Inherent 内在的，固有的Initiate 创始，发起Interchangeable 可以互换的Interpretation 理解，解释Package 打包，包裹Paramount 至高无上的Perceive 感知，意识到Possession 拥有Process-oriented 面向过程Quantifiable 可量化的Reiterate 反复重申三番五次的进行Representation 表示，表达，象征物Rigorous 严格的Sole 独有的Spectrum 谱，范围Statistic 统计数字statisticsStimulate 刺激Synonymous 同义的Synthesis 综合Terminology 术语Transmit 传递Ultimate 最后的Uncertainty 不确定性Variety 多样性Verified 查证的证实的acquire:取得、获得、学得（通常指学识、技艺）process：处理（工艺、程序）dedicated to献身于、致力于commodity: good 商品statistic: n.统计资料中的一项pl.统计学transmit: 传送（信号）embed: 固定于、存在于可以证明，信息以普通能量的形式加以传递或者出现。
Semantic Context-Aware Service Composition for Building Automation SystemSon N.Han,Gyu Myoung Lee,Senior Member,IEEE,and Noel Crespi,Senior Member,IEEEAbstract—Service-oriented architecture(SOA)is realized by in-dependent,standardized,and self-describing units known as ser-vices.This architecture has been widely used and verified for au-tomatic,dynamic,and self-configuring distributed systems such as in building automation.This paper presents a building automa-tion system adopting SOA paradigm with devices implemented by device profile for web service(DPWS)in which context informa-tion is collected,processed,and sent to a composition engine to co-ordinate appropriate devices/services based on the context,com-position plan,and predefined policy rules.A six-phased compo-sition process is proposed to carry out the task.In addition,two other components are designed to support the composition process: building ontology as a schema for representing semantic data and composition plan description language to describe context-based composite services in form of composition plans.A prototype con-sisting of a DPWSim simulator and SamBAS is developed to illus-trate and test the proposed parison analysis and experi-mental results imply the feasibility and scalability of the system. Index Terms—Building automation,device profile for web ser-vice(DPWS),semantic web,service composition,service-oriented architecture(SOA).I.I NTRODUCTIONT HE idea of a smart house or a smart building has been around and well-known for years as highly expected prod-ucts.A building automation system(BAS)residing at the heart of such smart environments interacts with all of its components, with hardware,software,and communication among them.It involves in several disciplines such as electronics,informatics, automation,or control engineering.BAS,since its debut,has been developed and promoted by a community of developers, technologists,and scientists with plenty of impressive proto-types and products.These products bring in comforts and con-veniences to daily life,freeing people from tedious house-works or offie cases vary from very simple ones,e.g.,au-tomatically turning on/off the lights to complex and critical sit-uations,e.g.,security surveillance.Furthermore,BAS also pro-vides value-added services by offering intelligent services such as customer tracking in shopping malls or elder healthcare ser-vices.All of these options make it a very promising business,Manuscript received November02,2012;revised January08,2013;accepted February21,2013.Date of publication March12,2013;date of current version December12,2013.Paper no.TII-12-0755.The authors are with the Department of Wireless Networks and Multimedia Services,Telecom SudParis,Institut Mines-Telecom,Evry,91011France (e-mail:son.han@it-sudparis.eu;gm.lee@it-sudparis.eu(corresponding au-thor);noel.crespi@it-sudparis.eu).Color versions of one or more of thefigures in this paper are available online at .Digital Object Identifier10.1109/TII.2013.2252356attracting the attention of the community to target not only or-ganizational customers but individual end-users.With such analysis in mind,savvy people in the industry and academia have been developing many new technologies for building automation such as communication protocols,data management,data bus systems,software components,and/or new hardware devices which can be integrated in the new systems.Thanks to all of these efforts,building automation has advanced over the last few decades with several communication protocols and a variety of BAS products from many different vendors.A comprehensive overview of communication pro-tocols in building automation can be found in[1],which also introduces different BAS products and other discussions on building automation.Traditionally,equipment in BAS products are interconnected by proprietary communication protocols such as LonWorks[2],Building Automation and Control Net-work(BACnet)[3],or KNX[4].These protocols have been used to cover all of the features of building automation, including heating,ventilating and air conditioning(HV AC), lighting,and alarming.The industry also has been making significant efforts to standardize communication protocols.KNX,for example,is one of the main communication standards used in building au-tomation.It is device-independent and allows monitor/control of lighting,blinds/shutters,security systems,energy manage-ment,and HV AC systems.HomeConnex(Peracom Networks) is a home entertainment network which unites PCs,TVs, audio/video components,and set-top devices into an integrated system.X-10(X10Inc.)is another industry standard using power lines and radio for communication among electronic devices used for home automation.Other proprietary standards include Easy-Radio(Low Power Radio Solution Inc.),No New Wires(Intellon Corporation),Sharewave(Sharewave Inc.), SoapBox(VTT Electronics),and Z-wave(Zensys).So far,consumers are overwhelmed by the many appealing BAS solutions offered by the industry and are well aware of the value of such smart systems.However,it is not difficult to rec-ognize the reluctance among customers in adopting available BAS products on the market.The main reasons are identified as the cost and the scalability of these proprietary systems.This normally leads to the suspension or partial deployment of sev-eral on-the-table building renovation projects.In recent years,there has been a recognizable movement from distributed systems controlled by users to a new style of systems built over independent,autonomous,and standardized compo-nents or services.This new style of systems called service-ori-ented architecture(SOA)enables organizations to share logic and data among multiple applications and usage modes.SOA is1551-3203©2013IEEEalso an open concept supporting plug-and-play capabilities of heterogeneous software and hardware components.W3C Web Services[5]using simple object access protocol(SOAP)mes-sage[6]and Web Services Description Language(WSDL)[7] is probably the most popular implementation of SOA which is gaining increasing market penetration.As will be discussed in Section II,there have been many ap-proaches as well as a great deal of research on adopting SOA paradigm into BAS.Several technologies and standards also have been created to support the trend.These works mainly focus on how to deal with the heterogeneity of the devices as well as the scalability of managing and adding devices from different third-party providers in smart building environment. In terms of the functionality at the application layer,there have been only simple prototypes rather than a full-scale system for SOA-based building automation.Current solutions also appear static or semidynamic as they only take into account simple use cases and scenarios in context change,e.g.,in temperature or hu-midity.These efforts can be categorized under the name of en-abling technologies for integration of SOA into BAS.However, to successfully enable such integration,it requires more than just connectivity and interoperability of heterogeneous systems. Therefore,this paper aims to pave the way for the develop-ment of full-scale SOA-based BASs by the support of open Web technologies and device profile for web service(DPWS)[8]. There are two problems challenging the development of this kind of BASs.Thefirst problem is to coordinate devices in order to serve the diverse and complex requirements consisting of sev-eral services from users.To solve it,the concept of service com-position is exploited to compose services based on predefined policy rules with reference to building ontology to choose,bind, and execute appropriate services.The second problem is to deal with the dynamic changes in context of users and building en-vironment,and the solution is to use composite service plans to describe users’requirements by a proposed Composition Plan Description Language(CPDL).User and environment context are modeled and processed efficiently in the system through the context processor to help the decision making process to carry out the service composition.To put all together,an SOA-based BAS built over DPWS-supported devices is proposed.DPWS devices cover a large range of equipment from highly resource-constrained sensors, full TCP/IP stack devices,to new-trend Android devices,thanks to dedicated open-source DPWS implementations.The database of resource description framework(RDF)[9]graphs is repre-sented semantically by one of the textual syntax alternatives to RDF called Notation3(N3)[10].Building ontology containing the description of concepts and relationships in the building environment is designed and used as a reference schema for storing graph data in the database.Context information is mod-eled,processed,and passed to a service composition engine to coordinate appropriate devices/services based on predefined policy rules and six-phased composition process.The remainder of this paper is organized as follows.Section II provides back-ground and related work on applying SOA paradigm and open Web technologies to building automation.Section III describes architecture of the system.Section IV introduces the design of building ontology and graph database.Semantic context-awareTABLE IDPWS IMPLEMENTATIONservice composition is presented in Section V.Section VI brings in comparison analysis with state-of-the-art and a prototype of the system along with experimental results.Section VII con-cludes the presentation and discusses future works.II.R ELATED W ORKThere have been several SOA models in the industry tar-geting device-to-device communication with technologies as old as Home Audio/Video Interoperability(HA Vi)[11]and Java Intelligent Network Infrastructure(Jini)[12]to new and currently developed such as Open Service Gateway Initiative (OSGi)[13],DPWS[8],and/or Universal Plug and Play (UPnP)[14],in which DPWS is getting popular among devel-opers because of its reliance to W3C Web Services.DPWS was debuted in2004by a consortium led by Microsoft and natively integrated in Microsoft’s Windows Vista platform.It defines a set of implementation constraints to provide secure and effective mechanism for describing,discovering,messaging, and eventing of services for resource-constrained devices.An open-source implementation of DPWS was also developed under several projects funded by Information Technology for European Advancement(ITEA)including SIRENA,SODA, and SOCRATES.Research results of the SIRENA project[15] have been presented widely by industry and academia.It is also available through an open-source software initiative Web Service for Devices(WS4D)[16]which is currently being actively maintained.WS4D has been demonstrated as feasible and powerful for several domains including industrial automa-tion.Up to date,several DPWS stack implementations have been provided by the WS4D initiative in various platforms and languages.Four core implementations are summarized in Table I. DPWS-gSOAP provides C/C++toolkits for deploying Web services consumers and providers.It is multiplatform im-plementation supporting Linux i386,Windows-native,Win-dows-cygwin,and embedded Linux.DPWS-uDPWS is DPWS implementation in C language designed for Linux systems and especially for highly resource-constrained devices such as sensor nodes.It paves the way for DPWS protocols to be implemented directly into a wireless sensor network(WSN) without any intermediary gateway or so.DPWS-JMEDS is Java framework for DPWS supporting different Java editions.The latest release of DPWS-JMEDS hosts the feature of Android OS which paves the way for implementing services on Android devices.In addition,academia has been also actively supporting the SOA paradigm as well as DPWS in building automation with a great deal of research and prototypes.Many other solutions for implementing the SOA on networked embedded deviceshave been proposed to prove the feasibility of the paradigm re-gardless of the variety of devices and the heterogeneity of the network with different communication protocols like ZigBee, Bluetooth,or TCP/IP.Distributed operating system(DOS)[17] presents an operating system based on SOA to manage all em-bedded devices in a home network.It sets to solve four prob-lems regarding the connectivity between devices and central managing point:multiple simultaneous connections to a device, request packet redundancy,inflexibility of direct access to em-bedded devices,and compact-SOA message.Leong et al.[18] try to solve the interoperability problem concerning message ex-change between two or more subsystems and performing inter-operation without the need for external intervention.The solu-tion involves not only the integration of components in subsys-tems but also their behavior.It is a rule-based framework with event-condition-action(ECA)pattern inherited from expert sys-tems domain for representing,sharing,and managing data in a smart home environment.Kyusakov et al.[19]introduce an improved DPWS architec-ture which considers a particular requirements of WSN.Fur-thermore,for the exchange of SOAP messages,the work used the IETF Constrained Application Protocol(CoAP).The work in[20]discusses the problem of using Web services over IPv6 over low-power wireless personal area networks(6LoWPAN) in WSN.Also,in[21],a modified DPWS protocol stack is pro-posed to be used in WSNs which comply with6LoWPAN.They all show that it is applicable for implementing DPWS and SOAP for WSN without any change of existing solution on conven-tional resource-rich devices.In the meantime,researches on Web service and open future Web technologies such as Semantic Web have been blooming up in the last decade,making them the most supported and promising technologies.Derived technologies such as service discovery and service composition have been also extensively exploited.Initially,service composition is used in the business processes to manually,semi-automatically,or automatically mobilize component services to create composite services which can satisfy various complex business requirements.The idea of service composition then has been adopted widely into the domain of ubiquitous computing or pervasive computing, especially with respect to context,in which dynamic user and environment context should be taken into account to ensure the successful deployment of the system.The work in[22] proposes a framework for context-aware dynamic service com-position in ubiquitous environment.It argues that changes in ubiquitous ambient environments occur frequently in different situations,and the system therefore needs to adapt dynamically. The work in[23]presents a prototype with UPnP devices and OSGi gateway to apply the concept of service composition for building automation.The concept has also penetrated other fields of industrial automation such as the semantic compo-sition model presented in[24]aims to managing production processes in factory automation.The model coordinates three Web services to achieve production goals using the domain Web services.Semantic Web as envisioned by Berners-Lee et al.[25]is an-other promising technology for building automation.Though it is not yet a full-fledged technology,it does offer a new concept of presenting data in a meaningful way which can improve the communication between human and machine.This means that a certain level of automation can be achieved through Semantic Web.Consequently,Semantic Web is very promising for indus-trial automation.Runde and Fay[26]from the point of view of software engineering in building automation introduce only a partial adoption of Semantic Web technologies but still hint at many open issues for future work.Another long-term effort for the adoption of Semantic Web into building automation is an ongoing SESAME-S project[27].The solution concerns the ef-ficient energy consumption in the house which is equipped with sensors,smart meters,and a semantic software module to per-form reasoning and control the house energy consumption.Key achievements and technologies of SESAME-S project are de-scribed in[28].To summarize,with several standards,prototypes,as well as products proposed by industry and academia,applying SOA and open Web technologies to building automation has been shown to be possible and promising.The above review also provides an outlook over the readiness of the state of the art of enabling technologies for the development of real BAS based on SOA and Web technologies.Even though several systems have been developed in this way or another,there’s still an absence of a full-scale SOA-based BAS products offering some intelligence to cover all the aspects of real and complicated scenarios.Next, the proposed system in such a manner is going to be presented and thoroughly explained.III.S YSTEM A RCHITECTUREHere,the key functionalities of the system are presented with its architecture in the background.Main explanations include DPWS Service Discovery followed up here.Building ontology and graph database will be described in Section IV,and se-mantic context-aware service composition will be discussed in Section V.During discussion,devices and theirs hosting ser-vices are going to be used interchangeable and sometimes men-tioned as devices/services.The system configuration shown in Fig.1depicts the main components and a typical setup of equipments inside a room of a building.There are four groups of devices which are all DPWS-supported consisting of a wide range of building appliances including sensors/actuators,radio-frequency iden-tification(RFID)readers,other appliances with TCP/IP stack and low-power wireless protocols,and Android devices.The first group is sensors/actuators,which are attached to devices to provide networked functionalities.They are implemented by uDPWS over the Contiki OS[29].The second group is DPWS devices which consists of devices natively support DPWS with full TCP/IP stack.These devices are developed using DPWS-gSOAP and can be connected directly to the IP network.The third group is newborn Android devices which are attracting much attention recently.In2011,some big electronics companies like Panasonic or Archos released home appliances based on an Android operating system.Google itself introduced the Android@Home at the2011Google annual I/O developer conference with the intention of turning the home into a network of Android accessories.Along with that event, Google also announced that it had collaborated with a partnerFig.1.System configuration.Typical four groups of devices which consist of sensors/actuators,DPWS devices,Android devices,and context collectors are deployed at the same time in a room of a building.to launch Android-enabled LED light bulbs.Though there has been some delay in launching the aforementioned Android home appliances,with the constant development of Android platform these days,Android-based home and building devices and appliances are very promising products in the near future. The fourth and last group is classified as context collectors consisting of sensors to provide sensing capacities and RFID readers to receive users’identifiers.All hardware components are connected to Building Application Server(BApS)directly or indirectly via gateways to expose their services.BApS hosts the core functionalities of the system with the details to be discussed in the following sections.The system architecture shown in Fig.2consists of several subsystems and modules.Thefirst subsystem DATABASE is composed of a building ontology,semantic graph database,an-other database for composition plan,and a caching component of service cache to improve the operation of the service exe-cution process.Building ontology provides the description of concepts and their relationships in building environment.The second subsystem is COMMUNICATION,which represents functionalities over heterogeneous communication methods including TCP/IP and several other low-power wireless proto-cols.There are different types of hardware devices ranging from sensor nodes,sensor/actuator nodes,DPWS devices,Android devices,and RFID readers which are connected to the network by different types of communication protocols.Note that there are two types of sensors:one attached to device to provide sensing functionality and the other used for collecting context information such as temperature or moisture level in building environment.The third subsystem DISCOVERY consists of two modules service discoverer and service cacher,but they work closely with each other under the DPWS WS-Discovery specification.Service discoverer plays the role of an interface between the core of BAS and devices.It sends a request to the network to discover connected devices and accompanied services and then receives information of available devices/ser-vices.Service cacher runs frequently to update the service cache and carries out the update process whenever service discoverer is inoperation.Fig.2.System architecture consists of four main subsystems DATABASE, COMMUNICATION,DISCOVERY and COMPOSITION and four other mod-ules:composition plan creator,semantic reasoner,composition broker,and con-text processor.COMPOSITION,the fourth and the central subsystem re-sides at the center of the architecture.Its six-phased composi-tion process helps to realize and deliver appropriate composite services to user based on the context of user and environment. The subsystem can be functionally divided into selecting ser-vices,binding services,and executing services which are re-flected in three components of the COMPOSITION:Service Se-lector,Service Binder,and Service Executor,respectively. Among the other modules,Semantic Reasoner is also an im-portant part,as it plays a link between building ontology,graph database,and the software component of COMPOSITION. This module is described subsequently along with the building ontology and graph database in Section position Plan Creator has access to composition plan database and provides functionalities for users to create,modify,and delete compo-sition plans.The wrapping interface over this module can be a part of Web or smartphone applications providing typical graphical buttons or comboboxes for users to interact with the system.Context processor receives raw context data from context collectors,removes headers and redundant packets to extract important information,and then sends them to the composition broker in form of structured position broker decides whether to call the COMPOSITION or not with a simple decision-making mechanism based on the received context information.DPWS service discovery as the main content of DIS-COVERY subsystem is going to befirst introduced to explain the communication between devices and the core of the system. Then,context-awareness in the context of BAS is also ex-plained later on in this section to give the details of context data and how to process them for the composition process.A.DPWS Service DiscoveryDifferent types of hardware components built over DPWS specification share the same dynamic mechanism of exposing and discovering in the network which is called DPWS-Dis-covery as part of DPWS specification.Therefore,the service discoverer module can universally detect and communicate with each service and all of their operators regardless of the origin of the hosting device,hardware,or operating system. Fig.3shows the process of discovering devices.DPWS de-vices send“Hello”and“Bye”message to join and leave the net-Fig.3.Service discovery.Devices in different types send hello/bye messages to the network to join/leave the network.A client sends a probe message to request for the service of a speci fic device and a matching service returns probe match message to con firm itsexistence.Fig.4.Servicesearch.Fig.5.Service cache.work.When a DPWS client wants a DPWS device,e.g.,with the identi fier as a printer ns:ExamplePrinterDevice ,it sends a Probe:ExamplePrinterDevice message to know if one is con-nected on the network.A DPWS printer receives this probe and answers back information that it is a printer (by sending a Probe-Match ).Other DPWS devices also receive this probe but they do not answer (as not ExamplePrinterDevice printer ).Fig.4shows typical Java codes used to search for the device with informa-tion described in SearchParameter .The DPWS device speaks directly to the printer about its services metadata and the printer sends its metadata back.In case of discovering all of the devices available in the network,the client puts null in the device iden-ti fier parameter to get a set of connected devices.A callback function or a handler is called when there is a matching service found.Information about each service associated with each device is collected through the service discovery process and is fre-quently updated to a data structure called service cache.Fig.5shows an example of a light service in the room 803stored in the service cache with three pieces of information Service Type ,Endpoint Reference Address ,and WSDL .B.Context-AwarenessContext-awareness plays an important role in the pervasive computing architectures to enable the automatic modi fication ofthe system behavior according to the current situation with min-imal human intervention.Since appeared in [30],context has be-come a powerful and longstanding concept in human–machine interaction.As human beings,we can more ef ficiently interact with each other by fully understanding the context in which the interactions take place.It is dif ficult to enable a machine to un-derstand and use the context of human beings.Therefore the concept of context-awareness becomes critical and is generally de fined by those working in ubiquitous/pervasive computing,where it is a key to the effort of bringing computation into daily lives.One major task in context-aware computing is to acquire and utilize information about the context of participating enti-ties of a system in order to provide the most adequate services.The service should be appropriate to the particular person,place,time,event,etc.where it is required.In the scope of this building automation,user,device and environment context are consid-ered in order to bring more ef ficient service composition.Context information is collected by sensors and RFID readers which are classi fied as context collectors.There are two types of context including User (U)and Environment (E).The raw data are sent to and processed by the context broker to yield the structured data in the format of {Type,Location,Source }.We present two examples of context here.1){U,Room803,Smith }2){E,Room803,TempSensor803}The processed data then are sent to the composition broker which plays the role as a composition decision maker.It de-cides whether to call the COMPOSITION or not.For example,if the context information of room temperature is over 10C,no composition will be carried out;otherwise,the composition broker checks the temperature with current status of the system to launch the COMPOSITION in case the situation is labeled as context change.IV .B UILDING O NTOLOGY AND G RAPH D ATABASEBuilding ontology de fines concepts and relationships be-tween entities within the building environment.It provides a schema to build up semantic database in the form of graph data.This is a new concept of database for Semantic Web which consumes RDF to present the domain knowledge.RDF is a common acronym within the semantic web community as it creates one of the basic building blocks for forming the Web of semantic data.A graph consists of resources related to other resources,with no single resource having any particular intrinsic importance over another.The RDF database includes of RDF statements or sometimes called an RDF triples.The term triple is used to describe the components of a statement with three constituent parts:the subject,predicate,and object of the statement.The primary purpose of this ontology is to classify things in terms of semantics,or meaning and especially for describing policies used in composition process.A class in Web Ontology Language (OWL)[31]is a classi fication of individuals into groups which share common characteristics.If an individual is a member of a class,it tells a machine that it falls under the semantic classi fication given by the OWL class.This provides the meaning of the data that helps reasoning engine to draw inferred information from the database.Fig.7shows a part。
