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程炜面向W e b服务的业务流程管理系统的研究和实现Standardization of sany group #QS8QHH-HHGX8Q8-GNHHJ8-HHMHGN#分类号_______ 密级_______ U D C _______硕士学位论文面向Web服务的业务流程管理系统的研究和实现学位申请人：程炜学科专业：通信与信息系统指导教师：杨宗凯教授论文答辩日期 2003年5月10日学位授予日期答辩委员会主席刘文予评阅人刘文予谭运猛A Thesis Submitted in Partial Fulfillment of the Requirementsfor the Degree of Master of EngineeringResearch and Implementation of Web Service-Oriented BusinessProcess Management SystemCandidate: Cheng WeiMajor: Communication & Information SystemSupervisor : Prof. Yang ZongkaiHuanghzong University of Science & technologyMay 2003摘要近几年，随着电子商务的深入发展，对企业信息化程度提出了更高的要求，如何利用现代网络技术来帮助企业管理各类业务流程，实现业务流程自动化已成为企业关注的热点。
所谓业务流程（Business Process，BP），是指为了在一定时期内达到特定的商业目标，而按照各种商业规则连接起来的业务功能的集合。
这些业务功能是抽象定义的：业务功能的具体实现受限于业务功能运行所需的可用资源。
业务功能的构成由商业目标决定。
业务流程中商业规则的目的是为了业务管理决策的实现。
而业务流程管理（Business Process Management，BPM）是理解、系统化、自动化以及改进公司业务运作方式的一门艺术，它可以看作是文档工作流和企业应用集成的紧密结合。
新版新视野大学英语读写教程第四册unit1答案<DIV&NBSP;&NBSP;CLASS=MSONORMAL>Unit One<DIV&NBSP;&NBSP;CLASS=MSONORMAL>III.1. idle2. justify3. discount4. distinct5. minute6.accused7. object8. contaminate9. sustain 10. worshipIV.1. accusing... of2. end up3. came upon4. at her worst5. pay for6. run a risk of7. participate in8. other than9. object to/objected 10. at bestV1. K2. G3. C4. E5. N6.O7.I8. L9. A 10. DCollocationVI.1. delay2. pain3. hardship4. suffering5. fever6. defeat7. poverty8. treatment9. noise 10. agonyWord buildingVII.1. justify2. glorify3. exemplifies4. classified5. purified6. intensify7. identify8. terrifiedVIII.1. bravery2. jewelry3. delivery4. machinery5. robbery6. nursery7. scenery8. discoverySentence StructureIX.1. other than for funerals and weddings2. other than to live an independent life3. other than that they appealed to his eye . . `4. but other than that, he'll eat just about everything .5. other than that it's somewhere in the town centerX.1. shouldn't have been to the cinema last night2. would have; told him the answer3. they needn't have gone at all4. must have had too much work to do5. might have been injured seriouslyTranslationXI. -1. The plant does not grow well in soils other than the one in which it has been developed.2. Research findings show that we spend about two hours dreaming every night, no matter what wemay have done during the day.3.Some people tend to justify their failure by blaming others for not trying their best.4.We remain tree to our commitment: Whatever we promised to do; we would do it.5.Even Beethoven's father discounted the possibility that his son would one day become the greatest musician in the world. The same is true of Edison, who seemed to his teacher to be quite dull.6. They were accused by authorities of threatening the state security.XII.l.出入除自己家以外的任何场所时，如果你带有宠物，一定要了解有关宠物的规定。
信息管理专业英语试题及答案一、写出以下单词的中文意思（每小题0.5分，共10分）1 algorithm 11 object-oriented2 optimization 12 subsystem3 transportation 13 analogous4 dissemination 14 authorization5 evaluate 15 collection6 reliability 16 database7 verification 17 distributing8 computerize 18 payment9 practical 19 warehouse10 manipulation 20 agility二、根据给出的中文意思，写出单词（每小题0.5分，共10分）1 n．组件，群件11 adj．分布式的2 n．反馈，反应12 n．构造，配置3 n．增殖，扩散13 vt．维持，维修4 n．扩散，传播14 vt．使标准化5 n．全球化，全球性15 n．识别，鉴定6 adj．多国的，跨国的16 adj．可接受的7 vt．分配，指派17 adj．兼容的8 adj．交互式的18 n．防火墙9 n．变化，转化，转换19 n．基本设施10 n．应用，应用程序20 vt．& vi．使同步三、根据给出的短语，写出中文意思（每小题1分，共10分）1 application program interface2 back up3 bar code4 base upon5 Business Intelligence6 c loud computing7 commercial service provider8 c ustomer churn9 customer relations management10 data independence四、根据给出的中文意思，写出短语（每小题1分，共10分）1 数据挖掘2 数据转换3 数据仓库4 脏数据，废数据5 外部存储设备6 遗传算法7 网格计算8 投资决策9 知识发现10 最小冗余五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）缩略语完整形式中文意思1 B2B2 B2C3 CRM4 DBMS5 ERP6 GIS7 HTTP8 IS9 SCM10 SQL六、把以下句子翻译为中文（每小题1.5分，共15分）1) Do you have any idea how to promote the sales of this product?2) Peter is confident of winning the post as the assistant to the managing director.3) There must be fair play whatever the competition is.4) She showed strong leadership during her first term in office.5) If you have any requirements, ask me.6) The managing director's only concern was how to improve the quality of their products.7) The cost of consumption articles is the first consideration, as far as most ordinary people are concerned.8) Appreciation of works of art is bound to be dominated by a particular kind of interest.9) We sought an answer to the question, but couldn't find one.10) The program was implemented with great efficiency and speed.七、把以下句子翻译为英文（每小题1.5分，共15分）1) 没有好的管理，生意是不可能做好的。
2024年重庆一中高2025届高三11月期中考试英语试题卷注意事项：1. 答卷前，考生务必将自己的姓名、准考证号码填写在答题卡上。
2. 作答时，务必将答案写在答题卡上。
写在本试卷及草稿纸上无效。
3. 考试结束后，将答题卡交回。
第一部分听力 (共两节，满分30分)第一节 (共5小题; 每小题1.5分, 满分7.5分)听下面5段对话。
每段对话后有一个小题，从题中所给的A、B、C三个选项中选出最佳选项，并标在试卷的相应位置。
听完每段对话后，你都有10秒钟的时间来回答有关小题和阅读下一小题。
每段对话仅读一遍。
1. Why won't the boy go on the ride?A. He is too short for it.B. He doesn't like it.C. He has to wait for his parents.2. What happened to the woman?A. She borrowed a wrong book.B. She bought a book at a high price.C. She was fined for not returning the book on time.3. What will the man most likely do this weekend?A. Buy a new tank.B. Visit the woman again.C. Get some goldfish delivered.4. What is the main topic of the conversation?A. A way to lose weight.B. The benefit of exercise.C. A workout plan.5. How much had the man borrowed before?A. $50.B. $100.C. $150.第二节 (共15小题; 每小题1.5分, 满分22.5分)听下面5段对话或独白。
081202计算机软件与理论计算机软件与理论专业主要培养具有良好科学作风、坚实理论基础和专业知识、能够独立从事计算机软件和系统的研究、开发与应用的高级专门人才。
硕士学位获得者应深入了解计算机软件理论和软件工程的研究现状和发展趋势，具有计算机软件与网络方面的理论基础和研究能力，能够研究和解决与本学科有关的科学和技术问题，掌握和精通若干软件编程工具和开发环境，熟练掌握一门外国语。
本学科近年培养和引进了一批高层次的人才，已建成了一支职称、学历、年龄结构合理，学术研究活跃，充满活力的师资队伍。
所从事的研究处在本学科前沿，取得了一批高水平的成果，在本学科领域的重要期刊上发表了一批有影响的论文，形成了有自己特色的研究方向，我校主要研究方向有：模式识别与数据挖掘、计算机图形图像处理、Web与服务计算、生物信息等。
附一：主要研究方向情况1、模式识别与数据挖掘随着电子技术和网络技术的飞速发展，各行各业积累大量的数据，如生物信息、商业数据、金融保险等行业。
于是大数据时代应运而生，如何利用这些海量、超高维和存在噪声的海量数据，挖掘到有用的数据，并提炼出知识是一个应用非常广泛的领域，是当前大数据时代面临的一个具有挑战性的问题。
主要研究领域包括：各种复杂生物数据、金融数据、和网络数据的分析方法和挖掘技术、基于机器学习方法的医疗诊断设计、开发和应用；基于网络的分析方法及应用、博弈论的应用与研究等。
2. 计算机图形图像处理计算机图形图像处理技术渗透在我们生活的方方面面，可以利用电子表格轻松地创建能够看到数据的图表，可以通过各种动画、游戏、特殊效果来增强娱乐性，还可以借助图形和图像更直观地进行视觉交流和理解科学原理等。
主要研究方向包括三维数字娱乐与交互技术、图像编辑与特效技术、科学计算可视化、高性能并行计算等，研发三维游戏、立体电影、平面设计、可视化分析等方面的软件系统，服务地方产业应用。
如今，计算机显卡核心（GPU）已经成为CPU的并行协处理器，其计算能力越来越强大，使以往许多计算机图形图像处理系统和算法成为实时自然交互的高效并行方法，不断地改变人们与计算机交流的方式。
收稿日期：2012-05-11；修回日期：2012-07-16基金项目：国家“973”计划资助项目（2009CB3020402）；江苏省自然科学基金资助项目（BK2010103）作者简介：王珏（1987-），男，黑龙江哈尔滨人，助理工程师，主要研究方向为Web 服务匹配（415638921@qq．com ）；向朝参（1987-），男，博士研究生，主要研究方向为SOA 架构、无线传感网；王萌（1983-），男，助教，主要研究方向为Web 服务发现；田畅（1963-），男，教授，博导，主要研究方向为网络信息系统、无线分组网；赵文栋（1972-），男，副教授，主要研究方向为SOA 体系架构、计算机网络；代登坡（1986-），男，助理工程师，主要研究方向为Web 服务发现．语义Web 服务发现研究现状与发展*王珏1，向朝参1，王萌1，田畅1，赵文栋1，代登坡2（1．解放军理工大学通信工程学院，南京210007；2．中国人民解放军78083部队，成都610011）摘要：从不同方面对语义Web 服务发现相关技术进行综述。
阐述了语义Web 服务的基本概念和特点，总结和分析了语义服务发现的基础———语义服务描述语言，重点从单服务匹配和服务组合匹配两个方面对语义Web 服务发现的关键问题———语义Web 服务匹配近年来的研究，进行了全面的总结和讨论，并指出了语义Web 服务发现研究领域的挑战和未来的研究方向。
关键词：面向服务架构；语义Web 服务；服务发现；服务组合；服务匹配中图分类号：TP393文献标志码：A文章编号：1001-3695（2013）01-0007-06doi ：10．3969/j．issn．1001-3695．2013．01．002Survey on semantic Web services discoveryWANG Jue 1，XIANG Chao-can 1，WANG Meng 1，TIAN Chang 1，ZHAO Wen-dong 1，DAI Deng-po 2（1．Institute of Communication Engineering ，PLA University of Science ＆Technology ，Nanjing 210007，China ；2．78083PLA Troops ，Chengdu 610011，China ）Abstract ：This paper presented the state-of-the-art of semantic Web service from various aspects．First of all ，this paper ex-plained the basic concepts and character of semantic Web service．And then ，it summarized and analyzed several kinds of ser-vice description languages which were the base of service discovery．Moreover ，it comprehensively summarized and discussed service matching as the key problem of service discovery from two aspects —single service matching and service compositionmatching．In the end ，this paper proposed the challenge and the future work of the study on semantic Web service discovery．Key words ：SOA （service-oriented architecture ）；semantic Web service ；service discovery ；service composition ；servicematching0引言信息技术的发展带动了各类应用系统的不断涌现，在为用户带来更多便利的同时，也给IT 系统的设计和开发带来了新的挑战。
新版新视野大学英语读写教程第四册unit1答案Unit 1III.1. idle2. justify3. discount4. distinct5. minute6.accused7. object8. contaminate9. sustain 10. worshipIV.1. accusing... of2. end up3. came upon4. at her worst5. pay for6. run a risk of7. participate in8. other than9. object to/objected10. at bestV1. K2. G3. C4. E5. N6.O7.I8. L9. A 10. DCollocationVI.1. delay2. pain3. hardship4. suffering5. fever6. defeat7. poverty8. treatment9. noise 10. agonyWord buildingVII.1. justify2. glorify3. exemplifies4. classified5. purified6. intensify7. identify8. terrifiedVIII.1. bravery2. jewelry3. delivery4. machinery5. robbery6. nursery7. scenery8. discoverySentence StructureIX.1. other than for funerals and weddings2. other than to live an independent life3. other than that they appealed to his eye . . `4. but other than that, he'll eat just about everything .5. other than that it's somewhere in the town centerX.1. shouldn't have been to the cinema last night2. would have; told him the answer3. they needn't have gone at all4. must have had too much work to do5. might have been injured seriouslyTranslationXI. -1. The plant does not grow well in soils other than the one in whichit has been developed.2. Research findings show that we spend about two hours dreaming everynight, no matter what wemay have done during the day.3.Some people tend to justify their failure by blaming others for nottrying their best.4.We remain tree to our commitment: Whatever we promised to do; wewould do it.5.Even Beethoven's father discounted the possibility that his sonwould one day become the greatest musician in the world. The same istrue of Edison, who seemed to his teacher to be quite dull.6. They were accused by authorities of threatening the state security.XII.l.出入除自己家以外的任何场所时，如果你带有宠物，一定要了解有关宠物的规定。
I.J. Computer Network and Information Security, 2014, 5, 9-20Published Online April 2014 in MECS (/)DOI: 10.5815/ijcnis.2014.05.02Visualization of Influencing Nodes in OnlineSocial NetworksPrajit Limsaiprom, Prasong Praneetpolgrang, Pilastpongs SubsermsriSchool of Information Technology, Sripatum University, Bangkok 10900, Thailandcrossprajit@, prasong.pr@spu.ac.th, pilastpongs@Abstract—The rise of the Internet accelerates the creation of various large-scale online social networks. The online social networks have brought considerable attention as an important medium for the information diffusion model, which can be described the relationships and activities among human beings. The online social networks’ relationships in the real world are too big to present with useful information to identify the criminal or cyber attacks. The methodology for information security analysis was proposed with the complementary of Cluster Algorithm and Social Network Analysis, which presented anomaly and cyber attack patterns in online social networks and visualized the influencing nodes of such anomaly and cyber attacks. The closet vertices of influencing nodes could not avoid from the harmfulness in social networking. The new proposed information security analysis methodology and results were significance analysis and could be applied as a guide for further investigate of social network behavior to improve the security model and notify the risk, computer viruses or cyber attacks for online social networks in advance.Index Terms—visualization, influencing nodes, anomaly and cyber attacks, online social networks, clustering, social network analysis.I.I NTRODUCTIONNew services will widen the Internet into an interactive medium for online social networks (Web 2.0) such as Facebook, MySpace, Twitter, Hi5, Google, etc. The online social network services increase the benefits of users, however these services may cause additional risks for users due to their trusted these online social networks. The attackers, spammers and scammers may have the opportunities to exploit their information easily or construct convincing social engineering attacks with all their data.Many organizations have become vulnerable from the intrusion of cyber attackers that compromise the security of their networks. The security incidents comprise the violation of the given security policies. To be able to detect violations of the security, it must be able to observe all activities that could potentially be part of such violations [1]. Many computer security techniques have been intensively studied in the last decade to defend against various cyber attacks and computer viruses, namely cryptography, firewalls, anomaly and intrusion detection [2], [3], [4], [5], [6].Online social networks can also play an important role as a medium for the spread of information. For example, innovation, hot topics and even malicious rumors can propagate through online social networks among individuals, and computer viruses can diffuse through email networks. Social Network Analysis (SNA) has become a powerful methodological tool alongside with statistics. The social network analysis is an approach and a set of techniques, which can use for studying the exchange of resources among actors (i.e., individuals, groups, or organizations). Regular patterns of information exchange reveal themselves as online social networks. The actors are nodes in the networks and information exchange relationships are connectors between nodes [7].The previous research paper was studied about computer virus distribution in online social networks and found that the users who visited MySpace was the first order40.58%, next was Hi5 35.12% and Facebook was the last one 24.30%. The virus behavior analysis was JS/PackRedir. A! tr. dldr90.36% from private IP address 172.16.10.96. The result of correlation analysis between the usage of online social networks and virus distribution was significant at the 0.01 level (2-tailed) [8].In this paper, we have carried out the anomaly and cyber attack patterns in online social networks, identified and visualized the influencing nodes of such anomaly and cyber attack patterns in online social networks.The organization of this paper as follows: Section I demonstrates about the background. Section II reviews the related works. Section III introduces the proposed approach. Section IV explains about the researching methodologies. Section V presents the data source that used in this research. Section VI illustrates the result and analysis. Section VII presents the discussion. Finally, section VIII shows the conclusion and references.II.R ELATED W ORKSData Mining has been interested in various research applications such as improved the performance of intrusion detection system, detected the anomaly and attack patterns, classified network behaviors, etc. Recently, many researchers have interested to propose the frameworks or the methods in data mining to improve the performance of intrusion detection system:C. Azad et al., studied many research papers to classify the area of interesting in Data Mining. Anomaly detection was interested in first priority that presented 67% [9]. R. Smith et al., introduced a new clustering algorithm based on the neural network architecture called an autoassociator (AA) [10].D. Fu et al., presented the improved association analysis algorithm based on FP-Growth and FCM network intrusion detection technologies based on statistical binning [11]. The result of this research found the intrusion activity in time and solved the problem of data mining speed effectively; enhanced the detective ability of intrusion detection. J. Song et al., presented an approach that was different from the traditional detection models based on raw traffic data. The proposed method could extract unknown activities from IDS [12].Data Mining was used to detect the anomaly and attack patterns: M. Bordie et al., developed a data-mining system with an integrated set of analysis methods that used to analyze a large amount of IDS log data to discover interesting, previous unknown and actionable information [13]. They presented an architecture and data mining process that used a set of integrated tools including visualization and data analysis and utilized several data-mining algorithms, including temporal association, event burst, and clustering, to discover valuable patterns. H. Yang et al., presented an anomaly detection approach based on clustering and classification for intrusion detection [4]. They used connections obtained from raw packet data of the audit trail and then performed clustering to group training data points into cluster, from which selected some clusters as normal and non-attack profile.The influence-based classification algorithm was used to classify network behaviors. Z. Qi et al., proposed k-walks in (1+A) k matrix for studying the connective relationship of a network. They normalized the walker vector of each node into unit n-dimensional unit spherical surface, and then they computed geodesic lines between two nodes on the spherical surface, and got the best part of the community by k-means clustering method [14]. Z. Jiliu et al., used the criminal data mining by applying data mining technology and graph theory to Social Network Analysis (SNA) in crime groups or terrorist organizations [15]. They proposed a Crime Group Identification Model (CGIM) based on time step by calculating the group similarity and the group members aggregation under different time steps. Besides, Social network is a phenomenon of the interaction among the people in a group, community or Internet world. It can visualize as a graph, where a vertex corresponds to a person in that group and an edge represents some kind of connections between the corresponding persons. Online social networks are interested in various research areas as well such as social network analysis, searching and discovering mechanisms in Social Networks, information diffusion, the problem of information dissemination, etc.Some researchers have interested in the applications of social network analysis: A. Apolloni et al., used a probabilistic model to determine whether two people would converse about a particular topic based on their similarity and familiarity [16]. H. Xia et al., analyzed the key factors influencing knowledge diffusion and innovation through quantitative analysis about network density, centrality and the cohesive subgroup [17].In addition, S.H. Sharif et al., reviewed on search and discovery mechanisms in Social Networks. They classified the existing methods into four categories: people search, job search, keyword search and web service discovery [18]. S. Sharma et al., presented a centrality measurement and analysis of the social networks for tracking online community by using betweenness, closeness and degree centrality measures [19].The approaches and techniques were proposed in order to study of information diffusion: C. Haythornthwaite introduced social network analysis as an approach and a set of techniques for the study of information diffusion [7]. T. Fushimi et al., attempted to answer a question “What does an information diffusion model tell about social network structure?”. They proposed a new scheme for an empirical study to explore the behavioral characteristics of information diffusion model [20]. C.T. Butts applied the notion of algorithmic complexity to the analysis of social network, based on theoretical motivation regarding constraints on the graph structure [21]. C. Lo Storto presented an approach useful to analyze the performance of the product development process [22].Some researchers have concentrated on the problem of information diffusion in social networks: M. Kimura et al., addressed the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible/infected/susceptible (SIS) model [23]. A. Pablo et al., solved the influence maximization problem in social networks with greedy algorithm [24]. They considered two information diffusion models, Independent Cascade Model and Linear Threshold Model. Their proposed algorithm compared with traditional maximization algorithms such as simple greedy and degree centrality using three data sets.III.P ROPOSED A PPROACHBased on the objectives of this research, a new approach was proposed with the complementary of cluster algorithm and social network analysis.The clustering technique was proposed to detect informative patterns of cyber attacks in online social networks. Consequently, the centroid of the cluster could use to represent the member within each cluster and discover attacks in the audit trail.Visualization the influencing nodes of anomaly and cyber attacks in online social networks, active nodes would be discovered by social network analysis (SNA) into two classes-Influencing nodes (Influencing Nodes:IN) and other nodes (Other Nodes: ON), IN was a class which indicated the influencing nodes and ON was a class which indicated the other nodes. The proposed architecture of this research presented as shown in Fig.1. The software, namely WEKA was used to analyze the IDS data streams of online social networks and present anomaly and cyber attack patterns. The software, namely Pajek was used to analyze and visualize the influencing nodes with high degrees’ nodes in onlin e social networks’ relationships with 7,035 clients and 25,778 edges of data in April 2012.Fig 1: Architecture of the proposed systemIV.M ETHODOLOGIESThe new approach was proposed in this research, which developed with the complementary of cluster algorithm and Social Network Analysis. The cluster algorithm was used to analyze IDS logs data and discover anomaly and cyber attack patterns in online social networks. Social network analysis (SNA) was used to identify and visualize the influencing nodes of anomaly and cyber attacks in online social networks, which could be applied in many applications such as identifying the structure of a criminal network or monitoring and controlling information diffusion, or cyber attacks for secure computer systems in network applications.A. Clustering AlgorithmClustering is a useful technique for the intrusion detection of online social networks as malicious activities will cluster together, separate itself from non-malicious activities.The clustering algorithm will group the similar objects from different objects by considering between similarity measure and distance measure. There are two most clustering analysis models, which are hierarchical cluster analysis and non-hierarchical cluster analysis. The hierarchical cluster analysis is included Agglomerative method and Division method while the method of non-hierarchical cluster analysis is K-Means clustering. The cluster analysis with distance measure and K-Means clustering method was used in this research.Formally, a cluster analysis can be described as the partitioning of a number N of classifying objects-a number of patterns with an endless dimension P in K groups or clusters {Ck; k = 1,…, K}. Given N objects X = {xi, i = 1,…, N}, where xi, j denotes the jth element of xi, the grouping of all objects with index i = 1,…, N in clusters k = 1,…, K can be defined as follows:(1) The association of each object to a cluster is unique by applying two conditions for the matrix.(2)Furthermore, let the following definition denominate the number of objects belonging to a cluster Ck:(3) The Euclidean distance was used as a similarity measure function to identify the similarity of two feature vectors. Whether a set of points is close enough to be considered a cluster. The distance measure D (x, y) was used to tell how far points x and y are. Often, the points may think to live in k-dimensional Euclidean space, and the distance between any two points, say asx= [x1, x2,…, x k] and y = [y1, y2,…, yk] (4)(5)The correlation coefficient provides another possibility to measure similarities between two classification objects:(6)B. Social Network Analysis (SNA)Social network analysis is based on the principles of graph theory, which consists of a set of mathematical formulae and concepts in the study of patterns of lines. Actors are the points in the graph, and relationships are the lines between actors, Sociograms are graphs of socialnetworks. Centrality is the extent to which a person is in the center of a network. Central people have more influence in their network than people who are less central. Measures of centrality include degree, betweenness and closeness centrality.1. A network is represented by a matrix that calledthe adjacency matrix A, which in the simplestcase is a (n x n) symmetric matrix, where n is thenumber of nodes in the network. The adjacencymatrix has elements.(7) 2.Let a graph G= (V, E) represents the graph ofonline social networks where vertices Vcorresponds to contact nodes (users) in thenetwork, and edges E corresponds to theinformation sending events among users.3.The degree of a vertex in a network is the numberof edges attached to it. In mathematical terms, thedegree Di of a vertex i is:(8) 4.The betweenness Ba of a node a is defined as thenumber of geodesics (shortest paths between twonodes) passing through it:(9)Where indicates whether the shortest pathbetween two other nodes i and j passes throughnode a.5.The Closeness Ca is the sum of the length ofgeodesics between a particular node a and all theother nodes in a network. It actually measureshow far away one node is from other nodes andsometimes called farness. Where l(i, a) is thelength of the shortest path connecting nodes i anda:(10) 6.For node i, we definitely InDg (I) as the uniquenumber of edges are sent to node i.(11)7.For node i, we definitely OutDg (I) as the uniquenumber of edges are received from node i.(12) 8.The total unique number of edges is sent to thenode i and the number of edges are received fromnode I definitely in TotalDg(i)(13)V.D ATA S OURCESThe organization in this research is the public health sector, which concerns about high privacy and security of information system and needs to avoid from the harmful of network applications. The event log data from IDS (Intruder Detection System) of Health Care Organization with Head Office in Bangkok and 12 regional centers (RC1, RC2… RC12) had located in the different region cover 76 provinces of Thailand in April 2012 with high severity were used in this research. Sample record of raw IDS logs file presented as shown in Table I.Table 1.N ETWORK IDS L OG R ECORD D ATA D EFENITIONIn this example, besides date and time stamp, the attributes of IDS log file were: attacked ID (attack_id) for representing the type of intruder signature; severity presented the level of risk; source IP address, destination IP address, source port, destination port, and a detailed message for additional explanation of this signature. The meaning of the example alert above, the attack id was 10873; the source IP was 172.16.10.96 with port number 2156; the destination IP was 172.16.10.137 with port number 80; and activity wa s “The remote attackers can gain control of vulnerable systems”.As mention above, this research focused on log data of intrusion detection system of online social networks toidentify and discover unknown patterns, the data preprocessing as follows:1.The attack logs data of online social networkswere filtered from Intrusion Detection System logto analyze and discover anomaly and cyber attackpatterns.2.The raw events were enhanced by augmenting theattack log data with other relevant information notfound in the raw event logs, such as the type ofweb (web 1.0, web 2.0), group number of onlinesocial networks in each real IP destinationaddress, so on.3.The attack logs data in April 2012 were filtered toIDS log data of online social networks (web 2.0)to analyze and discover unknown patterns withClustering Technique and Social NetworkAnalysis (SNA).4.This research merged online social networks’relationships with members, who were attackedfrom the attackers of such online social networks.The online social networks’relationships in thereal world are too big to present usefulinformation as shown in Fig.2, which was theexample of online social networks’relationshipswith 7,035 nodes and 25,778 edges in April 2012.They became very difficult to read as the memberof actors’ increases.Fig 2: The relationships in online social networksVI.R ESULTS AND A NALYSIS R ESULTSThe approach was proposed with the complementary of cluster algorithm and Social Network Analysis. The cluster algorithm was used to analyze IDS log data to discover anomaly and cyber attack patterns in online social networks. Social network analysis (SNA) was used to identify and visualize the influencing nodes of anomaly and cyber attacks in online social networks. The results of this research presented as follows;A. Anomaly and Cyber attacks Patterns in Online Social NetworksThe anomaly and cyber attacks patterns in online social networks were analyzed with a cluster algorithm by using source IP address. The model and evaluation of test set categorize signatures into four groups. The anomaly and cyber attacks patterns caused from attackers, which executed an arbitrary program on infected systems with 40% of the test set. The anomaly with HTTP with 37% of the test set. The remote attackers can gain control of vulnerable systems with 19% of the test set. Denial of Service (DOS) with 4% of test set. The result presented as shown in Fig. 3.Fig 3: Social networks anomaly and cyber attacks patterns analysisresultB. The Proportion of Anomaly and Cyber attacks Patterns in Each Branch of the OrganizationIn this experiment, 60% of the data stream was training set and 40% of the data stream was test set. When the training process finished and system model was built, the association rules of anomaly and cyber attacks patterns in online social networks would be discovered. The association predictive model was evaluated and best rules presented with eight principles. The anomaly and cyber attack patterns at head office and 12 regional centers were the same patterns. The attackers executed an arbitrary program on infected systems was the highest percentage of risk of RC6, located at Khon Khan. Anomaly with HTTP was the highest percentage of risk of RC2, located at Lopburi. The remote attackers could gain control of vulnerable systems was the highest percentage of risk of RC9and RC10, located at Pitsanuloke and Chiang Mai, respectively. DoS was the highest percentage of risk of Head office in Bangkok. The proportion of risk in each signature presented as shown in Table II and Fig. 4.Table 2. Percentage of Anomaly and Attack Classify by Site andSignatureFigure 4: Percentage of anomaly and cyber attacks patterns classify bysignatureC. The Proportion of Anomaly and Cyber attacksPatterns Classify by Online Social NetworksThe proportion of anomaly and cyber attack patternsclassified by online social networking presented asshown in Table III, Fig. 5 and Fig. 6.1.The proportion of risk caused by the remoteattackers could gain control of vulnerable systemswhen users access Facebook was 100%.2.The proportion risk caused by the attacker canexecute an arbitrary program on infected systemswhen users access googalz was 100%.3.The proportion of risk caused by Denial ofService when users access Skype was 55.56% andMSN was 44.44% as shown in Fig. 5.4.The proportion of risk caused by anomaly withHTTP when users access Facebook, imeem,YouTube, Hi5 and Twitter was 30%, 16%, 5 %,47% and 2%, respectively as shown in Fig. 6.Table 3. Percentage of Risks Classify by Online Social NetworksFig 5: Percentage of Denial of Service classifies by online socialnetworksFig 6: Percentage of Anomaly with HTTP classifies by online socialnetworksD. Identifying and Visualization the Influencing Nodes of Anomaly and Cyber attacks in Online Social Networks The centrality is the extent to which a person is in the center of a whole network. Central people have more influence in their network. Measures of centrality include degree, betweenness and closeness centrality.1. This research presented 42 nodes (node label 3, 60, 87, googalz, 224, 145, 119, B6, 216, 217, Facebook, Skype, MSN, Hi5, 234, 229, 30, 20, 179, 171, 96, 44, 229, A47, A12, B9, C26, B31, A24, C40, F48, J7, G24, E26, H21, M14, K16, L41, E29, E21, 235, and G19) these were the influencing nodes. A new network of 42 influencing nodes presented the cyber attack patterns, which called Egocentric network as shown in Fig. 7.2. A network member with a higher degree could be the leader or “hub” in a network.3. Top five In-degree nodes were node label 60, 87, 3, googalz and 224 with an In - degree equal 109, 109, 107, 72, and 60, respectively.4. Top five Out-degree nodes were node label 234, J7, 217, 229 and 216 with an Out - degree equal 1451, 101, 75, 68 and 61, respectively.5. Betweenness measures the extent to which a particular node lies between other nodes in a network. Top five Betweenness nodes were node label 216, 119, 234, C26 and J7 with Betweenness measure equal 1513.400, 1338.200, 1197.467, 785.233 and 695.000, respectively.6. Top five Closeness nodes were node label 119, 217, 216, 171 and 234 with closeness measure equal 8.456, 8.436, 8.413, 8.301 and 8.293, respectively. This meant that node label 119, 217, 216, 171 and 234 were persons in the center of the network and could be the leader in the network because they were both influencing nodes andhigh degrees. If they were attacked from many threats or cyber attacks such as social engineering or malware, they would be influenced in their network.A network member with a higher degree could be the leader or “hub” in the network. Betweenness measures the extent to which a particular node lies between other nodes in a network. Closeness is the sum of the length of geodesics between a particular node and all the other nodes in a network. It actually measures how far away one node is from other nodes. This research presented 42 influencing nodes, which could be partitioned to new network with high degree centrality.E. Anomaly and Cyber attacks Diffusion by Influencing Nodes in Online Social NetworksEgocentric Network is the graphs in subgroups of the whole network, builds a picture of a typical actor in any particular environment and shows how many ties they maintain, and what kinds of information they send to and receive from others in their network. This research presented the sociogram of each representative node, for example node label 119, 216, 217, 234 and their linkages of relationships as shown in Fig. 8, Fig. 9, Fig. 10, Fig. 11, and Fig. 12 respectively.Finally, the results of Egocentric Networks presented the closet vertices of node label 119 or node label 50 and node label 53. The closest vertices of node label 216 were node label 20 and node label 29. The closest vertices of node label 217 were node label 18 and node label 28. The closest vertices of node label 234 were node label 29 and node label 42. These could be implied that when node label 119, 216, 217, 234 were attacked from many threats such as social engineering or malware, they would send these attacks to node label 50, 53, 20, 29, 18, 28 29 and 42 with the highest proportionas shown in Fig. 13.Fig 7: Subgroup Identification of influence nodesFig 8: Egocentric network of representative node A24Fig. 8 presented the characteristics of node label A24 as follows: Node label A24 had 93 edges and average degree was equal 1.9565217. The closest vertices were node label 32 and node label 36 with distance: 0.08033. The ClosenessCentralization was equal 1.00000.Fig 9: Egocentric network of representative node 119Fig. 9 presented the characteristics of node label 119 as follows: Node label 119 had 107 edges and average degree was equal 1.9622642. The closest vertices were node label 50 and node label 53 with distance: 0.06899. The ClosenessCentralization was equal 1.00000.Fig 10: Egocentric network of representative node 216Fig. 10 presented the characteristics of node label 216 as follows: Node label 216 had 109 edges and average degree was equal 1.9629630. The closest vertices were node label 20 and node label 29 with distance: 0.06895. The ClosenessCentralization was equal 1.00000.Fig 11: Egocentric network of representative node 217Fig. 11 presented the characteristics of node label 217 as follows: Node label 217 had 105 edges and average degree was equal 1.9615385. The closest vertices were node label 18 and node label 28 with distance: 0.07135. The ClosenessCentralization was equal 1.00000.Fig 12: Egocentric network of representative node 234Fig. 12 presented the characteristics of node label 234 as follows: Node label 234 had 131 edges and average degree was equal 1.9692308. The closest vertices were node label 29 and node label 42 with distance: 0.05801. The ClosenessCentralization was equal 1.00000.Fig 13: Information Diffusion of representative nodes 119, 216, 217 and 234Fig. 13 presented the relationships of representative node label 119, 216, 217, and 234 as follows: Average degreewas equal 2.1604278. The closest vertices were node label 17 and node label 21 with distance: 0.00331. The ClosenessCentralization was equal 0.45397.。
新版新视野大学英语读写教程第四册Unit OneIII.1. idle2. justify3. discount4. distinct5. minute6.accused7. object8. contaminate9. sustain 10. worshipIV.1. accusing... of2. end up3. came upon4. at her worst5. pay for6. run a risk of7. participate in8. other than9. object to/objected 10. at best V 1. K 2. G 3. C 4. E 5. N 6.O 7.I 8. L 9. A 10. DCollocationVI.1. delay2. pain3. hardship4. suffering5. fever6. defeat7. poverty8. treatment9. noise 10. agonyWord buildingVII.1. justify2. glorify3. exemplifies4. classified5. purified6. intensify7. identify8. terrifiedVIII.1. bravery2. jewelry3. delivery4. machinery5. robbery6. nursery7. scenery8. discoverySentence StructureIX.1. other than for funerals and weddings2. other than to live an independent life3. other than that they appealed to his eye . . `4. but other than that, he'll eat just about everything .5. other than that it's somewhere in the town centerX.1. shouldn't have been to the cinema last night2. would have; told him the answer3. they needn't have gone at all4. must have had too much work to do5. might have been injured seriouslyTranslationXI. -1. The plant does not grow well in soils other than the one in which it has been developed.2. Research findings show that we spend about two hours dreaming every night, no matter what wemay have done during the day.3.Some people tend to justify their failure by blaming others for not trying their best.4.We remain tree to our commitment: Whatever we promised to do; we would do it.5.Even Beethoven's father discounted the possibility that his son would one day become the greatest musician in the world. The same is true of Edison, who seemed to his teacher to be quite dull.6. They were accused by authorities of threatening the state security.XII.l.出入除自己家以外的任何场所时，如果你带有宠物，一定要了解有关宠物的规定。
WebService原理及重要术语⼀：WebService简介1：WebService介绍 WebService是⼀个平台独⽴的、低耦合的、⾃包含的、基于可编程的web应⽤程序，可使⽤开放的XML来描述、发布、发现、协调和配置这些应⽤程序，⽤于开发分布式交互操作的应⽤程序。
WebService技术，能运⾏在不同机器上的不同应⽤⽆须借助附加的、专门的第三⽅软件或硬件，就可相互交换数据或集成。
依据WebService规范实施的应⽤之间，⽆论它们所使⽤的语⾔、平台或内部协议是什么，都可以相互交换数据。
这么说吧，其实WebService就是⼀种跨编程语⾔和跨操作系统平台的远程调⽤技术（RPC的⼀种实现⽅式）。
所谓可跨编程语⾔，就是说服务端程序和客户端程序可以以不同的语⾔编写也可以利⽤WebService互相调⽤；跨操作系统平台则是指服务端程序和客户端程序可以在不同的操作系统上运⾏。
远程调⽤，就是⼀台计算机的应⽤可以调⽤其他计算机上的应⽤。
例如：我⾃⼰编写⼀个⽹站，⾥⾯想要个天⽓预报的功能，这个时候我肯定去调⽤⽓象局的接⼝服务⽽不是我⾃⼰发射卫星来监测天⽓，再引⼊我⽹站⾥。
2：为什么使⽤WebService WebService能解决跨平台调⽤、跨语⾔调⽤、远程调⽤（RPC） 以各个⽹站显⽰天⽓预报功能为例，⽓象中⼼的管理系统将收集的天⽓信息并将数据暴露出来(通过WebService Server)，⽽各⼤站点的应⽤就去调⽤它们得到天⽓信息并以不同的样式去展⽰(WebService Client)，我们⽹站虽然提供了天⽓预报的服务，但其实它们什么也没有做，只是简单的调⽤了⼀下⽓象中⼼服务器服务接⼝⽽已。
3：WebService原理及重要术语 XML、SOAP、WSDL 是构成WebService平台的三⼤技术⼀：基本术语 UDDI：Universal Description, Discovery, and Integration（统⼀描述、发现和集成） UDDI是OASIS发起的⼀个开放项⽬，它使企业在互联⽹上可以互相发现并且定义业务之间的交互。
´«Ã½2021Äê4Ô¡¶´óѧӢÓԤ±¸Ò»£©¡·¿Î³Ì¿¼ÊÔ×÷Òµ¿¼ºËÊÔÌâÊÔ¾í×Ü·Ö:100 µÃ·Ö:100Ò»¡¢µ¥Ñ¡Ìâ (¹² 50 µÀÊÔÌâ,¹² 100 ·Ö)1.If you _________ his idea, you would surely be killing him.A.awardB.destroyedC.inventedD.cross´ð°¸:B2._---- May I put my bike here?---- No, you ______.A.may notB.can’tC.might notD.mayn’t´ð°¸:B3.You have to make the questions clear: Firstly, who does the text describe; secondly, what does the text __________?A.situationB.customC.describeD.suppose´ð°¸:C4.The president ___________ that the advertisement should include a well-written description to attract the buyers' attention.A.insistedB.necessaryC.carriedD.defeat´ð°¸:A5.Business has ___________ achieved a market (Êг¡) for children, because every child have learned from TV what a child should have and be like.A.firmlyB.avoidC.lieD.ceiling´ð°¸:A6.We cheered in a loud voice when our team __________ the other team at last.A.defeatedB.crazyC.treatD.discuss´ð°¸:A7.You can work it out by yourself, then _______ the result with that of your friends.A.enforcepareC.cigaretteD.defend´ð°¸:B8.If you try all kinds of jobs, not only will you find out what you want to do, but you will also ___________ up a surprising amount of useful information as you go along.A.pickB.equipmentC.drugD.director´ð°¸:A9.I was cooking in the ____________ when the doorbell rang.A.signB.kitchenC.approachD.introduce´ð°¸:B10.I¡¯m not hungry, but I don¡¯t _____________ having some cakes.A.mindB.beerC.lineD.stress´ð°¸:A11.I have seen the movie _______ times.A.the number ofB.a number ofC.muchD.a number´ð°¸:B12.I asked Marcus to __________ his dark glasses (ÑÛ¾µ) so that I could look at his face.A.webB.removeC.movementD.meaning´ð°¸:B13.It is a custom for people to __________ to others for stepping on their toes.A.pearB.apologizeC.delightD.chat´ð°¸:B14.People __________ if Carnegie would spent most of his money on poor people if he were still living.A.wonderB.supportC.momentD.wind´ð°¸:A15.The old man was remembered by people because he had ________ many useful things during his life.A.prizedB.knowC.finishD.invented´ð°¸:D16.People in our village respect him because he was a doctor with a ___________ position (µØλ)in society.A.respectableB.sortC.endingD.instead´ð°¸:A17.The policeman asked John ________ he knew the victim.A.whatB.thatC.ifD.which´ð°¸:C18.Over the past ten years, health, education, and __________ services(·þÎñ) have started to improve to answer the needs of the peopleA.stateB.forcepareD.welfare´ð°¸:D19.We bought the house ________ because it was convenient for the children to go to schoolA.leaderB.friendlyC.simplyD.share´ð°¸:C20.Do you know the lady ______ is interviewing our headmaster?A.whichB.whoC.whomD.whose´ð°¸:B21.The development of agriculture in this area has reached a new ________ in the last few years.A.beltB.bicycleC.buildD.level´ð°¸:D22.The children felt ______ when they heard the news.A.happilyB.happyC.sadlyD.angrily´ð°¸:B23.The farmer ________ himself on his new method of planting carrots.A.worthB.additionC.pridedD.smile´ð°¸:C24.I¡¯m afraid I won¡¯t ______ finish reading the book in two days.A.canB.couldC.be able toD.may´ð°¸:C25.Parents play a very important __________ in their children's education.A.classmateB.offerC.ripeD.role´ð°¸:D26.You should understand that they are only suggesting and not giving the ___________ advice.A.actualB.preferC.poetD.actress´ð°¸:A27.No school I have taught in has ever ignored spelling or ________ it unimportant as a basic skill.A.consideredB.choiceC.expectD.expert´ð°¸:A28.I have _____ question to ask you.A.more oneB.any moreC.some moreD.one more´ð°¸:D29.Let them take care of their children _____________ of the children taking care of them!A.describeB.officerC.insteadD.mail´ð°¸:C30.The teacher told the children, ¡°It is very important ________ careful ________ fire.¡±A.to be; withB.is; withC.being; withD.to be; to´ð°¸:A31.He couldn¡¯t decide ______.A.which sweater he boughtB.which sweater did he buyC.which sweater will I buyD.which sweater to buy´ð°¸:D32.The boy is ________ in ________¡¡on the ice.A.interesting; skatingB.interesting; to skateC.interested; skatingD.interested; to skate´ð°¸:C33.This was the chance I''d been waiting for --- a chance to learn a ___________ and make something of myself.A.birdB.appleC.coupleD.trade´ð°¸:D34.Can you hear someone _______ in the next room?A.singB.singsC.singingD.sang´ð°¸:C35.We will _________ all kinds of pollution(ÎÛȾ) control methods to protect our living conditions.A.extraB.accordingC.costD.enforce´ð°¸:D36.Exercise is most important to health, the report says; though it doesn''t ___________ body weight(ÌåÖØ) for sure.A.ignoreB.restrictionC.reduceD.area´ð°¸:C37.Well, I ___________ I have the time to give it a try, but I¡¯m not sure that I¡¯ll be able to finish it.A.changeB.influenceC.chairmanD.suppose´ð°¸:D38.Unlike the other men, Peter did not ______ they meet again.A.serviceB.suggestC.silentD.sport´ð°¸:B39.The firm is unable to ________ all the demands of customers for its new car this year.A.regretB.fairlyC.satisfyter´ð°¸:C40.The old man died but his shop _____________ to trade under its old name.A.inventedB.continuedputerD.discovery´ð°¸:B41.When a __________ tool (¹¤¾ß) has been developed, it is usually because nothing else will do the job as well.A.respectB.specialC.professorD.partly´ð°¸:B42.The biggest problem is the ________ of the research, which nobody knows but would be huge.A.personalB.experienceC.costD.similar´ð°¸:C43.There's a(n) __________ on the blackboard saying that there will be a meeting this afternoon.A.practicalB.cowC.noticeD.imagine´ð°¸:C44.________does it take you to travel from Beijing to Shanghai.A.How oftenB.How longC.How many timesD.How much´ð°¸:B45.The more we looked at the picture, the more lovely and surprising things ____________, until we were controlled by that wonder.A.heightB.fairC.appearedD.stay´ð°¸:C46._---- What a day! It is quite hot today.---- ________.A.So it isB.So is itC.So it doesD.So does it´ð°¸:A47.How many __________ do you plan to take this year?A.tireB.coursesC.mindD.tin´ð°¸:B48.They tried to pull (À-) the pole down, but it stood strong and __________.A.luckB.fitC.straightD.wipe´ð°¸:C49.Would you like to come to dinner this Sunday,Mr Wang?¡ª________________________________.A.I’d love to, but I have lots of things to do.B.Oh no. That’ll be too tired.C.I’ll stay at home.D.Yes, please.´ð°¸:A50.If the parents can __________ it, each child should have his or her own bedroom.A.affordB.varietyC.storeD.butter´ð°¸:A。
新视野第四册答案（1～10）新视野第四册答案（1～10）新版新视野大学英语读写教程第四册unit1答案Unit 1III.1. idle2. justify3. discount4. distinct5. minute6.accused7. object8. contaminate9. sustain 10. worshipIV.1. accusing... of2. end up3. came upon4. at her worst5. pay for6. run a risk of7. participate in8. other than9. object to/objected10. at bestV1. K2. G3. C4. E5. N6.O7.I8. L9. A 10. DCollocationVI.1. delay2. pain3. hardship4. suffering5. fever6. defeat7. poverty8. treatment9. noise 10. agonyWord buildingVII.1. justify2. glorify3. exemplifies4. classified5. purified6. intensify7. identify8. terrifiedVIII.1. bravery2. jewelry3. delivery4. machinery5. robbery6. nursery7. scenery8. discoverySentence StructureIX.1. other than for funerals and weddings2. other than to live an independent life3. other than that they appealed to his eye . . `4. but other than that, he'll eat just about everything .5. other than that it's somewhere in the town centerX.1. shouldn't have been to the cinema last night2. would have; told him the answer3. they needn't have gone at all4. must have had too much work to do5. might have been injured seriouslyTranslationXI. -1. The plant does not grow well in soils other than the one in which it has been developed.2. Research findings show that we spend about two hours dreaming every night, no matter what wemay have done during the day.3.Some people tend to justify their failure by blaming others for not trying their best.4.We remain tree to our commitment: Whatever we promised to do; we would do it.5.Even Beethoven's father discounted the possibility that his sonwould one day become the greatest musician in the world. The same is true of Edison, who seemed to his teacher to be quite dull.6. They were accused by authorities of threatening the state security. XII.l.出入除自己家以外的任何场所时，如果你带有宠物，一定要了解有关宠物的规定。
全国2023年10月高等教育自学考试电子商务英语试题课程代码：00888请将答案填在答题纸对应位置上一、词汇和语法（本大题共20小题，每题1分，共20分）根据句子旳意思选择一种对旳旳答案，错选、多选或未选均无分。
1. The time has come for the company to __________ after years of rapid expansion.A. consistB. considerC. consolidateD. combine2. __________ were naturally a musical family .A. A ShawB. The ShawsC. The ShawD. Shaws3. Nothing can __________ being unkind to children.A. adjustB. reasonC. justifyD. reassure4. That kind of shoes are __________ expensive for me.A. moreB. far moreC. far tooD. much5. Next time I __________ there, I’ll ask them about it.A. will goB. have goneC. am goingD. go6. His wife, to whom he __________ for thirty years, was childless.A. has marriedB. has been marriedC. had marriedD. had been married7. They are __________ a mass production movement.A. firingB. launchingC. introducingD. presenting8. Professor Tate __________ about him .A. heard the students to talkB. heard the talk by the studentsC. heard the students talkingD. heard the students to talking9. __________ some of the suggestions prove of value to you as well?A. CouldB. CanC. MightD. May10. The research project has only been under way for three months, so it’s too early to _______its success.A. figureB. considerC. rateD. evaluate11. What do you think of his motion that we __________ a play at the English evening?A. should put onB. shall put onC. would put onD. will have put on12. We can visit your company on Monday or Tuesday, our plans are fairly __________.A. flexibleB. elasticC. suppleD. compliant13. Nothing can stop us now, __________?A. can’t itB. can itC. could itD. does it14. All of my efforts to__________ peace failed.A. reserveB. conserveC. persistD. preserve15. I was greatly shocked by the way __________ things were done there.A. howB. thatC. asD. which16. There is a(n) __________ between logic and metaphysics.A. bargainB. treatyC. compactD. alliance17. Neither of the drivers who are being held by the police__________ whom to blame for his trouble.A. knowB. knowsC. to knowD. have known18. All citizens in this nation are__________ to the law.A. subjectB. objectC. exposedD. dependent19. I made up my mind that under no circumstances__________ to such principle.A. couldn’t I agreeB. I couldn’t agreeC. I could agreeD. could I agree20. Einstein’s new theory on optics changed__________ scientific idea about light.A. technicalB. originalC. classicalD. mechanical二、完型填空（本大题共10小题，每题1分，共10分）从选项中选择一种最适合短文旳选项，错选、多选或未选均无分。
Research on Web Service Discovery with Semantics and ClusteringTao Wen, Guojun Sheng,Yingqiu Li College of Information Science and Engineering Northeastern University,NEUShenYang,Chinae-mail: shengguojun@Quan GuoDepartment of Computer Science and Technology DaLian Neusoft Institute of InformationDaLian,ChinaAbstract—In tradition al Web service discovery methods, the search results from service registries are inadequate due to the lack of seman tic description s of the Web services. Moreover, with the heavily in creasin g amoun t of Web services, these methods usually cause low efficien cy an d poor performan ce. This paper presents a Web service discovery method based on semantics and clustering. The experiment result indicates that the algorithm proposed is more better and efficient than that of full scan search method.Keywords-component;Web service discovery˗ontology˗semantic Web service˗service clustering˗K-medoids algorithmI.I NTRODUCTIONThe increasing usage of Web services has brought the research community great interest in the area of service discovery[1,2].In traditional Web service discovery methods, the search results from service registries are inadequate due to the lack of semantic descriptions of the Web services. Moreover, with the increasing amount of Web services, these methods usually cause low efficiency and poor performance. In this paper, We present a Web service discovery method based on semantics and clustering, which utilizes the semantic representation of services to group similar Web services so as to improve the service discovery efficiency. Our method is three-fold. First, similarities among services are computed by using the semantic information of service elements, then a modified K-medoids method is used to cluster service set. Lastly, the semantics-clustering-based web service discovery algorithm is presented. The experiment result indicates that the algorithm proposed is more better and efficient than that of full scan search.II.B ASIC C ONCEPTSAn ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, in which it refers to the subject of existence. People often use ontology to represent semantic information via the relationships among concepts. In practical modeling work, A labeled tree structure is used to express ontology, and the line between concept nodes stands for a semantic relation. An example of ontology is shown as figure 1,where the connecting line from concept "Mammal" to "Animal" carries an inheritance relation.Definition I.An ontology is a 6-tuple::,,,,,C R CO C R A A H X!,where C and R are two disjoint sets whose elements are called concepts and relation identi¿HUV respectively,CA is the attributes set of concepts in C, and RA represents the attributes set of relations in R,CH is a directed, transitive relation setCH C Cu, this concept hierarchy is also called concepttaxonomy.12(,)CH c c means that1c is a sub-concept of2c. X indicates the axiom set in which each axiom forms a constraint to elements in CA or R A.Definition II.A Semantic Web Service(SWS) is described with OWL-S, it can be formalized as a5-tuple::,,,,SWS Name Description Inputs Outputs O!Where,Name refers to the name of the service,it can be used as an identifier of the service. Description provides a brief syntactic text for describing the service.O is a domain ontology from which the service references some relativeconcepts ,12{,...}mInputs in in in is the set of service inputparameters and eachiin in Inputs is annotated with a domain concept in ontology O,12{,...}nOutputs out out out isthe set of service output parameters, and eachjout in Outputs is also annotated with a domain concept of ontology O .Figure 1. A fragment of an animal ontologyIII.S IMILARITY M EASUREMENT OF S EMANTIC W EBS ERVICESTextual elements of a SWS such as the name and the___________________________________ 978-1-4244-8625-0/11/$26.00 ©2011 IEEEdescription can be extracted from the corresponding OWL-S file by interpreting the <profile:serviceName>and the <profile:textDescription>tags. In this paper, A WordNet-based similarity measurement method for these textual elements is used. WordNet is a lexical database for the E nglish language,it groups E nglish words into sets of synonyms called synsets, and records the various semantic relations between these synonym sets[3].While for input parameters and output parameters of a SWS, we employ a domain-ontology-based method to measure the similarity between two concepts.A.Service description similarity measurementService description is a small-size human-readable text to depict basic characteristics of a service. Service descriptions may contain rich information for service classification. Here we use a WordNet-based semantic similarity measurement method for service description. Only nominal words are considered in the method, because,compared with other parts of speech such as verbs and adjectives, nouns carry the most valuable information for classification, besides, the specified stopwords are filtered out as well for dimensionality reduction.Algorithm 1. Similarity measurement for service descriptions.Input: WordNet ontology graph:G=<V,E>,service description:i sd ,j sd ,stopwords set : sws={“web”,“service”,”soap”…}Output:(,)sd i j Sim sd sd : The similarity between texti sd and j sd .(1)()i i sd pretreate sd m : to make each two words in isd separated by only one space, and the same treatment to j sd .(2)()i i set split sd m ,()j j set split sd m : split the stringto words by separator space.(3)()i i set filter sd m ,()j j set filter sd m : to ensure thati set and j set contain only noun words and allstopwords in sws are excluded from them.(4)i j tvs set set m , where tvs is the text vector space,thus, tvs is the dimensionality of tvs .(5)Construct two space vectors for i sd ,j sd respectively:12[,,...,]T i i i ni sd w w w JJJ G ,12[,,...,]T j j j nj sd w w w JJJ G,Where,each dimension corresponds to a separate term,ki w and kj w are term weights, n tvs ,[1,]k n .(6)Compute the similarity between i sd and j sd using thefollowing formula:(,)nikjki jsd i j i jww sd sd Sim sd sd sd sd uu ¦JJJ G JJJ G JJJ G JJJ GIt is a measurement of cosine similarity between twovectors i sd JJJ Gand j sd JJJ G based on VSM model by finding thecosine of the angle between them.In step 5, with respect to the computation of term weights ki w and kj w , a term-frequency-based methodcalled TF-IDF is often used. However, it can not reach a better precision due to the lack of semantic considerations, so we employ a WordNet-based semantic method to estimate the term weights instead, as the following algorithm 2.Algorithm 2. Construct space vector and weights:Input: WordNet ontology graph:G=<V,E>,filtered i set of i sd ,tvs .Output: vector i sd JJJ G that contains corresponding term weights.(1)n tvs m ,Initialize i sd JJJ G as an n -length double array(2)for ,[1,]kterm tvs k n do (3)[1,]((,))((,))(,)t i i k t ki term set k t k t t sd d lca term term w Max d lca term term l term term §·m ¨¸ ©¹(4)[]i kisd k w m JJJ G(5)endforfunction (,)lca x y is the lowest common ancestor of term x and y ,function ()d z is the depth of term z , and function (,)l x y returns the path length from term x to y .B.Service name similarity measurementWe believe that abundant useful information are hidden in service names. However, at most cases, naming a service is a manual work, so the concrete text for a given service name may have some particularity and randomicity. For example, the service name “car_pricequality_service”can be easily split by the separator ‘_’, but the substring “pricequality”is a combination of two words which makes it difficult to differentiate whether it’s just one word or two, while the service name “NationalGovernmentScholarship Service”is separated by space and uppercase characters.Therefore, considering this facts, we use an edit distance(namely,Levenshtein Distance) and WordNet based similarity measurement method for service names. Definition III.Service Name Similarity Measurement(,)(,)(,),sn i j edit i j cosine i j Sim s s Sim s s Sim s s D E u u Where,i s and j s are two given service names, the function(,)(,)1i j edit i j i jd s s Sim s s s s , is used to compute the similarity of i s and j s , and i s and j s represent the stringlength of i s and j s respectively,(,)i j d s s is the editdistance of is and j s .(,)cosine i jSim s s is the cosinesimilarity which is described in (,)sd i j Sim sd sd of algorithm 1 ,DandEare two adjustable weight parameters which satisfy the rule:1D E , in this paper,their values are empirically preset as D =0.4, E =0.6.C.Similarity measurement for input/output parameters based on domain ontologyAs portrayed in definition 2, each input/output parameter of a SWS is annotated with a domain ontology concept. The similarity between two ontology concepts relies on their position in the concept taxonomy. In this paper, we take into account both the distance and the depth factors, namely the different information and the common information of two concepts when dealing with their similarity.Definition IV.Ontology Concepts Similarity (OCS)(,)i j OCS c c,Where, i c and j c are two ontology concepts in the concept taxonomy,(,)i j lca c c is a function to compute the Lowest Common Ancestor (LCA) of ,i j c c .((,))i j depth lca c c isused to obtain the depth of the LCA, namely the path length from (,)i j lca c c to the root node of the tree. Thus, theexpression ((,))i jdepth lca c c eO here is employed to represent the common information of i c and j c , while the expression (,)i jdistance c c eO stands for the different information of i c and j c ,O is an adjustable parameter which is preset as :O =0.5.The embedded function (,)i j distance c c returns the length of the shortest path from i c to j c , and it satisfies the rule:(,)()()2((,))i j i j i j distance c c depth c depth c depth lca c c In figure 1, for example, some OCS values are :OCS(Herbivore,Carnivore)=0.4566,OCS(Lion,Cow)=0.2361, OCS(Lion,Wolf)=0.6839.The curved surface of OCS is shown in figure 2.As can be seen in figure 2, the similarity value increases with both the rise of common information and the reduction of different information between two ontology concepts.Algorithm 3. Similarity computation of input parameters :Input: Domain ontology graph : O=<V,E>,i inputs ofi SWS ,j inputs of j SWS .Output:(,)in i j Sim inputs inputs :Similarity betweeni inputs and j inputs .(1)(,)i j m Max inputs inputs m here we presumei jinputs inputs ! (2)Initialize mset as an m -length set ,(3)for ,[1,]k i i c inputs k inputs do(4)(,)t jaddm k t c inputs set Max OCS c c m (5)endfor (6)return 1n m n sim set m sim set §·¨¸¨¸©¹¦Where,k i c inputs and t j c inputs are twocompared concepts with which the corresponding input parameters are annotated. The similarity computation of output parameters (,)out i j Sim outputs outputs takes the same way as algorithm 3.According to the aforementioned contents, we propose a total method to compute the similarity between two services:i SWS and j SWS :41(,)i j r r r Sim SWS SWS Sim Z u ¦, Where,r Z is theweight factor,411rr Z¦.1(,)sn i j Sim Sim s s,Figure 2. The curved surface of OCS2(,)sd i j Sim Sim sd sd ,3(,)in i j Sim Sim inputs inputs ,4(,)out i j Sim Sim outputs outputsIV.SWS C LUSTERING B ASED ON S EMANTIC S IMILARITYDivision-based clustering methods[4,5,6]such as K-means and K-medoids are commonly used to group items into k clusters. However, the clustering result may be widely different due to the randomly selected initial clustering centers. Regarding this problem, we adopt a modified K-medoids method, which obtains the initial clustering centers in a human-guided way, and is preexecuted to improve service discovery performance, as shown in the following algorithm 4:Algorithm 4. Modified K-medoids to cluster SWS set:Input:the original unclustered service set :12{,...}sws n set SWS SWS SWS , the specified numberof clusters k ,a vector set 12{,...}kv k set V V V J G J G JJ G which isprovided by domain experts , where c kv V set J G is akeyword-based vector and [1,]c k .Output:The clustered set cluster set of sws set :12{,...}cluster k set cluster cluster cluster ,and11k ksws c c c c set cluster cluster I §·§· ¨¸¨¸©¹©¹* ,where c clustercluster set (1)Construct a similaritymatrix:()sws ij n n SimMatrix a u ,where , sws n set ,(,)ij i j a Sim SWS SWS is thesimilarityvalue between two services :i sws SWS set and j sws SWS set . This matrix actsas a data center for the following steps. For a large service set, this matrix may be stored at a disk file or a database table.(2)Initialize cluster set as a set, and make it include k zero-size service sets .(3)for each ,[1,]c kv V set c k J Gdo(4),returnsc sws cserviceSet doSearch set V m JJ G: search the original service set swsset with the filter condition c V J Gand return the matched query result scserviceSet .Get the service c SWS , which is the most similar to the vector c V J G , from the returned sc serviceSet ,by using a (5)combination of the edit distance and the cosine similarity to measure the name and description ofc SWS . Take c SWS as the initial th c cluster centroid,add c SWS to the corresponding service set : c cluster .(6)endfor// now we have the cluster set in which each element contains //one SWS as the initial clustering centroid.(7)Repeat(8)for each ,[1,]m sws sws SWS set m set do(9)!=(,)c cluster c m max c m cluster set SWS SWS cluster Max Sim SWS SWS §·¨¸m ¨¸©¹,where c SWS is the centroid of c cluster .(10)Add m SWS to the nearest cluster :maxcluster (11)endfor(12)for each ,[1,]c cluster cluster cluster set c set do(13)Construct a similarity matrix for c cluster :(),c ij d d c SimMatrix a d cluster u ,(14)Get the maximum-average-similarity row numberof c SimMatrix :1,11([][])d c r x row getRow Max SimMatrix r x d §·§·m ¨¸¨¸¨¸©¹©¹¦(15)Get the corresponding row SWS according to this rownumber, and set the row SWS as the new clustering centroid of c cluster .(16)endfor(17)Evaluate the clustering result cluster set using:1kc E ¦ '''E E E ' ,where'E and ''E are respectivelycomputed in the current iteration and the previous iteration.(18)Until (E H ' ) or (cluster membership no longerchanges) ,where R H is a small threshold value.After the clustering process, services in the same clusterhave higher similarity to each other, and those in differentclusters have lower similarity mutually.V.S ERVICE D ISCOVERY A LGORITHMService discovery is a process to match user querycondition with services in the service registry. We first givea formal description of a user service query.Definition V.A Web Service Query(WSQ) is a 5-tuple::,,,,WSQ Name Description Inputs Outputs O !A WSQ may be considered as a virtual SWS which is used to match real services. The tuples in a WSQ fundamentally have the same meaning with those defined in the SWS definition,with the exception that they need notto be actual elements of a real service. For example, The Name here refers to an actual or approximate string for matching real service names.Thus, we can determine whether a real i SWS matchesa user-specified j WSQ according to the following condition :,((,)),((,))i j i j matched if Sim SWS WSQ unmatched if Sim SWS WSQ W H ! °®°¯where W and H are two threshold values,(0,1]R H ,(0,1]R W and W H !.Given the j WSQ ,if a matched i SWS is found , theremay be a high probability of matching other services within the same cluster of i SWS , then a subsequent search for more matched services can be carried out in this cluster.Otherwise, if an unmatched i SWS is found, then other services in the same cluster can be ignored. The service discovery algorithm is shown as follows:Algorithm 5. Web service discovery based on semantics and clustering :Input:the clustered service set:12{,...}cluster k set cluster cluster cluster , a user queryj WSQ , two threshold values W ,H .Output:The matched service set matched set .(1)Initialize matched set as a service set that contains no services.(2)for each ,[1,]c cluster cluster cluster set c set do (3)().tmp c SWS cluster centroid m (4)0,10counts O m m ,(5)((,)(,))tmp j while Sim SWS WSQ H W then(6)1()c if counts cluster O !Break.endif(7)().tmp c SWS cluster randomSWS m : randomlyselect a SWS from c cluster .(8)endwhile(9) ((,))tmp j if Sim SWS WSQ H then(10)continue to the next iteration(to next cluster).(11)endif(12)((,))tmp j if Sim SWS WSQ W ! then(19)for each ,[1,]m c c SWS cluster m cluster do (13)((,))m j if Sim SWS WSQ W ! then(14){}matched matched m set set SWS (15)endif(16)endfor(17)endif(18)endfor(19)return matchedset For an n -size service set which is clustered into k clusters, the algorithm complexity of algorithm 5 is :(1)np n k H W W §·2 ¨¸©¹,where p is a value indicates the probability of clusters falls into the interval (,)H W .VI.S IMULATION E XPERIMENT The experiment environment is set up on a computer withIntel Xeon E5450,3.0 GHZ CPU ˈ2GB memory, Windows Server 2003 Enterprise Edition SP2. Java is adopted as theprogramming language. So far, considering that there are not any standard test collections for the algorithms mentioned,we generate the relative data randomly with the tools ,OWL-S API[7], Protégé[8],JENA API[9]. First we prepare a set of noun words for describing web services, then construct the ontology trees with Protégéand extract concepts from these ontologies using JENA API,finally we randomly create 6500 OWL-S files(may be takenas virtual services) for test, ensuring that different groups of these services come from different domain ontologies inorder to obtain better clustering quality. Before clustering,all similarities among services are computed and stored to afile(or DB table), they are loaded into a matrix in thememory while used.To measure the clustering effectiveness and theperformance of algorithm 5, we propose the following definitions:Definition VI.Time-Saved Percentage(TSP) :121:()/TSP t t t ,Where, 1t is the time spent on searching the wholeservice set (full-scan) without clustering,and 2t is the timespent on searching the clusters(clustering-scan) for thematched service set. 0TSP !means that clustering-scan is quicker than full-scan.Definition VII.Recall Ratio(RECALL) :21:/RECALL set set , Where, 1set is the matchedservice set by full-scan, and 2set is the matched service setby clustering-scan.Definition VIII.T otal Performance Index(TPI) ::TPI TSP RECALL u ,The more TPI, the better performance of the algorithm. Thus, the following two experiments are done for this paper.As shown in figure 3, with the increase of the number of clusters,on the whole, the TPI value of the algorithm keeps stable. TPI>0 means that the performance of algorithm 5 is more better and efficient than the method of full-scan.In figure 4, with the increase of the number of services,the time spent of clustering-scan is lower than that of full-scan.In figure 5, with the increase of the number of services,the TPI value indicates that the performance of algorithm 5 is far better and efficient than the method of full-scan.VII.C ONCLUSIONThis paper proposes a web service discovery method based on semantics and clustering. Firstly, similarities among services must be computed by using the semantic information of their textual descriptions and ontology concepts,then a modified K-medoids method is used to cluster service set. Lastly, the semantics-clustering-based web service discovery algorithm is presented detailedly. The experiment result indicates that the algorithm proposed is more better than that of full scan search.TABLE I. P ARAMETER V ALUES OF E XPERIMENT 1Number of services 6500 fixedNumber of clusters [5,25] dynamically changed W (in algorithm 5)0.83H (in algorithm 5)0.30jWSQ a random service from service set, keeps fixed at each queryTABLE II. P ARAMETER VALUES OF EXPERIMENT 2Numberof services [500,6500] dynamically changed with a step of500Number of clusters 15 fixed W (in algorithm 5)0.83H (in algorithm 5)0.30jWSQ a random service from service set, keeps fixed at each queryFigure 3. The TSP-RECALL-TPI values of experiment 1Figure 4. The time spent of clustering-scan and full-scanFigure 5. The TSP-RECALL-TPI values of experiment 2R EFERENCES[1]Benatallah, B., Hacid, M., Leger, Alain., Rey, C.,& Toumani, F. (2005) On Automating Web service Discovery. VLDB Journal. 14 (1):84-96[2]Colgrave, J., Akkiraju, R., & Goodwin, R. (2004). External Matchingin UDDI . ICWS'04[3]G. A. Miller, R. 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