Predicting feature interactions in component-based systems
一种新的业务冲突检测和解决方案

一种新的业务冲突检测和解决方案李旭;胡国庆【摘要】详细分析了下一代网络(Next Generation Network,NGN)中的业务冲突问题,给出了现有的各种业务冲突检测和解决方法,并对业务交互管理模块(Feature Interaction Management,FIM)进行了重新设计,针对离线业务冲突和在线业务冲突分别进行检测和解决。
针对离线业务,采用二维分析表方法进行冲突检测和解决;针对在线业务,采用循环检测算法进行冲突的检测和解决。
该架构还可以扩展其冲突检测算法,以处理新的业务冲突,可以提高冲突检测解决的成功率。
%This paper analyses the feature interactions in the next generation network,and summarizes all the existing ways of feature interactions detection and resolution.A new architecture of feature interaction management is also present in this paper.The online feature interactions and offline feature interactions are detected and resolved separately.The offline problems are dealt with by dynamic two-dimension table and the online problems by circle detection.This new architecture can expend its interaction detection algorithm to deal with new feature interactions,so it can be more effective.【期刊名称】《无线电工程》【年(卷),期】2012(042)005【总页数】4页(P12-14,26)【关键词】IMS;业务冲突;SCIM【作者】李旭;胡国庆【作者单位】中国电子科技集团公司第五十四研究所,河北石家庄050081;总参信息化部驻石家庄地区军事代表室,河北石家庄050081【正文语种】中文【中图分类】TN960 引言随着通信技术和网络技术的快速发展,能够融合多种异构网络、提供多媒体综合业务和开放网络资源能力的下一代网络体系结构逐渐形成。
多模态生成关键技术

多模态生成关键技术主要涉及以下几个方面:
1. 多模态数据的融合:多模态生成要求模型能够处理来自不同来源和格式的数据,例如文本、图像、音频和视频等。
这需要一种高效的数据融合策略,以便在不丢失信息的情况下将不同模态的数据整合在一起。
2. 多模态注意力机制:注意力机制能够让模型在处理不同模态的数据时,根据其他模态的信息来调整其输出。
在多模态生成任务中,这种机制可以帮助模型更好地理解不同模态之间的关系,从而生成更准确和连贯的输出。
3. 跨模态迁移学习:在多模态生成中,模型可能需要在不同模态之间迁移学习。
例如,一个模型可能需要从文本生成图像,或者从音频生成文本。
这需要一种跨模态迁移学习的策略,以便在不同模态之间共享知识和能力。
4. 对抗训练和鲁棒性:多模态生成任务往往涉及到对抗性攻击和噪声,例如在图像中添加干扰或修改文本。
这需要一种对抗训练和鲁棒性的策略,以便提高模型的稳健性和可靠性。
5. 可解释性和可信任性:多模态生成任务需要模型具有一定的可解释性和可信任性。
这可以通过可视化技术和元学习等方法来实现,以便让用户理解模型的决策过程并信任其输出。
总的来说,多模态生成关键技术涉及数据融合、注意力机制、迁移学习、对抗训练和可信任性等方面,需要综合运用多种方法和策略来实现高质量的多模态生成任务。
多模态生物特征融合技术研究与应用

多模态生物特征融合技术研究与应用概述多模态生物特征融合技术是指通过同时利用多个生物特征进行识别和认证的技术。
传统的生物特征识别技术常常只使用单一的生物特征,如指纹、面部或虹膜等。
然而,随着科技的发展,融合多个生物特征的技术正在逐渐成为识别和认证领域的研究热点。
本文将重点探讨多模态生物特征融合技术的研究进展和应用前景。
1. 多模态生物特征融合技术的原理与方法多模态生物特征融合技术通过综合利用多个生物特征,旨在提高识别和认证系统的准确性和可靠性。
这些生物特征可以包括指纹、面部、虹膜、声音、书写、步态等等。
生物特征的融合可以通过以下两种主要方法实现:1.1 特征级融合特征级融合主要是将不同生物特征的信息进行融合。
例如,将指纹和面部特征进行融合,可以使用融合算法将两者的特征表示进行合并,创建一个新的特征向量。
这样可以综合利用不同生物特征的优势,提高系统的准确性。
1.2 决策级融合决策级融合是通过融合不同特征的决策结果来进行最终的判断。
例如,可以分别使用指纹和虹膜进行识别,并将它们的决策结果进行融合,从而得到更可靠的识别结果。
决策级融合主要依赖于多个生物特征的独立识别算法和决策规则。
2. 多模态生物特征融合技术的研究进展多模态生物特征融合技术的研究在过去几十年中取得了显著的进展。
下面介绍几个关键的研究方向:2.1 特征选择与提取在融合不同生物特征之前,首先需要对每个特征进行选择和提取。
特征选择的目标是选取具有代表性和互补性的特征,以提高融合系统的性能。
特征提取则是从原始生物数据中提取出具有判别性的特征表示。
当前的研究主要集中在开发高效的特征选择和提取方法,以满足多模态融合的需求。
2.2 融合算法融合算法是实现多模态生物特征融合的关键。
不同生物特征的融合算法可以分为基于特征的和基于决策的两种类型。
基于特征的融合算法通过将不同特征的表示进行融合,从而得到一个综合的特征向量,进而进行识别和认证。
而基于决策的融合算法则通过融合不同特征的决策结果,从而得到最终的判断。
如何使用前馈神经网络进行自然语言生成(八)

前馈神经网络是一种常见的人工智能模型,它在自然语言生成领域有着广泛的应用。
自然语言生成是人工智能领域的一个重要分支,它涉及到计算机系统如何理解和生成人类语言。
在这篇文章中,我们将探讨如何使用前馈神经网络进行自然语言生成,并介绍其中的一些关键概念和技术。
神经网络是一种模仿人类大脑神经元运作方式的计算模型。
前馈神经网络是其中的一种类型,它由一个或多个神经元层组成,每一层都与下一层全连接。
这种网络结构使得神经网络能够从输入数据中提取特征并进行预测。
在自然语言生成中,前馈神经网络可以被用来生成文本、回答问题、进行对话等任务。
首先,为了使用前馈神经网络进行自然语言生成,我们需要准备一个数据集。
这个数据集可以是大量的文本数据,比如文章、小说、对话记录等。
然后,我们需要对数据进行预处理,包括分词、去除停用词、标记化等操作。
接下来,我们可以利用前馈神经网络模型来训练这些数据。
在训练过程中,我们需要将文本数据转换成数字向量形式,以便于神经网络的处理。
这个过程被称为嵌入(embedding),它可以将文本数据映射到一个高维空间中。
这样一来,我们就可以将文本数据输入到神经网络中进行训练。
在训练过程中,神经网络会不断地调整模型参数,使得模型能够更好地拟合数据。
在训练完成后,我们就可以使用训练好的前馈神经网络模型来进行自然语言生成。
这时,我们可以将一个输入文本传入模型中,然后模型会生成一个对应的输出文本。
这个输出文本可以是对输入文本的回答、对话的继续、文章的续写等等。
当然,在实际应用中,使用前馈神经网络进行自然语言生成还涉及到许多其他技术和方法。
比如,我们可以使用注意力机制(attention mechanism)来提高模型的生成能力,使得模型能够更好地理解输入文本的上下文信息。
我们还可以使用循环神经网络(recurrent neural network)来处理长文本序列的生成任务,以及使用生成对抗网络(generative adversarial network)来提高生成文本的质量。
AcceptanceList:验收单

Regular Paper
B219 Sudeep Roy, Akhil Kumar, and Ivo Pro vazník, Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecule b ased inhibitors against metallothionein-III to study Alzheimer’s disease
B357 Qiang Yu, Hongwei Huo, Xiaoyang Chen, Haitao Guo, Jeffrey Scott Vitter, and J un Huan, An Efficient Motif Finding Algorithm for Large DNA Data Sets
B244 Ilona Kifer, Rui M. Branca, Ping Xu, Janne Lehtio, and Zohar Yakhini, Optimizing analytical depth and cost efficiency of IEF-LC/MS proteomics
B276 Yuan Ling, Yuan An, and Xiaohua Hu, A Symp-Med Matching Framework for Modeling and Mining Symptom and Medication Relationships from Clinical Notes
B333 Mingjie Wang, Haixu Tang, and Yu zhen Ye, Identification and characterization of accessory genomes in bacterial species b ased on genome comparison and metagenomic recruitment
大学英语综合教程1UNIT

True/False Statements
Students listen to a recording and decide whether a given statement is true or false.
Analysis of Listening Materials
Identifying the main idea
• Retrieving specific information: The ability to quickly and accurately identify and retrieve specific information from a given audio material, such as names, dates, or numbers.
Unit 2
Listening and Speaking Skills: 本单元将重点培养学生的英语听说技能。学生将通过听录音、看视频等方式练习听 力和口语表达,提高英语交际能力。
Unit 3
Reading and Writing Skills: 本单元将重点培养学生的英语阅读和写作技能。学生将通过阅读英文文章、 写英文短文等方式练习阅读和写作能力,提高英语书面表达能力。
Comprehensive College English Course 1 Unit
目录
• Course Introduction • Listening comprehension • reading comprehension • Oral expression • Writing skills • Cultural background knowledge
Gapfill Exercises
Provide gapfill exercises where students fill in missing information based on the text.
多模态生物特征

多模态生物特征
多模态生物特征是指采用多种生物特征识别技术来对个体进行
身份识别或辨认。
这些生物特征可以包括指纹、人脸、虹膜、声纹、手掌纹、视网膜等。
多模态生物特征识别技术的优势在于可以提高识别的准确性和可靠性,同时也可以降低识别误差率和欺骗率。
多模态生物特征识别技术在安防领域、公安领域、金融领域和医疗领域等有着广泛的应用。
在安防领域,多模态生物特征识别技术可以用于门禁系统、智能家居系统等场景,以提高安全性和便捷性。
在公安领域,多模态生物特征识别技术可以用于刑侦破案、警务管理等方面,以提高工作效率和准确性。
在金融领域,多模态生物特征识别技术可以用于身份验证、交易授权等场景,以提高交易的安全性和可靠性。
在医疗领域,多模态生物特征识别技术可以用于患者身份验证、医疗记录管理等方面,以提高医疗服务的质量和可靠性。
与传统的单一生物特征识别技术相比,多模态生物特征识别技术的识别准确性更高,同时也能够克服单一生物特征识别技术的局限性。
但是,多模态生物特征识别技术的应用还面临着技术成本高、隐私保护等问题,需要在实际应用中加以解决。
- 1 -。
基于注意力机制的人脸表情识别迁移学习方法

2021年3月计算机工程与设计Mar.2021第42卷第3期COMPUTER ENGINEERING AND DESIGN Vol.42No.3基于注意力机制的人脸表情识别迁移学习方法亢洁,李思禹+(陕西科技大学电气与控制工程学院,陕西西安710021)摘要:针对现有的在人脸表情识别中应用的卷积神经网络结构不够轻量,难以精确提取人脸表情特征,且需要大量表情标记数据等问题,提出一种基于注意力机制的人脸表情识别迁移学习方法。
设计一个轻量的网络结构,在其基础上进行特征分组并建立空间增强注意力机制,突出表情特征重点区域,利用迁移学习在目标函数中构造一个基于log-Euclidean距离的损失项来减小迁移学习中源域与目标域之间的相关性差异。
在数据集JAFFE和CK十上的实验结果表明,该方法相比其它人脸表情识别方法具有更优的识别能力&关键词:人脸表情识别;卷积神经网络;注意力机制;特征分组;迁移学习中图法分类号:TP391文献标识号:A文章编号:1000-7024(2021)03-0797-08doi:10.16208/j.issnl000-7024.2021.03.029Transfer learning method for facial expression recognitionbased on attention mechanismKANG Jie,LI Si-yu;(School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi'an710021,China) Abstract:To solve the problems that the existing convolutional neural network structure used in facial expression recognition is not lightweight enough to extract facial expression features accurately,and that a large amount of expression labeled data is required&a transfer learning method for facial expression recognition based on attention mechanism was proposed.A lightweight networkstructurewasdesigned andfeaturegroupsweregroupedonthebasisofit afterwardsaspatialenhanceda t ention mechanism was established to highlight the key areas of facial expression features.At the same time&transfer learning was used to construct a loss term based on log-Euclidean distance in the objective function to reduce the correlation difference between the source domain and the target domain.Experimental results on the data sets JAFFE and CK+show that the proposed method has better recognition ability than other facial expression recognition methods.Key words:facial expression recognition;convolutional neural network;attention mechanism;feature grouping;transfer learning1引言人脸表情⑴2识别最核心的部分是特征提取。
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Predicting Feature Interactions in Component-Based SystemsJudith Stafford and Kurt WallnauSoftware Engineering InstituteCarnegie Mellon UniversityPittsburgh,PA,USA+1412-268-5051+1412-268-3265jas@ kcw@ABSTRACTSoftware component technologies support assembly of systems from binary component implementations that may have been created in isolation from one and another.While these technologies provide assistance in wiring components together they fail to provide support for predicting the quality and behavior of configurations of components prior to actual system composition.We believe that all quality attributes manifested at runtime are emergent properties of component interactions, and hence arise as a consequence of planned,or unplanned,interactions among component features.In this paper we discuss the affinities among software architecture,software component technology,compositional reasoning,component property measurement,and component certification for the purpose of mastering component feature interaction,and for developing component technologies that support compositional reasoning,and that guarantee that design-time reasoning assumptions are preserved in deployed component assemblies.1.IntroductionSoftware component technologies provide a means for composing systems quickly from precom-piled parts.Technologies such as CORBA and COM have been developed to support composition of components that are created in isolation,perhaps by different people in different environments and in different languages.However,current component-based technologies do not support reasoning about system quality attributes,e.g.,performance,reliability,and safety.The quality of a software system is,in part,a function of the degree to which its features interact in predictable ers view systems from the perspective of system features whereas developers view systems in terms of functional decomposition into components.The former is a view in the problem domain;the latter is associated with the solution domain.Turner et al.study the relationship between these two domains as they define a conceptual framework for feature engineering[23].Quality attrib-utes such as performance,reliability,and safety are emergent properties of patterns of interaction in an assembly of components.Ultimately,all such patterns of interaction depend upon one or more features. Therefore,many critical system quality attributes are expressions of component feature interaction.In-deed,a failure to achieve system quality attributes may be attributable to unexpected feature interaction. We suggest that predicting and ensuring system-level quality attributes and controlling component fea-ture interactions are closely related.Moreover,we contend that the solution to both problems(to the In Proceedings of the Workshop on Feature Interaction of Composed Systems,in conjunction with the15th European Conference on Object-Oriented Programming,Budapest,Hungary,June2001.extent they are distinct)will be found in the form of compositional rmally,composi-tional reasoning posits that if we know something about the properties of two components,c1and c2,then we can define a reasoning function f such that f(c1,c2)yields a property of an assembly comprisingc1and c2.Many would argue that compositional reasoning is the holy grail of software engineering:a noblebut ultimately futile quest for an unobtainable objective.This argument usually has as its unspokenpremise that only a fully formal and rigorous f(c1,c2)will do.If we accept this premise,then progresswill indeed be slow.Instead,we suggest that it is possible to adopt a more incremental approach that in-volves many levels of formality and rigor.To begin,we suggest that three interlocking questions mustbe answered:1.What system quality attributes are developers interested in predicting?2.What analysis techniques exist to support reasoning about these quality attributes,and what compo-nent properties do they require?3.How are these component properties specified,measured,and certified?Since compositional reasoning ultimately depends upon the types of component properties that canbe measured,these questions are interdependent.Therefore,answers to these questions are mutuallyconstraining.Further,answering these questions will bean ongoing process:new prediction models will require Array new and/or improved component measures,which will inturn lead to more accurate prediction,and to demand forbetter or additional prediction models.The objective of our work in predictable assemblyfrom certifiable components(PACC)is to demonstratehow component technology can be extended to supportcompositional reasoning.To do this,PACC integratesideas from research in the areas of software architecture,trusted components,and software component technology.The rest of the paper is organized as follows:We be-gin by describing two areas of related work,architecture-based analysis and component certification.The formerdeals with issues antecedent to compositional reasoning,the latter with issues of component trust and specification.We then describe a reference model for using component technology to link compositional reasoningwith component certification,and close with a summary of our position.2.Background and Related WorkThe ideas of architectural analysis and component certification are not new but,to the best of ourknowledge,their integration is.In this section we describe prior work in these areas and discuss theirrelationship to our work on predictable assembly.2.1Architectural AnalysisSoftware architecture-based analysis provides a foundation for reasoning about system completeness and correctness early in the development process and at a high level of abstraction.To date,research in the area has focused primarily on the use of architecture description languages(ADLs)as a substrate for analysis algorithms.The analysis algorithms that have been developed for these languages have,in gen-eral,focused on correctness properties,such as liveness and safety[2,10,14,16].However,other types of analysis are also appropriate for use at the architecture level and are currently the focus of research projects.Examples include system understanding[13,21,27],performance analysis[3,20],and archi-tecture-based testing[4,24].One still unresolved challenge for architecture technology is to bridge the gap between architectural abstractions and implementation.Specification refinement is one approach that seeks to prove properties of the relationship between abstract and more concrete specifications,ei-ther across heterogeneous design notations[8]or homogeneous notations[17].2.2Component CertificationThe National Security Agency(NSA)and the National Institute of Standards and Technology (NIST)used the trusted computer security evaluation criteria(TCSEC),a.k.a.“Orange Book.1”as the basis for the Common Criteria2,which defines criteria for certifying security features of components. Their effort was not crowned with success,at least in part because it defined no means of composing criteria(features)across classes of component.The Trusted Components Initiative(TCI)3is a loose af-filiation of researchers with a shared heritage in formal specification of interfaces.Representative of TCI is the use of pre/post conditions on APIs[15].This approach does support compositional reasoning, but only about a restricted set of behavioral properties of assemblies.Quality attributes,such as secu-rity,performance,availability,and so forth,are beyond the reach of these assertion languages.Voas has defined rigorous mathematical models of component reliability based on statistical approaches to testing [26],but has not defined models of composing reliability mercial component vendors are not inclined to formally specify their component interfaces,and it is not certain that it would be cost ef-fective for them to do so.Shaw observed that many features of commercial components will be discov-ered only through use.She proposed component credentials as an open-ended,property-based interface specification[19].A credential is a triple<attribute,value,knowledge>,which asserts that a component has an attribute of a particular value,and that this value is known through some means.Credentials re-flect the need to address component complexity,incomplete knowledge,and levels of confidence(or trust)in what is known about component properties,but do not go beyond notational concepts.There-fore,despite many efforts,fundamental questions remain.What does it mean to trust a component?Still more fundamental:what ends are served by certifying(or developing trust)in these properties?3.PACC ApproachThe PACC approach is based on two fundamental premises:first,that system quality attributes are emergent properties adhere to patterns of interaction among components,and,second,that software component technology provides a means of enforcing predefined and designed interaction patterns,thus 1/tpep/library/tcsec/index.html2/cc/3/facilitating the achievement of system quality attributes by construction.3.1Premises of PACCThe study of software architectural styles supports the first premise.An architectural style is a recur-ring design pattern,usually expressed as a set of component types and constraints on their allowable in-teractions[1,7].Architectural styles provided the first link between structural design constraints and system properties.For example,the pipe and filter style yields systems that can be easily restructured. However,the link between system-level quality attribute and architectural style is informal and subjec-tive.To better formalize this link,Klein et al.have developed attribute-based architectural style (ABAS)[11].Informally,ABAS associates one or more attribute reasoning frameworks with an archi-tectural style.An attribute reasoning framework consists of a response variable,one or more stimuli variables,and an analysis model that links stimuli to response.ABAS is a key foundation for PACC.It provides the conceptual foundation for defining and analyzing the properties of assemblies(the response variables).It also provides the link between system properties and component properties(stimuli vari-ables).Component technology provides the means to realize ABAS concepts in software and,in fact,the concept of architectural style is quite amenable to a component-based interpretation[4].In or view,a component technology can play an analogous role to predictable assembly that structured programming languages and compilers played for structured programming—it limits the freedom of designers(pro-grammers)so that the resulting design(program)is more readily analyzed.In one of many possible ex-amples,the Enterprise JavaBeans(EJB)specification defines component types,such as session and en-tity beans,4and constraints on how they interact with one another,with client programs,and with the runtime environment.However serendipitous it may be,it is clear that EJB specifies an architectural style.It is our thesis that analogous component technologies can be defined that go still further to in-clude the additional style constraints needed to support ABAS-based reasoning.The result will be com-ponent technologies that support design-time quality attribute analysis,and guarantee,by construction, that the assumptions underlying these analyses are preserved in an assembly of components.At this point in our research,we are noncommittal about what a prediction-enabled component tech-nology should look like.However,we postulate the outlines of such a technology with the following reference model.3.2A Conceptual Reference Model for PACCComponent technologies comprise four levels of abstraction.We generally depict this as a layered reference model,but omit the graphic here for brevity.We describe this model beginning with the con-crete and work our way up to the abstract:–Assembly.The most concrete level of our reference model comprises a set of components whose resources(features)have been bound in such a way as to enable their interaction.–Assembly specification.At this level we find component specifications in place of components, and specifications of their interactions.It is at this level of abstraction that attribute analysis and 4Components are denoted as beans in EJB.prediction occur.–Types.At this level we specify component and connector types and their features,thereby defininga vocabulary to support design,that is,assembly specification and attribute analysis and prediction.–Metatypes.At this level one defines what it means to be a component type,or a connector type,or an assembly type,and define any constraints that must hold for all types to enable attribute predic-tion.3.3Reference Model InstantiationsWe have explored two complementary approaches to instantiate the PACC reference model:one that assumes that attribute reasoning models will be integrated into a component technology,and one that assumes the converse.We refer to the first as a component-centric instantiation,and the second as an architecture-centric instantiation.We have validated both approaches with(admittedly simple)proofs of feasibility.For the component-centric instantiation we used the WaterBeans[18]technology augmented with latency prediction.For the architecture-centric instantiation we used a security ABAS for attribute reasoning,and a Web-based enterprise system for the component technology(from the case study found in[25]).Table1summarizes the mapping of these instantiations to the reference model.Table1:Complementary Instantiation sModel Level Component Centric Architecture CentricMetatypes Properties shared by all WaterBeans compo-nents,e.g.,typed ports,connectors,and con-nection rules.Defined the latency attributeand associated it with the componentmetatype.A simple,behavior-less ADL of compo-nents,interactions,assemblies,and their properties.Analogous to a simplified meta-model of UML collaboration diagrams.Types Component type definitions for CD audiosampling and wave manipulation.Types in-troduced the additional Boolean property foraperiodic or periodic behavior,and,if peri-odic,the execution period.A quantitativemodel for end-to-end latency is also definedhere.Types that represent basic-level categories for analysis of security properties, e.g., peers,trusted computing base,key,crypto-graphic provider,threat agent,data asset. Each category is mapped to an element in the simple ADL.Specification A topology of audio components annotatedwith their latency attributes;assembly latencyprediction occurred here.Patterns of interaction comprising only basic categories,where patterns exhibit desired security rmal rules of attribute preserving pattern refinement.Assembly A benchmarked assembly,allowing compari-son of predicted versus actual assembly la-Pattern refinements where each basic cate-gory has been refined to(bound to)a moretency.specific category,ultimately grounding inspecific component and interaction features.4.Closing ThoughtsIn closing,we take the position that the identification of feature interactions in complex systems is closely tied to analysis of system-level quality attributes.Quality attributes of systems are a product of properties associated with both the components that comprise a system and their patterns of interaction. Designing systems as assemblies of components based on architectural styles produces systems that are analyzable by design.We are exploring the application compositional reasoning techniques to assem-blies of components in order to predict properties of systems.It is our belief that this line of work can support the identification of the potential for feature interaction before actual system assembly.5.AcknowledgementsThis work was supported by the United States Department of Defense.6.References1.G.D.Abowd,R.Allen and D.Garlan,Formalizing Style to Understand Descriptions of Software Architec-ture,ACM Transactions on Software Engineering and Methodology,Vol.4,No.4,October,1995,pp.319-364.2.R.Allen and D.Garlan,A Formal Basis for Architectural Connection,ACM Transactions on Software Engi-neering and Methodology,Vol.6,No.3,July.1997,pp.213-249.3.S.Balsamo,P.Inverardi and C.Mangano,An Approach to Performance Evaluation of Software Architec-tures,Proceedings of the1998Workshop on Software and Performance,October.1998,pp.77-84.4. F.Bachman,L.Bass,C.Buhman,ella-Dorda,F.Long,J.Robert,R.Seacord and K.Wallnau,VolumeII:Technical Concepts of Component-Based Software Engineering,Technical Report CMU/SEI-2000-TR-08, Software Engineering Institute,Carnegie Mellon University,Pittsburgh,PA.5. A.Bertolino,P.Inverardi,H.Muccini and A.Rosetti,An Approach to Integration Testing Based on Archi-tectural Descriptions,Proceedings of the1997International Conference on Engineering of Complex Com-puter Systems,September.1997,pp.77-84.6. E.Dijkstra,Structured Programming,Software Engineering,Concepts and Techniques,J.Buxton et al.(eds.),Van Nostrand Reinhold,1976.7. D.Garlan and M.Shaw,An Introduction to Software Architecture,Advances in Software Engineering andKnowledge Engineering,V.Ambriola and G.Tortora(eds.),World Scientific,1993.8. F.Gilham,R.Reimenschneider,V.Stavridou,Secure Interoperation of Secure Distributed Databases:An Ar-chitecture Verification Case Study,Proceedings of World Congress on Formal Methods(FM’99),Vol.I, LNCS1708,pp.701-717,1999,Springer-Verlag,Berlin.9.G.T.Heineman and W.T.Councill(eds.),Component-Based Software Engineering:Putting the Pieces To-gether,Addison-Wesley,Reading,Massachusetts,2001.10.P.Inverardi,A.L.Wolf,and D.Yankelevich,Static Checking of System Behaviors Using Derived Compo-nent Assumptions,ACM Transaction on Software Engineering and Methodology,Vol.9,No.3,July.2000, pp.238-272.11.M.Klein and R.Kazman,Attribute-Based Architectural Styles,Technical Report CMU/SEI-99-TR-022,Software Engineering Institute,Carnegie Mellon University,Pittsburgh,PA.12.M.Klein,T.Ralya,B.Pollak,R.Obenza and M.G.Harbour,A Practitioner’s Handbook for Real-TimeAnalysis,Kluwer Academic Publishers,1993.13.J.Kramer and J.Magee,Analysing Dynamic Change in Software Architectures:A Case Study,Proceedingsof the4th International Conference on Configurable Distributed Systems,May1998,pp.91-100.14.J.Magee,J.Kramer,and D.Giannakopoulou,Analysing the Behaviour of Distributed Software Architec-tures:A Case Study,Proceedings of the5th IEEE Workshop on Future Trends of Distributed Computing Sys-tems,October.1997,pp.240-247.15.B.Meyer,Object-Oriented Software Construction,Second Edition,Prentice Hall,London,1997.16.G.Naumovich,G.S.Avrunin,L.A.Clarke,and L.J.Osterweil,Applying Static Analysis to Software Archi-tectures,Proceedings of the6th European Software Engineering Conference Held Jointly with the5th ACM SIGSOFT Symposium on Foundations of Software Engineering Lecture Notes in Computer Science,No.1301,Springer-Verlag,1997,pp.77-93.17.J.Phillips and B.Rumpe,Refinement of Information Flow Architectures,Proceedings of the1st IEEE Inter-national Conference on Formal Engineering Models,pp.203-212,1997.18.D.Plakosh,D.Smith and K.Wallnau,Builder’s Guide for WaterBeans Components,Technical ReportCMU/SEI-99-TR-024,Software Engineering Institute,Carnegie Mellon University,Pittsburgh,PA.19.M.Shaw,Truth vs Knowledge:The Difference Between What a Component Does and What We Know ItDoes,Proceedings of the8th International Workshop on Software Specification and Design,March1996. 20.B.Spitznagel,D.Garlan,Architecture-Based Performance Analysis,Proceedings of the1998Conference onSoftware Engineering and Knowledge Engineering,San Francisco,California,1998.21.J.A.Stafford and A.L.Wolf,Architecture-Level Dependence Analysis in Support of Software Maintenance,Proceedings of the Third International Workshop on Software Architecture,November.1998,pp.129-132. 22.C.Szyperski,Component Software Beyond Object-Oriented Programming,Addison-Wesley,Boston,Massa-chusetts and ACM Press,1998.23.C.R.Turner,A.Fuggetta,vazza,and A.L.Wolf,A Conceptual Basis for Feature Engineering,Journalof Systems and Software,Vol.49,No.1,December1999,pp.3-15.24.M.E.R.Vieira,S.Dias and D.J.Richardson,Analyzing Software Architectures with Argus-I,Proceedings ofthe2000International Conference on Software Engineering,June2000,pp.758-761.25.K.Wallnau,S.Hissam and R.Seacord,Building Systems from Commercial Components,Addison WesleyLongman,To Appear July,2001.26.J.Voas and J.Payne,Dependability Certification of Software Components,Journal of Systems and Software,No.52,2000,pg.165-172.27.J.Zhao,Using Dependence Analysis to Support Software Architecture Understanding,New Technologies onComputer Software,September1997,pp.135-142.8。