信号处理应用的可视化建模与模型转换实现

针对数据流驱动的信号处理应用的可视化建模与模型转换问题,采用通用建模工具GME建立信号处理应用的可视化建模环境,并设计模型解析器实现图形化模型到XML架构模型的转换。参照GME提供的信号处理应用元模型,引入了描述具体信号处理应用的属性特征,并扩展了建模元素,构建了面向信号处理应用的可视化建模环境。在GME的解析器开发框架下,采用元生成器方法完成模型解析器的创建,实现模型解析。应用实例测试结果表明,此方法可以完成对信号处理应用的可视化建模与模型转换,提高信号处理应用的开发效率。     

网上虚拟实验室的设计与实现

提出了基于Java 3D技术网上虚拟实验室的设计模型。Java 3D作为Java在图形方面的扩展，它是基于Open—GL等底层的AFI，并结合了Java语言的网络功能，能很好的解决网络跨平台环境的三维可视化问题，并具有很强的交互功能。另一方面关于场景的建模我们采取的是分层建模。通过分层建模有效克服了模型数据量大而产生的客户端系统负担重等问题。     

一种基于概念模型的切点定义方法

针对面向方面编程中存在的切点软化问题,提出一种可视化的切点定义方法,切点定义依据一种概念模型而不再依赖于基础程序的具体结构,实现了切点定义与基础程序的解耦,提高了切点鲁棒性。概念模型是对AspectJ切点表达式的可视化的抽象概括,通过扩展UML元模型实现,文中称之为切点模型。同时,扩展了Rational Rose,使其支持切点模型的建模,并实现了由切点模型自动生成方面框架代码。     

企业时态化建模中的元模型研究

为了支持企业历史信息等与时间特征相关的建模,提出了包括日历对象、时间属性域类型、时间戳等在内的基本元模型。利用这些基本元模型,建模者可以在现有企业模型的基础上实现基于时间特征的时态化扩展建模,从而提高企业模型及其应用系统的弹性。     

支持CMMI的软件过程元模型的研究与实现

为解决软件组织在软件项目开发过程中难以实施CMMI(capability maturity model integration)过程改进的问题,基于模型驱动架构的思想和技术,提出了一种"裁剪+重型扩展+轻型扩展"的元模型建模策略.给出了该建模策略下的具体实施步骤和方法,实现了一种支持CMMI过程改进的软件过程元建模,同时给出了基于该元模型的建模实例.实验结果表明,该元模型有助于软件组织建立体现其组织特征并有效支持符合CMMI框架的软件过程用户模型.     

一种基于Petri网的工作流元模型研究

Petri网是建立离散系统模型有力的数学工具。为了解决基本Petri网建模能力不足的问题,Aalst提出了有代表性的WF-Net模型。提出的HSC-PN工作流元模型,基于WF-Net扩展,增加了模型元素静态属性、数据流全局共享和增强的活动块等特性。详细阐述了HSC-PN元模型的DTD文件规范,分析了该模型对工作流模式的支持、触发机制及验证方式。通过可视化的建模工具,生成符合DTD文件规约的流程模板XML文件表示。HSC-PN元模型具有更丰富的元素描述信息,简化了流程模型,具有更强的建模能力。     

基于 UML扩展的面向方面建模 *

面向方面编程（AOP）把横切关注点从系统中分离,解决了面向对象编程（OOP）中代码纠缠、散射的问题。但在面向方面建模中,横切方面没有得到很好的支持。首先,在统一建模语言（UML）元模型的基础上利用类图建立了整体框架模型,设计了核心类、方面、横切元素及其之间的关系;其次,利用UML扩展机制为切点、通知、方面等主要元素建立了新的模型,设计了它们与方面之间新的关联关系并为其定义了语义。通过UML扩展,使得UML能够直接表达面向方面概念,支持面向方面建模设计,实现了面向方面模型的可视化和可辨别性;整体框架模型使得面向方面整体结构清晰,各个核心元素之间关系明确,为面向方面建模提供了一个整体设计平台,提高了面向方面软件设计的模块化,增强了代码的重用性和系统的可维护性。     

Java 3D中的Text2D的扩展与应用

Java3D中Text2D类虽然能生成二维文本,但由于文本的字体、字号、颜色在文本生成后就不可改变,字的排列也只有“从左向右”方式。所以,Text2D不能满足交互式建立虚拟环境中二维文本的需要。Java3D中的Text3D类支持交互式地建立文本,但Text3D的三维文本却不能退化为二维文本,这使得Text3D不能替代Text2D。所以,有必要扩展Text2D类以支持交互地建立虚拟环境中二维文本。文章给出实现这一扩展的关键技术,为用Java3D开发交互式虚拟环境建模器的二维文本生成功能提供支持。扩展的Text2D在智能型虚拟汽车驾驶道路环境建模器中的使用表明它是有效的。     

基于网格服务的三维城市模型的可视化研究

对三维城市的建模方法进行了讨论，提出了一种网格环境下基于SOAP（Simple Object Access Protocol）消息包的三维空间数据表达方法，并通过动态网格服务的方式为客户端的三维可视化提供数据源，通过解析SOAP消息包便可得到三维可视化所需的矢量数据、属性数据以及纹理数据等，再通过Java和Java3D技术实现城市模型的三维显示和客户端的交互操作。最后，在Globus网格环境下给出了基于网格服务的三维城市模型可视化的实验例子。     

基于XML&WEB的系统建模与仿真方法的研究

XML为Web提供了一种新的组织信息和知识的途径。在基于Web的建模与仿真中,使用XML,“文档”这一概念可以扩展为对模型信息的封装。该文利用XML技术,设计了Web仿真系统建模的模型规范：建模可视化规范(MVL)和模型的可扩展标志语言(MXL)。并在此基础上,构建了模型融合的体系结构,形成了基于XML的Web仿真系统建模与仿真一体化方法。     

基于Annotation的Java单元测试框架

注解（Annotation）在广泛应用于企业级的Java框架中,注解并不影响代码的语义,但能为系统或容器提供大量的信息,使企业级的应用配置变得更加简洁。文章对如何利用Java的Annotation特性来实现单元测试自动化进行了探讨与实践。     

Supervised learning of semantic classes for image annotation and retrieval

A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning     

@Java: Bringing a richer annotation model to Java

The ability to annotate code and, in general, the capability to attach arbitrary meta-data to portions of a program are features that have become more and more common in programming languages.Annotations in Java make it possible to attach custom, structured meta-data to declarations of classes, fields and methods. However, the mechanism has some limits: annotations can only decorate declarations and their instantiation can only be resolved statically.With this work, we propose an extension to Java (named @Java) with a richer annotation model, supporting code block and expression annotations, as well as dynamically evaluated members. In other words, in our model, the granularity of annotations extends to the statement and expression level and annotations may hold the result of runtime-evaluated expressions.Our extension to the Java annotation model is twofold: (i) we introduced block and expression annotations and (ii) we allow every annotation to hold dynamically evaluated values. Our implementation also provides an extended reflection API to support inspection and retrieval of our enhanced annotations.     

Modeling Human Judgment of Digital Imagery for Multimedia Retrieval

The application of machine learning techniques to image and video search has been shown to boost the performance of multimedia retrieval systems, and promises to lead to more generalized semantic search approaches. In particular, the availability of large training collections allows model-driven search using a substantial number of semantic concepts. The training collections are obtained in a manual annotation process where human raters review images and assign predefined semantic concept labels. Besides being prone to human error, manual image annotation is biased by the view of the individual annotator because visual information almost always leaves room for ambiguity. Ideally, several independent judgments are obtained per image, and the inter-rater agreement is assessed. While disagreement between ratings bears valuable information on the annotation quality, it complicates the task of clearly classifying rated images based on multiple judgments. In the absence of a gold standard, evaluating multiple judgments and resolving disagreement between raters is not trivial. In this paper, we present an approach using latent structure analysis to solve this problem. We apply latent class modeling to the annotation data collected during the TRECVID 2005 Annotation Forum, and demonstrate how to use this statistic to clearly classify each image on the basis of varying numbers of ratings. We use latent class modeling to quantify the annotation quality and discuss the results in comparison with the well-known Kappa inter-rater agreement measure.     

Reusable model transformations

Model transformations written for an input metamodel may often apply to other metamodels that share similar concepts. For example, a transformation written to refactor Java models can be applicable to refactoring UML class diagrams as both languages share concepts such as classes, methods, attributes, and inheritance. Deriving motivation from this example, we present an approach to make model transformations reusable such that they function correctly across several similar metamodels. Our approach relies on these principal steps: (1) We analyze a transformation to obtain an effective subset of used concepts. We prune the input metamodel of the transformation to obtain an effective input metamodel containing the effective subset. The effective input metamodel represents the true input domain of transformation. (2) We adapt a target input metamodel by weaving it with aspects such as properties derived from the effective input metamodel. This adaptation makes the target metamodel a subtype of the effective input metamodel. The subtype property ensures that the transformation can process models conforming to the target input metamodel without any change in the transformation itself. We validate our approach by adapting well known refactoring transformations (Encapsulate Field, Move Method, and Pull Up Method) written for an in-house domain-specific modeling language (DSML) to three different industry standard metamodels (Java, MOF, and UML).     

Improving the accuracy of UML metamodel extensions by introducing induced associations

In the process of extending the UML metamodel for a specific domain, the metamodel specifier introduces frequently some metaassociations at MOF level M2 with the aim that they induce some specific associations at MOF level M1. For instance, if a metamodel for software process modelling states that a “Role” is responsible for an “Artifact”, we can interpret that its specifier intended to model two aspects: (1) the implications of this metaassociation at level M1 (e.g., the specific instance of Role “TestEngineer” is responsible for the specific instance of Artifact “TestPlans”); and (2) the implications of this metaassociation at level M0 (e.g., “John Doe” is the responsible test engineer for elaborating the test plans for the package “Foo”). Unfortunately, the second aspect is often not enforced by the metamodel and, as a result, the models which are defined as its instances may not incorporate it. This problem, consequence of the so-called “shallow instantiation” in Atkinson and Kühne (Procs. UML’01, LNCS 2185, Springer, 2001), prevents these models from being accurate enough in the sense that they do not express all the information intended by the metamodel specifier and consequently do not distinguish metaassociations that induce associations at M1 from those that do not. In this article we introduce the concept of induced association that may come up when an extension of the UML metamodel is developed. The implications that this concept has both in the extended metamodel and in its instances are discussed. We also present a methodology to enforce that M1 models incorporate the associations induced by the metamodel which they are instances from. Next, as an example of application we present a quality metamodel for software artifacts which makes intensive use of induced associations. Finally, we introduce a software tool to assist the development of quality models as correct instantiations of the metamodel, assuring the proper application of the induced associations as required by the metamodel.     

User interfaces metamodel based on graphs

Information systems are widely used in all business areas. These systems typically integrate a set of functionalities that implement business rules and maintain databases. Users interact with these systems and use these features through user interfaces (UI). Each UI is usually composed of menus where the user can select the desired functionality, thus accessing a new UI that corresponds to the desired feature. Hence, a system normally contains multiple UIs. However, keeping consistency between these UIs of a system from a visual (organisation, component style, etc.) and behavioral perspective is usually difficult. This problem also appears in software production lines, where it would be desirable to have patterns to guide the construction and maintenance of UIs. One possible way of defining such patterns is to use model-driven engineering (MDE). In MDE, models are defined at different levels, where the bottom level is called a metamodel. The metamodel determines the main characteristics of the models of the upper levels, serving as a guideline. Each new level must adhere to the rules defined by the lower levels. This way, if anything changes in a lower level, these changes are propagated to the levels above it. The goal of this work is to define and validate a metamodel that allows the modeling of UIs of software systems, thus allowing the definition of patterns of interface and supporting system evolution. To build this metamodel, we use a graph structure. This choice is due to the fact that a UI can be easily represented as a graph, where each UI component is a vertex and edges represent dependencies between these components. Moreover, graph theory provides support for a great number of operations and transformations that can be useful for UIs. The metamodel was defined based on the investigation of patterns that occur in UIs. We used a sample of information systems containing different types of UIs to obtain such patterns. To validate the metamodel, we built the complete UI models of one new system and of four existing real systems. This shows not only the expressive power of the metamodel, but also its versatility, since our validation was conducted using different types of systems (a desktop system, a web system, mobile system, and a multiplatform system). Moreover, it also demonstrated that the proposed approach can be used not only to build new models, but also to describe existing ones (by reverse engineering).     

Ann: A domain-specific language for the effective design and validation of Java annotations

This paper describes a new modelling language for the effective design and validation of Java annotations. Since their inclusion in the 5th edition of Java, annotations have grown from a useful tool for the addition of meta-data to play a central role in many popular software projects. Usually they are not conceived in isolation, but in groups, with dependency and integrity constraints between them. However, the native support provided by Java for expressing this design is very limited.To overcome its deficiencies and make explicit the rich conceptual model which lies behind a set of annotations, we propose a domain-specific modelling language. The proposal has been implemented as an Eclipse plug-in, including an editor and an integrated code generator that synthesises annotation processors. The environment also integrates a model finder, able to detect unsatisfiable constraints between different annotations, and to provide examples of correct annotation usages for validation. The language has been tested using a real set of annotations from the Java Persistence API (JPA). Within this subset we have found enough rich semantics expressible with Ann and omitted nowadays by the Java language, which shows the benefits of Ann in a relevant field of application.