Information and documentation services available on the Internet through web servers are growing in an exponential manner. The logical evolution of the Internet over the last 10 years has been producing a replacement of static web pages and documents by dynamically generated documents. This is due both to user interaction with work processes and flows defined by service creators and to the availability of growing information repositories. This has meant a progressive evolution from a concept of web page publishing which was quite simple in its origins to more complex and differentiated schemes relying on procedures and techniques based on information management. The increasing complexity of services and systems supporting them has made it necessary to formulate a theoretical and practical corpus capable of combining classical information management techniques within organizations with the particular features of the digital environment.
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We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG) from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
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The Vision 7.1 Development Module is for engineers and scientists who are developing machine vision and scientific imaging applications. The development module includes NI Vision Assistant 7.1—an interactive environment for developers who need to quickly prototype vision applications without programming—and IMAQ Vision 7.1 for LabVIEW, LabWindows™/CVI™, and Microsoft Visual Basic—a library of powerful functions for image processing. In addition, the development module includes NI-IMAQ 3.0, the National Instruments driver software for controlling IMAQ hardware products.
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This document outlines new functionality, system requirements, installation procedures, and descriptions of the documentation included with the NI Vision Development Module. The NI Vision Development Module is for engineers and scientists who are developing machine vision and scientific imaging applications. The NI Vision Development Module includes NI Vision and NI Vision Assistant. NI Vision is a library of powerful functions for image processing, and is available for LabVIEW, LabWindows™/CVI™, and Microsoft Visual Basic. NI Vision Assistant is an interactive environment for developers who need to quickly prototype vision applications without programming. In addition, the NI Vision Development Module ships with the NI Vision Acquisition Software CD, which includes National Instruments driver software for controlling image acquisition products.
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Over the last several years, computing systems based on adaptive learning with fine-grained parallel architectures have moved from obscurity to front-page prominence. These systems derive some of their novel architecture from ideas gleaned from biology, hence the name “neural network”. Although many of the ideas behind this field are not new, improved computing hardware, better understanding of learning algorithms, and limitations of traditional approaches have combined to renew interest in neural nets.
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Adaptive algorithms are an important technique to achieve portable high performance. They choose among solution methods and optimizations according to expected performance on a particular machine. Grid environments make the adaptation problem harder, because the optimal decision may change across runs and even during runtime. Therefore, the performance model used by an adaptive algorithm must be able to change decisions without high overhead. In this paper, we present work that is modifying previous research into rapid performance modeling to support adaptive grid applications through sampling and high granularity modeling. We also outline preliminary results that show the ability to predict differences in performance among algorithms in the same program.
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Texture caching systems are designed to overcome the texture budget limitations of 3D games. Only the textures required to display the current scene are held in RAM. When new textures need to appear in the scene, they are loaded from a larger and slower repository, or they are dynamically generated.
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Brains, unlike artificial neural nets, use sym- bols to summarise and reason about perceptual input. But unlike symbolic AI, they “ground” the symbols in the data: the symbols have meaning in terms of data, not just meaning imposed by the outside user. If neural nets could be made to grow their own symbols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine the good features of each.
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