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basics of synchronization This included the circumstances in which it's necessary to synchronize and some of the associated pitfalls In this chap ter, we'll look closely at the most fundamental support for synchronization offered by the Windows OS: kernel obj ects These objects serve as the basic building blocks for all concurrent programs and primitive data structures In fact, whether or not you use these objects directly in your code, you will almost always rely on them at some layer of software Just about all syn chronization primitives available in Win32 and the NET Framework, including Win32 critical sections and CLR monitors (see 6, Data and Control Synchronization), for example, use them in one way or another For this reason, we'll examine the details of them before looking at higher level data and control synchronization mechanisms in the next chapter Windows offers several different kinds of kernel objects Some kinds offer more sophisticated functionality in addition o being useful for syn chronization purposes-such as the thread kernel object representing an OS thread as reviewed in the past two chapters, file notification objects, and more-but we'll focus on synchronization behavior in this chapter. In Visual Basic .NET Using Barcode encoder for .Using Barcode encoder for Visual Studio .NET Control to generate, create ANSI/AIM ode 39 image in Visual Studio .NET applications.Related: .NET Codabar Generating , Generate ITF-14 .NET , Create Interleaved 2 of 5 .NET

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OnStartupComplete Method The OnStartupComplete method, which occurs after OnConnection, is called when the host application has completed loading and the COM addin is set o load at application startup time, which is the case for the add-in we are creating (If the add-in is not set to load at startup, this method does not occur) Here s where things get a little interesting In the OnStartupComplete method, you ll want to create any toolbars, buttons, menus, and so on that you need for your COM add-in In our case, we want to create a Form Utilities toolbar that contains two buttons Copy Form Data and Paste Form Data When clicked, these buttons will do exactly what their names imply copy or paste the data in the form Listing 204 shows the code for the OnStartupComplete method (minus comments and error handling) Let s look at this method and its helper methods in detail. EAN-13 Supplement 5 Printer In Java Using Barcode maker for Java Control to .Related: ITF-14 Generating .NET , .NET Codabar Generation , Java Intelligent Mail Generator

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speci ed by the VC-dimension [152, 732] Cohn and Tesauro [152] show that for experiments conducted, the generalization error decreases exponentially with the number of examples, rather than the 1/PT result of the VC bound Experimental results by Lange and M nner [502] show that more training examples do not necessarily improve a generalization In their paper, Lange and M nner introduce the notion of a critical a training set size Through experimentation they found that examples beyond this critical size do not improve generalization, illustrating that excess patterns have no real gain This critical training set size is problem dependent While enough information is crucial to e ective learning, too large training set sizes may be of disadvantage to generalization performance and training time [503, 948] Redundant training examples may be from uninteresting parts of input space, and do not serve to re ne learned weights it only introduces unnecessary computations, thus increasing training time Furthermore, redundant examples might not be equally distributed, thereby biasing the learner The ideal, then, is to implement structures to make optimal use of available training data That is, to select only informative examples for training, or to present examples in a way to maximize the decrease in training and generalization error To this extent, active learning algorithms have been developed Cohn et al [151] de ne active learning (also referred to in the literature as example selection, sequential learning, query-based learning) as any form of learning in which the learning algorithm has some control over what part of the input space it receives information from An active learning strategy allows the learner to dynamically select training examples, during training, from a candidate training set as received from the teacher (supervisor) The learner capitalizes on current attained knowledge to select examples from the candidate training set that are most likely to solve the problem, or that will lead to a maximum decrease in error Rather than passively accepting training examples from the teacher, the network is allowed to use its current knowledge about the problem to have some deterministic control over which training examples to accept, and to guide the search for informative patterns By adding this functionality to a NN the network changes from a passive learner to an active learner Figure 77 illustrates the di erence between active learning and passive learning With careful dynamic selection of training examples, shorter training times and better generalization may be obtained Provided that the added complexity of the example selection method does not exceed the reduction in training computations (due to a reduction in the number of training patterns), training time will be reduced [399, 822, 948] Generalization can potentially be improved, provided that selected examples contain enough information to learn the task Cohn [153] and Cohn et al [151] show through average case analysis that the expected generalization performance of active learning is signi cantly better than passive learning Seung et al [777], Sung and Niyogi [822] and Zhang [948] report similar improvements Results presented by Seung et al indicate that generalization error decreases more rapidly for active learning than for passive learning [777] Two main approaches to active learning can be identi ed, ie incremental learning.

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com.idautomation.linear.encoder.*; class CreateImageExample . Using ServletExec ISAPI on Windows Server: . image.createGraphics(); // paint barcode in graphics .Related: 

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This font encoder DLL also contains methods for the . Visit IDAutomation's Barcode Fonts page to download barcode fonts. The API of the Native Windows Barcode DLL. .Related: 

A Web server, which provides access to the application s functionality and information. he Web server software could be one of many common types such as Apache or Internet Information Services, but it must support the encryption and key exchange mechanisms being employed. Armed with the shared encryption key and an algorithm to implement his encryption mechanism, both client and server can encrypt and decrypt confidential data. for c#.net generate, create barcode data matrix .Related: Intelligent Mail Generation .NET

The Native Windows Barcode DLL API should only be used by advanced . Consider one of IDAutomation's many powerful font encoder tools as an alternative. .Related: 

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A design pattern describes a good solution to a recurring problem in a particular context The pattern follows a prescribed format that includes the pattern name, a description of the context, the forces (goals and constraints), and the solution The idea is to record the experience of experts in a way that can be used by others facing a similar problem In addition to the solution itself, the name of the pattern is important and can form the basis for a domain specific vocabulary that can significantly enhance communication between designers in the same area Design patterns were first proposed by Christopher Alexander The domain was city planning and architecture [AIS77] Design patterns were originally introduced to the software engineering community by Beck and Cunningham [BC87] and became prominent in the area of object oriented programming with the publication of the book by Gamma, Helm, Johnson, and Vlissides [GHJV95], affectionately known as the GoF (Gang of Four) book This book gives a large collection of design patterns for object oriented programming To give one example, the Visitor pattern describes a way to structure classes so that the code implementing a heterogeneous data structure can be kept separate from the code to traverse it Thus, what happens in a traversal depends on both the type of each node and the class that implements the traversal This allows multiple functionality for data structure traversals, and significant flexibility as new functionality can be dded without having to change the data structure class The patterns in the GoF book have entered the lexicon of object oriented programming references to its patterns are found in the academic literature, trade publications, and system documentation These patterns have by now become part of the expected knowledge of any competent software engineer An educational nonprofit organization called the Hillside Group [Hil] was formed in 1993 to promote the use of patterns and pattern languages and, more generally, to improve human communication about computers "by encouraging people to codify common programming and design practice" To develop new patterns and help pattern writers hone their skills, the Hillside Group sponsors an annual Pattern Languages of Programs (PLoP) workshop and several spinoffs in other parts of the world, such as ChiliPLoP (in the western United States), KoalaPLoP (Australia), EuroPLoP (Europe), and .

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