Experience in integrating Java with C# and .NET
Bibliographic record
Abstract
Abstract Java programmers cannot help but be aware of the advent of C#, the .NET network environment, and a host of new supporting technologies, such as Web services. Before taking the big step of moving all development to a new environment, programmers will want to know what are the advantages of C# as a language over Java, and whether the new and interesting features of C# and .NET can be incorporated into existing Java software. This paper surveys the advantages of C# and then presents and evaluates experience with connecting it to Java in a variety of ways. The first way provides evidence that Java can be linked to C# at the native code level, albeit through C++ wrappers. The second is a means for retaining the useful applet feature of Java in the server‐side architecture of Web services written in C#. The third is by providing a common XML‐based class for the development of graphical user interfaces (GUIs), which can be incorporated into Java or C#. An added advantage of this system, called Views, is that it can run independently of the resource‐intensive development environment that would otherwise be needed for using C#. A major advantage of the methods described in this paper is that in all cases the Java program is not affected by the fact that it is interfacing with C#. The paper concludes that there are many common shared technologies that bring Java and C# close together, and that innovative ways of using others can open up opportunities not hitherto imagined. Copyright © 2005 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".