Bibliographic record
Abstract
I n the first article of this series, Tim Matthews described how JavaSoft is developing a Java Cryptography Architecture (JCA) and extensions (Java Cryptography Extensions, or JCE). He described their contents and structure in the java.security package, and outlined their uses. In the second installment, I presented some actual code using the base functionality in the JCA. This third article describes programming using the JCE and multiple providers. After reading this article, you will, I trust, be able to write a program in Java (an application or applet) that can encrypt or decrypt data using DES and create an RSA digital envelope with the extensions package. Beyond the specific example presented here, though, I hope you will understand the JCE model enough to be able to quickly write code for any operation in the package, and to be able to use multiple providers. Before beginning, however, it is important to note that the security packages are not part of the JDK 1.0.2, only JDK 1.1 and above. Furthermore, there are significant differences between the security packages in JDK 1.1 and 1.2. This article (and the previous) describes features in 1.2. If you have not yet left 1.0.2 behind, now would be a good time to do so. After all, with 1.2, you are not only getting the security packages, you are also getting improved cloning, serialization and many other features. There is an important change from JDK 1.1 to 1.2, the JCE is in a different package.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".