Bibliographic record
Abstract
L ast month , 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. This month I will present some actual code using the base functionality in the JCA, and next month will program using the JCE and use multiple Providers. After reading this article, you will, I trust, be able to write a program in Java (an application or applet) that can sign or verify data using DSA with the security package. Beyond the specific DSA example presented here, though, I hope you will understand the JCA model enough to be able to quickly write code for any operation in the package. Before beginning, however, it is important to note that the java.security package is 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 next month's) 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 package, you are also getting improved cloning, serialization and many other features. Now let's look at what a Java program needs to do to use the JCA. Most everything in cryptography begins with the random number generator.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.124 |
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".