Bibliographic record
Abstract
Mobile communicating systems are ubiquitous in the modern world.The π-calculus proposed by Milner et al [Milner et al. 1992] sets a theoretical foundation for modeling mobile communicating systems such as the Internet, in which links can be sent from processes to processes and a process can use a received link to interact with another process.Here, we present a language named π-language for programming π-calculus processes and a compiler named pi2j for translating the π-programs to Java code, which can be compiled with the Java compiler to JVM bytecode and, then, executed on the Java Virtual Machine.Thus, we implement the π-calculus in the Java language.We present an ASCII-based language called π-language for coding π-calculus process expressions.Using keywords in ASCII to denote the Greek leters and other non-Implementing the π-Calculus in Java 158 J OURNAL OF OBJECT TECHNOLOGY V OL. 4, NO. 2ASCII symbols, the π-language represents π-calculus process expressions faithfully.Its grammar improves the syntactic rules of the π-calculus by eliminating ambiguities in the syntax rules of the π-calculus.We can compile the translated π-calculus process expressions into Java to explore the multithreading and synchronization mechanism of Java.We describe the Java concurrent classes that we developed to support the communication mechanism of the π-calculus.Based on the Java classes, we present a compiler named pi2j to translate the so-called π-programs into the Java language.Then, we can compile and run the Java code that implements π-calculus process expressions.This paper is organized as follows.The next section introduces the π-calculus as specified in [Milner 1999;Sangiorgi and Walker 2001].In Section 3, we briefly introduce the multithreading and synchronization constructs of the Java language, which are used to implement the π-calculus.We present the π-language for coding π-calculus process expressions in Section 4. We describe the various Java objects that support the dynamic communication mechanism of the π-calculus in Section 5.The compiler pi2j attached with the paper is described in Section 6.This paper is concluded in Section 7.The π-Calculus
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".