Network management application-oriented taxonomy of mobile code
Bibliographic record
Abstract
We present an application-oriented taxonomy of mobile code. We use a novel approach to managing telecommunication networks as a vehicle for describing the concepts through demonstrating the use of several types of mobile code in innovative network and system management applications. To make a clear distinction between the types of mobile code, we use a terminology that follows the Java conventions originated with the term applet. Therefore, in our jargon, we have servlets, exflets, deglets and netlets. We point to a piglet as a type of mobile code that constitutes a security risk to the network. We deliberately avoided the term agent in our taxonomy. In this paper, this term is used to refer to a general concept of code autonomy. Our approach to managing networks addresses the issues in traditional client/server, or in this context manager/agent, network management systems like the amount of data that needs to be transmitted, problems inherent to heterogeneous environment, like interoperability issues, problems with maintainability of the software, etc. With the techniques based on mobile code, we can harness many interoperability issues and work toward plug-and-play networks (PnPnets) by applying mobile agents that can take care of many aspects of configuring and maintaining networks in an autonomous way.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".