Havana: a mobile agent platform for seamless integration with the existing Web infrastructure
Bibliographic record
Abstract
Several research projects have been proposed to use mobile agents to deal with information overload. Their results, however, are not applicable in the existing Web infrastructure mainly because no Web sites are agent-enabled. There are two main reasons why Web site operators are not willing to let agents run on them: (1) security issues; and (2) many commercial Web sites make money from advertisement; if agents are going to do the work then who is going to see the ads? We have designed the Havana agent platform that addresses both of these issues. What is interesting about this platform is that it enables businesses to form beneficial business partnerships (e.g. between content providers and Internet Services Providers (ISPs) or even cellular network operators). In this paper, we discuss the design and implementation of the Havana platform. Some of the interesting features in the Havana platform include: (1) it can be seamlessly integrated into existing Web sites; (2) it enables business of different types to form beneficial partnerships; (3) supports location-based services and advertisements; (4) addresses the security issues in a revolutionary approach; and (5) can be accessed from any device.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".