Bibliographic record
Abstract
A distributed computing system, such as a Grid or Cloud, could be a very dynamic environment and the user groups are most likely become highly diverse. A user group could be formed by the users of different networks, organizations, or administrative-domains with different hardware/software infrastructures and managerial policies. Handling requests from a wide range of users from different domains becomes a challenge when attempting to accommodate all the differences. Service providers find it impossible to track all users (the number of users could be potentially very large) in a Grid. Therefore, an access control mechanism that provides users appropriate access to the resources in a dynamic environment is required. RBAC models have been demonstrated to be an effective and efficient approach for an administrator to manage accesses in a computing system. Much has been done to adapt the RBAC concept to Grids and focus on the authorization and verification of the dynamic factors or contexts of a user, such as time, location, rank, etc. Some applications also allow administrators to change the policies during the authorization process. However, no implementation has been found, from the reviewed literature that handles the real-time and on-demand authorization in a distributed system. Therefore, this problem motivated us to develop a new dynamic authorization protocol, Dynamic Role Lease Authorization (DRLA) that is suitable for a dynamic distributed computing environment.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".