Secured distributed discovery services in the EPCglobal network
Bibliographic record
Abstract
The EPCglobal Network is a global network developed to ensure global interoperability between trading partners in supply chains. Its main goal consists of providing real-time and accurate traceability of items in the supply chains. One of the major building blocks of the EPCglobal Network is the Object Naming Service (ONS) which is a central lookup service used mainly to locate the EPC manager information sources of a given EPC. Discovery services refer to a suite of services enabling any user, subject to authentication, to retrieve all relevant data, subject to access control policies, related to a given EPC in the EPCglobal Network. Many promising DHT-based distributed and secure architectures have been proposed to make the ONS more scalable and more secure than the current ONS specifications. Some of them focused on improving a specific aspect of the existing ONS architecture while others suggested integrated solutions for various weaknesses of the current ONS system. In this paper, we present a DHT-based, scalable and secure architecture for data lookup in the EPCglobal Network. The proposed architecture aims at replacing the current ONS system with a secure distributed Discovery Services system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".