Efficient group labeling for multi-group RFID systems
Bibliographic record
Abstract
Ever-increasing research effort has been dedicated to multi-group radio frequency identification (RFID) systems where all tags are partitioned into multiple groups, such as group-level queries, and multi-group missing tag detection. However, it is assumed in the existing work that all tags know their individual group IDs, which thus leaves group labeling problem unaddressed. To tackle the under-investigated problem, this paper is devoted to devising an efficient group labeling protocol to inform each tag of its corresponding group ID fast and accurately. To this end, we employ multiple seeds to build a Composite Indicator Vector (CIV) indicating the assigned seed in each slot, which reduces transmissions of useless information and thus improves time efficiency. Specifically, we first theoretically show that the Seed Assignment Problem (SAP) arising in establishing the CIV is NP-hard and then develop a myopic approximation algorithm. Finally, the simulation results confirm the superiority of the proposed protocol over the state-of-the-art solution in terms of time efficiency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".