New and Reliable Points Shifting - Based Algorithm for Indoor Location Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indoor localization is of great importance to several fields such as healthcare and asset tracking. However, many factors (e.g., multipath propagations) impact the quality of signals which are used to perform localizations. As a consequence, the precision and accuracy of the computed locations are heavily influenced. Therefore, the methodologies to compute indoor locations always need continuous refinements in terms of those metrics including the time complexity. For the last metric, It impacts the performance of mobile devices due to their limited resources. To address these challenges, a new set of fingerprinting algorithms was presented in this paper called Fingerprinting Line-Based Nearest Neighbour. This set shifts grid points potentially towards targets via a deterministic percentage. The running time of the set is upper bounded. Moreover, this paper presents the following: 1) an upper bound in terms of distance errors for the proposed algorithms, and 2) based on real experiments, the new algorithms (e.g., 90% shifting) improved the accuracy and precision, and had lower distance errors probabilities compared to those for the nearest neighbour-based algorithms (e.g., by 106% and 76%, respectively).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it