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
Device-free human sensing is a key technology to support many applications such as indoor navigation and activity recognition. By exploiting WiFi signals reflected by human body, there have been many WiFi-based device-free human sensing applications. Among these applications, person identification is a fundamental technology to enable user-specific services. In this paper, we present Rapid, a system that can perform robust person identification in a device-free and low-cost manner, using fine-grained channel information (i.e., CSI) of WiFi and acoustic information from footstep sound. In order to achieve high accuracy in real-life scenarios with both system and environment noise, we perform noise estimation and include two different confidence values to quantify the impact of noise to both CSI and acoustic measurements. Based on an accurate gait analysis, we then adaptively fuse CSI and acoustic measurements to achieve robust person identification. We implement low-cost Rapid nodes and evaluate our system using experiments at multiple locations with a total of 1800 gait instances from 20 volunteers, and the results show that Rapid identifies a subject with an average accuracy of 92% to 82% from a group of 2 to 6 subjects, respectively.
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.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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