Empowering Human-Computer Interaction in Securing Smartphone Sensing
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
In this paper, we propose and quantify the usability design of a continuous verification platform on smart mobile devices. The continuous verification platform aims at improving user experience in continuous verification of smartphone users, particularly in non-dedicated smartphone sensing campaigns. To this end, we re-design the mobile behaviometric platform in [1] by introducing enhanced usability features, as well as sustainability measures to prolong battery life of the devices while recruiting the users for non-dedicated sensing campaigns. Furthermore, we extend the continuous verification modules by introducing gesture recognition and a dual mode verification system. Through real time study, we show that the presented framework can achieve continuous verification of users on smart mobile devices by consuming 73% less memory and 46% less storage when compared to its predecessor. Moreover, the proposed framework can significantly reduce the battery drain down to an average percentage of 0.1% while operating with consistency, compliance and extensibility as opposed to its predecessor.
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 | 0.000 |
| 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 | 0.001 |
| 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