Behavioral Features for Different Haptic-based Biometric Tasks
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
The science of haptics or haptic technology has received enormous attention in the last decade for multiple applications. A new promising example is the use of haptic-based systems for individual authentication. A user's behavioral characteristics captured while interacting with a virtual scene are significantly more dfficult to compromise than traditional means (i.e. login ID and passwords). Moreover, another advantage is that this technology allows a user not only to gain access to the system but also to continuously verify individual authenticity during the whole session of haptic interaction. Our current haptic-based biometric system for authentication (the BioHaptic system) has integrated a variety of applications: solving a virtual maze, signing a virtual cheque and dialing phone codes on a virtual phone. In this paper, with the use of a 3D visual representation we study: (1) a subset of features with the greatest user-classificatory worth and (2) whether or not such features are dependent on the application used. We believe this study will enhance the success of individual authentication based on human-haptic interactions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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