Smart Phone User Behaviour Characterization Based on Autoencoders and Self Organizing Maps
Why this work is in the frame
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Bibliographic record
Abstract
Building applications that are cognizant of temporal and spatial changes in human behaviour under a one-class learning restriction represents a requirement for many user centric systems. We are particularly motivated to demonstrate the utility of algorithms for the self identification of smart phones. A framework is designed to quantify: (i) the dissimilarity in behaviours among any two users, (ii) the exclusivity of each user's behaviour (inclass) from the world (outclass). A central element of the proposed framework is to first identify a discriminating representation for each user. To this end, an autoencoder is employed in which the goal is to identify an encoding that rebuilds the original data with maximum accuracy/least loss. The hypothesis of this work is that such an autoencoding step provides an effective mechanism for discovering good data representations prior to the application of a data description technique, such as clustering. Both the autoencoder and the clustering steps are performed relative to a single user. We construct a user specific behavioural model using the most frequently used applications, cell towers and websites. We demonstrate that relative to the most up-to-date publicly available smart phone data set, the resulting behavioural models are capable of uniquely identifying each user under a one-class learning constraint.
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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) | 0.002 | 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