Smart Phone User Behaviour Characterization Based on Autoencoders and Self Organizing Maps
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".