Non-intrusive user identity provisioning in the internet of things
Bibliographic record
Abstract
The Internet of Things (IoT) represents an evolutionary vision and a new era of such smart environments that encompass all identifiable things in a dynamic and interacting network of networks. Each user has wide interactions with a huge number of entities. It would be impractical to require users to confirm themselves every time they cross various network boundaries, as the frequent verification process would disrupt the users' normal activities and degrades the overall performance. This paper presents a service provisioning framework for IoT that relies on verifying user identity using a non-intrusive method of monitoring and inferring certain types of user activities. The framework helps in supporting the purpose of the IoT for being smart, boundless, easier and safer to improve people's lives. The proposed framework reduces the risk of identity theft that results from losing user devices, where the user identity is usually stored. It copes with the loss of the user's ID or people impersonating other people, and raises an alarm to block an intruder from being verified as a legitimate user.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".