Bibliographic record
Abstract
The availability of powerful smartphones and the necessity of security in mobile devices have made researchers propose multiple security modes (e.g., home, office, outdoor, and financial) for such devices. In each mode, a user can install a different set of apps. However, in most of the cases, the user has to select the mode manually. If we can sense the smartphone's security context accurately, then it is possible to switch between different security modes automatically. Also, smartphone operating systems are becoming ubiquitous. As a result, mobile apps need to behave differently based on the security context (e.g., not sending the data if the network is insecure). There exist other research work that may detect the physical context of a smartphone. However, we focus on sensing different security parameters (e.g., location, is-network-encrypted) and calculating the security context from the parameters. In this paper, we propose Flamingo, a security context management framework that maintains a cache of security contexts and parameters to be used by the operating system and third-party applications. As detecting contexts requires the use of power-hungry smartphone sensors, a comprehensive framework for sharing security parameters among various applications can be beneficial in terms of energy and other resource expenses. The implementation of Flamingo as a part of the Android operating system shows that it is effective in managing security contexts and parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".