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
Big data based user authentication is a new approach that leverages the power of the Big Data analytics to develop a fertile field for the next generation authentication protocols. This new approach relies on “something you do”-based verification methods, where the users' dynamic behaviors are analyzed in order to generate real-time uniquely identifiable information about them. Once the unique user's identification is generated “authentication on demand” can be achieved through user challenging questions that are dynamic and user specific. In this paper, the 3Vs nature of Big Data (volume, variety and velocity) is utilized to propose an Innovative Data Authentication Model (IDA). IDA model is a new implementation for the Big Data based user authentication in finding out unique patterns of the users' dynamic behaviors to be used as a basis for the user challenging questions generation process. In other words, Big Data analytic techniques such as association learning and behavioral classification will be used to compile the human dynamics into flexible security user profiles. The term “human dynamics” comprises the actions of human and their impacts on behavioral outcomes. The real-time analysis of these users' profiles helps generate a random set of challenging questions thereby “authentication on demand” feature is obtained. A practical use case scenario has been given to illustrate how IDA works from creating user profiles, to studying and classifying human dynamics and generating questionnaire with security potentials to authenticate users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 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