Implementing online feature interaction detection in SIP environment: early results
Why this work is in the frame
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Bibliographic record
Abstract
The article proposes a solution for detecting feature interactions (FI) at runtime in SIP-based IP telephony architectures. The solution takes into account the special context of SIP that permits end user programmability, which means the possibility for end users to design their own tailored services and personalize them as much as they like. Programmability implies that services are roomed and run in the end user devices. This renders more frequent the so called multi-component FI situations, where conflicting services reside in different network components. This type of FI is the more complicated one. The presented solution is handled by FIMA, a feature interaction management agent that is introduced as a central network manager. We focus on the implementation requirements of an online detection method. The proposed solution deals with multi-component FI situations as well as another FI situation type, called single-component.
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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) | 0.000 | 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