A formal model to handle the adaptability of multimodal user interfaces
Bibliographic record
Abstract
In this paper we propose an approach for checking adaptability property of multimodal User Interfaces (UIs) for systems used in dynamic environments like mobile phones and PDAs. The approach is based on a formal description of both the multimodal interaction and the property. The SMV model-checking formal technique is used for the verification process of the property. The approach is defined in two steps. First, the system is described using a formal model, and the property is specified using CTL (Computation Tree Logic) temporal logic. Then, we assume that an environment changes such that at most one modality of the system is disabled. For this propose, Disable is defined as a formal operator that disables a modality in the system. The property is checked by using the SMV (Symbolic Model Verifier) model-checker on all systems resulting from desabling a modality of the system. The approach reduces the complexity of the model-checking process and allows the verification at earlier stages of the development life cycle. We apply this approach on a mobile phone case study.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".