Safety-Critical Mobile Systems – The RESCUER Interaction Evaluation Approach
Bibliographic record
Abstract
The infrastructure organization of large-scale events involves high safety requirements for the visitors and is a central issue for the officials in charge. To assist in dealing with this, we developed the RESCUER Mobile Crowdsourcing App, which runs on smartphones and allows the crowd to report an emergency, thereby improving the process for rescuing humans in an emergency. For the evaluation of the app, we faced the problem that people participating in a large event, such as a soccer match, are not willing to spend time on completing a long survey or interview. Also, people experiencing an emergency situation may have their cognitive capabilities affected by emotional burden, so a mobile app should be easy and intuitive to interact with. Hence, the goal of this contribution was to select and perform an on-site mobile evaluation approach that allows us to evaluate the user interaction. Two main evaluations were performed using two different versions of our application. The first evaluation took place during the FIFA World Cup 2014 and tested the app’s usability with 112 users in Brazil and in Germany. As a result of this evaluation, we found severe usability issues and gained concrete insights into how to solve them. The second, follow-up evaluation, using an improved version of our app, was performed during emergency exercises in Brazil, with 31 experts in emergency management. For our evaluation approach, the results indicated that on-site mobile evaluation is an appropriate method for improving the usability and interaction of safety-critical software systems.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".