A recommendation system for emergency mobile applications using context attributes: REMAC
Bibliographic record
Abstract
The extensive use of mobile devices had led to tremendous growth in not only the usage of different apps but also their capability to help people in moments of crisis. There are different emergency mobile apps published in the app markets; these apps can be of enormous assistance to victims as they can provide valuable information and guidance at the opportune moments. However, app store reviews, ratings, and relevant studies have revealed that users are often averse to using these apps or their different features. This draws our attention to the need for recognizing essential features and including them in the emergency apps to increase their usability. Our proposed recommendation system called REMAC combines different machine learning techniques to analyze the context characteristics of different organizations and suggest unique features that can be included in their emergency apps. REMAC is built by analyzing 24 potential context attributes of 1909 universities spread across North America. This research also includes a systematic attribute selection process that enables us to reach a local optimum for the given dataset. This tool carefully dissects the context attributes of each university and suggests top features that should be included in its emergency app. It leverages the data (other apps and features) provided by the app markets to suggest essential features.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".