A comparison of software architectures for data-oriented mobile applications
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Bibliographic record
Abstract
Mobile applications have empowered and extended the usability of mobile devices far beyond merely supporting voice communication. Mobile applications which rely on remote data sources and databases are particularly challenging given the need to engage in complex business logic and transmit data through wireless media. Previous research has looked at a variety of approaches to address these problems. Two software architectural approaches have emerged as the primary ways to develop mobile applications accessing remote data sources: client-agent-server and client-intercept-server. Both make use of middleware with the main difference being in the way agent components are used. In this work, we compare the performance of the two approaches under different scenarios. Statistical analysis shows that the client-agent-approach performs better. The results of this research provide useful guidelines for the development of mobile applications needing to connect to remote databases or data sources.
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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 | 0.002 | 0.001 |
| 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