Efficient and transparent dynamic content updates for mobile clients
Bibliographic record
Abstract
We introduce a novel infrastructure supporting automatic updates for dynamic content browsing on resource constrained mobile devices. Currently, the client is forced to continuously poll for updates from potentially different data sources, such as, e-commerce, on-line auctions, stock and weather sites, to stay up to date with potential changes in content. We employ a pair of proxies, located on the mobile client and on a fully-connected edge server, respectively, to minimize the battery consumption caused by wireless data transfers to and from the mobile device. The client specifies her interest in changes to specific parts of pages by highlighting portions of already loaded web pages in her browser. The edge proxy polls the web servers involved, and if relevant changes have occurred, it aggregates the updates as one batch to be sent to the client. The proxy running on the mobile device can pull these updates from the edge proxy, either on-demand or periodically, or can listen for "pushed" updates initiated by the edge proxy. We also use SMS messages to indicate available updates and inform the user of which pages have changed. Our approach is fully implemented using two alternative wireless networking technologies, 802.11 and GPRS, and evaluated on real world dynamic content traces. Our evaluation explores the data transfer savings enabled by our proxy-based infrastructure and the energy consumption when using each of the two networking capabilities. Our results show that our proxy system saves data transfers to and from the mobile device by an order of magnitude and battery consumption by up to a factor of 4.5, compared to the client-initiated continuous polling approach. Our results also show even in the case where users never visit the same page twice, energy consumption is reduced by the pre-fetching and batching or our proxy system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".