Mobile-Friendly HTTP Middleware with Screen Scrolling
Bibliographic record
Abstract
The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents requires the mobile Internet applications to download contents way beyond the user's viewing region (referred as viewport). In this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which interprets user touch screen inputs and optimize the HTTP downloading of media objects to improve quality of experience (QoE) and cost efficiency. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative applications, namely, web browsing and 360-degree video streaming.
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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 | 0.001 | 0.000 |
| 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 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".