An insight into mobile advertising and its impact on the resources of handheld devices: a survey
Bibliographic record
Abstract
With the rapid advancement of mobile devices, people become more attached to them than ever. This growth combined with millions of applications (apps) make smartphones a favourite means of communication among users. The available contents on smartphones, apps and web come into two versions: 1) free contents that are monetised via advertisements (ads); 2) paid ones that are monetised by users' subscription fees. However, the resources on-board are limited and the existence of ads can adversely impact them. These issues brought the need for good understanding of mobile advertising eco-system and how such limited resources should be efficiently used. This survey paper gives an overview on the mobile advertising eco-system and reviews the work done in the regard of the influence of such ads on smartphones' battery life and monthly data usage. It discusses and slightly addresses the open issues and research directions that need to be further investigated. This work is meant to motivate: 1) the researchers to investigate the energy and bandwidth issues further and hence, come up with more practical solutions; 2) app and web developers to consider the seriousness implications of embedding 'expensive' ads in their apps and web-pages on the end users limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".