Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System
Bibliographic record
Abstract
As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices. Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between humans in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support. Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. In addition, we present a mobile crowdsensing application, Smart City, developed on Vita to demonstrate the functionalities and practical usage of Vita.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".