uOS: A Resource Rerouting Middleware for Ubiquitous Games
Bibliographic record
Abstract
Ubiquitous computing (ubicomp) relies on the computation distributed over the environment to simplify the tasks performed by its users. A smart space is an instance of a ubiquitous environment, composed of a dynamic and heterogeneous set of devices that interact to support the execution of distributed smart applications. In this context, mobile devices provide new resources when they join the environment, which disappear when they leave it. This introduces the challenge of self-adaptation, in which smart applications may either include new resources as they become available or replace them when they become unavailable. Ubiquitous games combine ubicomp and computer game technologies to enrich user's experience and fun. Such games may benefit from different input and output resources offered by mobile devices. To support the development and deployment of ubiquitous games, this work presents the uOS middleware. Using a DSOA (Device Service Oriented Architecture) based architecture and lightweight service discovery protocols, uOS ensures compatibility among resources, providing resource rerouting between heterogeneous and limited software and hardware platforms. The uMoleHunt game is presented to illustrate the practical application of uOS.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".