Location-based services on a smart campus: A system and a study
Bibliographic record
Abstract
Knowledge about people's geographical location can be used to infer their possible needs, and to offer relevant services to satisfy these needs. Such targeted approach in service demand identification and service delivery is one of the reasons why Location-Based Services (LBS) are so popular in our days. In this paper, we describe a framework that we developed for offering location-based services, relying on infrastructure typically available in smart campus environments. Our framework includes three user-facing components: 1) an energy-aware Android application for end users to recognize their locations and access the services available to them; 2) a web application, which enables end users to search for services available on the campus as a whole; and 3) a web application for managers to specify what services are available on campus and in which areas. Each of these applications communicates with a server from the middle layer. Finally, a PostgreSQL database constitutes the framework's back-end, where information about the available services and their spatial scope is maintained. We evaluate the performance of our framework in terms of localization accuracy, since this is the most critical quality for the effective delivery of location-based services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".