Location-based services provisioning using WSN
Bibliographic record
Abstract
Ubiquitous computing has gained momentum over the last years with the expansion of mobile devices. One area of pervasive computing is context-aware systems which are applications designed to react to the constant changes in the environment. This paper presents a context aware platform that handles context acquiring, processing and service provisioning management. The platform alleviates the process of high level application development and sets a common ground for building context-aware applications and services. The context-based platform objective is to support mobile users with personalized services. It offers sophisticated mechanisms in matching the mobile user's preferences with services that are enabled at the visited location, and provides them in adaptive manner to the user. As a proof of concept, we present a case study on tourism where tourists are provided with services and information of interest based on their location and time. An application for e-tourism is deployed on top of the platform to assist tourists during their travels by providing them with context sensitive services. The user preferences, the current time, and the user current location are incorporated in the proactive formulation of suggestions on the tourist mobile devices about nearby points of interests (e.g. museums, restaurants....).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".