Enhancing Tourism in Riau Province through Augmented Reality and Near Field Communication-Enabled Smart Posters
Bibliographic record
Abstract
This research paper introduces a transformative approach to promoting tourism in Riau Province by integrating advanced technologies into smart systems.Utilizing Augmented Reality (AR) and Near Field Communication (NFC), smart posters were developed to elevate the tourist experience.AR technology was employed to overlay digital information, including historical insights, videos, and 3D visuals, directly onto tourists' smartphone screens when aimed at specific landmarks.Simultaneously, NFC technology allowed tourists to tap their smartphones on the smart posters, instantly downloading curated guides, maps, and coupons related to nearby attractions.The primary goal of this innovation was to provide tourists with a seamless and enriched experience, minimizing the often tedious task of manual information search.To validate the effectiveness of this system, user studies were conducted, analyzing interaction metrics with the smart posters and collecting direct feedback from participants.Preliminary findings showed an increased ease of information access, with tourists appreciating the intuitive nature of AR overlays and the immediate data transfer via NFC.By melding AR and NFC within smart posters, this research not only aligns Riau's tourism with contemporary Smart City concepts but also showcases the potential economic and experiential benefits for tourists and the local economy.Future implications suggest a broader application of such smart systems in tourism sectors globally.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".