Smart Signage: A Draggable Cyber-Physical Broadcast/Multicast Media System
Bibliographic record
Abstract
Digital signages are increasingly common for out-of-home advertising. Latest advancements in smart phones, wireless communication and displays make it possible to design smarter interactive signage systems for more effective advertising. Although existing research have started a trend of cyber-physical interactions, they are generally not scalable for multiple users and not intuitive to interact with. Smart Signage - a ``drag gable" cyber-physical broadcast/multicast (B/M) media system is therefore proposed here. With a novel cyber-physical B/M protocol that enables a display concurrently interacting with physical actions of multiple user smart phones, a large number of users can simultaneously acquire any running content on a display by simply using an intuitive ``dragging" hand gesture with their smart phones. Analytical formulations are derived to identify the key parameters and their dynamics in the system, which provide the condition for achieving the average response time of a user ``dragging" gesture within 1 second limit. Implementations are demonstrated for possible real-world deployments. Experiments with 30 primary students are conducted to prove the system offering scalable and intuitive interactions for the first-time users even when they are moving.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".