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Record W2007298320 · doi:10.1109/greencom.2012.55

Smart Signage: A Draggable Cyber-Physical Broadcast/Multicast Media System

2012· article· en· W2007298320 on OpenAlexaff
James She, Jon Crowcroft, Hao Fu, Pin‐Han Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
FundersEngineering and Physical Sciences Research Council
KeywordsComputer scienceDigital signageCyber-physical systemScalabilityMulticastMultimediaWirelessGestureImplementationKey (lock)Human–computer interactionSignageComputer networkComputer securityTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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.

Opus teacher head0.014
GPT teacher head0.248
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2012
Admission routes1
Has abstractyes

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