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Record W3013354539 · doi:10.1109/mmul.2020.2980098

Multimedia and the Tactile Internet

2020· article· en· W3013354539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Multimedia · 2020
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceLatency (audio)The InternetHuman–computer interactionPerceptionHaptic technologyVirtual realityMultisensory integrationMultimediaArtificial intelligenceWorld Wide WebTelecommunications

Abstract

fetched live from OpenAlex

With the rapid development in the areas of multisensory hard- and software and the emergence of Tactile Internet, new media such as haptics, smell, olfaction, etc., nowadays, play a prominent role in making virtual objects physically tangible in a collaborative and/or networked virtual environment. By allowing users to feel each other's presence and physically manipulate objects from their interacted environments within 1 ms. The Tactile Internet facilitates fast multimodal interactions with multisensory information over the 5G network. 1 ms is a critical threshold in human perception of tactile response. For auditory response, this threshold is 100 ms and for visual response, it is 10 ms, which means delays above these thresholds are within the latency limit sensed by the human brain. In 4G, the round trip latency is 25 ms for an ideal environment. Clearly, that indicates 4G is not able to meet the requirements of tactile response. For this reason, the efforts to reduce latency in 5G are critical for Tactile Internet. Low-latency communications will also enable other digital twins’ applications such as real-time control of smart grid, self-driving car, and so on.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.840

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.241
Teacher spread0.223 · 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