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Record W2918345001 · doi:10.1109/mitp.2018.2876534

Internet of Everything as a Platform for Extreme Automation

2019· article· en· W2918345001 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

VenueIT Professional · 2019
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsLakehead University
Fundersnot available
KeywordsDoorsAutomationComputer scienceThe InternetInternet of ThingsComputer securityWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Reports on the concept of the Internet of Everything (IoE). IoE is a notion for intelligently connecting people, processes, and data in one uniform way, enabling communications between machines (M2M), machine-topeople and technology- assisted peopleto- people interactions. IoE is expected to reinvent the business and the automation wheel all-together. From processes, models to business and manufacturing frameworks everything is expected to change with the change in data available and the smart connectivity between people and machines for critical decision making. It is bringing productivity and competitiveness to higher levels along with opening up many doors to new and exciting opportunities. IoE expands on the concept of the “Internet of Things” by connecting devices and people in one network. This connection goes beyond the basic M2Mcommunications to enable a democratization of skill and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time,which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network capabilities.

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: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.260

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.301
Teacher spread0.248 · 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