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Record W4313855988 · doi:10.1109/miot.2022.10012403

Guest Editorial: Pervasive, Efficient, and Smart Signal Processing for IoT

2022· editorial· en· W4313855988 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 Internet of Things Magazine · 2022
Typeeditorial
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersAgence Nationale de la Recherche
KeywordsInternet of ThingsWearable computerComputer scienceDigital signal processingFactory (object-oriented programming)Wearable technologyTelecommunicationsData scienceComputer securityEmbedded systemComputer hardware

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) lies at the core of the unprecedented connected era we are currently speeding towards. Both Academia and the Industry strive to push the limits of technology fueled by vertical applications such as: factory 4.0, eHealth, human-centric and tactile IoT, wearable IoT, smart city/building, digital twin, etc., which will shape our future society and significantly impact both economic and societal aspects. Signal Processing (SP) plays an important role in expanding the number of IoT technologies and capitalizing on their applications. This is due to the sophisticated processing of signals and data gathered and shared by connected things.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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