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Record W2738766362 · doi:10.1109/ccnc.2017.7983159

Sensorian Hub: An IFTTT-based platform for collecting and processing sensor data

2017· article· en· W2738766362 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRelational database management systemPython (programming language)SoftwareDatabaseRelational databaseOperating systemEmbedded systemCross-platformSoftware engineering

Abstract

fetched live from OpenAlex

The Sensorian Hub is a software platform designed and developed for using the Raspberry Pi Single Board Computer (SBC) as a sensor node, whether independently or in a larger network of sensors. The intention of the platform is to provide an easy to use and highly customizable system for collecting and processing sensor data in a variety of environments and applications. In this paper, the design and implementation of the system through the use of a Python client, a PHP and Flask Web Server/API, a Relational Database Management System (RDBMS) MySQL and external connections to services such as If This Then That (IFTTT) are discussed in detail. Some sample applications and a prototype implementation with the Sensorian Shield are presented as well to demonstrate the current capabilities as well as the opportunities for future work.

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 score0.924
Threshold uncertainty score0.801

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.090
GPT teacher head0.311
Teacher spread0.221 · 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

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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