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Record W2082557138 · doi:10.1109/icmew.2013.6618401

Control your smart home with an autonomously mobile smartphone

2013· article· en· W2082557138 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHome automationRobotInterface (matter)Smart phoneComputer scienceHuman–computer interactionPhoneMobile robotControl (management)Mobile phoneReading (process)Artificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Since smart homes will be more and more popular in the future, the needs of finding a friendly and intelligent interface between users and home environments are growing. However, conventional interfaces have reached their limits. Therefore, this paper describes a new concept of interface in a home environment. This work is characterized by building a smart robot: a smart phone is used as the brain, and a robot car is used as the body. With this system, a person will be allowed to carry the robot's brain when she is out of their home. Thus, the brain can not only be a normal smart phone, but will also monitor different parameters such as the temperature at home. On the other hand, when the person is at home, she will be allowed to put their phones on the robot car. By invoking the system, she will have an assist-robot. This assist-robot will act as a new interface in the smart home by following the person and recognizing the person's voice commands for taking notes, reading notes and controlling smart home devices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

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)0.0010.003

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 head0.005
GPT teacher head0.178
Teacher spread0.174 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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