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Record W2768178335 · doi:10.1145/3161587.3161589

Are Wearables Ready for Secure and Direct Internet Communication?

2017· article· en· W2768178335 on OpenAlexaff
Harini Kolamunna, Jagmohan Chauhan, Yining Hu, Kanchana Thilakarathna, Diego Perino, Dwight Makaroff, Aruna Seneviratne

Bibliographic record

VenueGetMobile Mobile Computing and Communications · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWearable computerThe InternetWearable technologyComputer scienceInternet of ThingsInternet privacySecure communicationComputer securityWorld Wide WebEmbedded systemEncryption

Abstract

fetched live from OpenAlex

Recent advances in wearable technology tend towards standalone wearables. Most of today's wearable devices and applications still rely on a paired smartphone for secure Internet communication, even though many current generation wearables are equipped with Wi-Fi and 3G/4G network interfaces that provide direct Internet access. Yet it is not clear if such communication can be efficiently and securely supported through existing protocols. Our findings show that it is possible to use secure and efficient direct communication between wearables and the Internet

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.007
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.007

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 head0.325
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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