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Record W2897010537 · doi:10.1142/s012962641850010x

The Importance of Applying Security Practices in Wireless Communication: Bluetooth Low Energy and RFID

2018· article· en· W2897010537 on OpenAlexaff
Joseph P. Tassone, Mike Biocchi

Bibliographic record

VenueParallel Processing Letters · 2018
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsAlgoma UniversityLakehead University
Fundersnot available
KeywordsSophisticationBluetoothComputer scienceExploitWirelessComputer securityBluetooth Low EnergyField (mathematics)Radio-frequency identificationIdentification (biology)Internet of ThingsEnergy (signal processing)CreativityInternet privacyTelecommunications

Abstract

fetched live from OpenAlex

Security is a concept which people recognize as important, yet regularly ignore for reasons such as cost or design constraints. The world is quickly shifting towards the wireless with phenomena akin to the Internet of Thing (IoT) accelerating this progression. Technologies like Bluetooth Low Energy and Radio Frequency Identification are greatly entwined with this trend, and research has been made into reinforcing protection methods. However, security is a choice made by the designer and more often than not is given decreased priority. With the improved creativity and sophistication of malicious exploits this is becoming far less acceptable. Theft of data is trivial for a user with the correct skillset and will be successful without proper defences. Further research needs to be done in the field, and encouraging consistent security practices is an appropriate start.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.012
Scholarly communication0.0070.009
Open science0.0010.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.002

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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