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Record W2960445241 · doi:10.1109/mitp.2019.2906442

Security and Vulnerability of Extreme Automation Systems: The IoMT and IoA Case Studies

2019· article· en· W2960445241 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

VenueIT Professional · 2019
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsLakehead University
Fundersnot available
KeywordsThe InternetComputer securityVulnerability (computing)AviationDigitizationComputer scienceEmerging technologiesTelecommunicationsHealth careRisk analysis (engineering)Internet privacyBusinessEngineeringWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

The Internet of Medical Things (IoMT) and the Internet of Aviation (IoA) are emerging waves of technologies that contributes to establishing-connected systems. It consists of smart devices, such as wearables, sensors technology, smart algorithms, and monitors, strictly for healthcare and aviation uses. It can reduce unnecessary hospital visits and the burden on aviation systems. However, because of increasing demand and its accessibility to high internet speed, IoMT, and IoA has opened doors for serious vulnerabilities to healthcare and aviation systems. The disastrous consequences of these issues will not only disrupt services causing financial losses but will also put the peoples' lives at risk. IoMT and IoA pass though a massive wave of digitization change in order to make both industries affordable, safe, and smart. This article sheds light on the security and vulnerability issues of these two technologies and suggests such remedies as echoed by the relevant industries.

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)0.000
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 score0.902
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.048
GPT teacher head0.327
Teacher spread0.279 · 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