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INGR Roadmap Security and Privacy Chapter

2023· article· en· W4396853406 on OpenAlexaff
Ashutosh Dutta, Eman Hammad, Michael A. Enright, Fawzi Behmann, Arsenia Chorti, Ahmad Raza Cheema, Kassi Kadio, Julia Urbina-Pineda, Khaled Alam, Ahmed Limam, Fred C. Chu, J.N. Lester, Jong‐Geun Park, Joseph Bio-Ukeme, Sanjay Pawar, Roslyn Layton, Prakash Ramchandran, Kingsley Okonkwo, Lyndon Ong, Marc Emmelmann, Omneya Issa, Rajakumar Arul, Sivarama Krishnan, Suresh Sugumar, Tk Lala, Baw Chng, Bharat S. Rawal, Taha Sajid, Haobo Lai, Glaucio H. S. Carvalho, Matthew Borst, Brad Kloza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsCarleton UniversityRogers Communications (Canada)Brock UniversityShared Services Canada
Fundersnot available
KeywordsComputer scienceInternet privacyComputer securityInformation privacy

Abstract

fetched live from OpenAlex

5G/6G technologies provide ubiquitous connectivity while also addressing the demands of both individual consumers and businesses <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . 5G/6G technologies are expected to provide higher throughput, lower latency, higher density and mobility range without compromising reliability. By virtue of its flexibility and an agile development methodology that uses modular network functions, 5G/6G Networks support various use cases that are both scalable and cost-effective. 6G can support exciting new use cases, including massive IoT, smart transportation, e-Health, smart cities, tactile computing, kinesthetic communication, and holographic interactions. 5G and beyond technologies present a paradigm shift of wireless mobile communication <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> .

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 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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.252

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 communication0.0000.001
Open science0.0000.001
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

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