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Record W4389028921 · doi:10.54097/hset.v70i.12166

Application Analysis of Security Technology in Computer Software Development

2023· article· en· W4389028921 on OpenAlexaff
Haiyang Guo, Kairui Zhang

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceComputer securityOrder (exchange)Field (mathematics)Network securityKey (lock)Computer technologyInformation technologyNetwork Access ControlInformation securityComputer security modelSoftwareCloud computing securityRisk analysis (engineering)BusinessMultimediaCloud computing

Abstract

fetched live from OpenAlex

At present, citizens benefit from the facilities provided by information network technology in all aspects of daily life. However, the particularity of the computer network environment also causes many unfavorable factors in the application of network technology. If these negative factors are not restricted, they will have a direct impact on the development of information technology industry. Therefore, creating a good business environment for this network has become a key issue in the field of computer software development. Facts have proved that computer security not only affects the economic development of enterprises, but also affects the security of information provided to users. In order to cope with these shortcomings, designers have developed many computer security tools to solve the problems of computer network operation caused by security risks.

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)0.006
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.210
Teacher spread0.205 · 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
Published2023
Admission routes1
Has abstractyes

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