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Record W2913503446 · doi:10.5539/cis.v12n1p93

Applicability Analysis of Semi-Network Operating System

2019· article· en· W2913503446 on OpenAlexaffvenue
Yin Sheng Zhang

Bibliographic record

VenueComputer and Information Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceFlexibility (engineering)AutonomyControllabilityTrustworthinessArchitectureHuman–computer interactionComputer security

Abstract

fetched live from OpenAlex

Innovation of computing technology is either to improve the security and performance, or to improve  the convenience of user operation of system, networking and device, but sometimes it’s hard to get a very good balance between the two, in particular, the traditional localized OS and computing devices are inherently incompatible  in some respects with today's network environment,  therefore, although many innovations help to improve the safety, autonomy and controllability of system and device, but they are still always lacking the trustworthiness of users, one of the reasons is that their performance is lower than  user's expectation, but their operation become more complicated than before. The semi-network operating system is designed according to user's actual experience, which not only utilizes the shared attributes of network to increase the flexibility of OS, but also utilizes the stability and user-autonomy attributes of local platform ensure the base functions of OS, in addition, it can greatly reduce the burden on user’s operation and maintenance of computing system, so which will be a trustworthy system architecture for users.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.006
Open science0.0010.000
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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2019
Admission routes2
Has abstractyes

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