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Record W4402055661 · doi:10.23977/acss.2024.080512

Construction of Traffic Collection Network for Banking Data Centers

2024· article· en· W4402055661 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2024
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionBusinessComputer networkComputer scienceTelecommunicationsTransport engineeringEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

In recent years, with the development of banking business and diversification of demands, especially the explosive growth of Internet business, banks have put forward higher requirements for the agile expansion capability of banking information systems. Banks are building a new generation of data centers based on the thinking of cloud architecture. The bank data center is undergoing a transformation from traditional network architecture to a new network architecture that supports multi-business and multi-point active data centers. In this process, network operation and maintenance capabilities are also being built, transformed and upgraded synchronously. Increasing the construction of network traffic collection and visual analysis technology applications to view the network performance, link status and business system operation of various types of devices in the data center in real time has become an important means of network operation and maintenance.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.381

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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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