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Android for Enterprise Automated Systems

2015· book-chapter· en· W2477707249 on OpenAlexaff
Fahmi Ncibi, Habib Hamam, Ezzedine Ben Braiek

Bibliographic record

VenueAdvances in business information systems and analytics book series · 2015
Typebook-chapter
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAndroid (operating system)Mobile phoneComputer scienceMobile deviceAndroid applicationArchitectureEmbedded systemOperating systemEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

In this chapter, various aspects pertaining to the open operating system Android OS such as its history, architecture, features, and utility for business purposes will be introduced, following which the role of Android in enterprise management will be explained. The chapter will be concluded by a detailed report of the BYOD approach that uses Android for industrial control and automation. Since mobile devices have become progressively more powerful and accessible, mobile computing has greatly changed our daily lives. As one of the most popular mobile operating systems, Android provides the tools and API for Android developers to develop Android applications. Android is an open source operating system for mobile devices. Today its primary use is lodged in the mobile phone industry. During the recent past years, many projects have been created, with the objective to elevate Android to other platforms, such as sub-notebooks or embedded systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.013
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 head0.015
GPT teacher head0.260
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2015
Admission routes1
Has abstractyes

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