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Record W1539000950 · doi:10.1108/09685220410542606

An exploration of wireless computing risks

2004· article· en· W1539000950 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Management & Computer Security · 2004
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsYork UniversityConcordia University
Fundersnot available
KeywordsWirelessComputer scienceWireless networkSoftware portabilityFlexibility (engineering)Risk analysis (engineering)Computer securityTelecommunicationsBusiness

Abstract

fetched live from OpenAlex

Wireless computing, as a way of providing mobile services, has been growing steadily during the past few years. While wireless communications offer organizations and users many benefits such as portability, flexibility, increased productivity, and lower installation costs; on the other hand, risks inherent and exacerbated by wireless connectivity are widely reported, wherein networks are open to intruders who may cause unwanted consequences to an organization's information resources. Hence, understanding these risks will help protect against unforeseen threats, delays and costs. This paper aims at the development of a taxonomy for wireless computing risks. Six levels are identified including risks associated with the users, mobile devices, wireless networks, wireless applications, the Internet and the corporate gateway. The findings show that there is a need for systematic studies on wireless risk assessment and management. The implications of these findings for both researchers and practitioners are discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.010
Open science0.0010.001
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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