Designing a mobile device automatic detector to support mobile librarysystems
Bibliographic record
Abstract
Content providers cannot ensure that digital material will be reformatted and accessed by any mobile device correctly, due to mobile device limitations. A mobile device automatic detector can provide accurate information about devices accessing mobile library systems. This information can be used to properly render content for the specific device. This chapter proposes an approach to designing a detector that will support mobile library systems so as to render web content dynamically and adaptively. The overall architecture of the detector is also discussed in this chapter and follows from a simple experimental study to evaluate the design proposed here. Introduction The rapid adoption of mobile devices with internet capabilities has allowed users to work or study at any time, in any place (Motiwalla, 2007). There have also been descriptions of implementations of library access via mobile devices (Needham and Ally, 2008; Yang et al., 2006). However, there are many limitations to mobile devices, which greatly restrict the relevant applications of mobile technology (Ally et al., 2006; Kojiri et al., 2007). Although some content providers have designed purposely digital material for mobile learning, providers cannot ensure that content is able to be correctly reformatted and accessed by any mobile device. This is due to the diverse characteristics among devices, which are not taken into account by most ubiquitous learning systems (Yang, 2007; Motiwalla, 2007). Some early attempts made effective use of proxy servers (Cheung et al., 2007) In any case, either with proxies or by other detection methods, it is essential to provide adaptive content based on the characteristics of mobile learners and mobile devices. In this chapter an automatic mobile device detector for mobile library systems is proposed to support content designers. This detector will provide adaptive content based on the capabilities of different mobile devices. This investigation concerns and is limited to the characteristics of the mobile devices. Learners’ characteristics may also affect adaptive content. The aim of this research is to construct an architecture that detects features of mobile devices, create RDFS (Resource Description Framework Schema) formatted mobile device profiles and provide content designers with services that are relevant to the mobile device profile. Constructing the mobile device profile is a great challenge for mobile library systems, the most difficult part being the immediate and accurate collection of mobile device features when content is accessed via the world wide web.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".