A prototype Haptic E-Book system to support immersive remote reading in a smart space
Bibliographic record
Abstract
Interactive book reading contributes profoundly to the language development in preschool students. Experimental research showed that shared book-reading between adults and children provides children with the opportunity to acquire new vocabulary. Moreover, the time spent reading together provides clear evidence to a child of a parent's love and care [1]. Inspired by such, we propose a remote reading framework in which parents or grandparents remotely participate in remote reading sessions. In this framework, we present an intuitive annotation based approach of hapto-audio-visual interaction with the traditional digital learning materials. We argue that picture and haptic modality enhanced book reading accelerates language development as students can relate the text with a known visual and tactile references. Hence, in the proposed Haptic E-Book system, by integrating the home entertainment system in the user's reading experience combined with haptic interfaces we examine whether such augmentation of modalities influence the user's learning behaviour. The proposed Haptic E-Book (HE-Book) system leverages the haptic jacket, haptic arm band as well as haptic sofa interfaces to provide haptic emotive signals to the remote story listener in the form of patterned vibrations of the actuators and expresses the learning material by incorporating image based augmented display in order to pave ways for intimate, shared, and immersive reading experience in the popular ebook platform.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".