Towards a smart world and ubiquitous intelligence: A walkthrough from smart things to smart hyperspaces and UbicKids
Bibliographic record
Abstract
A cyber world (CW) is a digitized world created on cyberspaces inside computers interconnected by networks including the Internet. Following ubiquitous computers, sensors, e‐tags, networks, information, services, etc., is a road towards a smart world (SW) created on both cyberspaces and real spaces. It is mainly characterized by ubiquitous intelligence or computational intelligence pervasion in the physical world filled with smart things. In recent years, many novel and imaginative researches have been conducted to try and experiment a variety of smart things including characteristic smart objects and specific smart spaces or environments as well as smart systems. The next research phase to emerge, we believe, is to coordinate these diverse smart objects and integrate these isolated smart spaces together into a higher level of spaces known as smart hyperspace or hyper‐environments, and eventually create the smart world. In this paper, we discuss the potential trends and related challenges toward the smart world and ubiquitous intelligence from smart things to smart spaces and then to smart hyperspaces. Likewise, we show our efforts in developing a smart hyperspace of ubiquitous care for kids, called UbicKids.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".