Augmented Reality – Implications toward Virtual Reality, Human Perception and Performance
Bibliographic record
Abstract
Augmented reality (AR) is defined as “a live direct or an indirect view of a physical, real-world environment whose elements are augmented by computer-generated sensory input, such as sound, graphics or GPS data.” It is not uncommon to come face-to-face with smart devices that are equipped with multiple embedded sensory inputs such as mega pixel camera, microphones, speakers, high definition (e.g. Retina) displays, 3D displays, holographic displays and pico-projection technologies. Such technology has enabled application designers and developers to package information succinctly and efficiently without loss of clarity. Recently, AR applications (e.g. iPhone World Lens, Google goggles) have drawn mainstream attention. The military also has programs that represent a leap forward (e.g. DARPA Sandblaster program). These advances in AR have been influenced by developments in variety of technologies including low cost of advanced processors, light weight displays, ubiquitous computing afforded by omnipresent devices such as smart phones, tablets, etc. However, there are currently no human factors standards to aid the development. These technologies have great potential to enhance our abilities, but there is also the risk that they represent an annoyance or a significant safety risk. Specifically, improper system lag, reliability, display design (e.g., clutter or resolution) could lead to errors. The goal of this session is to discuss what research is needed to define these standards. It is likely that there is no one set of standards, but developing a framework for these standards will go a long way towards bridging the research-application gap.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".