Bibliographic record
Abstract
Today, the digital community has strongly allied with rich sensory human computer interfaces (HCIs) to better understand how people interact via their sense of touch. A variety of touch interaction systems are essential for real environments, such as teleconferencing systems for remote interpersonal communications, and virtual environments, such as interacting with virtual scenes using personal computers for gaming applications. Through our sense of touch, we are capable of perceiving different types of stimuli such as pressure, vibration, pain, temperature and position. Psychologists, physiologists, and engineers have collaborated to study touch and advance the understanding of the human senses. In this research at the University of Ottawa, we leverage knowledge of the psychology and perception of haptics to better understand the human tactile sensory system. We utilize a human sensory illusion called the "funnelling illusion" to display a dynamic tactile sensation, such as a smooth, continuous sensation on the human skin, with low-resolution vibrotactile actuators. After obtaining the illusion of a continuous movement of one tactile stimulus, we investigate the influence of temporal intensity changes of adjacent vibrotactile actuators located on the dorsal of the human forearm and upper arm. Furthermore, we examine the quality of the continuous movement according to the intensity change of the vibrotactile actuators in a linear and logarithmic pattern. Initial psychophysical experiments have revealed correlations between the distance, orientation and temporal order of the vibrotactile actuators with the preferred intensity variation, substantiating our research direction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".