BLURRING THE BOUNDARY BETWEEN DIRECT & INDIRECT MIXED MODE INPUT ENVIRONMENTS
Bibliographic record
Abstract
The current computer input devices are either direct or indirect based on how interactive input data is interpreted by a computing system. Existing research have shown the benefits and limitations of both input modalities, and found that the limits of one input mode are the advantages of the other mode. In this thesis, we explore a new type of input device, which integrates direct and indirect input into a shared input space: mixed input mode. With a mixed mode input device, the two input modes could well complement each other in the shared input space, such that users can freely choose which mode to use to achieve optimized performance. We explore the hardware and software design space of mixed mode input device. On the hardware side, we created three novel prototype devices: 1) LucidCursor - a mixed mode input handheld device; 2) LensMouse - a mixed mode input computer mouse; and Magic Finger - a mixed mode finger-worn input device. We demonstrate how these devices can be built. We also evaluated the performance of the three proposed prototypes from various perspectives through a set of carefully designed user and system evaluations. On the software side, most of the current software user interfaces are only designed and optimized for a certain input mode, e.g. either mouse or finger input. To facilitate the mixed mode input on the existing software UIs, we proposed and evaluated two target expansion techniques - TouchCuts & TouchZoom. These techniques allow touchscreen laptop users to freely choose an input mode without impacting users' experience of the other input mode (e.g. mouse). An important finding of this thesis is that the mixed mode input devices have many add-on benefits, which can significantly improve the user experience of interacting with computer systems.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".