Demo Data from VLDB 2026 / reviewers in the wild / expert
Ying-Ju Lin
dblp:264/7312
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Personal fabrication and tangible interfaces · 77% Health and well-being technologies · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Personal fabrication and tangible interfaces › digital fabrication
food 3d printing |
0.4 | 1 | 2020 | FoodFab: Creating Food Perception Illusions using Food 3D Printing · CHI 2020 |
Methods — techniques the papers use, named apart from their topics
user study · 0.4computational model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | FoodFab: Creating Food Perception Illusions using Food 3D PrintingabstractPersonalization of eating such that everyone consumes only what they need allows improving our management of food waste. In this paper, we explore the use of food 3D printing to create perceptual illusions for controlling the level of perceived satiety given a defined amount of calories. We present FoodFab, a system that allows users to control their food intake through modifying a food's internal structure via two 3D printing parameters: infill pattern and infill density. In two experiments with a total of 30 participants, we studied the effect of these parameters on users' chewing time that is known to affect people's feeling of satiety. Our results show that we can indeed modify the chewing time by varying infill pattern and density, and thus control perceived satiety. Based on the results, we propose two computational models and integrate them into a user interface that simplifies the creation of personalized food structures. Ying-Ju Lin, Parinya Punpongsanon, Xin Wen 0025, Daisuke Iwai, Kosuke Sato, Marianna Obrist, Stefanie Mueller 0001 |
CHI | 1 |