Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hongzhe Du
dblp:358/9482
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0007-1234-2875ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
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 |
Accessibility and assistive technology · 87% Immersive interaction · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Accessibility and assistive technology › sign language technologies
sign language translation |
0.7 | 1 | 2023 | Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR Glasses · MobiCom 2023 |
Immersive interaction › see-through display
augmented reality glasses |
0.2 | 1 | 2023 | Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR Glasses · MobiCom 2023 |
Methods — techniques the papers use, named apart from their topics
traditional machine learning models · 0.7ASL linguistic feature exploitation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR GlassesabstractSign-to-911 offers a compact mobile system solution to fast and runtime American Sign Language (ASL) and English translations. It is designated as 911 call services for ASL users with hearing disabilities upon emergencies. It enables bidirectional translations of ASL-to-English and English-to-ASL. The signer wears the AR glasses, runs Sign-to-911 on his/her smartphone and glasses, and interacts with a 911 operator. The design of Sign-to-911 departs from the popular deep learning based solution paradigm, and adopts simpler traditional AI/machine learning (ML) models. The key is to exploit ASL linguistic features to simplify the model structures and improve accuracy and speed. It further leverages recent component solutions from graphics, vision, natural language processing, and AI/ML. Our evaluation with six ASL signers and 911 call records has confirmed its viability. Yunqi Guo, Boyan Ding, Congkai Tan, Weichong Ling, Zhaowei Tan, Jennifer Miyaki, Hongzhe Du, Songwu Lu |
MobiCom | 8 |