Demo Data from SIGIR 2026 / reviewers in the wild / expert
Takuto Yoshida
dblp:214/4853
· DBLP profile ↗
2ranked-venue papers
2as first author
1since 2021 · last 2023
0009-0004-3766-9303ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-authorSecurity and privacy · 1 · 1 first-author · 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 |
Wearable and physiological sensing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › inertial sensing
pedestrian dead reckoning |
0.4 | 1 | 2019 | Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019 |
Wearable and physiological sensing › gait analysis
walking speed estimation |
0.4 | 1 | 2019 | Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019 |
Wearable and physiological sensing › inertial sensing
smartphone inertial sensing |
0.1 | 1 | 2019 | Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019 |
Methods — techniques the papers use, named apart from their topics
deep learning · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Upper Bounds on the Number of Shuffles for Two-Helping-Card Multi-input and Protocols
Takuto Yoshida, Kodai Tanaka, Keisuke Nakabayashi, Eikoh Chida, Takaaki Mizuki |
CANS | 1 |
| 2019 | Gait Dependency of Smartphone Walking Speed Estimation using Deep LearningabstractThis paper proposes an accurate estimation method of walking speed using deep learning for smartphone-based Pedestrian Dead Reckoning (PDR).PDR requires to estimate speed and direction of pedestrians accurately using accelerometer and gyroscope.To improve the accuracy of PDR, existing works focused to improve the key factors of speed estimation (i.e., stride and/or step estimation) by adapting deep learning.On the contrary, our research proposes to adapt deep learning more directly to estimate walking speed from sensor data of smartphone. We evaluate the accuracy of proposed method by comparing with conventional PDR method. As a result, we confirmed that proposed method can estimate the speed more accurately. Takuto Yoshida, Junto Nozaki, Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi |
MobiSys | 1 |