Demo Data from EDBT 2026 / reviewers in the wild / expert
Zichao Shen
dblp:319/3480
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-4809-5905ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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.
| Computer networks
1 paper |
Wireless sensing and localization · 100% | |
| Artificial intelligence
1 paper |
3D vision · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Personal fabrication and tangible interfaces · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › point cloud analysis › point cloud learning
point-based deep neural networks |
0.7 | 1 | 2023 | MiliPoint: A Point Cloud Dataset for mmWave Radar · NeurIPS 2023 |
Computer vision › 3D vision › point cloud analysis
point cloud learning |
0.7 | 1 | 2023 | MiliPoint: A Point Cloud Dataset for mmWave Radar · NeurIPS 2023 |
Wireless sensing and localization
human activity recognition |
0.7 | 1 | 2023 | MiliPoint: A Point Cloud Dataset for mmWave Radar · NeurIPS 2023 |
Wireless sensing and localization › radar sensing
mmwave radar sensing |
0.7 | 1 | 2023 | MiliPoint: A Point Cloud Dataset for mmWave Radar · NeurIPS 2023 |
Wireless sensing and localization
radar sensing |
0.2 | 1 | 2023 | MiliPoint: A Point Cloud Dataset for mmWave Radar · NeurIPS 2023 |
Methods — techniques the papers use, named apart from their topics
point cloud neural networks · 0.7point cloud neural network · 0.7prototype demonstration · 0.6material analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | MiliPoint: A Point Cloud Dataset for mmWave RadarabstractMillimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors? To answer this question, our work, termed MiliPoint, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development. Shu Zhong, Zichao Shen, Naim Dahnoun |
NeurIPS | 4 |
| 2022 | FabricatINK: Personal Fabrication of Bespoke Displays Using Electronic Ink from Upcycled E ReadersabstractAbstract: FabricatINK explores the personal fabrication of irregularly-shaped low-power displays using electronic ink (E ink). E ink is a programmable bicolour material used in traditional form-factors such as E readers. It has potential for more versatile use within the scope of personal fabrication of custom-shaped displays, and it has the promise to be the pre-eminent material choice for this purpose. We appraise technical literature to identify properties of E ink, suited to fabrication. We identify a key roadblock, universal access to E ink as a material, and we deliver a method to circumvent this by upcycling broken electronics. We subsequently present a novel fabrication method for irregularly-shaped E ink displays. We demonstrate our fabrication process and E ink’s versatility through ten prototypes showing different applications and use cases. By addressing E ink as a material for display fabrication, we uncover the potential for users to create custom-shaped truly bistable displays. Ollie Hanton, Zichao Shen, Mike Fraser 0001, Anne Roudaut |
CHI | 2 |