Demo Data from EDBT 2026 / reviewers in the wild / expert
Joseph DeChicchis
dblp:252/3262
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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 |
Immersive interaction · 100% | |
| Network and information security
1 paper |
Systems and software security · 100% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Immersive interaction
augmented reality |
0.4 | 1 | 2019 | Adaptive AR visual output security using reinforcement learning trained policies: demo abstract · SenSys 2019 |
Systems and software security › application security
augmented reality security |
0.4 | 1 | 2019 | Adaptive AR visual output security using reinforcement learning trained policies: demo abstract · SenSys 2019 |
Machine learning › Reinforcement learning
policy learning |
0.1 | 1 | 2019 | Adaptive AR visual output security using reinforcement learning trained policies: demo abstract · SenSys 2019 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Adaptive AR visual output security using reinforcement learning trained policies: demo abstractabstractAugmented reality (AR) technologies have seen significant improvement in recent years with several consumer and commercial solutions being developed. New security challenges arise as AR becomes increasingly ubiquitous. Previous work has proposed techniques for securing the output of AR devices and used reinforcement learning (RL) to train security policies which can be difficult to define manually. However, whether such systems and policies can be deployed on a physical AR device without degrading performance was left an open question. We develop a visual output security application using a RL trained policy and deploy it on a Magic Leap One head-mounted AR device. The demonstration illustrates that RL based visual output security systems are feasible. Joseph DeChicchis, Surin Ahn, Maria Gorlatova |
SenSys | 1 |