Demo Data from VLDB 2026 / reviewers in the wild / expert
Parker Malachowsky
dblp:352/9513
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 50% Computational science and engineering · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › k-12 education
k-12 computing education |
0.7 | 1 | 2023 | An Introduction to Rule-Based Feature and Object Perception for Middle School Students · AAAI 2023 |
Computational science and engineering › pattern recognition
rule-based classification |
0.7 | 1 | 2023 | An Introduction to Rule-Based Feature and Object Perception for Middle School Students · AAAI 2023 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | An Introduction to Rule-Based Feature and Object Perception for Middle School StudentsabstractThe Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule-based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce students to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to classify higher-order objects. We discuss how this tool can be used as a "first step" to the more complex concept ideas of data representation and neural networks. Daniella DiPaola, Parker Malachowsky, Nancye Blair Black, Sharifa Alghowinem, Xiaoxue Du, Cynthia Breazeal |
AAAI | 2 |