Replicability and Transparency for the Creation of Public Human User Video Game Datasets
April 06, 2023 Β· Declared Dead Β· π 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Emma J. Pretty, Renan Guarese, Haytham M. Fayek, Fabio Zambetta
arXiv ID
2304.02861
Category
cs.HC: Human-Computer Interaction
Citations
4
Venue
2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Last Checked
4 months ago
Abstract
Replicability is absent in games research; a lack of transparency in protocol detail hinders scientific consensus and willingness to publish public datasets, impacting the application of these techniques in video games research. To combat this, we propose and give an example of the use of a set of experimental considerations, such as games and materials choice. This work promotes the communication of research protocols when publishing datasets, benefiting researchers when designing experiments.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted