Only YOU Can Make IEEE VIS Environmentally Sustainable
August 29, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Elsie Lee-Robbins, Andrew McNutt
arXiv ID
2308.15429
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The IEEE VIS Conference (or VIS) hosts more than 1000 people annually. It brings together visualization researchers and practitioners from across the world to share new research and knowledge. Behind the scenes, a team of volunteers puts together the entire conference and makes sure it runs smoothly. Organizing involves logistics of the conference, ensuring that the attendees have an enjoyable time, allocating rooms to multiple concurrent tracks, and keeping the conference within budget. In recent years, the COVID-19 pandemic has abruptly disrupted plans, forcing organizers to switch to virtual, hybrid, and satellite formats. These alternatives offer many benefits: fewer costs (e.g., travel, venue, institutional), greater accessibility (who can physically travel, who can get visas, who can get child care), and a lower carbon footprint (as people do not need to fly to attend). As many conferences begin to revert to the pre-pandemic status quo of primarily in-person conferences, we suggest that it is an opportune moment to reflect on the benefits and drawbacks of lower-carbon conference formats. To learn more about the logistics of conference organizing, we talked to 6 senior executive-level VIS organizers. We review some of the many considerations that go into planning, particularly with regard to how they influence decisions about alternative formats. We aim to start a discussion about the sustainability of VIS -- including sustainability for finance, volunteers, and, central to this work, the environment -- for the next three years and the next three hundred years.
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