SERENE: The Semi-Automatic User Experience Detector
May 29, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrea Esposito
arXiv ID
2407.11980
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
SERENE (uSer ExpeRiENce dEtector), also known as UX-SAD (User eXperience-Smells Automatic Detector), is a research project born in 2020, which comprises different components. As its name suggests, its primary goal is to provide a way to quickly and (semi-) automatically detect problems in the user experience of websites and web-based systems. Through a set of Artificial Intelligence (AI) models, SERENE detects users' emotions in web pages while guaranteeing users' privacy. Its main strength over typical user experience and usability evaluation is in the generalizability of its detections. While traditional methods use samples (that may not be representative), SERENE allows to tap into data provided by the whole user population. The platform is available at https://serene.ddns.net.
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