Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User Feedback

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

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Outfit Browser – An Image-data-driven User Interface for Self-service Systems in Fashion Stores

Procedia Manufacturing ◽

10.1016/j.promfg.2015.07.686 ◽

2015 ◽

Vol 3 ◽

pp. 3521-3528 ◽

Cited By ~ 1

Author(s):

Alexander Piazza ◽

Christian Zagel ◽

Sebastian Huber ◽

Matthias Hille ◽

Freimut Bodendorf

Keyword(s):

User Interface ◽

Image Data ◽

Service Systems ◽

Data Driven ◽

Self Service

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Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

BioMed Research International ◽

10.1155/2016/3939815 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Chang-Hee Han ◽

Jeong-Hwan Lim ◽

Jun-Hak Lee ◽

Kangsan Kim ◽

Chang-Hwan Im

Keyword(s):

Interindividual Variability ◽

Main Idea ◽

Individual Variability ◽

User Feedback ◽

Data Driven ◽

Neurofeedback Training ◽

Online Experiments ◽

The Individual ◽

Feedback Level ◽

Eeg Features

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

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Station Models Research Based on IFS Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.386 ◽

2014 ◽

Vol 539 ◽

pp. 386-389

Author(s):

Liu Li ◽

Guo Fei Zhang

Keyword(s):

Information System ◽

User Interface ◽

Management Information System ◽

Development Model ◽

Data Driven ◽

Management Information ◽

Information Models ◽

And Function ◽

Function Models

As a reaserch and development model, IFS is fast and efficient for MIS sysment.At first, we introduced the IFS models (information models, station models and function models) ,and it is proposed that the management information system (MIS) is build base on IFS models data-driven technologies and four tiers soft development technologies. Finally, we described abstract of the station models (user interface) and mechanism about the station models to the user interface.

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Mobile application user interface layout features for data-driven design

International Journal of Embedded Systems ◽

10.1504/ijes.2021.10044051 ◽

2021 ◽

Vol 14 (5) ◽

pp. 421

Author(s):

Qincheng Gao ◽

Yifan Wei ◽

Hao Yin ◽

Zexun Jiang

Keyword(s):

User Interface ◽

Mobile Application ◽

Data Driven

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Glass, Paper, Scissors: Investigating the Metaphors of the Glass Cast through Paper Prototyping

Scholarly and Research Communication ◽

10.22230/src.2015v6n2a219 ◽

2015 ◽

Vol 6 (2) ◽

Cited By ~ 1

Author(s):

Ernesto Peña ◽

Teresa Dobson

Keyword(s):

Social Sciences ◽

User Interface ◽

Graduate Students ◽

Design Process ◽

User Feedback ◽

Knowledge Networks ◽

Glass Block ◽

Negative Space ◽

Paper Prototyping ◽

Time Subject

This article reflects on the importance of user feedback in early stages of the design process of Glass Cast. A 3-D interface, Glass Cast is intended for the visualization of knowledge networks, including parameters such as authorship, time, subject, discipline, and connections between documents in a corpus. The name Glass Cast refers to the working metaphor of the prototype, which is a cast sculpture in which the object of representation appears as negative space in a glass block. The participants in this study, graduate students and faculty in the humanities and social sciences, provided feedback on a low-fidelity paper prototype in the context of interviews. Their feedback is organized following the taxonomy of user-interface metaphors.

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