It has been nearly 20 years since the U.S. Supreme Court ruled on the use of race/ethnicity, gender, and national origin in university admissions in the University of Michigan cases. It is now 2021 and universities are still struggling with how to ...

Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture ...

Recent content creation systems allow users to generate various high-quality content (e.g., images, 3D models, and melodies) by just specifying a parameter set (e.g., a latent vector of a deep generative model). The task here is to search for an ...

Organizing fingerings, i.e., choosing which fingers to press on which positions and strings, is a crucial step for playing the violin. As the violin fingering comprises several components, the mapping of a musical phrase to the corresponding fingering ...

Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer valuable help, such input is often limited, expensive, and generally not in ...

The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or ...

Rich multi-modal information - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. UI designs are composed of UI entities supporting different functions which together enable the ...

Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations. These approaches measure different qualities of explanations to achieve diverse goals in designing ...

Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted results are to the target. This simplifies away all the details of the ...

Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity ...

In this paper, we design novel interactive deep learning methods to improve semantic interactions in visual analytics applications. The ability of semantic interaction to infer analysts’ precise intents during sensemaking is dependent on the quality of ...

Creating a high-quality layout design from scratch is difficult for novices. Therefore, novices often consult the works of other skilled designers for ideas regarding layout designs. Researchers have previously investigated methods to support the layout ...

When designing a smart glove for gesture recognition, the set of sensors available and their layout on the glove are crucial. However, once a computational model reaches acceptable recognition accuracy, it is often not clear which sensors are more ...

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from ...

Building a perfect knowledge base in a certain domain is practically impossible, so it is effective for dialogue systems to acquire knowledge for enhancing an imperfect knowledge base through natural language dialogues with users. This paper proposes a ...

This paper contributes to the growing literature in empirical evaluation of explainable AI (XAI) methods by presenting a comparison on the effects of a set of established XAI methods in AI-assisted decision making. Specifically, based on our review of ...