After decades of digitization, large cultural heritage collections have emerged on the web,
which contain massive stocks of content from galleries, libraries, archives, and museums …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

With the emergent focus on human-centered artificial intelligence (HCAI), research is
required to understand the humanistic aspects of AI design, identify the mechanisms through …

Understanding and accounting for uncertainty is critical to effectively reasoning about
visualized data. However, evaluating the impact of an uncertainty visualization is complex …

Over the last decade, ensemble visualization has witnessed a significant development due
to the wide availability of ensemble data, and the increasing visualization needs from a …

As researchers are increasingly able to collect data on a large scale from multiple clinical
and omics modalities, multi-omics integration is becoming a critical component of …

Most visualization techniques have been designed on the assumption that the data to be
represented are free from uncertainty. Yet this is rarely the case. Recently the visualization …

With data comes uncertainty, which is a widespread and frequent phenomenon in data
science and analysis. The amount of information available to us is growing exponentially …

In topological data analysis and visualization, topological descriptors such as persistence
diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an …

Quantifying uncertainty is an increasingly important topic across many domains. The
uncertainties present in data come with many diverse representations having originated …