Data visualization can be defined as the visual communication of informa...
When telling a data story, an author has an intention they seek to conve...
A common way to evaluate the reliability of dimensionality reduction (DR...
The ability to monitor and interpret of hardware system events and behav...
Despite the value of VR (Virtual Reality) for educational purposes, the
...
With the most advanced natural language processing and artificial
intell...
With the rapid accumulation of text data produced by data-driven techniq...
Multivariate networks are commonly found in real-world data-driven
appli...
Adversarial attacks on a convolutional neural network (CNN) – injecting
...
Researchers have proposed various methods for visually interpreting the
...
Spatial statistical analysis of multivariate volumetric data can be
chal...
Neural networks have shown great potential in compressing volumetric dat...
Dimensionality reduction (DR) plays a vital role in the visual analysis ...
Analyzing air pollution data is challenging as there are various analysi...
Network data plays a vital role in much of today's visualization researc...
The rapid development of Convolutional Neural Networks (CNNs) in recent ...
Depending on the node ordering, an adjacency matrix can highlight distin...
The optimization of water distribution systems (WDSs) is vital to minimi...
Finding the similarities and differences between groups of datasets is a...
Visual data storytelling is gaining importance as a means of presenting
...
Air quality maps help users make decisions to mitigate the effects of
po...
Many real-world applications involve analyzing time-dependent phenomena,...
Deep Recurrent Neural Networks (RNN) continues to find success in predic...
Dynamic networks – networks that change over time – can be categorized i...
Autonomous multi-robot systems, where a team of robots shares informatio...
We present P6, a declarative language for building high performance visu...
Data-driven problem solving in many real-world applications involves ana...
A common network analysis task is comparison of two networks to identify...
Optimizing the performance of large-scale parallel codes is critical for...
Comparative analysis of event sequence data is essential in many applica...
Contrastive learning (CL) is an emerging analysis approach that aims to
...
Current text visualization techniques typically provide overviews of doc...
There is a growing trend of applying machine learning methods to medical...
Machine learning for data-driven diagnosis has been actively studied in
...
The use of adaptive workflow management for in situ visualization and
an...
Understanding and tuning the performance of extreme-scale parallel compu...
Deep Recurrent Neural Networks (RNN) is increasingly used in decision-ma...
The visualization of a network influences the quality of the mental map ...
Mind mapping is a popular way to explore a design space in creative thin...
This study describes a detailed analysis of museum visitors' decoding pr...
We examine the application of narrative strategies to present a complex ...
Dimensionality reduction (DR) methods are commonly used for analyzing an...
Dimensionality reduction (DR) is frequently used for analyzing and
visua...
As different layouts can characterize different aspects of the same grap...
The development and design of visualization solutions that are truly usa...
The use of large-scale multifaceted data is common in a wide variety of
...
Deep Recurrent Neural Network (RNN) has gained popularity in many sequen...
Using different methods for laying out a graph can lead to very differen...
This is the arXiv index for the electronic proceedings of the 25th
Inter...
Photo composition is an important factor affecting the aesthetics in
pho...