Related papers: Towards Understanding Decision Problems As a Goal …
In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The…
In contemporary information ecologies saturated with misinformation, disinformation, and a distrust of science itself, public data communication faces significant hurdles. Although visualization research has broadened criteria for effective…
We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice,…
How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its…
Software Visualization encompasses the development and evaluation of methods for graphically representing different aspects of methods of software, including its structure, execution and evolution. Creating visualizations helps the user to…
Common pitfalls in visualization projects include lack of data availability and the domain users' needs and focus changing too rapidly for the design process to complete. While it is often prudent to avoid such projects, we argue it can be…
Understanding player strategies is a key question when analyzing player behavior both for academic researchers and industry practitioners. For game designers and game user researchers, it is important to gauge the distance between intended…
Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the…
The main aim of decision support systems is to find solutions that satisfy user requirements. Often, this leads to predictability of those solutions, in the sense that having the input data and the model, an adversary or enemy can predict…
Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups…
Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have received little attention to date. This position paper maps…
This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and…
The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning…
Graphical perception studies typically measure visualization encoding effectiveness using the error of an "average observer", leading to canonical rankings of encodings for numerical attributes: e.g., position > area > angle > volume. Yet…
One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced. This is…
Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive…
With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or…
Researchers in the humanities are among the many who are now exploring the world of big data. They have begun to use programming languages like Python or R and their corresponding libraries to manipulate large data sets and discover brand…
Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characterization methods aiming to provide feedback for optimization or…
Understanding different types of users' needs can even be more critical in today's data visualization field, as exploratory visualizations for novice users are becoming more widespread with an increasing amount of data sources. The…