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Found 11 papers, 6 papers with code
Extracting Human Attention through Crowdsourced Patch Labeling
We demonstrated the effectiveness of our method in mitigating bias through improved classification accuracy and the refined focus of the model.
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Nonetheless, we lack knowledge of how people customize and interact with agent personas.
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity.
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
To address this issue, we present ZADU, a Python library that provides distortion measures.
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction
In this paper, we introduce two novel quality measures -- Label-Trustworthiness and Label-Continuity (Label-T&C) -- advancing the process of DR evaluation based on class labels.
Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
We then propose processes to generalize internal measures to fulfill these new axioms, and use them to extend the widely used Calinski-Harabasz index for between-dataset CLM evaluation.
Uniform Manifold Approximation with Two-phase Optimization
We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately.
Distortion-Aware Brushing for Interactive Cluster Analysis in Multidimensional Projections
Brushing is an everyday interaction in 2D scatterplots, which allows users to select and filter data points within a continuous, enclosed region and conduct further analysis on the points.
Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections
Our metrics consider two aspects of the inter-cluster reliability: Steadiness measures the extent to which clusters in the projected space form clusters in the original space, and Cohesiveness measures the opposite.
Interactive Visualization for Exploring Information Fragments in Software Repositories
Software developers explore and inspect software repository data to obtain detailed information archived in the development history.
Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis
Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history.
Software Engineering Human-Computer Interaction
