In information visualization, rapidly expanding data volumes and increasingly complex user interactions highlight the urgent need for systematic usability evaluations. This research introduces and validates an innovative framework that integrates Nielsen’s Ten Usability Principles with the Analytic Hierarchy Process (AHP), transforming qualitative usability heuristics into quantifiable and reproducible metrics. The two-stage evaluation first engaged 30 experts in pairwise comparisons to derive AHP-based weights, which were applied to assess five electrocardiogram (ECG) visualization systems. Subsequent surveys of 200 general users validated the framework, revealing a strong correlation between expert-weighted scores and user evaluations. Results confirm the framework’s efficacy in reducing evaluator bias, enhancing reproducibility, and prioritizing critical design elements. Merging qualitative and quantitative analyses ensures rigorous, objective insights for iterative interface refinement. The methodology provides actionable guidance for designers and researchers addressing data-intensive visualization challenges, demonstrating its potential to advance usability evaluation practices in healthcare analytics and beyond.

Multiview-based 3D object detection achieved great success in the past years. However, for some complex models with complex inner structures, the performances of these methods are not satisfactory. This paper provides a method based on slide sampling for 3D object classification. First, we slice and sample the model from the different depths and directions to get the model’s features. Then, a deep neural network designed based on the attention mechanism is used to classify the input data. The experiments show that the performance of our method is competitive on ModelNet. Moreover, for some special models with simple surfaces and complex inner structures, the performance of our method is outstanding and stable.

We present a general computational framework to locally characterize any shape space of meshes implicitly prescribed by a collection of non-linear constraints. We computationally access such manifolds, typically of high dimension and co-dimension, through first and second order approximants, namely tangent spaces and quadratically parameterized osculant surfaces. Exploration and navigation of desirable subspaces of the shape space with regard to application specific quality measures are enabled using approximants that are intrinsic to the underlying manifold and directly computable in the parameter space of the osculant surface. We demonstrate our framework on shape spaces of planar quad (PQ) meshes, where each mesh face is constrained to be (nearly) planar, and circular meshes, where each face has a circumcircle. We evaluate our framework for navigation and design exploration on a variety of inputs, while keeping context specific properties such as fairness, proximity to a reference surface, etc.