Visualizing Extensions of Argumentation Frameworks as Layered Graphs

• URL: http://arxiv.org/abs/2409.05457v1
• Date: Mon, 9 Sep 2024 09:29:53 GMT
• Title: Visualizing Extensions of Argumentation Frameworks as Layered Graphs
• Authors: Martin Nöllenburg, Christian Pirker, Anna Rapberger, Stefan Woltran, Jules Wulms,
• Abstract summary: We introduce a new visualization technique that draws an AF, together with an extension, as a 3-layer graph layout. Our technique supports the user to more easily explore the visualized AF, better understand extensions, and verify algorithms for computing semantics.
• Score: 15.793271603711014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The visualization of argumentation frameworks (AFs) is crucial for enabling a wide applicability of argumentative tools. However, their visualization is often considered only as an accompanying part of tools for computing semantics and standard graphical representations are used. We introduce a new visualization technique that draws an AF, together with an extension (as part of the input), as a 3-layer graph layout. Our technique supports the user to more easily explore the visualized AF, better understand extensions, and verify algorithms for computing semantics. To optimize the visual clarity and aesthetics of this layout, we propose to minimize edge crossings in our 3-layer drawing. We do so by an exact ILP-based approach, but also propose a fast heuristic pipeline. Via a quantitative evaluation, we show that the heuristic is feasible even for large instances, while producing at most twice as many crossings as an optimal drawing in most cases.

Related papers

• Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval [85.73149096516543]
  We address the choice of viewpoint during sketch creation in Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) A pilot study highlights the system's struggle when query-sketches differ in viewpoint from target instances. To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks.
  arXiv  Detail & Related papers  (2024-07-01T21:20:44Z)
• SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering [0.0]
  Scene graphs have emerged as a useful tool for multimodal image analysis. Current methods that utilize idealized annotated scene graphs struggle to generalize when using predicted scene graphs extracted from images. Our approach extracts a scene graph from an input image using a pre-trained scene graph generator.
  arXiv  Detail & Related papers  (2023-10-03T07:14:53Z)
• Rethinking Range View Representation for LiDAR Segmentation [66.73116059734788]
  "Many-to-one" mapping, semantic incoherence, and shape deformation are possible impediments against effective learning from range view projections. We present RangeFormer, a full-cycle framework comprising novel designs across network architecture, data augmentation, and post-processing. We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks.
  arXiv  Detail & Related papers  (2023-03-09T16:13:27Z)
• Expansion of Visual Hints for Improved Generalization in Stereo Matching [26.37702321092758]
  We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics.
  arXiv  Detail & Related papers  (2022-11-01T11:30:26Z)
• SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning [61.57887011165744]
  multimodal Transformers have made great progress in the task of Visual Commonsense Reasoning. We propose a Scene Graph Enhanced Image-Text Learning framework to incorporate visual scene graphs in commonsense reasoning.
  arXiv  Detail & Related papers  (2021-12-16T03:16:30Z)
• Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach [57.67932970472768]
  Mesh and Point Cloud simplification methods aim to reduce the complexity of 3D models while retaining visual quality and relevant salient features. We propose a fast point cloud simplification method by learning to sample salient points. The proposed method relies on a graph neural network architecture trained to select an arbitrary, user-defined, number of points from the input space and to re-arrange their positions so as to minimize the visual perception error.
  arXiv  Detail & Related papers  (2021-09-30T10:23:55Z)
• Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility [17.929307870456416]
  We present a novel framework for mesh reconstruction from unstructured point clouds. We take advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation.
  arXiv  Detail & Related papers  (2021-08-18T20:28:16Z)
• To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes [0.0]
  We compiled all state-of-the-art visualization methods in a single R-package (moPLOT) This enables - for the first time - to illustrate the landscape structures of three-dimensional MOPs. We additionally provide a dashboard that allows to compute the state-of-the-art visualizations for a wide variety of common benchmark functions.
  arXiv  Detail & Related papers  (2020-11-29T16:33:15Z)
• Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
  We propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
  arXiv  Detail & Related papers  (2020-11-06T12:17:01Z)
• Weakly Supervised Visual Semantic Parsing [49.69377653925448]
  Scene Graph Generation (SGG) aims to extract entities, predicates and their semantic structure from images. Existing SGG methods require millions of manually annotated bounding boxes for training. We propose Visual Semantic Parsing, VSPNet, and graph-based weakly supervised learning framework.
  arXiv  Detail & Related papers  (2020-01-08T03:46:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.