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PhonMatrix: Visualizing co-occurrence constraints of sounds 51

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PhonMatrix: Visualizing co-occurrence constraints of sounds

51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Mayer, Thomas Rohrdantz, Christian Research Unit Quantitative Language Comparison Philipps University of Marburg Data Analysis and Visualization Group University of Konstanz

This paper describes the online tool PhonMatrix, which analyzes a word list with respect to the co-occurrence of sounds in a specified context within a word. The co-occurrence counts from the user-specified context are statistically analyzed according to a number of association measures that can be selected by the user. The statistical values then serve as the input for a matrix visualization where rows and columns represent the relevant sounds under investigation and the matrix cells indicate whether the respective ordered pair of sounds occurs more or less frequently than expected. The usefulness of the tool is demonstrated with three case studies that deal with vowel harmony and similar place avoidance patterns. © 2013 Association for Computational Linguistics.

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Exploring Uncertainty in Image Segmentation Ensembles

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Exploring Uncertainty in Image Segmentation Ensembles

20th Eurographics/IEEE VGTC Conference on visualization, EuroVis 2018

作者： Fröhler, B. Möller, T. Weissenböck, J. Hege, H.-C. Kastner, J. Heinzl, C. Research Group Computed Tomography University of Applied Sciences Upper Austria Austria Visualization and Data Analysis Research Group Faculty of Computer Science University of Vienna Austria Visual Data Analysis Department Zuse Institute Berlin Germany

ISBN: (纸本)9783038680659

Finding the most accurate image segmentation involves analyzing results from different algorithms or parameterizations. In this work, we identify different types of uncertainty in this analysis that are represented by the results of probabilistic algorithms, by the local variability in the segmentation, and by the variability across the segmentation ensemble. We propose visualization techniques for the analysis of such types of uncertainties in segmentation ensembles. For a global analysis we provide overview visualizations in the image domain as well as in the label space. Our probability probing and scatter plot based techniques facilitate a local analysis. We evaluate our techniques using a case study on industrial computed tomography data. © 2018 The Author(s)

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Sketch-based 3D model retrieval using keyshapes for global and local representation

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Sketch-based 3D model retrieval using keyshapes for global a...

5th Eurographics Workshop on 3D Object Retrieval, EG 3DOR 2012

作者： Saavedra, J.M. Bustos, B. Schreck, T. Yoon, S. Scherer, M. KDW-PRISMA Research Group Department of Computer Science University of Chile Chile Data Analysis and Visualization Group University of Konstanz Germany Yonsei Institute of Convergence Technology Yonsei University Korea Republic of GRIS TU Darmstadt Germany

ISBN: (纸本)9783905674361

Since 3D models are becoming more popular, the need for effective methods capable of retrieving 3D models is becoming crucial. Current methods require an example 3D model as query. However, in many cases, such a query is not easy to get. An alternative is using a hand-drawn sketch as query. In this work, we present a new keyshape based approach named HKO-KASD for retrieving 3D models using rough sketches as queries. Our approach comprises two general steps. First, a global descriptor is used to determine the appropriate viewpoint for each model. Second, we apply a local matching process to determine the final ranking for an input sketch. To this end, we present a local descriptor capable of working with sketch representations. The global descriptors as well as the local descriptors rely on a set of keyshapes precomputed from 2D representations of 3D models and from the query sketch as well. We evaluate our method using the first-tier precision and compare it with current approaches (HELO, STELA). Our results show a significant increase in precision for many classes of 3D models. © The Eurographics Association 2012.

关键词： Self organizing maps

PointCloudXplore: A visualization Tool for 3D Gene Expression data 1

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PointCloudXplore: A Visualization Tool for 3D Gene Expressio...

1st Workshop of the DFG's International research Training group "visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering"

作者： Rübel, Oliver Weber, Gunther H. Keränen, Soile V. E. Fowlkes, Charless C. Hendriks, Cris L. Luengo Simirenko, Lisa Shah, Nameeta Y. Eisen, Michael B. Biggin, Mark D. Hagen, Hans Sudar, Damir Malik, Jitendra Knowles, David W. Hamann, Bernd International Research Training Group 'Visualization of Large and Unstructured Data Sets' University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)9783885794387

The Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of threedimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several extensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data. © 2006 Gesellschaft für Informatik, Bonn.

关键词： visualization

PointCloudXplore: Visual analysis of 3D Gene Expression data Using Physical Views and Parallel Coordinates

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PointCloudXplore: Visual Analysis of 3D Gene Expression Data...

8th Joint Eurographics/IEEE VGTC Symposium on visualization, EuroVis 2006

作者： Rübel, O. Weber, G.H. Keränen, S.V.E. Fowlkes, C.C. Hendriks, C.L. Luengo Simirenko, L. Shah, N.Y. Eisen, M.B. Biggin, M.D. Hagen, H. Sudar, D. Malik, J. Knowles, D.W. Hamann, B. International Research Training Group "Visualization of Large and Unstructured Data Sets" University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory CA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)3905673312

To allow a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos. Here we report the first components of a visualization tool, PointCloudXplore, that allows the relationships between different gene's expression to be analyzed using the BDTNP's datasets. PointCloudXplore uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PointCloudXplore, physical views of the data are linked to parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within the embryo. Parallel coordinates, on the other hand, show only some features of each gene's expression, but allow simultaneous analysis of data for many more genes than would be possible in a physical view. We have developed several extensions to standard parallel coordinates to facilitate brushing the visualization of 3D gene expression data. © The Eurographics Association 2006.

关键词： visualization

Deliberative XAI: How Explanations Impact Understanding and Decision-Making of AI Novices in Collective and Individual Settings

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arXiv 2024年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Research Network Data Science Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

XAI research often focuses on settings where people learn about and assess algorithmic systems individually. However, as more public AI systems are deployed, it becomes essential for XAI to facilitate collective understanding and deliberation. We conducted a task-based interview study involving 8 focus groups and 12 individual interviews to explore how explanations can support AI novices in understanding and forming opinions about AI systems. Participants received a collection of explanations organized into four information categories to solve tasks and decide about a system’s deployment. These explanations improved or calibrated participants’ self-reported understanding and decision confidence and facilitated group discussions. Participants valued both technical and contextual information and the self-directed and modular explanation structure. Our contributions include an explanation approach that facilitates both individual and collaborative interaction and explanation design recommendations, including active and controllable exploration, different levels of information detail and breadth, and adaptations to the needs of decision subjects. Copyright © 2024, The Authors. All rights reserved.

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Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

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arXiv 2023年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science UniVie Doctoral School Computer Science DoCS Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" [24] framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach. © 2023, CC BY.

关键词： Decision making

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

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arXiv 2023年

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems’ adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants’ thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants’ perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems. © 2023, CC BY.

关键词： Decision making

Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions

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arXiv 2024年

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the "XAI Novice Question Bank," an extension of the XAI Question Bank [60] containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task-based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences. Copyright © 2024, The Authors. All rights reserved.

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