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Guest Editor

Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20759, USA
Interests: HCI; visual analytics; big data analytics; information visualization

Dr. Soo-Yeon Ji

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Department of Computer Science, Bowie State University, Bowie, MD 20715, USA
Interests: data science; medical informatics and visualization; time-series analysis

Dr. Bong-Keun Jeong

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Guest Editor

Department of Management and Decision Sciences, Coastal Carolina University, Conway, SC 29528, USA
Interests: data analytics; visualization; management information systems; HCI

Special Issue Information

Dear Colleagues,

In data science, information visualization has become a central component to understanding complex scientific problems or data through visualization. Thus, designing innovative visualization systems is essential to address and solve various domain problems. For this, a theoretical understanding of visualization models and design principles through visual encoding, interaction, and/or analysis tasks is critical to solving domain problems and understanding data effectively. Identifying possible implications from theories of perception, cognition, design, and/or aesthetics is also important. Automated design guidelines and visualization recommendations should be determined to find scientific limitations of understanding data through visualization.

This Special Issue aims to seek high-quality papers that highlight visualization challenges to be accomplished, elucidate solutions for understanding domain problems through visualizations, and theoretical visualization principles for advancing visualization techniques and applications.

Dr. Dong Hyun Jeong
Dr. Soo-Yeon Ji
Dr. Bong-Keun Jeong
Guest Editors

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Keywords

information visualization
science of user interactions in visualization
visualization theory
knowledge-assisted visualization
visualization technique
visualization in machine learning
visualziatioin applications and design studies
evaluation and empirical research in visualization
visual data analysis and knowledge discovery
visual representation and interaction
visualization applications
visualization taxonomies and models
visualization algorithms and technologies
uncertainty visualization
visualization tools and systems for simulation and modeling

Published Papers (2 papers)

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Research

Open AccessArticle

Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System

and

Information 2023, 14(7), 401; https://doi.org/10.3390/info14070401 - 14 Jul 2023

Viewed by 586

Abstract

Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand the results given by an RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What–Why–How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N = 12) based on a thematic analysis of think-aloud sessions and semi-structured interviews with students and researchers to investigate the potential effects of providing Why and How explanations together in an explainable RS on users’ perceptions regarding transparency, trust, and satisfaction. Our study shows qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type. Full article

(This article belongs to the Special Issue Information Visualization Theory and Applications)

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Open AccessArticle

On Isotropy of Multimodal Embeddings

Kirill Tyshchuk

Polina Karpikova

Andrew Spiridonov

Anastasiia Prutianova

Anton Razzhigaev

and

Alexander Panchenko

Information 2023, 14(7), 392; https://doi.org/10.3390/info14070392 - 10 Jul 2023

Viewed by 751

Abstract

Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based text encoders, such as the BERT model. Anisotropic word embeddings do not use the entire space, instead concentrating on a narrow cone in such a pretrained vector space, negatively affecting the performance of applications, such as textual semantic similarity. Transforming a vector space to optimize isotropy has been shown to be beneficial for improving performance in text processing tasks. This paper is the first comprehensive investigation of the distribution of multimodal embeddings using the example of OpenAI’s CLIP pretrained model. We aimed to deepen the understanding of the embedding space of multimodal embeddings, which has previously been unexplored in this respect, and study the impact on various end tasks. Our initial efforts were focused on measuring the alignment of image and text embedding distributions, with an emphasis on their isotropic properties. In addition, we evaluated several gradient-free approaches to enhance these properties, establishing their efficiency in improving the isotropy/alignment of the embeddings and, in certain cases, the zero-shot classification accuracy. Significantly, our analysis revealed that both CLIP and BERT models yielded embeddings situated within a cone immediately after initialization and preceding training. However, they were mostly isotropic in the local sense. We further extended our investigation to the structure of multilingual CLIP text embeddings, confirming that the observed characteristics were language-independent. By computing the few-shot classification accuracy and point-cloud metrics, we provide evidence of a strong correlation among multilingual embeddings. Embeddings transformation using the methods described in this article makes it easier to visualize embeddings. At the same time, multiple experiments that we conducted showed that, in regard to the transformed embeddings, the downstream tasks performance does not drop substantially (and sometimes is even improved). This means that one could obtain an easily visualizable embedding space, without substantially losing the quality of downstream tasks. Full article

(This article belongs to the Special Issue Information Visualization Theory and Applications)

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