Huyen N. Nguyen

Huyen N. Nguyen, Nils Gehlenborg

Effective visualization retrieval necessitates a clear definition of similarity. Despite the growing body of work in specialized visualization retrieval systems, a systematic approach to understanding visualization similarity remains absent. We introduce the Similarity Framework for Visualization Retrieval (Safire), a conceptual model that frames visualization similarity along two dimensions: comparison criteria and representation modalities. Comparison criteria identify the aspects that make visualizations similar, which we divide into primary facets (data, visual encoding, interaction, style, metadata) and derived properties (data-centric and human-centric measures). Safire connects what to compare with how comparisons are executed through representation modalities. We categorize existing representation approaches into four groups based on their levels of information content and visualization determinism: raster image, vector image, specification, and natural language description, together guiding what is computable and comparable. We analyze several visualization retrieval systems using Safire to demonstrate its practical value in clarifying similarity considerations. Our findings reveal how particular criteria and modalities align across different use cases. Notably, the choice of representation modality is not only an implementation detail but also an important decision that shapes retrieval capabilities and limitations. Based on our analysis, we provide recommendations and discuss broader implications for multimodal learning, AI applications, and visualization reproducibility.

Huyen Nguyen, Haihua Chen, Lavanya Pobbathi et al.

Evaluating text summarization has been a challenging task in natural language processing (NLP). Automatic metrics which heavily rely on reference summaries are not suitable in many situations, while human evaluation is time-consuming and labor-intensive. To bridge this gap, this paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. We also conducts a comparative study on eight automatic metrics, human evaluation, and our proposed LLM-based method. Seven different types of state-of-the-art (SOTA) summarization models were evaluated. We perform extensive experiments and analysis on datasets with patent documents. Our results show that LLMs evaluation aligns closely with human evaluation, while widely-used automatic metrics such as ROUGE-2, BERTScore, and SummaC do not and also lack consistency. Based on the empirical comparison, we propose a LLM-powered framework for automatically evaluating and improving text summarization, which is beneficial and could attract wide attention among the community.

Huyen N. Nguyen, Jake Gonzalez, Jian Guo et al.

This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassification, and providing an analysis of underlying patterns, in response to the VAST Challenge 2020 Mini-Challenge 2. VisMCA facilitates tracking provenance and provides a comprehensive view of object detection results, easing re-labeling, and producing reliable, corrected data for future training. Our solution implements multiple analytical views on visual analysis to offer a deep insight for underlying pattern discovery.

Huyen N. Nguyen, Vinh T. Nguyen, Tommy Dang

Having a good Human-Computer Interaction (HCI) design is challenging. Previous works have contributed significantly to fostering HCI, including design principle with report study from the instructor view. The questions of how and to what extent students perceive the design principles are still left open. To answer this question, this paper conducts a study of HCI adoption in the classroom. The studio-based learning method was adapted to teach 83 graduate and undergraduate students in 16 weeks long with four activities. A standalone presentation tool for instant online peer feedback during the presentation session was developed to help students justify and critique other's work. Our tool provides a sandbox, which supports multiple application types, including Web-applications, Object Detection, Web-based Virtual Reality (VR), and Augmented Reality (AR). After presenting one assignment and two projects, our results showed that students acquired a better understanding of the Golden Rules principle over time, which was demonstrated by the development of visual interface design. The Wordcloud reveals the primary focus was on the user interface and shed some light on students' interest in user experience. The inter-rater score indicates the agreement among students that they have the same level of understanding of the principles. The results show a high level of guideline compliance with HCI principles, in which we witnessed variations in visual cognitive styles. Regardless of diversity in visual preference, the students presented high consistency and a similar perspective on adopting HCI design principles. The results also elicited suggestions into the development of the HCI curriculum in the future.

Huyen N. Nguyen, Tommy Dang

This paper introduces EQSA, an interactive exploratory tool for earthquake situational analytics using social media. EQSA is designed to support users to characterize the condition across the area around the earthquake zone, regarding related events, resources to be allocated, and responses from the community. On the general level, changes in the volume of messages from chosen categories are presented, assisting users in conveying a general idea of the condition. More in-depth analysis is provided with topic evolution, community visualization, and location representation. EQSA is developed with intuitive, interactive features and multiple linked views, visualizing social media data, and supporting users to gain a comprehensive insight into the situation. In this paper, we present the application of EQSA with the VAST Challenge 2019: Mini-Challenge 3 (MC3) dataset.