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Interactive Visual Analytics System for Criminal Intelligence Analysts with Multiple Coordinated Views
Seokweon Jung, Donghwa Shin, Jinwook Bok, Seokhyeon Park, Hyeon Jeon, Jinwook Seo, Insoo Lee, Sooyoung Park
http://doi.org/10.5626/JOK.2023.50.1.47
Data that criminal intelligence analysts have to analyze have become much larger and more complex in recent decades. However, the environment and methods of investigation have not yet kept up with those changes. In this study, we examined current investigation practices in Korean Government Agency. We focused on the sensemaking process of investigation and tried to adopt visual analytics approaches for sensemaking into the investigation. We derived tasks and design requirements and designed a multi-view visual analytics system that could satisfy them. We validated our design with a high-fidelity prototype through a case study to show realistic use cases.
Analyzing the Effect of the Twitter Corpus Selection on the Accuracy of Smartwatch Text Entry
http://doi.org/10.5626/JOK.2022.49.4.321
When a statistical decoder is used to support text entry on a smartwatch, fast and accurate typing is possible. In this paper, we analyzed the effect of a corpus, which is used to construct a language model necessary to implement the autocorrect function, on the accuracy of character input. Language models are based on the Brown corpus, which consists of text of various genres, and the Twitter corpus, extracted from tweet messages. We constructed a statistical decoder for the autocorrect function of the text entry using the two language models, and we simulated user touch input with the dual Gaussian distribution on the smartwatch keyboard to input Enron mobile phrases, composed of phrases written on real mobile devices. The test result shows that the average character error rate (CER) of the Brown corpus and the Twitter corpus is 8.35% and 6.44%, respectively, confirming a statistically significant difference.
Leveraging the Physical Properties of Real Objects to Manage Digital Photography in Augmented Reality
Han Joo Chae, Youli Chang, Minji Kim, Gwanmo Park, Jinwook Seo
http://doi.org/10.5626/JOK.2020.47.10.900
We introduced the concept of physical-object-oriented interaction that provides a natural user experience by leveraging the physical properties of real objects, and the development of ARphy, a tangible interface that enables people to manage and interact with digital photographs using real physical objects in augmented reality (AR). Unlike traditional mobile photo applications, ARphy utilizes the physical attributes and affordances of real objects for more intuitive usages. For example, people can hang travel photos on a souvenir, keep meaningful photos inside a box, or delete photos by putting them into a trash can. We designed the architecture of ARphy for use in various types of AR devices (e.g., mobile devices and headsets). Our qualitative user evaluation demonstrated that ARphy was intuitive, immersive, and fun to use and well-suited for managing digital photos in an AR environment.
Progressive Visual Analytics Using Scagnostics and an Automatic Partitioning Variables Selection Method
DongHwa Shin, Sehi L’Yi, Hyunjoo Song, Jinwook Seo
http://doi.org/10.5626/JOK.2018.45.8.801
In this paper, we propose a visual analytics system that combines progressive visualization with a partitioning variables selection method, one of the analytic techniques based on a scagnostics concept. In order to overcome the problems of scalability and performance associated with the existing method, all of the interface elements are designed so as to update the analysis progress in real time. The interface consists of two parts: an overview of the scatterplots to be analyzed and a detailed view for exploring interesting scatterplots in detail. We introduce the design rationale of our system and present a data analysis scenario to show how users can effectively use the system.
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