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A Visual Analytics System for Interpretable Machine Learning
http://doi.org/10.5626/JOK.2023.50.1.57
Interpretable machine learning is a technology that assists people understand the behavior and prediction of machine learning systems. This study proposes a visual analytics system that can interpret the relationship between how machine learning models relate output results from input data. It supports users to interpret machine learning models easily and clearly. The visual analytics system proposed in this study takes an approach to effectively interpret the machine learning model through an iterative adjustment procedure that filters and groups model decision results according to input variables, target variables, and predicted/classified values. Through use case analysis and in-depth user interviews, we confirmed that our system could provide insights into the complex behavior of machine learning models, gain scientific understanding of input variables, target variables, and model predictions, and help users understand the stability and reliability of models.
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.
Best Practices on Software Development and Management Process for the Republic of Korea Army Information System
Bo Kyung Park, Chae Yun Seo, Ki Du Kim, Jong Hoon Lee, R.Young Chul Kim
http://doi.org/10.5626/JOK.2020.47.10.911
Korea Army HQ Information System Management Group is in absolute need to construct a software development management process to develop high-quality software. Therefore, they tried to partially construct the Army information software development system based on commercial software and operating systems. But the current issue is the complete construction of the open source-based software development and management system. To solve this problem, we propose the means to enhance the previous Army Information System with an open OS-based software development management process for quality improvement of Army software. This approach brings a solution to build an independent, salable and flexible software development management process with all open sources based on CentOS. This process can be easily adapted with some mechanisms such as application of the redefined quality metrics, automatically generating development documentation, and identifying the code complexity on the entire software lifecycle. Also, the process possibly facilitates the development high-quality software. In the future, we will need to extend the process by involving software process training, designing, development, maintenance and establishment at each phase of the lifecycle.
A Practice of Software Development Process Visualization for Army Information System Management
Woo Sung Jang, Hyung Seung Son, R.Young Chul Kim, Jong Hoon Lee
http://doi.org/10.5626/JOK.2018.45.9.904
To increase the chance of success of the current software project, we need a software process that is like a manufacturing process. Specially, the army information system should focus on developing an effective information system and managing byproducts as a whole process. Moreover, maintenance will require a way to avoid maintenance delays, which increase a costs, and degrade quality. To solve this problem, we apply a process visualization mechanism for the Army’s system, one of process, architecture, and documentation visualization in NIPA. We can guarantee a high quality of software that will provide transparency and traceability for all stakeholders in the life cycle. We expect to maintain high quality for the army’s software with quality indicators, result visualization, scheduling control, and management.
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.
A Defect Management Process based on Open Source Software for Small Organizations
http://doi.org/10.5626/JOK.2018.45.3.242
For high-quality software development, it is necessary to detect and fix the defects inserted. If defect management activities are not properly performed, it will lead to the project delay and project failure due to rework. Therefore, organizations need to establish defect management process and institutionalize it. Process standard models handle defect management in the area of project monitoring and control. However, small organizations experience difficulties in implementing and applying defect management process in a real situation. In this paper, we propose a defect management process for small organization which is designed in accordance with the characteristics of a small projects such as few participants and short development period. The proposed defect management process will be based on a tool chain with open source software such as Redmine, Subversion, Maven, Jenkins that support a defect management process and SW Visualization in systematic way. We also proposed a way of constructing defect database and various methods of analyzing and controlling defect data based on it. In an effort to prove the effectiveness of the proposed process, we applied the process and tool chain to a small organization.
A Class Diagramming Tool for Visualizing the Latest Revision of Software Change History
Jaekyeong Sim, HeeTae Cho, Jongyeol Park, Seonah Lee
http://doi.org/10.5626/JOK.2018.45.2.150
Software visualization can assist developers to understand a software system and change its code. The recent development of bottom-up visualization tools demonstrates the advantages by revealing the code that is directly related to a software evolution task. However, the information provided by these tools is limited to the code already investigated by the developers in that task session. To broaden the scope and provide the code information that developers should explore, we propose to present the latest revision of a software system via a class diagram. When a developer clicks on a button, the proposed tool reveals the code changes committed to a configuration management system, and facilitates the understanding of code changes. We also conduct case studies illustrating the advantages of the proposed tool.
Keyword Network Visualization for Text Summarization and Comparative Analysis
Kyeong-rim Kim, Da-yeong Lee, Hwan-Gue Cho
Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the “big data” era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors’ experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.
Spatiotemporal Data Visualization using Gravity Model
Seokyeon Kim, Hanbyul Yeon, Yun Jang
Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.
Dynamic Parameter Visualization and Noise Suppression Techniques for Contrast-Enhanced Ultrasonography
This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.
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