Sungahn Ko

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics in road networks. The novel aspects of our approach mainly include spatial attention, temporal atte

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion

Osteocalcin (OC), a noncollagenous bone matrix protein, is expressed in high levels by osteoblasts. To determine whether the OC promoter mediates cell-specific gene expression in cells of osteoblast lineage, we constructed a recombinant adenovirus, Ad-OC-TK, which contains the OC promoter that drives the expression of herpes simplex virus thymidine kinase (TK). We tested the expression of TK by this virus in osteoblast cell lines as well as in non-osteoblastic cell lines by assessing the enzyme

Abstract Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploratio

Users may face challenges while designing graphical user interfaces, due to a lack of relevant experience and guidance. This paper aims to investigate the issues users face during the design process, and how to resolve them. To this end, we conducted semi-structured interviews, based on which we built a GUI prototyping assistance tool called GUIComp. This tool can be connected to GUI design software as an extension, and it provides real-time, multi-faceted feedback on a user's current design. Ad

We introduce WordBridge, a novel graph-based visualization technique for showing relationships between entities in text corpora. The technique is a node-link visualization where both nodes and links are tag clouds. Using these tag clouds, WordBridge can reveal relationships by representing not only entities and their connections, but also the nature of their relationship using representative keywords for nodes and edges. In this paper, we apply the technique to an interactive web-based visual an

Predicting the road traffic speed is a challenging task due to different types of roads, abrupt speed changes, and spatial dependencies between roads, which requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel Spatio-Temporal Graph Attention (STGRAT) that effectively captures the spatio-temporal dynamics in road networks. The features of our approach mainly include spatial attention, temporal a

The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transform

Abstract Competitive intelligence is a systematic approach for gathering, analyzing, and managing information to make informed business decisions. Many companies use competitive intelligence to identify risks and opportunities within markets. Point of sale data that retailers share with vendors is of critical importance in developing competitive intelligence. However, existing tools do not easily enable the analysis of such large and complex data. therefore, new approaches are needed in order to

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948-2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time-such as countries and continents with their demographic data-into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been s

Music creation is difficult because one must express one's creativity while following strict rules. The advancement of deep learning technologies has diversified the methods to automate complex processes and express creativity in music composition. However, prior research has not paid much attention to exploring the audiences' subjective satisfaction to improve music generation models. In this paper, we evaluate human satisfaction with the state-of-the-art automatic symbolic music generation mod

Abstract Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐tri

A novel method for the production of adenoviral vectors on a scale sufficient to support most research applications and early phase clinical trials is presented. This method utilizes serum-free cell culture medium and a hollow fiber cell culture apparatus. Significantly less time and space are required than in conventional methods, and the resulting adenovirus is collected in a much smaller volume, simplifying the purification steps. The protocol described is a reproducible, convenient, biologic

Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. A number of models have been proposed to solve this challenging problem with a focus on learning spatio-temporal dependencies of roads. In this work, we propose a new perspective of converting the forecasting problem into a pattern matching task, assuming that large data can be represented by a set of patterns. To evaluate the validness of the new perspective, we