Engineering topics

Han, Kyungsik

Publications and source records attributed to Han, Kyungsik.

8 records

DOE OSTI2017

Design Science Methodology Applied to a Chemical Surveillance Tool

Public health surveillance systems gain significant benefits from integrating existing early incident detection systems,supported by closed data sources, with open source data.However, identifying potential alerting incidents relies on finding accurate, reliable sources and presenting the high volume of data in a way that increases analysts work efficiency; a challenge for any system that leverages open source data. In this paper, we present the design concept and the applied design science research methodology of ChemVeillance, a chemical analyst surveillance system.Our work portrays a system design and approach that translates theoretical methodology into practice creating a powerful surveillance system built for specific use cases.Researchers, designers, developers, and related professionals in the health surveillance community can build upon the principles and methodology described here to enhance and broaden current surveillance systems leading to improved situational awareness based on a robust integrated early warning system.

Huang, Zhuanyi↗

DOE OSTI2017

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of : i) relationships among scientists’ familiarity, their perceived lev- els of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.

Dasgupta, Aritra↗

DOE OSTI2017

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

ChemAnalyzer: A Text Analytics Platform for Chemical Surveillance

ChemAnalyzer is designed to highlight and summarize important information in chemical incident reportings and other textual sources to reduce the burden on a chemical analyst. By reducing time spent identifying relevant information and summarizing important characteristics, the application (app) enables expedient and thorough surveillance of multiple sources to identify a potential chemical incident.

Gaurav, Sanket↗

DOE OSTI2016

Using Social Media to Measure Student Wellbeing: A Large-Scale Study of Emotional Response in Academic Discourse

Student resilience and emotional well-being are essential for both academic and social development. Earlier studies on tracking students' happiness in academia showed that many of them struggle with mental health issues. For example, a 2015 study at the University of California Berkeley found that 47% of graduate students suffer from depression, following a 2005 study that showed 10% had considered suicide. This is the first large-scale study that uses signals from social media to evaluate students' emotional well-being in academia. This work presents fine-grained emotion and opinion analysis of 79,329 tweets produced by students from 44 universities. The goal of this study is to qualitatively evaluate and compare emotions and sentiments emanating from students' communications across different academic discourse types and across universities in the U.S. We first build novel predictive models to categorize academic discourse types generated by students into personal, social, and general categories. We then apply emotion and sentiment classification models to annotate each tweet with six Ekman's emotions -- joy, fear, sadness, disgust, anger, and surprise and three opinion types -- positive, negative, and neutral. We found that emotions and opinions expressed by students vary across discourse types and universities, and correlate with survey-based data on student satisfaction, happiness and stress. Moreover, our results provide novel insights on how students use social media to share academic information, emotions, and opinions that would pertain to students academic performance and emotional well-being.

academic discourse↗

DOE OSTI2016

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profiles or tags.

behavioral patterns and detection of teens in soci↗

DOE OSTI2016

Exploring Effective Decision Making through Human-Centered and Computational Intelligence Methods

Decision-making has long been studied to understand a psychological, cognitive, and social process of selecting an effective choice from alternative options. Its studies have been extended from a personal level to a group and collaborative level, and many computer-aided decision-making systems have been developed to help people make right decisions. There has been significant research growth in computational aspects of decision-making systems, yet comparatively little effort has existed in identifying and articulating user needs and requirements in assessing system outputs and the extent to which human judgments could be utilized for making accurate and reliable decisions. Our research focus is decision-making through human-centered and computational intelligence methods in a collaborative environment, and the objectives of this position paper are to bring our research ideas to the workshop, and share and discuss ideas.

collaboration↗

DOE OSTI2016

Understanding Roles of Social Media in Academic Engagement and Satisfaction for Graduate Students

Research indicates positive effects of social media in academia and education. However its main populations have been faculty, teachers, high school or college students, and its primary contexts have been course or classroom settings. We realized there exists a lack of studies on how Ph.D. (broadly graduate) students use social media for academic purposes and how its use is associated with academic motivation, engagement, and satisfaction, which are salient factors for the success of their graduate degrees and life. Based on the survey responses from 91 current Ph.D. students, our study results highlight that (1) students mainly use social media for broadcasting and keeping up with up-to-date academic and research information; yet, making connections and developing professional networks is one of the primary reasons, and (2) social media use is positively associated with their academic engagement and satisfaction. We discuss implications and future work of our study.

social media↗