Blockchain technologies and cryptocurrency have made a significant impact on today's computing and financial sectors, and the use cases for blockchain applications are increasing day by day. However, there is little understanding of blockchain and cryptocurrencies amongst the general public. In this work, we present LACE, a kinetic sculpture and decentralized ledger created to educate audiences on the complexities of cryptocurrency creation through a visual form. We discuss the design and implementation of LACE as a modular system constructed of 10 kinetic units, each unit containing an array of Microsoft Surface tablets and one delta robot arm to perform touch based operations on each tablet with a modified stylus. Through this structure, we establish a distributed computing system in which each tablet represents blockchain nodes that maintain copies of the blockchain, mine for new blocks and process transactions through visual software interfaces. Additionally, we implement an interactive gaming module to help audiences understand the work of blockchain creation and the mining process. Finally, we evaluate the LACE project's effectiveness to teach audiences through a detailed questionnaire at the 2022 Accelerate Festival in Washington, DC. We found that 73% of visitors agreed they were able to learn something new from LACE and 82% enjoyed their interaction with LACE. / Master of Science / Global technology, computing, economic and financial sectors are all increasingly influenced by the use of the relatively new technologies known as blockchain and cryptocurrency. A blockchain is a publicly distributed digital ledger that keeps track of transaction data securely through cryptography. This technology is heavily associated with the global economy, following the introduction of the cryptocurrency Bitcoin in 2008. Cryptocurrency has often been compared to fiat currencies which are not backed by a commodity with intrinsic value like gold. Bitcoin is seen as a commodity due to its scarcity, with approximately 19 million bitcoins in existence and can be used as a monetary value to purchase goods and services. Studies have shown that a large segment of the general public has little to no understanding of these concepts, even those who have significant related investments. To help expand the understanding of these topics to general audiences, we present LACE; a kinetic sculpture and digital ledger designed to educate audiences on the complexities of cryptocurrency creation through visual and interactive demonstration. LACE demonstrates the processes of blockchain technologies through physical robotic movement and interactive software visualizations. Consisting of a collection of 10 acrylic hexagon units stacked together like building blocks to mimic a virtual network, each unit interacts with an array of Microsoft Surface tablets through an operating robot arm and modified stylus. These tablets illustrate the work of a blockchain through various visualizations, demonstrating the work of nodes and miners who operate and maintain a blockchain network. To help audiences understand the work of blockchain creation and the mining process, we implement an interactive gaming module where participants can act as a miner within a blockchain network and assist in the process of mining for new blocks, help maintain the blockchain and process transactions. We evaluate the LACE project's effectiveness to teach audiences through a detailed questionnaire at the 2022 Accelerate Festival in Washington, DC. We found that 76% of visitors had a better understanding of blockchain concepts following their interactions with LACE and 82% enjoyed their interaction with the sculpture overall.

In many US homes, 15% of the energy that can be saved is hidden beneath complex mathematical calculations. Hidden energy savings can be revealed by converting mathematical calculations to data visualizations, creating a story for residents to see how they are consuming energy. Cloud-based data visualization platforms offer the ability to appropriately communicate complex building energy data to a broad set of stakeholders. Unfortunately, proprietary solutions are too expensive and open-source options lack standardization for cloud-based energy monitoring. This study aims to create a comprehensive energy dashboard display to increase residents' energy awareness of how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight visualization chart types that are used on utility bills and energy monitoring devices. In addition, the literature review uncovered eight additional visualization chart types that have the functionality to visualize energy data. Next, the visualization chart types were combined with data modeling design techniques to create prototype energy dashboard displays to communicate energy insights to residents. Soon utility companies will begin to provide data visualizations for the majority of their customers. The insights from this study can help to inform and lead the development of commercially used data visualizations. In addition, this research can provide utility companies with a blueprint on how to share energy consumption data with customers. / Master of Science / For residents to live an energy-efficient lifestyle, they must first begin by learning about one's energy consumption behaviors in the home. Unfortunately, utility bills miss out on communicating energy insights to customers based on how the energy data appears on the utility bill. Graphs on utility bills that display aggregate monthly energy consumption do not provide enough information for residents to comprehend how energy is consumed through their homes or provide information on how to lower energy consumption. There are commercial energy consumption devices on the market such as CURB and eGauge that provide an energy dashboard display, but the visuals are too complex to draw conclusions. This study aims to create an energy dashboard display that allows residents to see how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight chart types used of utility bills and energy monitoring devices. In addition, the literature review results uncovered eight additional chart types not used on utility bills and energy monitoring devices that have the potential to visualize energy data. Next, the identified and uncovered chart types were combined with data modeling design techniques to create example energy dashboard displays. Changing the way energy data is displayed to residents, can educate residents on how energy is consumed throughout their home. In addition, the insights from this study can provide utility companies with a model for displaying energy data to increase their customers' energy awareness. Living an energy-efficient lifestyle, first began by understanding how energy is consumed throughout one's home.

Systems biology aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins and metabolites. Comprehensive visualization tools for exploring multivariate data are needed to gain insight into the physiological processes reflected in these molecular profiles. Data fusion methods are required to integratively study high-throughput transcriptomics, metabolomics and proteomics data combined before systems biology can live up to its potential. In this work I explored mathematical and statistical methods and visualization tools to resolve the prominent issues in the nature of systems biology data fusion and to gain insight into these comprehensive data. In order to choose and apply multivariate methods, it is important to know the distribution of the experimental data. Chi square Q-Q plot and violin plot were applied to all M. truncatula data and V. vinifera data, and found most distributions are right-skewed (Chapter 2). The biplot display provides an effective tool for reducing the dimensionality of the systems biological data and displaying the molecules and time points jointly on the same plot. Biplot of M. truncatula data revealed the overall system behavior, including unidentified compounds of interest and the dynamics of the highly responsive molecules (Chapter 3). The phase spectrum computed from the Fast Fourier transform of the time course data has been found to play more important roles than amplitude in the signal reconstruction. Phase spectrum analyses on in silico data created with two artificial biochemical networks, the Claytor model and the AB2 model proved that phase spectrum is indeed an effective tool in system biological data fusion despite the data heterogeneity (Chapter 4). The difference between data integration and data fusion are further discussed. Biplot analysis of scaled data were applied to integrate transcriptome, metabolome and proteome data from the V. vinifera project. Phase spectrum combined with k-means clustering was used in integrative analyses of transcriptome and metabolome of the M. truncatula yeast elicitation data and of transcriptome, metabolome and proteome of V. vinifera salinity stress data. The phase spectrum analysis was compared with the biplot display as effective tools in data fusion (Chapter 5). The results suggest that phase spectrum may perform better than the biplot. This work was funded by the National Science Foundation Plant Genome Program, grant DBI-0109732, and by the Virginia Bioinformatics Institute. / Ph. D.

Human behavior is a key factor influencing the spread of infectious diseases. Individuals adapt their daily routine and typical behavior during the course of an epidemic -- the adaptation is based on their perception of risk of contracting the disease and its impact. As a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data collection methods are not well suited to support the task of acquiring human behavior related information; especially as it pertains to epidemic planning and response. Internet-based methods are an attractive complementary mechanism for collecting behavioral information. Systems such as Amazon Mechanical Turk (MTurk) and online survey tools provide simple ways to collect such information. This thesis explores new methods for information acquisition, especially behavioral information that leverage this recent technology. Here, we present the design and implementation of a crowd-sourced surveillance data acquisition system -- WiSDM. WiSDM is a web-based application and can be used by anyone with access to the Internet and a browser. Furthermore, it is designed to leverage online survey tools and MTurk; WiSDM can be embedded within MTurk in an iFrame. WiSDM has a number of novel features, including, (i) ability to support a model-based abductive reasoning loop: a flexible and adaptive information acquisition scheme driven by causal models of epidemic processes, (ii) question routing: an important feature to increase data acquisition efficacy and reduce survey fatigue and (iii) integrated surveys: interactive surveys to provide additional information on survey topic and improve user motivation. We evaluate the framework's performance using Apache JMeter and present our results. We also discuss three other extensions of WiSDM: Adapter, Synthetic Data Generator, and WiSDM Analytics. The API Adapter is an ETL extension of WiSDM which enables extracting data from disparate data sources and loading to WiSDM database. The Synthetic Data Generator allows epidemiologists to build synthetic survey data using NDSSL's Synthetic Population as agents. WiSDM Analytics empowers users to perform analysis on the data by writing simple python code using Versa APIs. We also propose a data model that is conducive to survey data analysis. / Master of Science

Cheating, beguiling, and misleading information exist all around us; understanding deception and its consequences is crucial in our information environment. This study investigates deception in phishing emails that successfully bypassed Microsoft 365 filtering system. We devised a model that explains why some people are deceived and how targeted individuals and organizations can prevent or counter attacks. The theoretical framework used in this study is Anderson's functional ontology construction (FOC). The methodology involves quantitative and qualitative descriptive design, where the data source is the set of phishing emails archived from a Tier 1 University. We looked for term frequency-inverse document frequency (Tf-idf) and the distribution of words over documents (topic modeling) and found the subjects of phishing emails that targeted educational organizations are related to finances, jobs, and technologies. Also, our analysis shows the phishing emails in the dataset come under six categories; reward, urgency, curiosity, fear, job, and entertainment. Results indicate that staff and students were primarily targeted, and a list of the most used verbs for deception was compiled. We uncovered the stimuli being used by scammers and types of reinforcements used to misinform the target to ensure successful trapping via phishing emails. We identified how scammers pick their targets and how they tailor and systematically orchestrate individual attack on targets. The limitations of this study pertain to the sample size and the collection method. Future work will focus on implementing the derived model into software that can perform deception identification, target alerting and protection against advanced email phishing.

Data visualization has the power to portray an informative message when designed with the end user in mind. Energy data visualizations must be tailored to the resident's energy, graphical, and data literacy level. A resident's energy, graphical, and data literacy level depicts their understanding and life experience with energy. Current utility companies standardize data visualization formats for all customers, regardless of their literacy level. My aim for this dissertation is to evaluate how data visualization mediums (2D chart types and virtual reality visual aids) aid residents when reading, working with, analyzing, and arguing energy consumption data of household appliance pairs. The data visualization chart types explored include the area, bar, and circular column charts. The visual aids displayed in the virtual environment explored include color coding, electricity flow, and the power meter. The energy data of the household appliances is embedded within the visual aids without displaying energy metrics. The household appliances include lighting (LED vs incandescent bulb), cooking (air fryer and stove), and heating appliances (heat pump and space heater). The participants included 32 graduate students from Virginia Tech engineering programs. Results from the study showed that some participants had a hard time interpreting axis unit metrics energy such as watts, watt*minutes, and kWhs in all three 2D chart types. If participants could not read and work with the units on charts, their ability to analyze and argue about the energy data was diminished quickly. In addition, when participants were interacting with the visual aids, researchers discovered that the power meter was the easiest to convey because it provided participants with a way to qualitatively and quantitatively answer the questions presented by the questionnaire. This dissertation provides insights for researchers, utility companies, and policymakers to move away from standardized data visualizations and utilize alternative visuals for reading, working with, analyzing, and arguing residential energy consumption data. Researchers can utilize the dissertation insights to explore other data visualization mediums that have the potential to convey energy insights. Utility companies can begin implementing these alternative data visualizations in pilot programs to test their effectiveness with the public. And lastly, policymakers can enforce utility companies to prioritize customer literacy levels when administering utility bills. / Doctor of Philosophy / Data visualization has the power to tell a wonderful, meaningful, and thoughtful story when created with the end user in mind. The same thing can be said about visualizations on utility bills when tailored to a resident's energy, graphical, and data literacy level. A residents' literacy level results from their life experiences and circumstances dealing with energy data. Currently, data visualizations are standardized by utilizing companies, meaning that all individuals receive the same energy data visualization no matter their literacy level. My aim for this dissertation is to evaluate which data visualization chart types and VR visual aids enable reading, working with, analyzing, and arguing energy consumption data of household appliance pairs for residents. The data visualization chart types explored include the area, bar, and circular column charts. The visual aids explored include color coding, electricity flow, and the power meter. The household appliances include lighting (LED vs incandescent bulb), cooking (air fryer and stove), and heating appliances (heat pump and space heater). The participants included 32 graduate students from Virginia Tech engineering programs. Results from the study showed that some participants had difficulty reading and working with energy metrics on all three 2D chart types. If participants did not understand the units when reading and working with the charts their ability to analyze and argue the energy data was insufficient. In addition, when participants were interacting with the visual aids, researchers discovered that the power meter was the easiest to convey because it provided participants with a way to qualitatively and quantitatively answer the questions presented by the questionnaire. This dissertation provides insights for researchers, utility companies, and policymakers to move away from standardized data visualizations and utilize alternatives that enable reading, working with, analyzing, and arguing residential energy consumption data.

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<p dir="ltr">The present era of industrial growth, along with the rise in big data, has led to an increase in the demand for data-savvy professionals employing visualization techniques and software to fully leverage the value of this data. Since data visualization is an expansive and intricate field, it leads to challenges for novice learners as they seek to understand it. The Data Visualization Capacity (DVC) Tool is an online learning platform designed to enhance data visualization literacy amongst learners. The DVC Tool encompasses fundamental principles and techniques essential for proficient data visualization, by including external resources, quizzes, and tutorials in a distance-based modular format.</p><p dir="ltr">This study investigates the usability of the DVC Tool using a mixed-methods approach combining quantitative analysis of Google Analytics data, System Usability Scale (SUS) questionnaires, and qualitative insights from usability testing sessions and interviews. The research aims to assess the effectiveness of the DVC Tool across diverse user profiles and identify strategies for optimizing user experience. User studies were conducted with participants from various backgrounds and experience in data visualization to gain insight into the strengths and weaknesses of the DVC Tool, as well as gain recommendations for effective learning strategies and user experience design. The findings reveal a high overall usability rating for the DVC Tool, with users from various educational backgrounds and levels of expertise expressing satisfaction with its functionality and organization. The SUS usability scores indicate a mean usability score of 81.8, highlighting the tool's effectiveness in providing a user-friendly learning experience for all users across diverse profiles. Interviews also give insight into the importance of clear organization, visual aids, and custom learning plans to enhance the learning experience of the student.</p><p dir="ltr">In general, this research contributes to the advancement of data visualization education by providing insights into effective instructional strategies and components of digital learning platforms. The findings offer practical implications for educators and developers looking to enhance data visualization literacy among learners, while also addressing theoretical gaps in usability research within the field.</p>

如何透過視覺化探索勢力消長情形，是近年來頻繁被探討的問題，常見之做法會針對帶有時間屬性的時間關聯資料 (time-oriented data)來進行觀察，而以社群媒體為例，重大議題通常是透過意見領袖提出具有關鍵性之觀點，而得以分歧出新議題並吸引其他社群媒體上之閱聽人加入討論，上述之過程牽涉評論之階層資料其層次隨著時間變化分歧與合併，然而，能夠透過視覺化之方式同時觀察上述特性有其挑戰性。本篇論文將針對階層式資料提出一套整合方式，稱為TopicWave，特別是帶有時間變化屬性的資料，希望透過改良動態圖形視覺化工具，結合 Sunburst 與 ThemeRiver Graph，實作 Facebook 上公開文章之評論(comments)行為隨時間變化的趨勢，而透過直覺式互動功能之設計。透過案例分析和使用者測試，本論文提出的方法能清楚呈現評論關係隨時間之變化與階層式結構，達到組合式創新之效果。 / In recent research, it is a frequently asked question about how to explore the topic trend during a time interval. If we want to analysis and discuss this question, time-oriented data will be the most appropriate dataset. For example, on social media platform, major issues are commonly formed by opinion leaders, people will be attracted by opinion leaders and join in the commentary on a topic. The above-mentioned procedure will involve in commentary hierarchy level increasing or decreasing while time changes, however, it is challenging when we want to explore these properties using traditional visualization techniques. We propose TopicWave, a visualization design that combines ThemeRiver Graph (time-oriented visualization) and Sunburst (hierarchical data visualization). It can visualize the trend of a post’s comment on Facebook Page. TopicWave can clearly present hierarchy and time-varying trend of a Facebook post’s comment data at the same time through the intuitive design of interactive on visualization.

本篇論文針對社群使用者在社群網路文章上的互動行為進行研究，以視覺化工具Shareflow探索互動過程中造成資訊擴散的意見領袖以及傳播路徑。本研究主要分成兩個部分，第一部分為單一篇文章的分享路徑視覺化，基於階層化邊線綑綁(Hierarchical edge bundles)方法，根據控制點的引導將鄰近邊線進行綑綁，透過邊線捆綁舒緩資料量過大時造成之視覺混亂(visual clutter)問題。第二部分為粉絲專頁文章視覺化，分析多篇文章中具有多次分享、留言行為之使用者，呈現整體社群中積極活動的使用者以及其相關文章的視覺化。最後提供即時互動操作介面，以並列方式呈現出資料的廣度和深度，本研究的貢獻為提供一套視覺化工具，協助使用者探索臉書社群網路中的資訊散播過程以及發掘積極活動的臉書使用者。 / In this thesis, we propose a visualization tool, “Shareflow”, for observing the user activities in social media posts, and to explore opinion leaders and the propagation path caused by the information diffusion. Our approach contains two parts. The first part is a visualization of propagation path for a post in Facebook fanpage. Based on hierarchical edge bundles, we optimize the layout to reduce visual clutter caused by excessive information. The second part is visualization for a summary of posts in Facebook fanpage. It provides a tool for analysis the active users through their sharing and comments activities; In addition, we provide a real-time interactive interface, which demonstrates the breadth and depth of information concurrently.