Browsing by Author "Alhakamy, A'aeshah"

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    Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
    (MDPI, 2020-09) Trajkova, Milka; Alhakamy, A'aeshah; Cafaro, Francesco; Vedak, Sanika; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.
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    Real-time Illumination and Visual Coherence for Photorealistic Augmented/Mixed Reality
    (ACM, 2021-05) Alhakamy, A'aeshah; Tuceryan, Mihran; Computer and Information Science, School of Science
    A realistically inserted virtual object in the real-time physical environment is a desirable feature in augmented reality (AR) applications and mixed reality (MR) in general. This problem is considered a vital research area in computer graphics, a field that is experiencing ongoing discovery. The algorithms and methods used to obtain dynamic and real-time illumination measurement, estimating, and rendering of augmented reality scenes are utilized in many applications to achieve a realistic perception by humans. We cannot deny the powerful impact of the continuous development of computer vision and machine learning techniques accompanied by the original computer graphics and image processing methods to provide a significant range of novel AR/MR techniques. These techniques include methods for light source acquisition through image-based lighting or sampling, registering and estimating the lighting conditions, and composition of global illumination. In this review, we discussed the pipeline stages with the details elaborated about the methods and techniques that contributed to the development of providing a photo-realistic rendering, visual coherence, and interactive real-time illumination results in AR/MR.
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    Show Me How You Interact, I Will Tell You What You Think: Exploring the Effect of the Interaction Style on Users’ Sensemaking about Correlation and Causation in Data
    (ACM, 2021-06) Alhakamy, A'aeshah; Trajkova, Milka; Cafaro, Francesco; Human-Centered Computing, School of Informatics and Computing
    Findings from embodied cognition suggest that our whole body (not just our eyes) plays an important role in how we make sense of data when we interact with data visualizations. In this paper, we present the results of a study that explores how different designs of the ”interaction” (with a data visualization) alter the way in which people report and discuss correlation and causation in data. We conducted a lab study with two experimental conditions: Full body (participants interacted with a 65” display showing geo-referenced data using gestures and body movements); and, Gamepad (people used a joypad to control the system). Participants tended to agree less with statements that portray correlation and causation in data after using the Gamepad system. Additionally, discourse analysis based on Conceptual Metaphor Theory revealed that users made fewer remarks based on FORCE schemata in Gamepad than in Full-Body.