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- Genre: Masters Thesis
Description
Working memory plays an important role in human activities across academic,professional, and social settings. Working memory is dened as the memory extensively
involved in goal-directed behaviors in which information must be retained and
manipulated to ensure successful task execution. The aim of this research is to understand
the effect of image captioning with image description on an individual's
working memory. A study was conducted with eight neutral images comprising situations
relatable to daily life such that each image could have a positive or negative
description associated with the outcome of the situation in the image. The study
consisted of three rounds where the first and second round involved two parts and
the third round consisted of one part. The image was captioned a total of five times
across the entire study. The findings highlighted that only 25% of participants were
able to recall the captions which they captioned for an image after a span of 9-15
days; when comparing the recall rate of the captions, 50% of participants were able
to recall the image caption from the previous round in the present round; and out of
the positive and negative description associated with the image, 65% of participants
recalled the former description rather than the latter. The conclusions drawn from the
study are participants tend to retain information for longer periods than the expected
duration for working memory, which may be because participants were able to relate
the images with their everyday life situations and given a situation with positive and
negative information, the human brain is aligned towards positive information over
negative information.
ContributorsUppara, Nithiya Shree (Author) / McDaniel, Troy (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / Bryan, Chris (Committee member) / Arizona State University (Publisher)
Created2021
Description
While significant qualitative, user study-focused research has been done on augmented reality, relatively few studies have been conducted on multiple, co-located synchronously collaborating users in augmented reality. Recognizing the need for more collaborative user studies in augmented reality and the value such studies present, a user study is conducted of collaborative decision-making in augmented reality to investigate the following research question: “Does presenting data visualizations in augmented reality influence the collaborative decision-making behaviors of a team?” This user study evaluates how viewing data visualizations with augmented reality headsets impacts collaboration in small teams compared to viewing together on a single 2D desktop monitor as a baseline. Teams of two participants performed closed and open-ended evaluation tasks to collaboratively analyze data visualized in both augmented reality and on a desktop monitor. Multiple means of collecting and analyzing data were employed to develop a well-rounded context for results and conclusions, including software logging of participant interactions, qualitative analysis of video recordings of participant sessions, and pre- and post-study participant questionnaires. The results indicate that augmented reality doesn’t significantly change the quantity of team member communication but does impact the means and strategies participants use to collaborate.
ContributorsKintscher, Michael (Author) / Bryan, Chris (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Hansford, Dianne (Committee member) / Johnson, Erik (Committee member) / Arizona State University (Publisher)
Created2020
Description
Augmented Reality (AR) has progressively demonstrated its helpfulness for novicesto learn highly complex and abstract concepts by visualizing details in an immersive
environment. However, some studies show that similar results could also be obtained
in environments that do not involve AR. To explore the potential of AR in advancing
transformative engagement in education, I propose modeling facial expressions
as implicit feedback when one is being immersed in the environment. I developed a
Unity application to record and log the users' application operations and facial images.
A neural network-based model, Visual Geometry Group 19 (VGG19, Simonyan
and Zisserman (2014)), is adopted to recognize emotions from the captured facial
images. A within-subject user study was designed and conducted to assess the sentiment
and user engagement differences in AR and non-AR tasks. To analyze the
collected data, Dynamic Time Warping (DTW) was applied to identify the emotional
similarities between AR and non-AR environments. The results indicate that users
showed an increase in emotion patterns and application operations throughout the
AR tasks in comparison to non-AR tasks. The emotion patterns observed in the
analysis show that non-AR provides less implicit feedback compared to AR tasks.
The DTW analysis reveals that users' emotion change patterns appear to be more
distant from neutral emotions in AR than non-AR tasks. Succinctly put, the users
in the AR task demonstrated more active use of the application and yielded ranges
of emotions while operating it.
ContributorsPapakannu, Kushal Reddy (Author) / Hsiao, Ihan (Thesis advisor) / Bryan, Chris (Committee member) / Glenberg, Mina Johnson (Committee member) / Arizona State University (Publisher)
Created2021
Description
Component-based models are commonly employed to simulate discrete dynamicalsystems. These models lend themselves to formalizing the structures of systems at multiple levels of granularity. Visual development of component-based models serves to simplify the iterative and incremental model specification activities. The Parallel Discrete Events System Specification (DEVS) formalism offers a flexible yet rigorous approach for decomposing a whole model into its components or alternatively, composing a whole model from components. While different concepts, frameworks, and tools offer a variety of visual modeling capabilities, most pose limitations, such as visualizing multiple model hierarchies at any level with arbitrary depths. The visual and persistent layout of any number of hierarchy levels of models can be maintained and navigated seamlessly. Persistence storage is another capability needed for the modeling, simulating, verifying, and validating lifecycle. These are important features to improve the demanding task of creating and changing modular, hierarchical simulation models. This thesis proposes a new approach and develops a tool for the visual development of models. This tool supports storing and reconstructing graphical models using a NoSQL database. It offers unique capabilities important for developing increasingly larger and more complex models essential for analyzing, designing, and building Digital Twins.
ContributorsMohite, Sheetal Chandrakant (Author) / Sarjoughian, Hessam S (Thesis advisor) / Bryan, Chris (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
Visual Question Answering (VQA) is an increasingly important multi-modal task where models must answer textual questions based on visual image inputs. Numerous VQA datasets have been proposed to train and evaluate models. However, existing benchmarks exhibit a unilateral focus on textual distribution shifts rather than joint shifts across modalities. This is suboptimal for properly assessing model robustness and generalization. To address this gap, a novel multi-modal VQA benchmark dataset is introduced for the first time. This dataset combines both visual and textual distribution shifts across training and test sets. Using this challenging benchmark exposes vulnerabilities in existing models relying on spurious correlations and overfitting to dataset biases. The novel dataset advances the field by enabling more robust model training and rigorous evaluation of multi-modal distribution shift generalization. In addition, a new few-shot multi-modal prompt fusion model is proposed to better adapt models for downstream VQA tasks. The model incorporates a prompt encoder module and dual-path design to align and fuse image and text prompts. This represents a novel prompt learning approach tailored for multi-modal learning across vision and language. Together, the introduced benchmark dataset and prompt fusion model address key limitations around evaluating and improving VQA model robustness. The work expands the methodology for training models resilient to multi-modal distribution shifts.
ContributorsJyothi Unni, Suraj (Author) / Liu, Huan (Thesis advisor) / Davalcu, Hasan (Committee member) / Bryan, Chris (Committee member) / Arizona State University (Publisher)
Created2023
Description
Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. However, existing design tools for mid-air haptics primarily support the creation of static tactile sensations (tactons), which lack adaptability at runtime. These tactons do not offer the required expressiveness in interactive scenarios where a continuous closed-loop response to user movement or environmental states is desirable. This thesis proposes AdapTics, a toolkit featuring a graphical interface for the rapid prototyping of adaptive tactons—dynamic sensations that can adjust at runtime based on user interactions, environmental changes, or other inputs. A software library and a Unity package accompany the graphical interface to enable integration of adaptive tactons in existing applications. The design space provided by AdapTics for creating adaptive mid-air ultrasound tactons is presented, along with evidence that the design tool enhances Creativity Support Index ratings for Exploration and Expressiveness, as demonstrated in a user study involving 12 XR and haptic designers.
ContributorsJohn, Kevin (Author) / Seifi, Hasti (Thesis advisor) / Bryan, Chris (Committee member) / Schneider, Oliver (Committee member) / Arizona State University (Publisher)
Created2024
Description
This research project seeks to develop an innovative data visualization tool tailored for beginners to enhance their ability to interpret and present data effectively. Central to the approach is creating an intuitive, user-friendly interface that simplifies the data visualization process, making it accessible even to those with no prior background in the field. The tool will introduce users to standard visualization formats and expose them to various alternative chart types, fostering a deeper understanding and broader skill set in data representation. I plan to leverage innovative visualization techniques to ensure the tool is compelling and engaging. An essential aspect of my research will involve conducting comprehensive user studies and surveys to assess the tool's impact on enhancing data visualization competencies among the target audience. Through this, I aim to gather valuable insights into the tool's usability and effectiveness, enabling further refinements. The outcome of this project is a powerful and versatile tool that will be an invaluable asset for students, researchers, and professionals who regularly engage with data. By democratizing data visualization skills, I envisage empowering a broader audience to comprehend and creatively present complex data in a more meaningful and impactful manner.
ContributorsNarula, Jai (Author) / Bryan, Chris (Thesis advisor) / Seifi, Hasti (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2024
Description
This thesis explores the impact of artificial intelligence (AI) interventions on player decision-making in an investment-themed serious game, The Strategists. This Monopoly®-inspired game incorporates rule-based advice, machine learning-based predictions, and data-driven visualizations to create a unique platform for studying user interactions with AI-assisted decision support systems. The research investigates how players perceive and utilize AI-driven features in a game environment and how these interventions influence gameplay outcomes. Through a series of user studies involving 37 participants across 29 game sessions, the study compares player behavior and performance between control groups with and without access to AI features. Key findings reveal that players who followed AI advice more frequently were likelier to win the game. However, the study also uncovered an interesting paradox: players with more gaming experience tend to distrust and undervalue AI assistance despite evidence of its effectiveness. The findings show gender differences in gameplay outcomes, with female players more likely to win than male players. Players who finished last viewed AI advice more frequently but were less likely to follow it, suggesting a complex relationship between advice visibility and following. This research adds to human-computer interaction and game studies by providing insights into AI-driven decision support systems design in gaming contexts. It also addresses the need for interdisciplinary approaches in game research by combining elements from game design, artificial intelligence, data visualization, and financial education. The study highlights the significance of understanding user perceptions and trust in AI systems.
ContributorsChawla, Shubham (Author) / Bryan, Chris (Thesis advisor) / Seifi, Hasti (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Distributed databases, such as Log-Structured Merge-Tree Key-Value Stores (LSM-KVS), are widely used in modern infrastructure. One of the primary challenges in these databases is ensuring consistency, meaning that all nodes have the same view of data at any given time. However, maintaining consistency requires a trade-off: the stronger the consistency, the more resources are necessary to replicate data across replicas, which decreases database performance. Addressing this trade-off poses two challenges: first, developing and managing multiple consistency levels within a single system, and second, assigning consistency levels to effectively balance the consistency-performance trade-off. This thesis introduces Self-configuring Consistency In Distributed LSM-KVS (SCID), a service that leverages unique properties of LSM KVS properties to manage consistency levels and automates level assignment with ML. To address the first challenge, SCID combines Dynamic read-only instances and Logical KV-based partitions to enable on-demand updates of read-only instances and facilitate the logical separation of groups of key-value pairs. SCID uses logical partitions as consistency levels and on-demand updates in dynamic read-only instances to allow for multiple consistency levels. To address the second challenge, the thesis presents an ML-based solution, SCID-ML to manage consistency-performance trade-off with better effectiveness. We evaluate SCID and find it to improve the write throughput up to 50% and achieve 62% accuracy for consistency-level predictions.
ContributorsThakkar, Viraj Deven (Author) / Cao, Zhichao (Thesis advisor) / Xiao, Xusheng (Thesis advisor) / Bryan, Chris (Committee member) / Arizona State University (Publisher)
Created2023