AdapTics: A Toolkit for Creative Design and Integration of Real-Time Adaptive Mid-Air Ultrasound Tactons

193025-Thumbnail Image.png
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.

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.
Date Created
2024
Agent

Towards Robust VQA: Evaluations and Methods

190815-Thumbnail Image.png
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

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.
Date Created
2023
Agent

A Computational Analysis on the Efficacy of Independent Redistricting Commissions

Description
Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit.

Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to select an elected official that accurately represents their interests. Instead, gerrymandering has legislators to choose their voters. In recent years, the Supreme Court has heard challenges to state legislature-drawn districts, most recently in Allen v. Milligan for Alabama and Moore v. Harper for North Carolina. The highest court of the United States ruled that the two state maps were gerrymandered, and in coming to their decision, the 9 justices relied on a plethora of amicus briefs- one of which included the Markov Chain Monte Carlo method, a computational method used to find gerrymandering. Because of how widespread gerrymandering has become on both sides of the political aisle, states have moved to create independent redistricting commissions. Qualitative research regarding the efficacy of independent commissions is present, but there is little research using the quantitative computational methods from these SCOTUS cases. As a result, my thesis will use the Markov Chain Monte Carlo method to answer if impartial redistricting commissions (like we have in Arizona) actually preclude unfair redistricting practices. My completed project is located here: https://dheetideliwala.github.io/honors-thesis/
Date Created
2023-12
Agent

Explaining the Vulnerabilities of Machine Learning through Visual Analytics

189385-Thumbnail Image.png
Description
Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and

Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and safety of these models, given the potential negative consequences of model vulnerabilities. The complexity of machine learning models, along with the extensive data sets they analyze, can result in unpredictable and unintended outcomes. Model vulnerabilities may manifest due to errors in data input, algorithm design, or model deployment, which can have significant implications for both individuals and society. To prevent such negative outcomes, it is imperative to identify model vulnerabilities at an early stage in the development process. This will aid in guaranteeing the integrity, dependability, and safety of the models, thus mitigating potential risks and enabling the full potential of these technologies to be realized. However, enumerating vulnerabilities can be challenging due to the complexity of the real-world environment. Visual analytics, situated at the intersection of human-computer interaction, computer graphics, and artificial intelligence, offers a promising approach for achieving high interpretability of complex black-box models, thus reducing the cost of obtaining insights into potential vulnerabilities of models. This research is devoted to designing novel visual analytics methods to support the identification and analysis of model vulnerabilities. Specifically, generalizable visual analytics frameworks are instantiated to explore vulnerabilities in machine learning models concerning security (adversarial attacks and data perturbation) and fairness (algorithmic bias). In the end, a visual analytics approach is proposed to enable domain experts to explain and diagnose the model improvement of addressing identified vulnerabilities of machine learning models in a human-in-the-loop fashion. The proposed methods hold the potential to enhance the security and fairness of machine learning models deployed in critical real-world applications.
Date Created
2023
Agent

Optimizing Consistency and Performance Trade-off in Distributed Log-Structured Merge-Tree-based Key-Value Stores

189344-Thumbnail Image.png
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.

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.
Date Created
2023
Agent

A Unified Visual and Persistent RESTful Tool for Modular and Hierarchical Modeling

189217-Thumbnail Image.png
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

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.
Date Created
2023
Agent

"Can I Consider You My Friend?" Moving Beyond One-Sided Conversation in Social Robotics

171933-Thumbnail Image.png
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with

As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with the individual. One solution is to keep older adults in their homes longer through smart home and smart living technology, allowing them to age in place. People have many choices when choosing where to age in place, including their own homes, assisted living facilities, nursing homes, or family members. No matter where people choose to age, they may face isolation and financial hardships. It is crucial to keep finances in mind when developing Smart Home technology. Smart home technologies seek to allow individuals to stay inside their homes for as long as possible, yet little work looks at how we can use technology in different life stages. Robots are poised to impact society and ease burns at home and in the workforce. Special attention has been given to social robots to ease isolation. As social robots become accepted into society, researchers need to understand how these robots should mimic natural conversation. My work attempts to answer this question within social robotics by investigating how to make conversational robots natural and reciprocal. I investigated this through a 2x2 Wizard of Oz between-subjects user study. The study lasted four months, testing four different levels of interactivity with the robot. None of the levels were significantly different from the others, an unexpected result. I then investigated the robot’s personality, the participant’s trust, and the participant’s acceptance of the robot and how that influenced the study.
Date Created
2022
Agent

A Visual Analytics Workflow for Detecting Transition Regions in Large Scale Molecular Dynamics Simulations

171880-Thumbnail Image.png
Description
Molecular Dynamics (MD) simulations are ubiquitous throughout the physical sci-ences; they are critical in understanding how particle structures evolve over time given a particular energy function. A software package called ParSplice introduced a new method to generate these simulations in parallel that

Molecular Dynamics (MD) simulations are ubiquitous throughout the physical sci-ences; they are critical in understanding how particle structures evolve over time given a particular energy function. A software package called ParSplice introduced a new method to generate these simulations in parallel that has significantly inflated their length. Typically, simulations are short discrete Markov chains, only captur- ing a few microseconds of a particle’s behavior and containing tens of thousands of transitions between states; in contrast, a typical ParSplice simulation can be as long as a few milliseconds, containing tens of millions of transitions. Naturally, sifting through data of this size is impossible by hand, and there are a number of visualiza- tion systems that provide comprehensive and intuitive analyses of particle structures throughout MD simulations. However, no visual analytics systems have been built that can manage the simulations that ParSplice produces. To analyze these large data-sets, I built a visual analytics system that provides multiple coordinated views that simultaneously describe the data temporally, within its structural context, and based on its properties. The system provides fluid and powerful user interactions regardless of the size of the data, allowing the user to drill down into the data-set to get detailed insights, as well as run and save various calculations, most notably the Nudged Elastic Band method. The system also allows the comparison of multiple trajectories, revealing more information about the general behavior of particles at different temperatures, energy states etc.
Date Created
2022
Agent