Artificial Intelligence and Robotics Commons^™

Mivt: Medical-Informed Vision Transformer For Early Epilepsy Diagnosis, Md Masum Rana

Dissertations and Theses

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures, and early diagnosis is crucial for effective management and treatment. However, the diagnosis of epilepsy, particularly in its early stages, remains challenging due to the subtle nature of seizures and the complexity of brain activity patterns. In this research, we introduce the Medical-Informed Vision Transformer (MIVT), a deep learning architecture specifically designed to improve early epilepsy diagnosis from multimodal neuroimaging data. Our model integrates insights from both medical knowledge and state-of-the-art Vision Transformers (ViTs) to enhance the accuracy and interpretability of seizure detection and localization. The MIVT leverages the rich …

Development Of A Modular Lab Automation System With Applications To Animal And Bacteria Cell Culture, Timothy William Hartman

Dissertations and Theses

The challenges faced while executing wet lab protocols encourage the development of automation systems to come alongside human scientists. Today’s cutting-edge experiments involve complex protocols with precise measurements usually performed manually. Even simpler biological protocols can be tedious and prone to error, as was seen during the COVID-19 pandemic and society’s demand for high-volume, rapid sample analysis. Moreover, reproducibility suffers when there is excessive variability and insufficient data. Here, we leveraged the Stanford Biodesign process to develop a modular lab automation system and image analysis workflow to address challenges like these. This flagship automation platform at The University of South …

Artificial Intelligence And News Consumption: A Study Of Trust, Credibility And Transparency In Automated Journalism, Julia Lobo Paes

Dissertations and Theses

According to Gallup poll (2023), over the last 50 years there has been a decline in how much Americans trust mass media. While in the 1970’s 72% of the population responded that they trust the media a 'great deal/fair amount', this number dropped to 34% in 2023. Given the decreasing public trust in news, this thesis focused particularly on analyzing trust in the organization, trust in the news story and perceived credibility in AI generative content. In addition to articles created by AI, this study also aimed to analyze how the public perceives information that has been personalized and distributed …

Can Informed Consent Solve Ai Bias?, W. Nicholson Price Ii

Reviews

Artificial intelligence (AI) is moving increasingly rapidly into health care (as indeed into everything else). But it has problems there (as indeed everywhere else!). What’s to be done, in particular, about the deeply embedded biases along racial and other lines that permeate the whole world of health and, as such, are likely to be encoded in AI?

Khiara Bridges gives an answer that seems mild but carries roots of revolution. In Race in the Machine: Racial Disparities in Health and Medical AI, she argues that informed consent is a key lever to pull in fighting these racial disparities. But not …

Impossibility Of Artificial Inventors, Matt Blaszczyk

Fellow, Adjunct, Lecturer, and Research Scholar Works

Recently, the United Kingdom Supreme Court decided that only natural persons can be considered inventors. A year before, the United States Court of Appeals for the Federal Circuit issued a similar decision. In fact, so have many the courts all over the world. This Article analyses these decisions, argues that the courts got it right, and finds that artificial inventorship is at odds with patent law doctrine, theory, and philosophy. The Article challenges the intellectual property (IP) post-humanists, exposing the analytical and normative perils of their argumentation, and recommends against getting rid of the nominally central place of humans in …

Review Of Who Wrote This?: How Ai And The Lure Of Efficiency Threaten Human Writing, By Naomi S. Baron, Taylor J. Greene

Library Articles and Research

A review of Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing, by Naomi S. Baron.

Fr1: Comics, Cyborgs, And “In Between” Identities, Ella Lehavi

Scripps Senior Theses

As a queer Jew who grew up surrounded by immigrant cultures and communities, I find myself in a liminal space between my identities and the dominant culture of my country– one where my perspective on gender and my cultural experiences aren’t fully understood by the world I exist in. Comics and cartoons are an explorational platform for concepts of reality and identity; they are one of very few spaces where I see my identities explored with so much depth and care.

Cartoons and comics exist in between realistic depictions and abstraction. This makes them a great place to express all …

Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry

Theses and Dissertations

Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …

Low-Resource Automatic Speech Recognition Domain Adaptation – A Case-Study In Aviation Maintenance, Nadine Amin, Tracy L. Yother, Julia Rayz

Journal of Aviation/Aerospace Education & Research

With timeliness and efficiency being critical in the aviation maintenance industry, the need has been growing for smart technological solutions that optimize and streamline the different underlying tasks (Bergkvist & Sabbagh, 2021). One such task is the technical documentation of the performed maintenance operations (Chandola et al., 2022). Instead of manual documentation, voice tools that transcribe spoken logbook entries allow technicians to document their work right away in a hands-free and time efficient manner. However, an accurate automatic speech recognition (ASR) model requires large training corpora (Siyaev & Jo, 2021a), which are lacking in the domain of aviation maintenance. In …

Flexible Attenuation Fields: Tomographic Reconstruction From Heterogeneous Datasets, Clifford S. Parker

Theses and Dissertations--Computer Science

Traditional reconstruction methods for X-ray computed tomography (CT) are highly constrained in the variety of input datasets they admit. Many of the imaging settings -- the incident energy, field-of-view, effective resolution -- remain fixed across projection images, and the only real variance is in the detector's position and orientation with respect to the scene. In contrast, methods for 3D reconstruction of natural scenes are extremely flexible to the geometric and photometric properties of the input datasets, readily accepting and benefiting from images captured under varying lighting conditions, with different cameras, and at disparate points in time and space. Extending CT …

Cyclistai: A Smartphone Solution For Cyclist Stress Assessment Using Deep Learning, Aairish Singh

Computer Science and Engineering Theses - Archive

Cycling presents a compelling solution for promoting personal health and environmental well-being, particularly for short-distance travel. Despite its numerous advantages, cycling uptake in the United States remains disproportionately low, primarily due to safety concerns. Traditional frameworks for assessing cyclist stress are hindered by their impracticality and inability to provide real-time evaluations. Self-report surveys and physiological measurements offer alternative approaches but suffer from limitations such as retrospective reporting biases and accessibility challenges, respectively. This thesis introduces CyclistAI, a novel smartphone-based cyclist stress assessment model that leverages context sensing. By combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques, CyclistAI …

When Brain Meets Artificial Intelligence, Lu Zhang

Computer Science and Engineering Dissertations - Archive

When we review the history of development of artificial intelligence (AI), we will find that brain science plays a pivotal role in fostering breakthroughs in AI, such as artificial neural networks (ANNs). Today, AI has made remarkable strides, particularly with the emergence of large language models (LLMs), surpassing expectations and achieving human-level performance in certain tasks. Nonetheless, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI, promoting their mutual enhancement and collaborations. This involve establishing connections from brain science to AI (brain-inspired AI), and reversely, from AI to …

Content Moderation On Social Media: Social And Computational Standards And Implications, Mohit Singhal

Computer Science and Engineering Dissertations - Archive

Social media has become a powerful tool that reflects human communication's best and worst aspects. They allow individuals to freely express opinions, communicate with others, and learn about new stories. On the other hand, they have become fertile grounds for several forms of abuse, harassment, and the dissemination of misinformation. Social media platforms have established and employed content moderation to counteract the spread of abuse and misinformation.

Some critical challenges hinder the understanding of the social media content moderation ecosystem. This dissertation investigates various aspects of content moderation, including their coverage, fairness, and effectiveness. Firstly, it investigates how, in practice, …

Natural Language Generation From Large-Scale Open-Domain Knowledge Graphs, Xiao Shi

Computer Science and Engineering Dissertations - Archive

This dissertation delves into the realm of natural language generation (NLG) from expansive open-domain knowledge graphs, aiming to bridge the gap between existing methods primarily tested on limited datasets and the demands of real-world large-scale, diverse graph structures. Prior works in NLG often relied on small-scale or restricted datasets, neglecting the complexities of broader knowledge graphs. To address this, we introduce a new dataset called GraphNarrative, designed to encompass a wide range of graph structures and enhance the realism of NLG tasks.

The core contribution of this research lies in devising a novel approach to mitigating information hallucination, a common …

Predicting The Need For Cardiovascular Surgery: A Comparative Study Of Machine Learning Models, Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi

Engineering Management & Systems Engineering Faculty Publications

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques …

Comment On Chapters 1 And 4: Health Ai, System Performance, And Physicians In The Loop, W. Nicholson Price Ii

Book Chapters

Accounts of artificial intelligence (AI) in medicine must grapple, in one way or another, with the interaction between AI systems and the humans involved in delivering healthcare. Humans are, of course, involved throughout the process of developing , deploying, and evaluating AI systems, but a particular role stands out: the human in the loop of an algorithmic decision. In medicine, when an algorithm is involved in a decision , a typical view of the system envisions a human healthcare professional mediating that algorithm - deciding whether and how to implement or react to any recommendation, prediction, or other algorithmic output. …

Topic Mining And Dynamic Evolution Analysis Of Patent Technology From The Perspective Of Binary Evolution：Take The Field Of Industrial Robots As An Example, Luyao Dou, Zhigang Zhou, Yi Li, Tao Jiang

Journal of Scientific Information Research

[Purpose/significance]From the perspective of "overall ecology + local stage", mining the technical theme and its evolution law in the field of industrial robots can not only know the overall process of technological development, but also clarify the specific paradigm of technology combination, which has important practical significance for insight into the technological progress and capital investment focus in the field of industrial robots.[Method/process]Based on incoPat patent database, taking the industrial robot field from 2003 to 2022 as an example, combined with Word2vec word vector model and LDA topic model, data mining and corpus expansion of patent texts were carried out, …

Embracing Ai In English Composition: Insights And Innovations In Hybrid Pedagogical Practices, James Hutson, Daniel Plate, Kadence Berry

Faculty Scholarship

In the rapidly evolving landscape of English composition education, the integration of AI writing tools like ChatGPT and Claude 2.0 has marked a significant shift in pedagogical practices. A mixed-method study conducted in Fall 2023 across three sections, including one English Composition I and two English Composition II courses, provides insightful revelations. The study, comprising 28 student respondents, delved into the impact of AI tools through surveys, analysis of writing artifacts, and a best practices guide developed by an honors student. Initially, the study observed a notable anxiety and mistrust among students regarding the use of AI in writing. However, …

A Comprehensive Study Of Patent Litigation In The Pharmaceutical Sector: Employing Network Theories, Graph Neural Networks, Agent Based Modeling, Bayesian Network Autocorrelation Models, Sreehas Gopinathan

Information Systems & Operations Management Dissertations - Archive

Understanding the dynamics and predictors of patent litigation is crucial in intellectual property management, especially given the competitive edge patents offer companies. Also, patents serve as both legal tools and repositories of innovation. This research delves into the complex world of patent litigation within the pharmaceutical industry, focusing on creating and applying advanced computational models to study litigation propensities. Techniques such as Graph Neural Networks (GNN), Agent-Based Modeling (ABM), and Bayesian Analysis of Network Autocorrelation Models (BANAM) are employed to explore the litigation phenomenon

Selecting And Evaluating Key Mds-Updrs Activities Using Wearable Devices For Parkinson's Disease Self-Assessment, Yuting Zhao, Xulong Wang, Xiyang Peng, Ziheng Li, Fengtao Nan, Menghui Zhuo, Jun Qi, Yun Yang, Zhong Zhao, Lida Xu, Po Yang

Information Technology & Decision Sciences Faculty Publications

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities combination scheme based …

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

35. Using Generative Ai To Perform Stacked Evaluations Of Educational Documents: Provoking Students To Think On Successively Higher Levels, Susan Codone

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This chapter describes the use of ChatGPT in a graduate class assignment and explores how Al can scaffold student work to promote successively higher levels of thinking. The assignment asked students to compose a "stacked evaluation" of school district technology plans, which included a rubric generated by ChatGPT, evaluation data generated by ChatGPT using the rubric criteria, and the students' evaluation of both the technology plan and of the ChatGPT evaluation. Student deliverables were more thorough than in previous semesters and included clear demarcation of Al-generated text and original writing. Because students asked ChatGPT to act in the persona of …

26. Working Alongside, Not Against, Ai Writing Tools In The Composition Classroom: A Dialectical Retrospective, Daniel Frank, Jennifer K. Johnson

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This article presents a dialectical retrospective on thoughtfully integrating Generate AI tools such as ChatGPT into composition classrooms. Drawing on their experiences and research, the authors outline key principles for using AI as a supplemental aid rather than a replacement for student writing, promoting academic integrity, and fostering critical perspectives on the technology's capabilities and limitations. They share experimental classroom activities and assignments that engage students in hands-on exploration and reflection on their AI-assisted writing processes. Student responses reveal nuanced engagement with the tools to support rather than shortcut learning. The authors argue that attempting to simply prohibit AI use …

23. Cake-Making Analogy For Setting Generative Ai Guidelines/Ethics, Maha Bali

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This is a lesson plan that offers metaphor as an innovative approach to teaching about the ethical use of generative Al. The cake-making analogy equates different ways of acquiring a cake (baking from scratch, using a readymade mix from a box, buying from a bakery or buying preserved cake from a supermarket) with varying degrees of reliance on Al as a shortcut for tasks or assignments. The lesson invites participants (who may be students or teachers) to critically consider the implications of each mode, examining factors such as quality, time, cost, and personal investment. This analogy is then applied to …

7. Automated Aid Or Offloading Close Reading? Student Perspectives Of Ai Reading Assistants, Marc Watkins

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

Generative Al technologies offer new opportunities for enhancing student learning that go beyond chatbot interfaces like ChatGPT. This chapter presents reflections from a small study about the possible benefits and challenges posed by integrating Al-powered reading assistants in first-year writing courses. Careful integration of these tools suggests potential benefits that do not simply generate text on students' behalf. For example, reading assistants like Explainpaper and SciSpace are powered by large language models like OpenAl's GPT and can help students augment reading. This application of generative technology could aid non-native speakers, students with disabilities, and those struggling with reading comprehension. However, …

6. More Is Less?: Using Generative Ai For Idea Generation And Diversification In Early Writing Processes, Franziska Tsufim, Lainie Pomerleau

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

As writing teachers, we are strong proponents of process writing. At the same time, we are aware that early process work, especially in a group setting, can be time consuming and anxiety-inducing. Students may also self-censor when sharing work with peers especially if they are not confident in their ideas. Drawing on the process of nominal, electronic brainstorming, we created two different prompts that allowed students to incorporate generative Al into their idea generation process. This first activity improves the efficiency of individual idea generation, while the second exercise helps increase student confidence in their ideas in collaborative brainstorming situations. …

Digital Resurrection Of Historical Figures: A Case Study On Mary Sibley Through Customized Chatgpt, James Hutson, Paul Huffman, Jeremiah Ratican

Faculty Scholarship

This study investigates the emerging realm of digital resurrection, focusing on Mary Sibley (1800–1878), the esteemed founder of Lindenwood University. The core objective was to demonstrate the capability of advanced artificial intelligence, specifically a customized version of ChatGPT, in revitalizing historical figures for educational and engagement purposes. By integrating comprehensive diaries from Sibley with Claude 2.0, the research utilized a substantial autobiographical dataset to develop a GPT beta version that replicates her distinct voice and tone. The incorporation of her official portrait and diaries into the GPT Builder was pivotal, creating an interactive platform that accurately reflects her perspectives on …

Analysis Of Speech Recognition Systems And Error Correction Approaches, Saki Imai

Honors Theses

Despite significant advances in automatic speech recognition (ASR) accuracy, challenges remain. Naturally occurring conversation often involves multiple overlapping speakers, of different ages, accents and genders, as well as noisy environments and suboptimal audio recording equipment, all of which reduce ASR accuracy. In this study, we evaluate the accuracy of state of the art open source ASR systems across diverse conversational speech datasets, examining the impact of audio and speaker characteristics on WER. We then explore the potential of ASR ensembling plus post-ASR correction methods to improve transcription accuracy. Our findings underscore the need for robust error correction techniques and of …

Development Of A Two-Finger Haptic Robotic Hand With Novel Stiffness Detection And Impedance Control, Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi, Ahad Behboodi

Mechanical & Aerospace Engineering Faculty Publications

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive …

Kinodynamic Motion Planning For A System With Squid Dynamics, Logan E. Beaver, Cong Wei, Wei-Kuo Yen

Mechanical & Aerospace Engineering Faculty Publications

This paper introduces a path planning algorithm for a system with squid dynamics in a cluttered environment. We capture the complex interactions of fin, arms, and body patterning by analyzing experimental data collected from observing squid motion. We extract nine motion primitives to build the control sequence for a time-optimal trajectory. This task is formulated as a mixed-integer program, and we generate the minimum-time trajectory using a sample-based approach. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to exploration of squid motion features, improvement of the modeling, and experimental demonstrations of the motion planning …