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Articles 1621 - 1650 of 11151
Full-Text Articles in Computer Sciences
Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi
Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi
UNLV Theses, Dissertations, Professional Papers, and Capstones
Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …
Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam
Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam
Computer Science ETDs
We develop distributed robotics algorithms with analytical tools needed to define and analyze angle turned and distance traversed by robots executing geometric algorithms. We then use these analytical tools to obtain information, via sensor measurements, about an a priori unknown surface. Our contributions are threefold. First, we develop the Sketch Algorithm, which estimates the boundary of any unknown contour and is asymptotically optimal in terms of distance traversed and angle turned. Second, we present experimental field work that validates the Sketch Algorithm. Finally, we propose an approach to find multiple sources of a surface with potential applications to approximate that …
Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi
Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi
LSU Doctoral Dissertations
The construction industry faces significant challenges, including labor shortages, high physical demands, and safety risks, necessitating advanced assistive technologies like exoskeletons to enhance worker efficiency and reduce injuries. However, effective exoskeleton control in dynamic construction environments requires accurate locomotion prediction, a task complicated by the diversity of activities and reliance on supervised learning methods that struggle to generalize. This study investigates a multimodal approach to locomotion prediction, leveraging speech commands and visual data from smart glasses to enable adaptive and safe human-exoskeleton interaction. The research unfolds in two stages: the first develops a framework to evaluate the zero-shot capability and …
Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher
Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Engineering Faculty Articles and Research
Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …
Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi
Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi
SKMC Student Presentations and Publications
Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. …
Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri
Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri
McKelvey School of Engineering Graduate Student Theses & Dissertations
Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …
Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu
Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu
Engineering Faculty Articles and Research
Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …
Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng
Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng
Dartmouth College Ph.D Dissertations
Listening to fast-tempo piano sonatas of the Classical period (circa 1750-1820) has been shown to have therapeutic effects for neurological disorders such as epilepsy. The limited existing repertoire of music in this style motivates the creation of more long-form, coherent compositions with clearly defined structure. Despite the long history of computer-based music generation and recent progress in deep learning, particularly transformer-based models, generating structurally coherent long-form music remains a major challenge. This difficulty stems from the scarcity of reliable structural annotation datasets, the computational demands of modeling very long musical sequences, and the lack of effective structural encoding in both …
Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby
Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby
Center for Medical Ethics and Health Policy Staff Publications
Background: Increasing interest in deploying artificial intelligence tools in clinical contexts has raised several ethical questions of both normative and empirical interest. One such question in the literature is whether "responsibility gaps" (r-gaps) are created when clinicians utilize or rely on such tools for providing care, and if so, what to do about them. These gaps are particularly likely to arise when using opaque, "black box" AI tools. Compared to normative and legal analysis of AI-generated responsibility gaps in health care, little is known, empirically, about health care providers views on this issue. The present study examines clinician perspectives on …
Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi
Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi
Open Educational Resources
This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.
Robotic Rhythm: A Contemporary Look At Ai-Driven Editing Tools’ Efficacy In Understanding And Replicating Creative Rhythm Editing Techniques., Alexander Selby-Lara, Charles Howard
Robotic Rhythm: A Contemporary Look At Ai-Driven Editing Tools’ Efficacy In Understanding And Replicating Creative Rhythm Editing Techniques., Alexander Selby-Lara, Charles Howard
Honors Thesis
It is undeniable that artificial intelligence (AI) has made its way into the film world. From AI-generated imagery and sound design in two 2025 Oscar nominees — The Brutalist and Emilia Pérez (Pulver) — to weekly updates and monthly beta releases of existing and new generative image and video models, AI-driven filmmaking tools are here to stay. But what does this mean for the post-production workflow? Much like camera development, editing technology has come a long way from flatbed film editors to Adobe Premiere Pro v. 25.0. Yet, despite each technological leap from system to system, the delicate task of …
Evolving Enemy Behavior In Video Games, Hermie H. Adams Iii
Evolving Enemy Behavior In Video Games, Hermie H. Adams Iii
Honors Theses
The video game I developed for my senior project lacked complex and engaging enemy artificial intelligence. The standard implementations of AI systems such as finite state machines and behavior trees felt like side-steps rather than innovative solutions. Upon seeing the 'magic' of machine learning in perfecting games such as Snake, Super Mario, and Flappy Bird, I was inspired to seek my answer in the field of evolutionary computation. However, my challenge differed in that the problem space would be defined by dynamic player strategies, making it not well-defined or static. As such, my evaluations are based on enemies exhibiting emergent …
Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez
Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez
Physical Therapy Faculty Articles and Research
Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested …
Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev
Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev
Publications and Research
The virtualization of Javanese shadow puppetry (Wayang Kulit) offers a unique opportunity to preserve and revitalize traditional performance art through immersive digital platforms. This project explores the development of a virtual Wayang Kulit experience using real-time 3D engines like Unity/Unreal Engine while focusing on simulating the mechanics and aesthetics of shadow puppet performance. The puppets are designed using detailed 2D planes and rigged with skeletal systems to reflect the stylized motion of traditional puppetry. An aspect of this project is integrating an AI-driven control system that autonomously animates the puppets, learning from recorded puppeteer performances to replicate gesture, rhythm, and …
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Doctoral Dissertations and Master's Theses
This study presents a reinforcement learning (RL) approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously reorienting the satellite’s antenna toward Earth while charging the battery via solar panels. A generic reward function, designed for the RL-based method, enables the controller to adapt to diverse failure scenarios, including severe actuator noise, misalignment, and complete actuator failure. Simulations are conducted in the Basilisk environment and trained with the tonic framework and demonstrate ranging capabilities of …
Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao
Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao
Dartmouth College Ph.D Dissertations
The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and …
Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal
Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal
Computer Science: Faculty Publications and Other Works
The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …
Voice Interaction With Conversational Ai Could Facilitate Thoughtful Reflection And Substantive Revision In Writing, Jiho Kim, Philippe Laban, Xiang 'Anthony' Chen, Kenneth C. Arnold
Voice Interaction With Conversational Ai Could Facilitate Thoughtful Reflection And Substantive Revision In Writing, Jiho Kim, Philippe Laban, Xiang 'Anthony' Chen, Kenneth C. Arnold
University Faculty Publications and Creative Works
Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. …
Interaction-Required Suggestions For Control, Ownership, And Awareness In Human-Ai Co-Writing, Kenneth C. Arnold, Jiho Kim, Jason G. Chew, Jooha Yoo, Juyeong Kim, Ray Flanagan, Heonjae Kwon
Interaction-Required Suggestions For Control, Ownership, And Awareness In Human-Ai Co-Writing, Kenneth C. Arnold, Jiho Kim, Jason G. Chew, Jooha Yoo, Juyeong Kim, Ray Flanagan, Heonjae Kwon
University Faculty Publications and Creative Works
This paper explores interaction designs for gen-
erative AI interfaces that necessitate human in-
volvement throughout the generation process.
We argue that such interfaces can promote
cognitive engagement, agency, and thoughtful
decision-making. Through a case study in text
revision, we present and analyze two interac-
tion techniques: (1) using a predictive-text in-
teraction to type the assistant’s response to a
revision request, and (2) highlighting potential
edit opportunities in a document. Our imple-
mentations demonstrate how these approaches
reveal the landscape of writing possibilities and
enable fine-grained control. We discuss impli-
cations for human-AI writing partnerships and
future interaction design …
Unpaired Virtual Histological Staining Of Tissue From Autofluorescence Using Regularized Cycle-Consistent Adversarial Networks, Zhesi Wen
Theses and Dissertations
We present a regularized CycleGAN with a Dense Residual U-Net to virtually stain autofluorescence images of tissue into H&E-like images. Our method outperforms standard architectures, reduces artifacts, and achieves superior FID scores, enabling efficient, label-free, and accurate digital pathology for unpaired datasets using multi-channel fluorescence inputs.
Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces, Jeffrey Hsu
Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces, Jeffrey Hsu
Theses and Dissertations
This research presents MARCO—a cognitive framework for Intelligent User Interfaces that uses meta-reasoning for context-aware adaptation across diverse tasks. It integrates multiple reasoning modules coordinated by a Meta-Cognitive Unit that selects strategies based on evolving demands. Evaluations show MARCO outperforms baselines in reasoning accuracy and computational efficiency.
Optimizing Small Ai Models For Biomedical Tasks Through Efficient Knowledge Transfer From Large Domain Models, Girish Sundaram
Optimizing Small Ai Models For Biomedical Tasks Through Efficient Knowledge Transfer From Large Domain Models, Girish Sundaram
Theses and Dissertations
The PICO (Population, Intervention, Comparison, Outcome) framework is a widely adopted methodology for structuring clinical research questions and extracting relevant information from unstructured medical texts. However, traditional approaches for PICO classification demand computationally expensive domain-specific language models, such as BioBERT and ClinicalBERT, which require extensive training and large annotated datasets. This dissertation introduces Distilled Rapid Embedding Transfer (DRET), a novel knowledge transfer method designed to enable resource-constrained domain adaptation. DRET aims to efficiently transfer biomedical domain knowledge from large, specialized models to a compact, general-purpose model, DistilBERT, thereby enhancing its ability to perform domain-specific tasks without access to the original …
Designing Ai-Driven Dining: A Ux Approach To Enhancing The Self-Service Experience, Kaylin Joung, Yuki Hayashi, Dailuaine Esguerra
Designing Ai-Driven Dining: A Ux Approach To Enhancing The Self-Service Experience, Kaylin Joung, Yuki Hayashi, Dailuaine Esguerra
Undergraduate Research Symposium Posters
Artificial intelligence has transformed many industries, yet its integration into self-dining experiences is still emerging. This research explores how AI can enhance self-dining by introducing technology like interactive kiosks and robot servers to improve efficiency, personalization, and customer convenience. By addressing current gaps, we aim to create a more seamless and engaging dining experience.
Artificial Intelligence In Higher Education: A Case Study Of Faculty Teaching Methodologies At A Private University, Ellen Ramsey, George Antoniou, Matteo Peroni, Karima Lanfranco, Brent Muckridge, Raouf Ghattas, Philip L. Fazio, Wendy Wallberg, Saidi Porta, Mary Smith, Gary Solomon, Kristen Migliano, David G. Wolf
Artificial Intelligence In Higher Education: A Case Study Of Faculty Teaching Methodologies At A Private University, Ellen Ramsey, George Antoniou, Matteo Peroni, Karima Lanfranco, Brent Muckridge, Raouf Ghattas, Philip L. Fazio, Wendy Wallberg, Saidi Porta, Mary Smith, Gary Solomon, Kristen Migliano, David G. Wolf
Faculty and Staff Publications & Presentations
This research study examined the integration of artificial intelligence (AI) in higher education from the perspective of the faculty of a private university. It inquired into the impact of AI on pedagogical methods, administrative procedures, and ethical values. Qualitative case study methodology and in-depth semi-structured interviews were designed and conducted with faculty from four academic departments. Responses related to impressions, challenges, and opportunities for AI integration were gathered. The study findings from qualitative and quantitative data analysis indicated that AI is perceived to help improve educational outcomes with student-personalized learning pathways through streamlined administrative processes. The study revealed that participating …
Harnessing Neurodiversity And Artificial Intelligence In Education To Bridge The Cybersecurity Workforce Gap, George Antoniou
Harnessing Neurodiversity And Artificial Intelligence In Education To Bridge The Cybersecurity Workforce Gap, George Antoniou
Faculty and Staff Publications & Presentations
This perspective paper examines how neurodiversity and artificial intelligence (AI) can jointly address the critical workforce shortage in cybersecurity. Drawing on peer-reviewed research, industry reports, and case studies, it explores how neurodivergent individuals—such as those with autism spectrum disorders, ADHD, and dyslexia—possess strengths in pattern recognition, logical reasoning, and attention to detail that align with cybersecurity demands. AI-based educational tools, including adaptive tutoring systems, scenario-based simulations, and real-time analytics, can personalize learning for neurodiverse students, enhancing engagement and skill mastery. The paper discusses how these targeted interventions not only accelerate knowledge retention and practical competence but also foster greater inclusion …
Pure Nash Equilibrium And Strong Nash Equilibrium Computation In Additive Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan
Pure Nash Equilibrium And Strong Nash Equilibrium Computation In Additive Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan
Research & Publications
Aggregate games, first conceptualized by Nobel laureate Reinhard Selten in 1970, model the decision-making of interdependent agents where each agent’s utility depends on their own action and the aggregation of everyone’s actions. We consider computational questions on pure Nash equilibrium (PNE) and pure strong Nash equilibrium (SNE) for aggregate games. On the way, we define a new subclass of aggregate games we call additive aggregate games, which encompasses popular games like congestion games, anonymous games, Schelling games, etc. We show that PNE existence is NPcomplete for very simple cases of additive aggregate games. We devise an efficient aggregate-space algorithm for …