Artificial Intelligence and Robotics Commons^™

Hack24f: Using Ai To Support Teachers Working With Young Children With Challenging Behaviors, Muqing Zhang, Bennie Bendiksen, Songtian Zeng

Paul English Applied Artificial Intelligence (AI) Institute Publications

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While coaching or mental health consultation can be a promising approach to support educators (Kang-Yi et al., 2018), the cost can be substantial and many school districts find it difficult to hire qualified personnel to coach the educators in need. Previous research has attempted to utilize mobile technology to support parents in addressing children with challenging behaviors (Meadan et al., 2016). There are few tools available to support teachers and that attempt to integrate AI technology in the program design and implementation. Our objective is to develop a prototype AI-based tool called Trick-or-Treat to support teachers in identifying and developing …

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Hack24f: Beacon Pathway, Michael Agbesi, Ciara Santiago, David Martinez, Olivia Moos, Mohammed Mustafa

Paul English Applied Artificial Intelligence (AI) Institute Publications

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The problem: There are a multitude of students from different backgrounds who are feeling lost in their academic journey at UMass Boston & need help with finding opportunities that fit their needs.

The solution: Developing Beacon Pathway, a user-centered platform designed to empower UMass Boston students from diverse backgrounds by streamlining their academic journey. This solution focuses on providing personalized resources, tailored opportunities, and community engagement, ensuring every student feels supported and connected as they navigate their unique paths to success

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Hack24f: Tori Project: An Llm Powered Personal Assistant That Speaks, Inal Mashukov, Avi Felzenstein

Paul English Applied Artificial Intelligence (AI) Institute Publications

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The problem: remember Jarvis from Iron Man? If not, Jarvis is an AI personal assistant to Tony Stark, aka Iron Man, which can speak in a human-like manner, and is in charge of the systems of the Iron Man suit. Tony Stark also relies on Jarvis for direct access to information, automation of his projects, and helping with work in general. Currently, the “best” real-world alternatives to Jarvis are Siri and Alexa. These are NOT particularly intelligent. Wouldn't it be nice to have a Jarvis-like assistant that is: actually intelligent and resourceful, sounds human, runs locally/offline on your phone or …

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Hack24f: Ai And Mental Health: Addressing The Therapy Gap, Samatrai Piam, Digvijay Mahawar, Meric Kinali, Luxman Surentha, Jackson Comeau, Gail Rauch, Cody Turner

Paul English Applied Artificial Intelligence (AI) Institute Publications

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Real-World Problem: There is a global shortage of mental health professionals, especially in underserved areas. AI-based chatbots could help fill this gap, but there are serious ethical concerns about the quality of care and efficacy of such chatbots.

Solution: This is a non-technical project that involved conversing with existing chatbots in an effort to develop a normative framework that could be used by future AI therapy bot developers. This framework seeks to build on, and operationalize, some of the insights from the recently published whitepaper on the ethics of chatbot therapy by the UMass Boston Applied Ethics Center. This presentation …

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Hack24f: Examine The Human Creativity And Productivity In Ai Generated Content, Jongsoo Ha, Dante Barton, Onkar Khedkar, Neeti Shah, Yash Gondkar, Youxiang Zhu, Xiaohui Liang

Paul English Applied Artificial Intelligence (AI) Institute Publications

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This project, presented at the Fall 2024 Hackathon, explores the balance between human creativity and productivity in the age of AI-generated content. With tools such as ChatGPT and Grammarly becoming integral to students’ academic workflows, questions surrounding originality, intellectual property, and academic integrity have grown increasingly urgent. The team addresses the challenge of distinguishing between human and AI contributions in content creation, particularly as current AI detection tools prove unreliable. Their proposed solution involves a system that not only detects AI-generated text using watermarking techniques but also quantifies contributions from humans and AI throughout the writing process.

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Identifying And Predicting Patterns Of Snowpack Ripening With Machine Learning Methods, Clement Cherblanc

Graduate Student Theses, Dissertations, & Professional Papers

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The timing of water release from the snowpack plays key roles in ecosystem services, groundwater recharge, and water resource management. However, two internal barriers in a standing snowpack must be overcome before runoff can outflow from the base: 1) the cold content must be exhausted, and 2) the interconnected network of snow grains must be filled with liquid water to residual saturation. Expressing the liquid water as latent heat allows the two barriers to be grouped as an energy (J/m²) to define a snowpack’s Runoff Energy Hurdle (REH). The growth and loss of REH is driven by evolution of pore …

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Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

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Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

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The Combined Focal Loss And Dice Loss Function Improves The Segmentation Of Beta-Sheets In Medium-Resolution Cryo-Electron-Microscopy Density Maps, Yongcheng Mu, Thu Nguyen, Bryan Hawickhorst, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

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Although multiple neural networks have been proposed for detecting secondary structures from medium-resolution (5–10 Å) cryo-electron microscopy (cryo-EM) maps, the loss functions used in the existing deep learning networks are primarily based on cross-entropy loss, which is known to be sensitive to class imbalances. To monitor and tune the performance of various loss functions for the secondary structure detection problem, we investigated five loss functions: cross-entropy, Focal loss, Dice loss, and two combined loss functions. Using a U-Net architecture in our DeepSSETracer method and a dataset composed of 1,355 box-cropped atomic-structure/density-map pairs, we found that a newly designed loss function …

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Disentangling Cyclic Causality: An Instance-Based Framework For Causal Discovery, Chase A. Yakaboski

Dartmouth College Ph.D Dissertations

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Correlation does not imply causation" is one of the fundamental principles taught in science, emphasizing that associations between variables do not necessarily indicate causality. Yet, over the past three decades, extensive research has begun to challenge this perspective by developing sophisticated methods to differentiate causal from correlative relationships. This research suggests that correlations often involve a blend of confounded and causal interactions, which, given certain assumptions, can be disentangled to uncover actionable insights and deepen our understanding of physical, biological, and societal systems.

Accurately discovering causal relationships from data amidst cyclic dynamics remains a challenging open problem in causality research. …

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Mitigating Safety Issues In Pre-Trained Language Models: A Model-Centric Approach Leveraging Interpretation Methods, Weicheng Ma

Dartmouth College Ph.D Dissertations

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Pre-trained language models (PLMs), like GPT-4, which powers ChatGPT, face various safety issues, including biased responses and a lack of alignment with users' backgrounds and expectations. These problems threaten their sociability and public application. Present strategies for addressing these safety concerns primarily involve data-driven approaches, requiring extensive human effort in data annotation and substantial training resources. Research indicates that the nature of these safety issues evolves over time, necessitating continual updates to data and model re-training—an approach that is both resource-intensive and time-consuming. This thesis introduces a novel, model-centric strategy for understanding and mitigating the safety issues of PLMs by …

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Scalable Methods For Resource-Constrained Adaptive Sampling, Kizito Masaba

Dartmouth College Ph.D Dissertations

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As the global community confronts the pressing issues of climate change, the importance of environment monitoring cannot be overstated. This process is essential in identifying and tracking critical environmental trends to facilitate efficient and effective conservation efforts. Traditionally, scientists perform this task using hand-held, in-situ instruments. With the recent robot advancements, this task can be fully automated by applying multi-robot systems to a task referred to as multi-robot adaptive sampling. However, the cost of purchasing and operating robot systems for this application are still prohibitive due to various environmental, technological, and logistical constraints. Some of the outstanding constraints to the …

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A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor

UNF Graduate Theses and Dissertations

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Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …

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Feedback Loops: Feedback Machines, Patrick Barry

Articles

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Yes, AI raises serious concerns about bias, privacy, copyright infringement, environmental sustainability, and a whole bunch of other important topics. But if you are looking for a positive use case - and a new way to approach professional development - try asking chatgpt or some other AI chatbot for feedback, especially on something you've written.

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Transformer-Enabled Deep Reinforcement Learning For Coverage Path Planning, Daniel B. Tiu

UNF Graduate Theses and Dissertations

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Coverage path planning (CPP) is the problem of covering all points in an environment and is a well-researched topic in robotics due to its sheer practical relevance. This paper investigates such an offline CPP problem where the primary objective is to minimize the path length to achieve complete coverage. Furthermore, the literature suggests that taking turns leads to a higher energy use than going straight. To this end, we design a novel objective function that aims to minimize the number of turns as well. We have proposed a deep reinforcement learning (DRL)-based framework that uses a Transformer model. Unlike state-of-the-art …

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Media Haze Classification In Retinal Images Using Deep Learning, Jonathan O'Berry

UNF Graduate Theses and Dissertations

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Media Haze (MH) is a condition that affects an individual’s quality of life by affecting their eyes. Current practice is to detect MH by manually examining retinal fundus (retinal) images. The analysis of images being used as the prevalent technique for identifying the MH condition strongly suggests that automation of this process may be possible. In recent years, machine learning, specifically computer vision, has allowed for the automation of tasks relating to image analysis. This ability to automate has also recently been shown in the medical field for some eye conditions and diseases. This thesis centers around the problem of …

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Using Pose Estimation Software To Predict Actions In Sabre Fencing, Micah Edwin Peters Ii

Honors College Theses

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Fencing is a combat sport that uses three different swords: epee, foil, and sabre. Due to its fast-paced nature and employment of right of way, sabre fencing is often considered the most difficult of the three to learn. Computer vision and pose estimation software can be used to lower the barrier of entry to sabre fencing by identifying the different actions in sabre fencing. This project focuses on using open-source software to design a program that can identify the sabre parries as well as the main sabre movements. This program could be used to help newer fencers and spectators better …

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Ethical Decision-Making In Older Drivers During Critical Driving Situations: An Online Experiment, Amandeep Singh, Sarah Yahoodik, Yovela Murzello, Samuel Petkac, Yusuke Yamani, Siby Samuel

Psychology Faculty Publications

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The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze …

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Leveraging Machine Learning To Study How Temperature Scores Predict Pre-Term Birth Status, Erich Seamon, Jennifer A. Mattera, Sarah A. Keim, Esther M. Leerkes, Jennifer L. Rennels, Andrea J. Kayl, Kristy M. Kulhanek, Darcia Narvaez, Sarah M. Sanborn, Jennifer B. Grandits, Christine Dunkel Schetter, Mary Coussons-Read, Amanda R. Tarullo, Sarah J. Schoppe-Sullivan, Mariah E. Thomason, Julie M. Braungart-Rieker, Julie C. Lumeng, Shannon N. Lenze, Lisa M. Christian, Darby E. Saxbe, Laura R. Stroud, Christina M. Rodriguez, Stephanie Anzaman-Frasca

Psychology Faculty Publications

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Background

Preterm birth (birth at <37 completed weeks gestation) is a significant public heatlh concern worldwide. Important health, and developmental consequences of preterm birth include altered temperament development, with greater dysregulation and distress proneness.

Aims

The present study leveraged advanced quantitative techniques, namely machine learning approaches, to discern the contribution of narrowly defined and broadband temperament dimensions to birth status classification (full-term vs. preterm). Along with contributing to the literature addressing temperament of infants born preterm, the present study serves as a methodological demonstration of these innovative statistical techniques.

Study design

This study represents a metanalysis conducted with multiple samples (N = 19) including preterm (n = 201) children and (n = 402) born at term, with data combined across investigations to perform classification analyses.

Subjects …

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Synthetic Health Data: Real Ethical Promise And Peril, W. Nicholson Price Ii, Daniel Susser

Other Publications

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Modern health research and development faces a dilemma. On the one hand, there is more data than ever — in electronic health records, in lab research, in public datasets, and on the internet — from which to extract potentially transformative scientific insights and to use as the basis for developing breakthrough health care technologies. On the other hand, using this data entails various risks: threats to patient privacy, skewed samples and approaches to analysis that can perpetuate demographic and other biases, and uneven access to data about rare conditions and small patient subgroups. Generating synthetic data has emerged as one …

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Hack24f: Ai Audio Extractor, David Wu, Tiffany Nham

Paul English Applied Artificial Intelligence (AI) Institute Publications

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I want to make a next.js website locally and then be able to hopefully deploy on Vercel. Within the website I want to be able to use AI to separate the instruments (vocals, piano, guitar, drums, bass, etc.) and also identify which notes are being played. I was thinking that we might be able to use an AI stem splitter to separate the audio tracks and use another AI model for note detection.

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