Artificial Intelligence and Robotics Commons^™

Enhancing Community College Leadership Development Through Ai: Enriching The Andragogical Approach, James Bartlett, Michelle Bartlett

Educational Leadership & Workforce Development Faculty Publications

This proposal is for a work-in-progress that proposes a study to explore the innovative use of Artificial Intelligence (AI), specifically ChatGPT, in advancing the development of training for leadership skills within community college settings. It aims to investigate how AI can be leveraged to provide a personalized and effective learning experience, aligning with the principles of andragogy to cater to adult learners in leadership roles. The study, currently in the data collection phase, anticipates revealing significant insights into the effectiveness of AI in professional development. It seeks to highlight the innovative aspects of AI integration in educational leadership, focusing on …

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

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 …

Hack24f: Ai Audio Extractor, David Wu, Tiffany Nham

Paul English Applied Artificial Intelligence (AI) Institute Publications

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.

Hack24f: Ai Attacking Ai, Emry Hankins, Sofia Escobar, Yinxin Wan

Paul English Applied Artificial Intelligence (AI) Institute Publications

As MLaaS gains popularity, it also attracts new threats, in particular, model extraction attacks. These attacks involve unauthorized attempts to access and replicate AI models by querying and analyzing the response. Not only do these attacks pose a threat to security and safety but also compromises valuable intellectual property. Because businesses are increasing accessibility to their models that contain sensitive data, it is crucial that they are able to keep them safe and secure.

Hack24f: Careplus: Ai Chatbot, Arianna Fernandez, Sophia Cherkaoui

Paul English Applied Artificial Intelligence (AI) Institute Publications

90% of patients Google symptoms before they talk to their doctor. What are we developing? An app that asks for user symptoms and personal information, users AI to determine medical conditions and provides a plan of action, including recommended medications and suggested doctors to visit.

Hack24f: Llms And World Knowledge Problems, Chengtaon Lin, Joseph Lobo, Huuthanhvy Nguyen, Brian Santos, Ting Xu

Paul English Applied Artificial Intelligence (AI) Institute Publications

While AI “hallucinations” have been widely publicized by the media as shortcomings of Large Language Models (LLMs), there is much less awareness about shortcomings that derive from the potential lack of understanding of the world, from a potential lack of commonsense knowledge. We want to raise awareness among our student body about the potential fallibility of LLMs when they the problems that they need to solve require a basic understanding of the world. We hope that this awareness will prompt everyone to question more carefully the outputs generated by LLMs.

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

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.

Hack24f: Ai Conversations In Healthcare, Patrick Finger, Hannah Neale, Anthony Ferreira, Ayaz Mohammed

Paul English Applied Artificial Intelligence (AI) Institute Publications

Nursing students often complete clinical hours under the supervision of instructors in traditional hospital settings. However, obtaining individualized, consistent feedback from patients about their interactions with nursing students is often not feasible. This limits students' ability to fully understand how their communication skills are perceived and how they can improve. Currently, there are no models that represent realistic real life conversations with patients. Most virtual simulation models used for nursing students provide scripted responses that do not feel genuine.

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

Paul English Applied Artificial Intelligence (AI) Institute Publications

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 …

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

Paul English Applied Artificial Intelligence (AI) Institute Publications

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

Hack24f: Alzcare Ai Assist: Empowering Alzheimer's Care, Yao Zhang, Chengjie Zheng, Oliver Francois, Lingling Zhang

Paul English Applied Artificial Intelligence (AI) Institute Publications

AlzCare AI Assist is a groundbreaking solution that leverages the power of artificial intelligence to revolutionize Alzheimer's care. By delivering personalized assessments, psychological support, and caregiver assistance, we aim to transform the lives of those affected by this debilitating condition.

Hack24f: Painsync, Zihan Li, Zhen Lu, Ping Chen

Paul English Applied Artificial Intelligence (AI) Institute Publications

Pain is one of the most disruptive human experiences, influencing not only physical well-being but also emotional and mental health. The PainSync project proposes a technology-assisted framework for pain recognition, monitoring, and management, using AI-driven tools to bridge the gap between patient experiences and professional care. PainSync begins by recognizing an individual’s discomfort and offering immediate support through a chatbot that collects symptom information and provides preliminary guidance. The system then tracks vital signs and daily activities, generating data that is subsequently analyzed by custom-built AI models to detect patterns, assess severity, and identify potential causes of pain. This analysis …

Hack24f: The Empathy Architects, Michael Rejtig, Ajanee Igharo, Emma Kelminson, Jackson Comeau

Paul English Applied Artificial Intelligence (AI) Institute Publications

Problem Statement: We want to help patients from underserved communities feel more safe, respected and comfortable in interacting with healthcare providers during the initial in-person encounters while also helping providers build empathy and implement socio-emotionally competent communication behaviors. Solution: Empathy Evolution is an AI-powered role-playing game that helps patients simulate their ideal communication with healthcare providers.

Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …

Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

In this collaborative research endeavor at the intersection of biological safety and cybersecurity for BiosView labs, the authors highlight their engagement with a diverse student cohort. The chapter delves into the motivation behind collaborations extending beyond traditional academic research environments, emphasizing inclusivity. The meticulous examination of student demographics, including gender, self-reported ethnicity, and national origin, is detailed in the methodology. A student-centric approach is central to the exploration, focusing on aligning teaching and management styles with unique student needs. The chapter elaborates on effective teaching methodologies and management practices tailored for BiosView labs. A dedicated section emphasizes the purpose of …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang

Electrical & Computer Engineering Faculty Publications

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a_c, a_m, b_m, and c_m in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach …

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …