Artificial Intelligence and Robotics Commons^™

Music Recommendation Using Exemplars And Contrastive Learning, Tina Tran

Honors Undergraduate Theses

The popularity of AI audio applications is growing, it is used in chatbots, automated voice translation, virtual assistants, and text-to-speech translation. Audio classification is crucial in today’s world with a growing need to sort and classify millions of existing audio data with increasing amounts of new data uploaded over time. In the area of classification lies the difficult and lucrative problem of music recommendation. Research in music recommendation has trended over time towards collaborative-based approaches utilizing large amounts of user data. These approaches tend to deal with the cold-start problem of insufficient data and are costly to train. We look …

Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai

Theses and Dissertations--Electrical and Computer Engineering

Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Advancing Household Robotics: Deep Interactive Reinforcement Learning For Efficient Training And Enhanced Performance, Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla

Engineering Management & Systems Engineering Faculty Publications

The market for domestic robots—made to perform household chore, is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labour, in contrast to industrial robots, which are frequently criticised for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in a number of minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviours. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to …

Improving Neuropathological Analysis With Aβgan: Addressing Morphology Imbalance For Efficient Alzheimer's Disease Diagnosis, Sujatha Alla, Prasanthi Chidipudi, Nagesh Bheesetty, Vedvikash Reddy Velur, Joshit Mohanty, Puneeth Bheesetty, Marisha Jmukhadze, Narendra Lakshmana Gowda, Sai Gireesh Komaragiri

Engineering Management & Systems Engineering Faculty Publications

Histopathologists are experiencing a digital revolution in their field thanks to the digitization of Whole Slide Images (WSIs), which are microscope slides of tissue that can measure gigapixels in size. With so much high resolution data at their disposal, computer vision techniques can now be used to automate laboratory processes, create visual standards, and increase analysis throughput, all of which reduce the workload of pathologists [1]. The "gold" standard in neuropathology, particularly for Alzheimer's Disease- is pathological diagnosis made by looking at White Matter Inclusions (WSIs) in brain tissue. Semi-quantitative scoring in accordance with the standards established by the Consortium …

Exploring Instructional Designers' Utilization And Perspectives On Generative Ai Tools: A Mixed Methods Study, Tian Luo, Pauline S. Muljana, Xinyue Ren, Dara Young

STEMPS Faculty Publications

The emergence of generative artificial intelligence (GenAI) has caused significant disruptions on a global scale in various workplace settings, including the field of instructional design (ID). Given the paucity of research investigating the impact of GenAI on ID work, we conducted a mixed methods study to understand instructional designers (IDs)’ perceptions and experiences of utilizing GenAI across a spectrum of ID tasks. A total of 70 IDs completed an online survey, and 13 of them participated in the semi-structured interviews. The survey results indicated IDs’ familiarity with and perceived usability of GenAI tools in performing various ID responsibilities in their …

Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu

Engineering Technology Faculty Publications

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …

Federated Learning: Overview, Strategies, Applications, Tools And Future Directions, Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Maliha Tabassum

Engineering Technology Faculty Publications

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

A Benchmark Framework For Data Visualization And Explainable Ai (Xai), Murat Kuzlu, Gokcen Ozdemir, Umut Ozdemir

Engineering Technology Faculty Publications

This research introduces a benchmark framework, called EDUMX, designed for machine learning (ML)-based forecasting and XAI tasks, leveraging the Streamlit open-source Python library. The framework offers a comprehensive suite of functionalities, including data loading, feature selection, relationship analysis, data preprocessing, model selection, metric evaluation, training, and real-time monitoring. Users can easily upload data in diverse formats, explore relationships between variables, preprocess data using various techniques, and assess the performance of the ML model using customizable metrics. With its user-friendly interface, this framework offers invaluable insights for forecasting tasks in various domains, catering to the evolving needs of predictive analytics. EDUMX …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Short, Full, Best: Analysis Of Different Conference Papers, Miguel Williams

Graduate Research Theses & Dissertations

Research, publish, repeat. This is the basic cycle of anyone in academia. Individuals in academia conduct research, write up your research into a paper or journal article, submit to a conference or journal, and repeat the process. If you're skilled you may even obtain the coveted best paper award from the conference. In this research, I compare full papers to short papers and full papers to best papers. I start by fine-tuning three transformer models for classification capabilities. After I calculate lexical diversity and readability metrics of the papers, I use the features to train three traditional machine learning models. …

Simulation Of A Pick And Place System For Electronic Cards Using A Yumi Cobot, Derrick Sze, Rosula Sj Reyes, Patricia Angela R. Abu

Electronics, Computer, and Communications Engineering Faculty Publications

Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing repetitive work and requiring highly skilled workers. The goal of the research is to be able to simulate a pick and place environment with electronic cards using a YuMi cobot and mobile platforms in Coppeliasim. The mobile robot is responsible for transporting the electronic cards to the target location through path planning implemented using the OMPL plug-in. After arriving at the target location, YuMi will then perform the …

"Ai's Pompous Vocabulary Sounds Like It's Trying To Impress Someone": Preservice Teachers' Respond To Ai Reviews Of Their Writing, Jennifer Kidd, Keri Parker, Samantha Myers

Teaching & Learning Faculty Publications

This study examines pre-service teachers’ (PSTs’) responses to using AI to review their writing. As part of a class activity, PSTs prompted an AI-chatbot for feedback and revisions on a short essay based on a provided assignment rubric. Afterward, they reflected on the pros and cons of using AI in this way. While some PSTs initially encountered functionality issues, all were able to obtain feedback and the majority reported finding it useful. In general, the PSTs appreciated AI’s speed and expertise and the potential to customize feedback based on their needs. However, many were skeptical of AI’s evaluation of their …

Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai

Dartmouth College Master’s Theses

Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, …

A Benchmark Framework For Data Visualization And Explainable Ai (Xai), Murat Kuzlu, Gokcen Ozdemir, Umut Ozdemir

Engineering Technology Faculty Publications

This research introduces a benchmark framework, called EDUMX, designed for machine learning (ML)- based forecasting and XAI tasks, leveraging the Streamlit open-source Python library. The framework offers a comprehensive suite of functionalities, including data loading, feature selection, relationship analysis, data preprocessing, model selection, metric evaluation, training, and real-time monitoring. Users can easily upload data in diverse formats, explore relationships between variables, preprocess data using various techniques, and assess the performance of the ML model using customizable metrics. With its user-friendly interface, this framework offers invaluable insights for forecasting tasks in various domains, catering to the evolving needs of predictive analytics. …

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: 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

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 …

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: 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: 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: 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.

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

Graduate Student Theses, Dissertations, & Professional Papers

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 …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …

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

Computer Science Faculty Publications

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 …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …

Identifying New Cancer Genes Based On The Integration Of Annotated Gene Sets Via Hypergraph Neural Networks, Chao Deng, Hong-Dong Li, Li-Shen Zhang, Yiwei Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Results

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …