Physical Sciences and Mathematics Commons^™

Spring Ai 2026 Workshop And Speaker Series Report, Gregory Blike, Hannah (Nyingi) Brown, Dora (Dawn) Nguyen, Rami Huu Nguyen, Ajanee Igharo, Frayni Calderon, Moumita Saha, Chengjie Zheng

Paul English Applied Artificial Intelligence (AI) Institute Publications

The AI Workshop and Speaker Series was organized by the Student Advisory Council of the Paul English Applied Artificial Intelligence Institute at the University of Massachusetts Boston with the guidance of Distinguished Professor of Computer Science Wei Ding to create a practical and student-centered AI learning space. The Spring 2026 series included workshops on LinkedIn Optimization, Data Mining, GitHub, Prompt Engineering, Machine Learning of Structured Data, and Applied LLMs with Responsible AI Use in Research, while the broader speaker series introduced students to AI career development, generative AI and LLMs for cybersecurity, transportation security, political sciences, and AI in biomedicine. …

A Simulation Assessment Of The 'Law Of One Price', Caleb Wilkins

Computational and Data Sciences (MS) Theses

The ‘law of one price’ is an appealing notion regarding pricing of tradeable commodities that are priced in different currencies. It states that the prices of the same good in different markets should be equal after adjustment for exchange rates and that equality should persist through exchange rate fluctuations.

My research simulates the market conditions that should precipitate the ‘law of one price.’ Data was obtained from the simulated trade between algorithmic artificial intelligence agents that operated under induced boundedly rational market behaviors. Trade took place in two initially separate markets, a high-price market with a higher equilibrium price and …

Lessons Learned From The Adrenalin Load Disaggregation Challenge, András Balázs Tolnai, Zheng Ma, Igor Sartori, Clayton Miller, Stephen White, Matt Amos, Gustaf Bengtsson, Akram Hameed, Nørregaard Bo Jørgensen

Research Collection College of Integrative Studies

Crowdsourced data science competitions have emerged as a powerful mechanism for advancing research in energy informatics, offering scalable pathways for developing machine learning solutions that enhance energy efficiency and smart building operations. The ADRENALIN Load Disaggregation Challenge addressed a central problem in energy analytics—non-intrusive load monitoring (NILM) of heating and cooling loads in commercial buildings—while emphasizing the importance of model generalization across different buildings. This paper presents a comprehensive reflection on the lessons learned from organizing and executing the ADRENALIN competition, including technical insights, organizational challenges, and recommendations for future energy data challenges. In addition to the ADRENALIN case, a …

Ai-Ready Libraries Require Ai-Ready Librarians: Building Organisational Capability For Digital Transformation, Salihin Mohammed Ali

Research Collection Library

Academic libraries worldwide are rapidly experimenting with artificial intelligence (AI) to enhance research, learning, discovery, operations, and user engagement. However, many institutions continue to approach AI adoption primarily through isolated pilots, individual experimentation, or technology-centric initiatives. While these efforts generate innovation, they often struggle to scale sustainably without corresponding organisational capability development. This presentation argues that AI-ready libraries require AI-ready librarians and proposes an organisational capability approach for sustainable AI transformation in academic libraries. Drawing from the development of a library-wide AI strategy plans at Singapore Management University, the presentation explores how AI capability-building can be operationalised across diverse functional …

Viability Assessment Of Bovine Embryos: A Public Dataset And Deep Learning Baselines, Erfan Khayyati

All Graduate Theses and Dissertations, Fall 2023 to Present

Improving the success rates of cattle breeding is essential for sustainable agriculture, global food security, and high-quality livestock production. Currently, determining whether a lab-grown bovine embryo is healthy enough for a successful pregnancy requires highly trained experts to manually evaluate days of continuous time-lapse video footage. This process is not only incredibly time-consuming but also highly subjective; human reviewers often suffer from visual fatigue when tracking subtle, microscopic cellular changes over a seven-day period, leading to significant disagreement among even top experts on an embryo’s true potential. Furthermore, assessing bovine embryos is notoriously difficult due to their dark, lipid-dense cellular …

Counterfactual Explanations For Time Series Classification: From Localized Perturbations To Realistic Generation, Peiyu Li

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning models are often used to classify signals collected over time, such as heart rhythms, movement recordings, industrial sensor measurements, and scientific observations. These models can be accurate, but they are often difficult to understand. Users may need to know not only what a model predicted, but also what would have needed to change for the model to reach a different decision.

This dissertation studies counterfactual explanations for time series data. A counterfactual explanation answers a “what-if” question. For example, if a model classifies a signal as one activity instead of another, the explanation shows how the signal would …

Fostering Safe Space For Children In Online Navigation And Parent-Child Interactions, Rizu Paudel

All Graduate Theses and Dissertations, Fall 2023 to Present

As children and teenagers spend increasingly more time online, digital devices have become a major source of family friction. Disagreements frequently arise over privacy boundaries, and online activities. When these conflicts are unresolved, they often lead to broken trust and secretive behavior, leaving children vulnerable to digital harms like cyberbullying, toxic content, or account hacking. Therefore, it is important to create a safe and open environment for children where in order for them to share their feelings with parents. This dissertation investigates the human and technological dynamics of parent-child interactions, developing new ways to support collaborative conflict resolution and online …

A Data-Driven Nutrient Density Scoring Framework For Beef Using Principal Component Analysis, Teja Vuppala

All Graduate Theses and Dissertations, Fall 2023 to Present

Beef is one of the most nutrient-rich foods in the human diet, providing high-quality protein, iron, omega-3 fatty acids, B vitamins, and a wide range of other compounds important to health. However, current nutrition scoring systems used on food labels were designed to compare different foods to one another — for example, beef versus broccoli — and do not work well for judging the nutritional quality of different beef samples relative to each other. A grass-fed steak and a conventionally-finished steak can carry nearly identical Nutrition Facts panels while differing substantially in their content of omega-3 fatty acids, vitamins, and …

Causal Discovery In Photospheric Magnetic Field Time Series For Interpretable Solar Flare Prediction, Nathan W. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are capable of damaging many valuable resources, including satellites, power grids, and even human lives. Being able to predict solar flares can allow for proactive measures to be taken that can prevent that damage. Many new deep learning methods for predicting solar flares have shown promise in this task, but the decisions they make are harder to explain to humans. This makes understanding why these models make mistakes difficult, which in turn makes fixing and maintaining them more challenging. We test a recent deep learning method that helps discover relationships between different measurements of the Sun as they …

The Relativity Of Education: Student Perspectives On Artificial Intelligence In Community College, Ashley Greta Magana

Electronic Theses, Projects, and Dissertations

This hermeneutic phenomenological study examined how diverse community college students experience and make meaning of the integration of generative artificial intelligence (AI) into their educational contexts. Although AI is quickly transforming higher education through automated grading, personalized learning systems, and new models of assessment, the discourse surrounding its implementation remains dominated by administrators, faculty, and institutional stakeholders, while the perspectives of students, specifically community college students who are often historically underrepresented and economically marginalized, are systematically excluded. Most existing research is quantitative and centered on universities, leaving a critical gap in qualitative understanding of the most diverse population in higher …

Artificial Intelligence (Ai) In Forensic Psychology: An Umbrella Review Of Potentials And Pitfalls, Ysabel Thereze Ang Guevarra, Nur Eva Alisha Binte Mohamed Hisham, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) is becoming increasingly embedded within forensic psychological practice, shaping how criminal risk, legal responsibility and public safety are assessed. AI tools are now used in recidivism prediction, behavioural analysis, deception detection and investigative support, high-stakes domains where errors can have profound consequences. Despite this rapid adoption, the existing literature remains fragmented, with most reviews confined to narrow subdomains and offering limited integrated synthesis of AI′s broader role in forensic psychology. Thus, this umbrella review addresses this gap by synthesising findings from 43 reviews obtained from five major databases, namely EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web …

Caring With Ai: The Efficacy Of A Customised Chatgpt-Delivered Self-Compassion Intervention On College Students' Well-Being And Academic Functioning, Tracy X. Chen, Chi-Ying Cheng, Andree Hartanto

Research Collection School of Social Sciences

College students face various challenges, including academic pressure, social stress, and the transition into adulthood, which can lead to increased anxiety and other mental health issues. By recognizing personal struggles as part of a shared human experience and responding with kindness, self-compassion serves as a powerful strategy for enhancing resilience, facilitating better well-being and performance outcomes. Although effective, Compassion-Focused Therapy often requires substantial resources and time, limiting its applicability to college students. To overcome these barriers, the current study designed and evaluated Your Self-Compassion Companion, a ChatGPT-powered AI chatbot intervention grounded in self-compassion theory and delivered over three weekly 20-min …

Llm-Based Early Rumor Detection With Imitation Agent, Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, Cheng Niu

Research Collection School Of Computing and Information Systems

Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive for both training and inference. In this work, we propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for \textit{early time point determination}, while the LLM serves as a powerful \textit{rumor …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Technique-Level Normalization For Cybersecurity Intelligence: An Empirical Evaluation Of Att&Ck Attribution From Hids Alerts Using Fine-Tuned Transformers And Metadata Re-Ranking, Emad Sherif

International Journal of Cybersecurity Intelligence & Cybercrime

Cybercrime investigations increasingly depend on the ability to interpret large volumes of automated security events. For organizations without dedicated security operations centres, a situation common among small and medium enterprises, the manual translation of raw alerts into structured threat intelligence represents a critical bottleneck that slows investigative triage and limits cross-case comparability. This paper evaluates an automated enrichment pipeline designed to address this bottleneck by mapping security events to standardised adversary behaviour labels drawn from the MITRE ATT&CK framework, supporting both operational response and cybercrime investigation workflows. We compare three pipeline configurations, a general-purpose encoder model, a cybersecurity domain-adapted variant, …

Alfred Russel Wallace Notes 42: How Accurate Are The Transcriptions Presented At The Alfred Russel Wallace Page Website?, Charles H. Smith

Faculty/Staff Personal Papers

A look is taken at the level of accuracy displayed by the transcriptions of Wallace writings offered at the Alfred Russel Wallace Page website, as determined by a ChatGPT analysis.

Lessons From The Club Homeschool Capstone: Testing, Data Discipline, And The Computer Science Curriculum, Shane Brown

University Honors Theses

This thesis looks at the CLUB Homeschool Capstone project to argue that Portland State University's Computer Science curriculum should introduce testing and data quality discipline earlier and more intentionally than it does now. As team lead of a seven-person team, I coordinated sprint planning, communicated with the sponsor, and developed custom Discourse plugins that enhanced an existing forum platform instead of creating a separate application database, as requested by the sponsor. The project's requirements document called for a formal testing plan, but our team lacked the practical experience to implement one. This gap became evident through my internships as a …

Enabling Multi-Task Neural Network Inference On Heterogeneous Edge Devices, Redwanul Islam Arif

Master's Theses

Deep neural networks are increasingly required to run on the devices that generate the data. If such a device must perform more than one task, the standard practice is deploying one model per task, which makes memory grow linearly with task count, which is unacceptable when the entire budget is kilobytes. This thesis asks one question in three settings: how much capability can a network acquire without incurring deployment cost?

The first study takes an ImageNet-pretrained ResNet-18, sweeps the branch point across every residual stage and the classification-head depth across one, ten, and twenty layers, and deploys the resulting multi-head …

Federated Learning For Early Medical Diagnosis: Enhanced Diabetic Retinopathy Detection In Smart Healthcare, Mohammad Nasajpour Esfahani

Master's Theses

This thesis investigates the role of federated learning as a privacy-preserving solution for modern healthcare challenges. In traditional machine learning, sensitive medical data must be centralized for model training, raising concerns about privacy, security, and regulatory compliance. Federated learning offers an alternative by allowing hospitals, clinics, and personal health devices to collaboratively train shared models without exchanging raw patient data. The study first explores how federated learning is being used across various healthcare domains, including cancer detection, medical imaging, and disease prediction— highlighting its potential to support secure collaboration across institutions. It addresses key benefits such as data privacy, scalability, …

Dbssnet: Dual-Branch Spectral-Spatial Network With Data-Driven And Knowledge-Guided Band Selection For Uav Hyperspectral Wheat Rust Detection, Subin Kim

All Graduate Theses and Dissertations, Fall 2023 to Present

Wheat rust is a serious plant disease that can reduce crop yield and quality. In practice, the disease is often noticed only after visible symptoms appear, when some damage may already be difficult to reverse. This thesis studies whether drone-based imaging can help detect wheat rust earlier and more reliably in field environments. 

Unlike an ordinary color photograph, a hyperspectral image records reflected light at many narrow wavelengths. These measurements can reveal useful information about plant condition, but they are also high dimensional, noisy, and difficult to analyze when only a limited number of labeled field samples are available. To …

Understanding The Correlation Between Prediction Performance And Trust In Ai Through Scenario-Based Tasks, Likhitha Kammara

Theses and Dissertations

Trust in artificial intelligence is commonly assessed through self-reported scales or behavioral reliance, yet behavioral reliance is retrospective and can only be observed after a decision has already been made. This thesis examines whether prediction accuracy — a user's ability to predict what an AI system will recommend before its output is revealed — can serve as a prospective correlate of trust in the same empirical sense as behavioral reliance. The study was conducted in two phases using scenario-based AI decision tasks across disaster response, healthcare, and infrastructure restoration contexts, employing a between-group design in which participants either predicted AI …

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

A Fuzzy–Neutrosophic Suitability Index For Selecting An Appropriate Reasoning Model Under Vagueness, Incompleteness, And Conflict, Nada A. Nabeeh, Ahmed Samy

Neutrosophic Systems with Applications

Fuzzy reasoning and neutrosophic reasoning are both used to handle uncertainty, but they are not intended for the same uncertainty structure. Fuzzy reasoning is suitable when uncertainty appears mainly as gradual vagueness, where a value may belong to a concept such as ``high risk'' or ``good performance'' to a certain degree. In this case, a membership value is often sufficient. Neutrosophic reasoning is more suitable when the problem also contains incomplete information, undecided evidence, or conflict between sources. In such cases, one membership degree may be too limited because it cannot represent support, rejection, and indeterminacy separately. This study introduces …

Foundations Of Neutrosophic N-Semirings Theory And Structural Properties, Raja Muhammad Hashim, Muhammad Gulistan, Muhammad Shahzad

Neutrosophic Systems with Applications

In this paper the concept of neutrosophic n-semirings (S∪ I, ∗ ₁, ∗ _2, ∗ ₃,..., ∗ _n, ∗ _n+1) has been introduced. The substructure of n-semirings (S∪ I, ∗ ₁, ∗ _2, ∗ ₃,..., ∗ _n, ∗ _n+1) has been defined and some useful results have been proved. Moreover, in order to familiarize the readers with these concepts some worthy examples have been coined. The left, right and two sided ideals of neutrosophic n-semirings have been paid a special heed. Finally we have turned our discussion towards the compatible and congruence …

Evaluating Generative Ai-Based User Interfaces Using An Integrated Neutrosophic Multi-Criteria Decision-Making Framework, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated …

Digraphicsoft Sets And Bidigraphicsoft Sets: Directed And Bidirected Extensions Of Graphicsoft Modeling, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran, Mithun Datta

Neutrosophic Systems with Applications

Uncertainty has been modeled through a wide variety of mathematical frameworks, including fuzzy sets, neutrosophic sets, rough sets, and plithogenic sets. Among these approaches, soft sets offer a parameterized representation of uncertain information and have inspired numerous extensions, such as multisoft sets, double-framed soft sets, hypersoft sets, SuperHyperSoft sets, TreeSoft sets, ForestSoft sets, IndetermSoft sets, and IndetermHyperSoft sets.
This paper focuses on GraphicSoft Sets, which extend the classical soft-set framework by assigning a subset of the universe to each subgraph of an attribute graph. In this way, relationships among attributes are incorporated directly into the parameterized model. Building on …

Neutrosophic Time-Truncated Acceptance Sampling Plans Based On The Exponentiated Weibull Distribution For Reliability Applications, Divya P.R., Preethi John

Neutrosophic Systems with Applications

Classical acceptance sampling plans require precisely specified parameter values, an assumption routinely violated by measurement uncertainty and gauge imprecision in practice. This article develops Neutrosophic Time-Truncated Acceptance Sampling Plans (N-TTASP) for the Exponentiated Weibull (EW) distribution by representing the scale parameter as a neutrosophic interval. The neutrosophic sample size n_N ∈ [n_L, n_U] and acceptance number c_N ∈ [c_L, c_U] are obtained by minimizing n_U subject to dual producer and consumer risk constraints on the neutrosophic Operating Characteristic interval. A new indeterminacy ratio η is introduced as a …

Psychological Needs And Ai Delegation Across Four Social Domains - A Cross-Cultural Analysis Of 35 Nations, Magnus Liebherr, Ala Yankouskaya, Mohamed Basel Almourad, Justin Thomas, Guandong Xu, Raian Ali

All Works

As artificial intelligence (AI) systems increasingly assume roles with social, educational, and emotional significance, understanding the psychological drivers behind individuals' readiness to delegate such roles to AI is crucial. Drawing on Self-Determination Theory (SDT), this study examines how the satisfaction of basic psychological needs (autonomy, competence, and relatedness) predicts individuals' readiness to delegate socially significant roles to AI across four domains (education, healthcare, mental health, and companionship) and 35 nations. Using data from over 35,000 participants in the 2023 Global Digital Wellbeing Survey, we applied Bayesian multilevel multivariate modelling to assess both global and culture-specific motivational associations. Results revealed that …