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Articles 691 - 720 of 58434
Full-Text Articles in Entire DC Network
Public Pool Usage As Adaptation Against Urban Heat, Stefan Borsky, Eric Fesselmeyer
Public Pool Usage As Adaptation Against Urban Heat, Stefan Borsky, Eric Fesselmeyer
Research Collection College of Integrative Studies
This paper examines the relationship between urban heat and outdoor public pool usage. Using attendance data from all 53 outdoor public pools in New York City, we analyze nonlinear effects of heat on pool usage across socioeconomic contexts. Pool attendance rises sharply with heat, especially in low-income neighborhoods where alternative coping options are likely limited. We also find that public pools reduce heat-related emergency medical service calls. Our findings highlight the need for equitable investment in blue infrastructure to enhance urban climate resilience and demonstrate how this type of adaptive infrastructure can play a critical role in managing urban heat.
Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu
Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu
Research Collection School Of Computing and Information Systems
The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove …
Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang
Detecting Doubt In Reflective Learning: A Learning Analytics Study With Large And Small Language Models, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Yuhao Zhang
Research Collection School Of Computing and Information Systems
Reflective learning enhances understanding, especially when instructors promptly address difficulties raised in student reflections. Automated doubt detection can reduce time for instructors, yet existing classification approaches take substantial time for manual annotation and model training. This paper investigates whether large and small language models (LLMs, SLMs) can automate doubt detection without time-consuming training. Using a dataset of anonymized student reflections, we evaluate zeroshot, few-shot prompting, and multi-step reasoning against prior supervised classification baselines. We show that LLMs (GPT-4o, Claude-4, Gemini-2.5) surpass earlier F1 scores without prompting, while prompting further improves their performance. However, using proprietary LLMs can raise cost and …
Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao
Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we …
The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing
The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing
All-Inclusive List of Electronic Theses and Dissertations
This dissertation evaluates whether a reusable assurance architecture, the Quality Assurance Machine (QAM), can provide effective product and process quality assurance for ML-enabled software platforms. The QAM is a system-level SQA architecture that turns plans and policies into versioned configurations, executes them in controlled environments, and produces preserved run evidence that supports traceability, auditability, and controlled change. The study follows Design Science Research and evaluates the instantiated artifact using eight assurance requirements (AR1–AR8) synthesized from standards-based guidance, including IEEE 730 and ISO/IEC/IEEE 15026. A four-year longitudinal evaluation combines two methods. First, operational evidence from routine regression and release-validation runs, defect …
Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan
Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan
Research Collection School Of Computing and Information Systems
The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …
Mease: Multi-Agent Episodic Action Sequence Explanation, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan
Mease: Multi-Agent Episodic Action Sequence Explanation, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Multi-agent reinforcement learning (MARL) achieves remarkable performance in complex coordination tasks, yet interpreting the emergent behaviors of trained agents remains a fundamental challenge. Most current explainability methods focus on individual agent decisions, overlooking the critical interplay of joint strategiesand temporal coordination patterns that define successful multi-agent policies. We present MEASE (Multi-agent Episodic Action Sequence Explanation), a novel explainable MARL (XMARL) framework that explains trained MARL policies as human-interpretable emergent cooperative joint behaviors. MEASE employs a cognition-inspired episodic memory model to learn spatio-temporal multi-agent interaction patterns, coupled with abstraction algorithms that identify significant cooperative agent behaviors. We evaluate MEASE on diverse …
Smart-Charge: Stable Matching Algorithm For Electric Vehicle Charging In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Sajal K. Das
Smart-Charge: Stable Matching Algorithm For Electric Vehicle Charging In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Sajal K. Das
Computer Science Faculty Research & Creative Works
The transition from internal combustion engine (ICE) to electric vehicles (EVs) introduces several challenges, including limited charging infrastructure, unpredictable charging wait times, and inefficient selection of charging points (CPs). To address these issues, we propose SMART-CHARGE, a framework that efficiently assigns EVs to CPs through an edge-level coordination mechanism within each service region, enforced by roadside units (RSUs). Operating under a novel subscription-based charging model, SMART-CHARGE enforces predefined charging time limits via service-level agreements (SLAs). The EV-CP assignment problem is formulated as a one-to-many matching game that captures EV user preferences. To construct bounded yet efficient EV coalitions at each …
Safepass: Efficient Emergency Vehicle Passage With Minimal Disruption To Traffic Flow, Osho Osho, Suchetana Chakraborty, Sajal K. Das
Safepass: Efficient Emergency Vehicle Passage With Minimal Disruption To Traffic Flow, Osho Osho, Suchetana Chakraborty, Sajal K. Das
Computer Science Faculty Research & Creative Works
Emergency vehicle passage in congested urban networks poses a dual challenge: ensuring rapid response while minimizing disruption to surrounding traffic. This study addresses this challenge in the context of Connected Autonomous Emergency Vehicles (CA-EVs), proposing SafePass , a lightweight distributed framework for seamless CA-EV passage through decentralized, cooperative maneuvering of surrounding Connected Autonomous Non-Emergency Vehicles (CA-NEVs). At its core, SafePass employs the Target Lane Potential (TLP), a novel utility-based metric combining lane-choice utility with probabilistic gap acceptance, augmented by a cascade-aware penalty that suppresses upstream shockwaves triggered by gap-creation maneuvers. Evaluated in Simulation of Urban Mobility (SUMO) using synthetic traffic …
Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic
Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. given a threshold t the goal is to find …
Collaborative Practices And Tool Utilization In Software Development Projects: A Student Perspective, Yi Meng Lau, Muhammad Syahmi Bin Abbas, Lingxiao Jiang
Collaborative Practices And Tool Utilization In Software Development Projects: A Student Perspective, Yi Meng Lau, Muhammad Syahmi Bin Abbas, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Software development is a collaborative activity that depends on effective teamwork, shared understanding, and coordinated use of development practices and tools. While these aspects are well studied in professional environments, they are less frequently examined within software engineering education. This study investigates how students collaborate in group projects, focusing on collaborative practices, tool usage, and their perceptions of software quality. We conducted a quantitative post-project survey with 143 second-year undergraduate students enrolled in a software development course. The results show that students actively share information and often establish team norms to support coordination and collaboration. However, students face challenges in …
Causality-Driven Test Case Minimisation For Cyber-Physical Systems, Michael Foster, Christopher M. Poskitt, Nicholas R. Latimer, Neil Walkinshaw, Richard Somers, Robert M. Hierons
Causality-Driven Test Case Minimisation For Cyber-Physical Systems, Michael Foster, Christopher M. Poskitt, Nicholas R. Latimer, Neil Walkinshaw, Richard Somers, Robert M. Hierons
Research Collection School Of Computing and Information Systems
Cyber-physical systems allow digital control systems to interact with the physical world using sensors and actuators. They are increasingly being used to automate critical infrastructure, where software faults can have dire consequences. Due to the complex nature and unpredictability of these systems, their resilience is often tested using a technique called fuzzing, which generates quasi-random sequences of sensor and actuator manipulations with the goal of forcing a system into unsafe states. However, there is currently no way of determining which manipulations of a test case cause a failure without systematically removing each one and re-running the test, which can be …
Natural Adversaries: Fuzzing Autonomous Vehicles With Realistic Roadside Object Placements, Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun
Natural Adversaries: Fuzzing Autonomous Vehicles With Realistic Roadside Object Placements, Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical misjudgements. It is important that these systems are tested not only with respect to other vehicles on the road, but also with respect to objects placed on the roadside. Trash bins, billboards, and greenery are examples of such objects, typically positioned according to guidelines developed for the human visual system, which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes or patches, which …
Enhancing Action And Ingredient Modeling For Semantically Grounded Recipe Generation, Guoshan Liu, Bin Zhu, Yian Li, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Enhancing Action And Ingredient Modeling For Semantically Grounded Recipe Generation, Guoshan Liu, Bin Zhu, Yian Li, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and …
Practicing Conflict Transformation Skills Through Role-Playing Games For Diversity, Equity, And Inclusion In Higher Education, Alexandra Schreiber, Kjell H. Hugaas, Sarah L. Bowman
Practicing Conflict Transformation Skills Through Role-Playing Games For Diversity, Equity, And Inclusion In Higher Education, Alexandra Schreiber, Kjell H. Hugaas, Sarah L. Bowman
Journal of Roleplaying Studies and STEAM
No abstract provided.
Ai Institute Summer Camp Webinar Summary, Dora Nguyen
Ai Institute Summer Camp Webinar Summary, Dora Nguyen
Paul English Applied Artificial Intelligence (AI) Institute Publications
This report summarizes the AI Institute Summer Camp informational webinar hosted by the Paul English Applied Artificial Intelligence Institute (PEAAII) at the University of Massachusetts Boston. The webinar introduced the structure, curriculum, learning objectives, and student outcomes associated with the 2026 AI Institute Summer Camp. In addition to summarizing the webinar content, this report analyzes participant questions, engagement trends, and areas of audience interest. Findings indicate strong interest in coding accessibility, mentorship opportunities, student outcomes, research experiences, and program flexibility. The report also identifies opportunities for improving future webinar delivery, including expanded eligibility guidance, pre-camp learning resources, enhanced presentation of …
Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach
Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach
Honors Theses
Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …
It's Not Nde Without U And X: Preparing For Change With Inclusive Research, Sanjeet Mann, Heather L. Cribbs
It's Not Nde Without U And X: Preparing For Change With Inclusive Research, Sanjeet Mann, Heather L. Cribbs
Library Faculty Publications & Presentations
The upcoming Next Discovery Experience (NDE) introduces major changes to how users search, interpret information, and navigate Primo. Preparing for NDE is an opportunity to center the diverse students and faculty who rely on discovery systems every day, ensuring that their lived experiences, accessibility needs, and research practices guide interface design, configuration, and communication. This session presents a consortial approach to NDE readiness that positions students and community members as partners in the development process. We describe strategies for creating ethical and rigorous UX research workflows that include IRB approval, purposeful recruitment, accessible study design, and clear documentation on the …
A Comparative Study Of Traditional Training And Xr-Based Simulation In Healthcare Professional Education, Fazal Qudus Khan, Gohar Khan, Ibrar Ahmad, Owais Khan, Suleman Shah
A Comparative Study Of Traditional Training And Xr-Based Simulation In Healthcare Professional Education, Fazal Qudus Khan, Gohar Khan, Ibrar Ahmad, Owais Khan, Suleman Shah
All Works
Immersive Virtual Reality or VR/VX stands poised to revolutionize healthcare education through interactive learning modules, overcoming current deficiencies in existing methodologies for instruction. This research compared the effectiveness of VR/VX-based training to traditional CT scanner operator training using a within-subjects design, involving 30 subjects, and concluded the effectiveness of using VR/VX in enhancing knowledge retention, task accomplishment, and engagement, and proved it by showing significant enhancement in immediate knowledge acquisition scores (Δ = 8.87, t(29) = 6.71, p < .0001), relative to delayed knowledge retention scores (Δ = 11.03, t(29) = 6.85, p < .0001), procedural achievement scores (Δ = 5.40, t(29) = 4.45, p = .0001), reduced overall task completion time using VR/VX for increased speed of execution (t(29) = 10.74, p < .0001), as well as reduced task errors for lower error rates using VR/VX in comparison to existing methodologies, as testified by the results, t(29) = 8.14, p < .0001, respectively, while showing no significant difference in usability, although assessed superior in terms of engagement and relative usefulness by the involved subjects.
Research Remix: Teams, Tech, And Texts, Jaime Carbajal
Research Remix: Teams, Tech, And Texts, Jaime Carbajal
UNLV Best Teaching Practices Expo
The pedagogical innovation that enhanced student learning in the Research Methodologies in Health Sciences course is described as Integrated Digital Collaborative Inquiry-Based Learning (IDCIBL). The IDCIBL approach leveraged digital tools (lecture videos, podcasts, recorded poster presentations, and Artificial Intelligence (AI) platforms), integrated journal article analysis, utilized research-informed active learning, and included team-based learning activities. The combination of multiple innovative strategies into the IDCIBL model intentionally transforms the educational environment and optimizes the student learning experience, relating to the TLC priority area of teaching and assessments in the age of Gen AI.
Ai Literacy: An Annotated Oer Bibliography, Houy Yvonne
Ai Literacy: An Annotated Oer Bibliography, Houy Yvonne
UNLV Best Teaching Practices Expo
"Scalable, discipline-agnostic AI literacy instruction can be implemented incrementally without requiring full course redesign in higher education, supporting both technical understanding and critical engagement with the social and ethical dimensions of AI: The curated list of open educational resources (OER) on this poster enable a flexible, modular approach to teaching foundational AI literacy. The annotated list includes self-paced, hands-on projects with complementary educator-guided activities and discussion to support conceptual understanding of machine learning, training data, and algorithmic bias, drawing on OER such as Code.org’s AI curriculum, MIT RAISE’s Day of AI, and the multi-lingual Elements of AI course. Many resources …
Redefining Certainty: Non-Euclidean Geometry And Theology Transformation Throughout The Intellectual Unrest Of The Early 1800s, Luke Bensinger
Redefining Certainty: Non-Euclidean Geometry And Theology Transformation Throughout The Intellectual Unrest Of The Early 1800s, Luke Bensinger
Honors Theses
To bridge the gap between mathematics and theology, it is necessary to explore their intersection and challenge the notion that these fields are incompatible. This study focuses on the 19th century, a period when non-Euclidean geometries emerged and disrupted mathematical certainty, while Protestant theologians such as Barton W. Stone and Alexander Campbell grappled with Calvinism and shifting theological perspectives. By analyzing mathematicians studying geometry, such as Gauss, Lobachevsky, and Riemann, this research examines how both disciplines balance change with enduring truths.
Consistency-Based Computing Of Fuzzy Eigenvalues And Fuzzy Eigenvectors: Method And Application, Kamala. R. Aliyeva Prof., Nihad Mehdiyev, Shamil Mehdi
Consistency-Based Computing Of Fuzzy Eigenvalues And Fuzzy Eigenvectors: Method And Application, Kamala. R. Aliyeva Prof., Nihad Mehdiyev, Shamil Mehdi
Chemical Technology, Control and Management
The computation of eigenvalues and eigenvectors under uncertainty is a fundamental problem in fuzzy linear algebra and decision analysis. When matrix elements are represented by fuzzy numbers, classical spectral methods cannot be directly applied due to nonlinearity, ambiguity in ordering, and the propagation of uncertainty. Moreover, in many practical applications, particularly those involving pairwise comparison matrices, the reliability of eigenvalue-based results strongly depends on the consistency of the underlying data. This paper proposes a consistency-based framework for computing fuzzy eigenvalues and fuzzy eigenvectors that explicitly integrates consistency analysis into the spectral derivation process. The proposed method preserves the fuzzy structure …
Volume 12 Full Text, Bjur Staff
Volume 12 Full Text, Bjur Staff
Butler Journal of Undergraduate Research
No abstract provided.
Front Matter And Table Of Contents, Bjur Staff
Front Matter And Table Of Contents, Bjur Staff
Butler Journal of Undergraduate Research
No abstract provided.
A Deep Learning-Based Approach For Bot Detection In Trending Hashtags On X, Mehboob Hussain, Muhammad Rizwan Rashid Rana, Muhammad Imran, Muhammad Shoaib, Muhammad Hasaan Mujtaba
A Deep Learning-Based Approach For Bot Detection In Trending Hashtags On X, Mehboob Hussain, Muhammad Rizwan Rashid Rana, Muhammad Imran, Muhammad Shoaib, Muhammad Hasaan Mujtaba
Makara Journal of Technology
The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is …
Cooperation And Conflict: Oswald Veblen And Abraham Flexner, Juan Carvajalino, Della Dumbaugh
Cooperation And Conflict: Oswald Veblen And Abraham Flexner, Juan Carvajalino, Della Dumbaugh
Department of Math & Statistics Faculty Publications
In 1929, Caroline Bamberger Fuld and her brother, Louis Bamberger, sold their department stores, Bamberger & Co., at a particularly timely moment. They invested their sizeable fortune in a new academic initiative in America, an institute devoted to research that would go by the name of the Institute for Advanced Study. This paper introduces two colleagues, Abraham Flexner and Oswald Veblen, and explores the cooperation and conflict between them as they worked alongside one another to shape the Institute for Advanced Study and the School of Mathematics within it.
Building Human-Aware Ai: Learning From And Assisting Human Decision-Makers, Saumik Narayanan
Building Human-Aware Ai: Learning From And Assisting Human Decision-Makers, Saumik Narayanan
McKelvey School of Engineering Graduate Student Theses & Dissertations
As artificial intelligence systems become more capable, they are increasingly used not only as standalone problem-solvers, but as systems that learn from and interact with humans. In these settings, success depends not only on the strength of the model in isolation, but also on how well it fits the humans it is trained on or deployed alongside. This dissertation argues that human heterogeneity is a central ingredient in the design of effective human-aware machine learning systems. Rather than treating differences between people as noise to be averaged away, I show that variation in human expertise and preferences can provide useful …
Assessing Hyperlocal And Local Air Quality Through Passive Samplers And Low-Cost Monitors, Yan He
Assessing Hyperlocal And Local Air Quality Through Passive Samplers And Low-Cost Monitors, Yan He
McKelvey School of Engineering Graduate Student Theses & Dissertations
Air pollution is a major environmental health concern and has been associated with respiratory disease, cardiovascular morbidity, metabolic disorders, premature mortality, and additional adverse health outcomes. Air pollution often exhibits substantial variability at fine spatial scales, yet conventional regulatory monitoring networks are too sparse to resolve neighborhood-level exposure gradients that may exist in many urban and rural communities. Hyperlocal differences in pollutant concentrations—driven by local emission sources, land use characteristics, atmospheric chemistry, and meteorological conditions—can lead to systematic differences in long-term exposure among populations living only short distances apart. Improving exposure characterization therefore requires dense monitoring strategies and spatial modeling …
Smart Kitchen: Towards Real-Time Ai Systems For Cognitive Support In Daily Activities, Ruiqi Wang
Smart Kitchen: Towards Real-Time Ai Systems For Cognitive Support In Daily Activities, Ruiqi Wang
McKelvey School of Engineering Graduate Student Theses & Dissertations
The rapid growth of the aging population and the rising prevalence of Subjective Cognitive Decline (SCD) highlight the need for continuous, unobtrusive assessment of functional cognition during everyday activities. Vision-based smart home systems offer a promising pathway for monitoring behavior and supporting independent living. However, enabling real-time cognitive assistance remains challenging. It requires not only accurately interpreting complex human behaviors to detect cognitive errors, but also supporting real-time deployment on resource-constrained edge devices and under dynamic wireless network conditions. This dissertation presents Smart Kitchen, an AI-driven system for real-time cognitive error detection through the monitoring of daily cooking activities. Under …