Computer Sciences Commons^™

Deep Learning In Reproducing Kernel Banach Spaces, Mingsong Yan

Mathematics & Statistics Theses & Dissertations

Deep learning has achieved immense success in the past decade. The goal of this dissertation is to understand deep learning through the framework of reproducing kernel Banach spaces (RKBSs), which were originally proposed for promoting sparse solutions. We begin by considering learning problems in a general functional setting, and establishing explicit and data-dependent representer theorems for both minimal norm interpolation (MNI) problems and regularization problems. These theorems provide a crucial foundation for the subsequent results derived for both sparse learning and deep learning. Next, we investigate the essential properties of RKBSs capable of encouraging sparsity in learning solutions. With the …

Explainable Artificial Intelligence: Methods And Evaluation, Gayane Grigoryan

Engineering Management & Systems Engineering Theses & Dissertations

A wide array of techniques within explainable artificial intelligence (XAI) have been developed to measure the importance of features in machine learning models. A notable portion of these methods draws upon principles of cooperative game theory (CGT), with the Shapley value emerging as a widely used solution concept. Despite the rising prominence of the Shapley value, other promising solutions from cooperative game theory—such as the Nucleolus, Banzhaf power index, Shapley-Shubik power index, and solutions to conflicting claims problems—have been comparatively overlooked, even though they hold significant potential. In this dissertation, multiple XAI methods based on these other CGT solutions are …

Raemap: Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardena

Computer Science Theses & Dissertations

Eye-tracking offers insights into human cognition and attention allocation by capturing and interpreting eye movements. Traditionally, eye tracking research has relied on offline analysis methods, requiring post-experiment processing to derive insights from gaze data. This approach limits its applicability in contexts requiring immediate feedback and adaptive responses based on real-time gaze behaviors. Recent advancements in computing power and eye tracking technology have enabled a shift towards real-time analysis, allowing modern systems to interpret gaze data with minimal latency, facilitating integration into interactive applications and experiments. Despite these advancements, existing tools often face challenges with real-time computation of advanced gaze measures …

Leveraging Blockchain For Trust Enhancement In Decentralized Marketplaces: A Reputation System Perspective, Meshari Mohammd Aljohani

Computer Science Theses & Dissertations

Centralized marketplaces provide reliable reputation services through a central authority, but this raises concerns about single points of failure, user privacy, and data security. Decentralized marketplaces have emerged to address these issues by enhancing user privacy and transparency and eliminating single points of failure. However, decentralized marketplaces face the challenge of maintaining user trust without a centralized authority. Current blockchain-based marketplaces rely on subjective buyer feedback. Additionally, the transparency in these systems can deter honest reviews due to fear of seller retaliation. To address these issues, we propose a trust and reputation system using blockchain and smart contracts. Our system …

A Portable Numerical Library For The Calculation Of Multi-Dimensional Integrals, Ioannis Sakiotis

Computer Science Theses & Dissertations

Multi-dimensional numerical integration is a prevalent task in physics and other scientific fields, e.g., in the simulation of particle-beam dynamics and Bayesian parameter estimation. Scientific computing applications that simulate complex phenomena may require the solution to numerous multi-variate integrals. However, functions that have features such as sharp peaks or oscillations in high dimensional spaces, can result in an exorbitant number of computations. For many cases, convergence to accurate results in a reasonable amount of time is infeasible with existing numerical libraries. One approach towards making multi-dimensional integration viable is to parallelize existing algorithms. No commonly available algorithms or libraries exist …

Safe And Efficient Operation Of Mobile Robots In Indoor Environments: A User-Centric Shared Control System With High-Level Navigation Capabilities, Ahmet Saglam

Electrical & Computer Engineering Theses & Dissertations

Hospitalization and isolation can be a traumatic experience for immunocompromised children, especially because they are separated from their families and friends. Social robots have been proposed as a way to improve the quality of care for children hospitalized in isolation by providing alternative means of social interaction and support. Remote control of such robots in a hospital setting, particularly where safety is a major concern, can be a daunting task for young patients.

This dissertation introduces a multilevel shared control system for mobile robots, specifically companion robots in hospital-like indoor spaces. The system integrates user inputs with algorithmic semi-autonomous control …

Accelerating The Efficiency Of Multiscale Hybridizable Discontinuos Galerkin Methods For Flows In Heterogeneous Media, Tony Charles Haines

Mathematics & Statistics Theses & Dissertations

A plethora of scientific and engineering problems encountered are multiscale in nature. This multiscale feature often influences simulation efforts wherever large disparities in spatial scales are experienced. Notable examples include composite materials, fluid flow through porous media and turbulent transport in high Reynolds number flow. Although there are promising results from the advancement of modern supercomputer, obtaining direct numerical solution of multiscale problems is very laborious. This difficulty stems from the tremendous amount of computer memory and CPU time required. Parallel computing may be one obvious choice in remedying this issue. However, the complexity and size of the discrete problem …

Mesostructure Reconstruction Of Prepreg Platelet Molded Composite With Artificial Intelligence, Richard Larson

Mechanical & Aerospace Engineering Theses & Dissertations

Prepreg platelet molded composites (PPMC) are long, discontinuous fiber reinforced polymer materials. PPMC are an important subcategory of composite materials as they are processible into geometrically complex structures and can be produced via high-throughput manufacturing processes, however they have higher stiffness and strength as compared to traditional discontinuous fiber reinforced polymers. However, there is inherent randomness in the structure of PPMCs and as such, PPMC parts frequently require per part testing that is cost prohibitive.

Herein, a method using artificial intelligence is proposed as a more cost-effective method of inspecting PPMC parts. Different artificial intelligence (AI) architectures are explored to …

Contextualizing Interpersonal Data Sharing In Smart Homes, Weijia He, Nathan Reitinger, Atheer Almogbil, Yi-Shyuan Chiang, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people's perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make …

A Framework For Evaluating The Security And Privacy Of Smart-Home Devices, And Its Application To Common Platforms, Ravindra Mangar, Timothy Pierson, David Kotz

Dartmouth Scholarship

In this article, we outline the challenges associated with the widespread adoption of smart devices in homes. These challenges are primarily driven by scale and device heterogeneity: a home may soon include dozens or hundreds of devices, across many device types, and may include multiple residents and other stakeholders. We develop a framework for reasoning about these challenges based on the deployment, operation, and decommissioning life cycle stages of smart devices within a smart home. We evaluate the challenges in each stage using the well-known CIA triad—Confidentiality, Integrity, and Availability. In addition, we highlight open research questions at each stage. …

React: Recognize Every Action Everywhere All At Once, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Page Daniel Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

In the realm of computer vision, Group Activity Recognition (GAR) plays a vital role, finding applications in sports video analysis, surveillance, and social scene understanding. This paper introduces Recognize Every Action Everywhere All At Once (REACT), a novel architecture designed to model complex contextual relationships within videos. REACT leverages advanced transformer-based models for encoding intricate contextual relationships, enhancing understanding of group dynamics. Integrated Vision-Language Encoding facilitates efficient capture of spatiotemporal interactions and multi-modal information, enabling comprehensive scene understanding. The model’s precise action localization refines joint understanding of text and video data, enabling precise bounding box retrieval and …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Cognitive Technologies, Tom Davenport

Asian Management Insights

AI and the revolution of work.

Professor Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, speaks about how companies can integrate generative Artificial Intelligence (GenAI) into their operations while ensuring workforce adaptation and skills development.

Development Of An Algorithm To Identify And Calculate The Amount Of File Slack On An Image Of A Given Drive, Nicholas Flynn

Honors Theses

As society increasingly relies on technology, the rates of cyber crime have been increasing at exponential rates. Cyber criminals are also discovering new ways to hide evidence of their crimes. This study develops a forensic analysis algorithm to evaluate the amount of file slack on an image of a drive. Slack space, leftover drive space on a disk sector after a file has been written, can be exploited to hide data. The algorithm aims to detect and calculate this slack space to help direct forensic investigations. The algorithm was evaluated on a population dataset of 100,000 files with random data …

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Multi-Scale Deep Representation Learning In Synthetic Biology, Xiaoyi Liu

Theses and Dissertations

Synthetic biology advances and combines the expertise of engineers and biologists, bridging the gap between engineering and natural life. Synthetic biology has been generally categorized into two broad branches by developing new biological components, networks, and systems to reprogram organisms. The first branch involves using synthetic molecules to mimic natural biological functions. The second branch focuses on assembling natural biological components in novel ways, aiming to produce systems with unique, practical functions. Thus, the de novo engineering of biological modules and synthetic pathways is used in related practical bioengineering applications, such as drug-targeting strategies and microbial product manufacturing. Therefore, synthetic …

Quantitative Evaluation Of Security Intelligence Policy Texts In China: Text Analysis Based On Pmc Model, Bin Zhang

Journal of Scientific Information Research

[Purpose/significance]Analyzing the laws, regulations and policies related to security intelligence in China can not only provide reference for decision-making, but also effectively enrich the connotation of China's overall national security concept. [Method/process]Using the LDA topic model, text mining was conducted on laws, regulations and policies related to security intelligence in China, and theme words were extracted from them. At the same time, based on the selection of policy indicators by existing scholars, scientifically select and design evaluation indicators for China's security intelligence laws, regulations, and policies. Referring to the overall national security concept, several representative policy contents were selected for …

Green Finance Growth Prediction Model Based On Time-Series Conditional Generative Adversarial Networks, Aya Salama Abdelhady, Nadia Dahmani, Lobna M. Abouel-Magd, Ashraf Darwish, Aboul Ella Hassanien

All Works

Climate change mitigation necessitates increased investment in green sectors. This study proposes a methodology to predict green finance growth across various countries, aiming to encourage such investments. Our approach leverages time-series Conditional Generative Adversarial Networks (CT-GANs) for data augmentation and Nonlinear Autoregressive Neural Networks (NARNNs) for prediction. The green finance growth predicting model was applied to datasets collected from forty countries across five continents. The Augmented Dickey-Fuller (ADF) test confirmed the non-stationary nature of the data, supporting the use of Nonlinear Autoregressive Neural Networks (NARNNs). CT-GANs were then employed to augment the data for improved prediction accuracy. Results demonstrate the …

Establishing Metrics To Encourage Broader Use Of Atomic Requirements – A Call For Exchange And Experimentation, William L. Honig

Computer Science: Faculty Publications and Other Works

There are seemingly many advantages to being able to identify, document, test, and trace single or “atomic” requirements during system development and maintenance. Ongoing work with Agile development has focused on “user stories” that can capture individual features for implementation. However, it is still difficult to evaluate the quality of such requirements and teaching their creation is difficult.

Based on a working definition of atomic requirement, this paper proposes a set of metrics for their evaluation. Ten metrics designed to measure atomic requirements are presented here: five used on individual requirements statements and five applied to a requirements document or …

Empowering Informed Decision-Making In Mental Health Care: A Web-Based Recommendation System For Mobile App Selection, Md Romael Haque

Dissertations (1934 -)

In 2022, 23.1% of adults in the United States (77 million individuals) were affected by mental health (MH) concerns. Due to the inaccessibility and high cost of traditional treatment, around 55% of people with severe mental illnesses do not receive treatment. Mental health concerns are prevalent, affecting a significant portion of the population in the United States. Traditional treatment options are often inaccessible and expensive, leaving many people without essential mental healthcare. However, the rise of mobile technologies has given rise to a promising solution: mobile mental health applications (MMHAs). These apps offer greater accessibility and affordability, potentially expanding mental …

Integrating Remote Sensing And Machine Learning To Determine Past, Current And Future Crop Water Use From The Nubian Sandstone Aquifer System, Moaz Ishag

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The agriculture sector is a significant consumer of water, and sustainable water use begins with monitoring irrigated land. Delineating irrigated land supports decision-makers and promotes the sustainable use of this crucial resource. This study focuses on the Nubian Sandstone Aquifer System (NSAS), the largest aquifers in the world, which spans Egypt, Sudan, Libya, and Chad. The study aims to: 1) quantify the increase in irrigated hectares (both pivot and non-pivot) from 2000-2001 to 2023-2024; 2) identify major irrigated crop types and their water requirements; and 3) quantify groundwater crop water use from the NSAS using remote sensing via the Google …

Missing History Of A Modern Domesticate: Historical Demographics And Genetic Diversity In Farm-Bred Red Fox Populations, Halie M. Rando, Emmarie P. Alexander, Sophie Preckler-Quisquater, Cate B. Quinn, Jeremy T. Stutchman, Jennifer L. Johnson, Estelle R. Bastounes, Beata Horecka, Kristina L. Black, Michael P. Robson, Darya V. Shepeleva, Yury E. Herbeck, Anastasiya V. Kharlamova, Lyudmila N. Trut, Jonathan N. Pauli, Benjamin N. Sacks, Anna V. Kukekova

Computer Science: Faculty Publications

The first record of captive-bred red foxes (Vulpes vulpes) dates to 1896 when a breeding enterprise emerged in the provinces of Atlantic Canada. Because its domestication happened during recent history, the red fox offers a unique opportunity to examine the genetic diversity of an emerging domesticated species in the context of documented historical and economic influences. In particular, the historical record suggests that North American and Eurasian farm-bred populations likely experienced different demographic trajectories. Here, we focus on the likely impacts of founder effects and genetic drift given historical trends in fox farming on North American and Eurasian farms. A …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen

Department of Sociology: Dissertations, Theses, and Student Research

The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …

Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós

Computer Science Theses & Dissertations

Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …

A Microservices Approach To Electroencephalography Research In The Public Cloud, Bathsheba Farrow

Computer Science Theses & Dissertations

The rich information content of the brain is embedded in electroencephalography (EEG) data that instantaneously measures its electrophysiological activity. Expansion of EEG data usage for health risk predictions and other applications requires reliable and consistent methods for extracting features from raw signals. However, the intricate nature of EEG signal data analysis is further complicated by the magnitude of variability in current research practices, including data preprocessing strategies. The heavy reliance on antiquated, stove-piped applications and pipelines also highlights a need for improved solutions that enable efficient, distributed preprocessing of large EEG data collections.

To address these challenges, we propose a …

Privacy-Preserving Deep Learning Framework For Iot Malware Detection, Sabbir Ahmed Khan

Computer Science Theses & Dissertations

Cyberattacks on IoT devices are accelerating at an unprecedented rate, largely driven by IoT malware activities. The IoT malware attacks typically comprise three stages: intrusion, infection, and monetization. Existing IoT malware detection methods fail to identify malicious activities at the intrusion and infection stages and thus cannot stop potential attacks timely. In our research, we have leveraged power side-channel information as input to our deep learning model to identify malware at early stages of intrusion on IoT devices. But, deploying a resource-intensive deep learning model on highly resource-constrained IoT devices is a significant challenge. Consequently, utilizing a Machine Learning as …

Predicting Iot Distributed Ledger Fraud Transactions With A Lightweight Gan Network, Charles Rawlins, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security …

Attribute-Based Fine-Grained Access Control Using Verifiable Credentials, Srinivasa Dumpa

Student Theses

In the era of digital transformation, ensuring secure and privacy-preserving access control mechanisms is of paramount importance. Traditional identity-based access control systems often fall short in providing granular control and user autonomy over digital identities. This thesis presents a novel approach to access control by leveraging the power of verifiable credentials and attribute-based access control. The proposed system introduces a decentralized and user-centric framework that enables fine-grained access control based on specific attributes encapsulated within verifiable credentials. These tamper-evident digital credentials, stored in a user's digital wallet, contain a rich set of attributes that can be selectively disclosed to grant …