Computer Sciences Commons^™

Real Time Panoramic Image Processing, Matthew Gerlits

Master's Projects

Image stitching algorithms are able to join sets of images together and provide a wider field of a vision when compared with an image from a single standard camera. Traditional techniques for accomplishing this are able to adequately produce a stitch for a static set of images, but suffer when differing lighting conditions exist between the two images. Additionally, traditional techniques suffer from processing times that are too slow for real time use cases. We propose a solution which resolves the issues encountered by traditional image stitching techniques. To resolve the issues with lighting difference, two blending schemes have been …

Querying The Past: Automatic Source Attribution With Language Models, Ryan Muther, Mathew Barber, David Smith

Faculty & Staff Publications

This paper explores new methods for locating the sources used to write a text by 昀椀ne-tuning a variety of language models to rerank candidate sources. These methods promise to shed new light on traditions with complex citational practices, such as in medieval Arabic where citations are ambiguous and boundaries of quotation are poorly defined. After retrieving candidates sources using a baseline BM25 retrieval model, a variety of reranking methods are tested to see how effective they are at the task of source attribution. We conduct experiments on two datasets—English Wikipedia and medieval Arabic historical writing—and employ a variety of retrieval- …

An Interactive System For Generating Music From Moving Images, Hanlin Wang

Dartmouth College Master’s Theses

Moving images contain a wealth of information pertaining to motion. Motivated by the interconnectedness of music and movement, we present a framework for transforming the kinetic qualities of moving images into music. We developed an interactive software system that takes video as input and maps its motion attributes into the musical dimension based on perceptually grounded principles. The system combines existing sonification frameworks with theories and techniques of generative music. To evaluate the system, we conducted a two-part experiment. First, we asked participants to make judgements on video-audio correspondence from clips generated by the system. Second, we asked participants to …

Disciplines Different Of Fine-Grained Acknowledged Entity：A Case Of Doctoral Dissertations In Chinese Humanities And Social Sciences, Jiaxin He, Shijin Zhang, Jiayu Zheng, Xinlong Chu, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Thesis acknowledgment is a public text in which the authors express their gratitude to individuals and institutions those support their research. It is helpful to understand the individuals and institutions that play a role in the cultivation of doctoral candidate in different disciplines by extracting the entities of acknowledgment and comparing the distribution of the objects of acknowledgment in the dissertations of different disciplines.[Method/process]In this paper, we gained more than 60 000 Chinese doctoral dissertations acknowledgment of 21 humanities and social sciences disciplines, extracted acknowledged entities from acknowledgment, and constructed a fine-grained classification system of acknowledgment entities. The classification system …

A Study Of The Local Deep Galerkin Method For The Modified Cahn Hilliard Equation, Shi Wen Wong

Electronic Theses and Dissertations

Solving higher order partial differential equations (PDEs) can often prove to be a challenging task due to the involvement of higher-order derivatives of the unknown function, particularly for complex problems. The higher the order of the PDE, the more challenging it becomes to obtain an analytical solution. In such cases, alternative numerical methods are often used, such as finite element method or finite difference method. However, these methods can be computationally expensive and require a significant amount of mathematical expertise to implement. In recent times, there has been significant progress in applying neural networks to various fields, including the solution …

Collaborative Consultation Doctors Model: Unifying Cnn And Vit For Covid-19 Diagnostic, Trong-Thuan Nguyen, Tam Nguyen, Minh-Triet Tran

Computer Science Faculty Publications

The COVID-19 pandemic presents significant challenges due to its high transmissibility and mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder timely and accurate screening. In response, AI-powered computer-aided imaging analysis techniques have emerged as a promising alternative for COVID-19 diagnosis. In this paper, we propose a novel approach that combines the strengths of Convolutional Neural Network (CNN) and Vision Transformer (ViT) to enhance the performance of COVID-19 diagnosis models. CNN excels at capturing spatial features in medical images, while ViT leverages self-attention mechanisms inspired by human radiologists. Additionally, our approach draws inspiration from subclinical diagnosis, a …

Model Checking Time Window Temporal Logic For Hyperproperties, Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque

Computer Science Faculty Publications

Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties in domains such as cyber-physical systems, smart grids, and automotive. This paper introduces HyperTWTL, which extends Time Window Temporal Logic (TWTL)-a domain-specific formal specification language for robotics, by allowing explicit and simultaneous quantification over multiple execution traces. We propose two different semantics for HyperTWTL, synchronous and asynchronous, based on the alignment of the timestamps in the traces. Consequently, we demonstrate the application of HyperTWTL in formalizing important information-flow security policies and concurrency for robotics applications. Furthermore, we …

Uit-Adrone: A Novel Drone Dataset For Traffic Anomaly Detection, Tung Minh Tran, Tu N. Vu, Tam Nguyen, Khang Nguyen

Computer Science Faculty Publications

Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that have attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to street monitoring systems. However, there is a notable lack of adequate drone-based datasets available to detect unusual events in the urban traffic environment, especially in roundabouts, due to the density of interaction between road users and vehicles. To promote the development of anomalous event detection with …

Assessing Spurious Correlations In Big Search Data, Jesse T. Richman, Ryan J. Roberts

Political Science & Geography Faculty Publications

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random …

Exploring The Integration Of Patient Generated Health Data In A Fair Digital Health System In Low-Resourced Settings: A User-Centered Approach, Abdullahi Abubakar Kawu, Rens Kievit, Adamu Abubakar, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman

Conference papers

This article presents the initial user-centered research exploring the opportunities in the collection of Patient-Generated Health Data (PGHD) within the context of a project aimed at improving health management and outcomes among residents in African countries. Through interviews with a doctor, a patient and two data managers, the local status and opinions regarding PGHD collection, integration and use are investigated. The findings suggest that PGHD have only been encountered in paper forms - and are mostly patient driven, however opportunities for PGHD for the facility and patient were identified and included supporting the treatment of whitecollar hypertension, treatment planning and …

A Lak Of Direction Misalignment Between The Goals Of Learning Analytics And Its Research Scholarship, Benjamin A. Motz, Yoav Bergner, Christopher A. Brooks, Anna Gladden, Geraldine Gray, Charles Lang, Warren Li, Fernando Marmolejo-Ramos, Joshua D. Quick

Articles

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments.

Enabling Dapps Data Exchange With Hardware-Assisted Secure Oracle Network, Yue Li

Theses and Dissertations--Computer Science

Decentralized applications (dApps), enabled by the blockchain and smart contract technology, are known for allowing distrustful parties to execute business logic without relying on a central authority. Compared to regular applications, dApps offer a wide range of benefits, including security by design, trustless transactions, and resistance to censorship. However, dApps need to access real-world data to achieve their full potential, relying on the data oracles. Oracles act as bridges between blockchains and the outside world, providing essential data to the smart contracts that power dApps. A significant challenge in integrating oracles into the dApp ecosystem is the Oracle Problem …

Equitable Ecosystem: A Two-Pronged Approach To Equity In Artificial Intelligence, Rangita De Silva De Alwis, Amani Carter, Govind Nagubandi

Michigan Technology Law Review

Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining …

A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19, İhsan Şahi̇n, Erhan Akdoğan, Mehmet Emi̇n Aktan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical …

An Effective Hilbert-Huang Transform-Based Approach For Dynamic Eccentricity Fault Diagnosis In Double-Rotor Double-Sided Stator Structure Axial Flux Permanent Magnet Generator Under Various Load And Speed Conditions, Makan Torabi, Yousef Alinejad Beromi

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert-Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is …

Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …

A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Since displacement is a vital variable to be considered in many industrial applications, displacement sensing devices have been extensively studied both theoretically and experimentally. There have been also many studies on Hall effect-based displacement measurement, but for many systems linearity still remains a problem. This paper discusses different approaches to calculate the magnetic field due to a cylindrical permanent magnet and proposes a new setup geometry with 2-Hall effect sensors and a permanent magnet between them to overcome the linearity problems. Furthermore, theoretical and experimental studies of the discussed displacement sensor were presented by focusing on the linear range and …

Thinking Local With Original Data In Ai And Machine Learning Research, David G. Taylor, Robert Mccloud

WCBT Working Papers

Sacred Heart University spent significant funds to establish an AI lab. Initially there is no ongoing research and no real plan for a research agenda. This paper details how the Jack Welch College of Business and Technology created and implemented an active meaningful research plan. It involves two key elements: thinking local and using business connections to foster active, impactful research. Surrounding communities, business connections, area environment, and other Sacred Heart University departments all played a part. The research plan also identifies a specific issue in working with local and business contact sources: the AI researcher almost never gets data …

Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper

CCAC Theses and Dissertations

No abstract provided.

A Study Of The Effect Of Types Of Organizational Culture On Information Security Procedural Countermeasures, Sheri James

CCAC Theses and Dissertations

This study examined the impact of specific organizational cultures on information security procedural countermeasures (ISPC). With increasing security incidents and data breaches, organizations acknowledge that people are their greatest asset as well as a vulnerability. Previous research into information security procedural controls has centered on behavioral, cognitive, and social theories; some literature incorporates general notions of organization culture yet there is still an absence in socio-organizational studies dedicated to elucidating how information security policy (ISP) compliance can be augmented by implementing comprehensive security education, training, and awareness (SETA) programs focusing on education, training, and awareness initiatives.

A theoretical model was …

Comparing Phishing Training And Campaign Methods For Mitigating Malicious Emails In Organizations, Jackie Christopher Scott

CCAC Theses and Dissertations

Although there have been numerous technological advancements in the last several years, there continues to be a real threat as it pertains to social engineering, especially phishing, spear-phishing, and Business Email Compromise (BEC). While the technologies to protect corporate employees and network borders have gotten better, there are still human elements to consider. No technology can protect an organization completely, so it is imperative that end users are provided with the most up-to-date and relevant Security Education, Training, and Awareness (SETA). Phishing, spear-phishing, and BEC are three primary vehicles used by attackers to infiltrate corporate networks and manipulate end users …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCAC Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

An Empirical Assessment Of The Use Of Password Workarounds And The Cybersecurity Risk Of Data Breaches, Michael Joseph Rooney

CCAC Theses and Dissertations

Passwords have been used for a long time to grant controlled access to classified spaces, electronics, networks, and more. However, the dramatic increase in user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure a high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. The increased use of IS as a working tool for employees increases the number of accounts and passwords required. Despite being more aware of password entropy, users still often participate in deviant password behaviors, known as ‘password workarounds’ …

A Functioning Code May Not Be A Secure Code : A Preliminary Study On The Students' Complacency With Secure Coding, Jeremiah Niiquaye Kotey

Theses, Dissertations and Culminating Projects

Eleanor Roosevelt once said: "Learn from the mistakes of others. You can’t live long enough to make them all yourself". Mistakes are almost inevitable while coding or designing a system. Therefore, patches are created to fix the issues in the code either by a manual review, or through a static analysis tool. Oftentimes, mistakes in programming emanate from lack of skills thus, competence with a particular programming language but negligence also plays a role in other instances. A functioning code that solves a particular problem does not guarantee that the code is secure, hence the code should be structured to …

Privacy-Enhanced And Outsourced Power Usage Control In Smart Grids, Hemadri Patel

Theses, Dissertations and Culminating Projects

Due to the numerous advantages of smart grids like reliability, availability, and efficiency, it has been emerging as an extraordinary contribution to the economic and environmental health. This project mainly focuses on the power outage issue in a smart grid environment. Power outages occur when electricity demand exceeds the supply, more specifically, consider a utility company which sets a threshold on the total power usage of households from a neighborhood. Whenever the total power usage from a neighborhood exceeds the threshold, some of the households needs to reduce their energy consumption; to avoid the power outage. This problem is referred …

Integrating The Spatial Pyramid Pooling Into 3d Convolutional Neural Networks For Cerebral Microbleeds Detection, Andre Accioly Veira

CCAC Theses and Dissertations

Cerebral microbleeds (CMB) are small foci of chronic blood products in brain tissues that are critical markers for cerebral amyloid angiopathy. CMB increases the risk of symptomatic intracerebral hemorrhage and ischemic stroke. CMB can also cause structural damage to brain tissues resulting in neurologic dysfunction, cognitive impairment, and dementia. Due to the paramagnetic properties of blood degradation products, CMB can be better visualized via susceptibility-weighted imaging (SWI) than magnetic resonance imaging (MRI).CMB identification and classification have been based mainly on human visual identification of SWI features via shape, size, and intensity information. However, manual interpretation can be biased. Visual screening …

Sequential User Modeling And Recommendation Under Partially Observable Environment, Chunpai Wang

Legacy Theses & Dissertations (2009 - 2024)

A tremendous amount of user data is collected daily due to technological progress that enables us to understand users better. The availability of this data also advances machine learning-based technologies, which aim to learn generic global patterns of user behavior from large volumes of data. User modeling is the process of building user profiles and finding the inherent representation of the user. Precise user modeling is critical for predicting users' future behavior and providing personalized services or products to individuals. Many machine learning models have been explored for user modeling to meet the increasing demand for user-centric technologies. Machine learning …

Learning From Hierarchical And Noisy Labels, Wenting Qi

Legacy Theses & Dissertations (2009 - 2024)

One branch of machine learning algorithms is supervised learning, where the label is crucial for the learning model. Numerous algorithms have been proposed for supervised learning with different classification tasks. However, fewer works question the quality of the training labels. Training a learning model with noisy labels leads to decreased or untruthful performance. On the other hand, hierarchical multi-label classification (HMC) is one of the most challenging problems in machine learning because the classes in HMC tasks are hierarchically structured, and data instances are associated with multiple labels residing in a path of the hierarchy. Treating hierarchical tasks as flat …

Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar

CMC Senior Theses

This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …

Credit Worthiness Tool For Credit Unions, Rylee Christoffersen, Mario Ramalho

ICT

The core objectives for the Capstone project were to mine data in order to create a tool for Credit Unions (and banks) that will evaluate customers credit worthiness based on an ethical standardised criteria that is transparent to all. We explored why this was necessary and explored how important it could be to the business. Our focus is on helping Credit Unions have a stronger online presence as the banking sector has been changing rapidly and moving online and Credit Unions are currently behind in the market in this regard .This tool would help automate the credit approval process, reducing …