Physical Sciences and Mathematics Commons^™

Designing Human-Centered Algorithms For The Public Sector: A Case Study Of The U.S. Child Welfare System, Devansh Saxena

Dissertations (1934 -)

Public sector agencies in the United States are increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithmic systems. These data-driven systems purportedly improve decision-making; however, the public sector poses its own unique challenges where policies, practices, and organizational constraints mediate all decisions. Algorithms that do not account for these pertinent aspects of professional practice frustrate practitioners, diminish the quality of human discretionary work, and amplify biases in decision-making. A human-centered research agenda can help us develop algorithms centered in social-ecological theories that support the decision-making processes of …

Artificial Intelligence-Based Smarter Accessibility Evaluations For Comprehensive And Personalized Assessment, Sayeda Farzana Aktar

Dissertations (1934 -)

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions …

Using Object Detection To Navigate A Game Playfield, Peter Kearnan Hyde-Smith

Master's Theses (2009 -)

Perhaps the crown jewel of AI is the self-navigating agent. To take many sources of data as input and use it to traverse complex and varied areas while mitigating risk and damage to the vehicle that is being controlled, visual object detection is a key part of the overall suite of this technology. While much efforts are being put towards real-world applications, for example self-driving cars, healthcare related issues and automated manufacturing, we apply object detection in a different way; the automation of movement across a video game play field. We take the TensorFlow Object Detection API and use it …

Exploring Post-Quantum Cryptographic Schemes For Tls In 5g Nb-Iot: Feasibility And Recommendations, Kadir Sabanci

Master's Theses (2009 -)

Narrowband Internet of Things (NB-IoT) is a wireless communication technology that enables a wide range of applications, from smart cities to industrial automation. As a part of the 5G extension, NB-IoT promises to connect billions of devices with low-power and low-cost requirements. However, with the advent of quantum computers, the incoming NB-IoT era is already under threat due to conventional cryptographic algorithms that might be adapted to secure devices in NB-IoT being susceptible to be broken soon. In this context, we investigate the feasibility of using post-quantum key exchange and signature algorithms for securing NB-IoT applications. We develop a realistic …

A Package Of Smartphone And Sensor-Based Objective Measurement Tools For Physical And Social Exertional Activities For Patients With Illness-Limiting Capacities, Arafat Mahmood

Dissertations (1934 -)

Patients with several incompletely diagnosed and understood chronic diseases suffer from symptoms that limit their functional capacity. In particular, patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and long covid syndromes have variable fatigue, malaise, poor and unrefreshing sleep, and delayed post-exertional exacerbations of these symptoms. There are no specific tests for these patients to diagnose their diseases properly. These patients must be aware of their daily activities and energy expenditure. Even a little physical effort or socially extroverted behavior can make them tired and incapable of continuing their daily routine. A comprehensive summary of the measured activities at any particular …

Enhancing Motor Imagery Decoding Via Transfer Learning, Olawunmi George, Sarthak Dabas, Abdur Sikder, Roger Smith, Praveen Madiraju, Nasim Yahyasoltani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

Motor imagery (MI) is arguably one of the most common brain–computer interface (BCI) paradigms. The decoding process, in many cases, involves the use of small amounts of data gathered over a period. The decoding performance might therefore be limited, due to the size of available data. Also, the non-stationarity of signals across sessions and subjects can pose a challenge to effective decoding. To solve these challenges, transfer learning is proposed as the suitable approach, which could yield optimal performance even with small amounts of data and handle the non-stationarity of signals with adaptation. It has been applied across domains and …

Gray Counters For Non-Volatile Memories, Arockia David Roy Kulandai, John Rose, Thomas Schwarz

Computer Science Faculty Research and Publications

New technologies for non-volatile memories combine the speed and byte addressability of current memory technologies with the low cost, density, and non-volatility of current storage technologies. They use energy only when writing or reading data. While some newer technologies have practically unlimited endurance, others, such as Phase Change Memory do not. However, this limited endurance surpasses that of solid state drives by several orders of magnitude. They can be integrated into the current memory storage hierarchy as a replacement for DRAM. To manage limited endurance, age-based wear leveling divides the memory into pages and counts the number of writes to …

Bit-Flip Aware Data Structures For Phase Change Memory, Arockia David Roy Kulandai

Dissertations (1934 -)

Big, non-volatile, byte-addressable, low-cost, and fast non-volatile memories like Phase Change Memory are appearing in the marketplace. They have the capability to unify both memory and storage and allow us to rethink the present memory hierarchy. An important draw-back to Phase Change Memory is limited write-endurance. In addition, Phase Change Memory shares with other Non-Volatile Random Access Memories an asym- metry in the energy costs of writes and reads. Best use of Non-Volatile Random Access Memories limits the number of times a Non-Volatile Random Access Memory cell changes contents, called a bit-flip. While the future of main memory is still …

Designing A Patient-Centered Clinical Workflow To Assess Cyberbully Experiences Of Youths In The U.S. Healthcare System, Fayika Farhat Nova

Dissertations (1934 -)

Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities …

Predicting Mental Health Crisis In Veterans: Early Warning Signs, Precursors And Protective Factors, Priyanka Annapureddy

Dissertations (1934 -)

Mental Health (MH) conditions have recently increased to a large extent due to socio-demographic changes. Posttraumatic Stress Disorder (PTSD) is one of the most common mental health disorders prevalent in US. PTSD is even more troubling at double the rate in combat veterans leaving their service compared to general population. Severity of PTSD is associated with risk taking behaviors such as substance abuse, non-suicidal self-injury, and sexual risk behaviors. Psychological disorders are often preceded by early warning signs and recognizing the early warning signs of PTSD will help in preventing the returning or worsening of PTSD symptoms. Ecological momentary assessment …

Emerging Technologies, Evolving Threats: Next-Generation Security Challenges, Tamara Bonaci, Katina Michael, Pablo Rivas, Lindsay J. Roberston, Michael Zimmer

Computer Science Faculty Research and Publications

Security is a fundamental human requirement. We desire the security of our person against injury, security of our capability to provide for our families, security of income linked to needs (food, water, clothing, and shelter), and much more. Most also hope for security of a way of life that is fulfilling and pleasant and peaceful [1] . In 2003, Alkire [2] defined “human security” as: “[t]he objective … to safeguard the vital core of all human lives from critical pervasive threats, in a way that is consistent with long-term human fulfillment.” Today most of the world’s population is highly dependent, …

Data Augmentation Strategies For Eeg-Based Motor Imagery Decoding, Olawunmi George, Roger Smith, Praveen Madiraju, Nasim Yahyasoltani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding. To combat this challenge, data augmentation can be performed, to enhance decoding performance. In this study, we performed data augmentation by synthesizing motor imagery (MI) electroencephalography (EEG) trials, following six approaches. Data generated using these methods were evaluated based …

Adaptive Pedagogy Framework For Risk Management, Incident Response And Disaster Recovery Education, Hsiao-An Wang

Dissertations (1934 -)

The field of Cybersecurity, both in cybersecurity education and cybersecurity workforce demands, has been growing steadily as the dangers of cyber-threats continue to rise. The gap between the supply and demand of the cybersecurity workforce has been widening throughout the past decade. In response to the increased demand, many government agencies have actively engaged in collaborative efforts with higher education institutions to produce more capable graduates to address the need. However, with the various educational utilities available to instructors, few utilities offer content related to risk management, incident response, and disaster recovery practices. Furthermore, many students lack the awareness to …

A Smartphone-Based Non-Invasive Measurement System For Blood Constituents From Photoplethysmography (Ppg) And Fingertip Videos Illuminated With The Near-Infrared Leds, Md Hasanul Aziz

Dissertations (1934 -)

At least two billion people are affected by hemoglobin (Hgb), diabetic-related, and other blood-related diseases. Regular clinical assessments of these problems are conducted by analyzing venipuncture-obtained blood samples in laboratories. A non-invasive, cheap, point-of-care, and accurate test is needed everywhere. We started with Hgb measurement, and after an extensive literature survey, we came up with a non-invasive solution with 10-second Smartphone videos of the index fingertips using custom hardware sets to illuminate the fingers. We tested four lighting conditions with wavelengths in the near-infrared spectrum suggested by the absorption properties of two primary components of blood- oxygenated Hgb and plasma. …

Context-Aware Graph-Based Self-Supervised Learning Of Whole Slide Images, Milam Aryal, Nasim Yahyasoltani

Computer Science Faculty Research and Publications

The gigapixel resolution of a single whole slide image (WSI), and the lack of huge annotated datasets needed for computational pathology, makes cancer diagnosis and grading with WSIs a challenging task. Moreover, downsampling of WSIs might result in loss of information critical for cancer diagnosis. Motivated by the fact that context such as topological structures in the tumor environment may contain critical information in cancer grading and diagnosis, a novel two-stage learning approach is proposed. Self-supervised learning is applied to improve training through unlabled data and graph convolutional network (GCN) is deployed to incorporate context from tumor and surrounding tissues. …

Quantitative Multidimensional Stress Assessment From Facial Videos, Lin He

Dissertations (1934 -)

Stress has a significant impact on the physical and mental health of an individual and is a growing concern for society, especially during the COVID-19 pandemic. Facial video-based stress evaluation from non-invasive cameras has proven to be a significantly more efficient method to evaluate stress in comparison to approaches that use questionnaires or wearable sensors. Plenty of classification models have been built for stress detection. However, most do not consider individual differences. Also, the results for such models are limited by a uni-dimensional definition of stress levels lacking a comprehensive quantitative definition of stress. The dissertation focuses on building a …

Load Balancing Algorithms For Parallel Spatial Join On Hpc Platforms, Jie Yang

Dissertations (1934 -)

Geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of geospatial computations and analytics on large scale datasets, parallel processing is necessary. To exploit fine-grained parallel processing on large scale compute clusters, partitioning of skewed datasets in a load-balanced way is challenging. The workload in spatial join is data dependent and highly irregular. Moreover, wide variation in the size and density of geometries from one region of the map to another, further exacerbates the load imbalance. This dissertation focuses on spatial join operation used in Geographic Information Systems (GIS) and spatial databases, where the inputs are two …

Causal Inference In Healthcare: Approaches To Causal Modeling And Reasoning Through Graphical Causal Models, Riddhiman Adib

Dissertations (1934 -)

In the era of big data, researchers have access to large healthcare datasets collected over a long period. These datasets hold valuable information, frequently investigated using traditional Machine Learning algorithms or Neural Networks. These algorithms perform great in finding patterns out of datasets (as a predictive machine); however, the models lack extensive interpretability to be used in the healthcare sector (as an explainable machine). Without exploring underlying causal relationships, the algorithms fail to explain their reasoning. Causal Inference, a relatively newer branch of Artificial Intelligence, deals with interpretability and portrays causal relationships in data through graphical models. It explores the …

Acceleration Of Computational Geometry Algorithms For High Performance Computing Based Geo-Spatial Big Data Analysis, Anmol Paudel

Dissertations (1934 -)

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that …

Privacy Concerns With Using Public Data For Suicide Risk Prediction Algorithms: A Public Opinion Survey Of Contextual Appropriateness, Michael Zimmer, Sarah Logan

Computer Science Faculty Research and Publications

Purpose

Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms.

Design/methodology/approach

A survey was developed to measure …

Accelerating Spatial Autocorrelation Computation With Parallelization, Vectorization And Memory Access Optimization, Anmol Paudel, Satish Puri

Computer Science Faculty Research and Publications

No abstract provided.

A Novel Framework For Mixed Reality–Based Control Of Collaborative Robot: Development Study, Md. Tanzil Shahria, Md. Samiul Haque Sunny, Md. Ishrak Islam Zarif, Md. Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H. Rahman

Computer Science Faculty Research and Publications

Background:

Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.

Objective:

This study aims to present the development of a novel framework for the collaborative robot using mixed reality.

Methods:

The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. …

Guest Editorial: Introduction To Aoir 2021 Papers On Emerging Ethical Practices And Platform Challenges, Michael Zimmer

Computer Science Faculty Research and Publications

No abstract provided.

Crowdsourced Archiving Of The January 6th Us Capitol Insurrection: An R/Datahoarders Case Study, Edward Miezio Chapman

Master's Theses (2009 -)

The crowdsourced archiving that occurred in the wake of the January 6th US Capitol insurrection exemplifies the potential for agile, collaborative evidence gathering during a crisis situation. This paper studies the r/DataHoarders subcommunity of Reddit and the collective and spontaneous archiving project that users initiated. Users were drawn to the thread out of a desire to contribute to law enforcement efforts, enact punitive justice upon the rioters, engage in public discourse, and preserve information for posterity. They did this by gathering and preserving social media evidence that may have otherwise been lost. I discovered that this constituted a crowdsourced archive …

Explainable Retinal Screening With Self-Management Support To Improve Eye-Health Of Diabetic Population Via Telemedicine, Jannatul Ferdause Tumpa

Dissertations (1934 -)

Diabetic Retinopathy (DR) is one major complication of diabetes and is the leading cause of blindness worldwide. Progression of DR and complete vision loss can be prevented by keeping diabetes in control and by early diagnosis through annual eye screenings. However, cost, healthcare disparities, cultural limitations, lack of motivation, etc., are the main barriers against regular screening, especially for a few ethnically and racially minority communities. On the other hand, to well-manage and control diabetes, the diabetic population needs to be physically active and keep their weight healthy. From the perspective of Behavioral Science, Some self-management techniques based on motivational …

Workers’ Attitudes Toward Increased Surveillance During And After The Covid-19 Pandemic, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

Amid the Covid-19 pandemic, the transition of many offices to remote work has led to new ways for employers to track workers’ movements, behavior, and productivity. Through their SSRC-funded research, Jessica Vitak and Michael Zimmer surveyed remote workers in the US about perceptions of current workplace monitoring practices. They argue that worker concerns about reductions in privacy and independence at work might have negative outcomes on worker productivity, satisfaction, and well-being.

Efficient Service For Next Generation Network Slicing Architecture And Mobile Traffic Analysis Using Machine Learning Technique, Billian Khan Tapan

Master's Theses (2009 -)

The tremendous growth of mobile devices, IOT devices, applications and many other services have placed high demand on mobile and wireless network infrastructures. Much research and development of 5G mobile networks have found the way to support the huge volume of traffic, extracting of fine-gained analytics and agile management of mobile network elements, so that it can maximize the user experience. It is very challenging to accomplish the tasks as mobile networks increase the complexity, due to increases in the high volume of data penetration, devices, and applications. One of the solutions, advance machine learning techniques, can help to mitigate …

Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George

Dissertations (1934 -)

Motor imagery (MI) has been one of the most used paradigms for building brain-computer interfaces (BCI), widely used in neurorehabilitation, for restoring functionality to damaged parts of a neurologically deficient person. The existing motor imagery techniques have largely employed feature extraction techniques such as the power spectral density (PSD) and the common spatial patterns (CSP) before classification, using traditional machine learning algorithms such as support vector machines (SVM) and linear discriminant analysis (LDA). These algorithms are quite limited in their ability to generate feature representations for certain types of signals, limiting the potential for improvements in the decoding process. Also, …

Needles In A Haystack: How Pooling Can Control Error Rates In Noisy Tests, Arockia David Roy Kulandai, J. Stella, John Rose, Thomas Schwarz

Computer Science Faculty Research and Publications

Testing many individuals for a reasonably rare condition using imperfect, time consuming, and expensive tests can be facilitated by pooling. Pooling groups samples from different individuals that are then tested for the existence of a pathogen. An individual is diagnosed as a carrier if a threshold of the tests to which the individual contributed samples is positive. Our assumptions dictate a testing strategy that is not adaptive, with the exception of retesting positively diagnosed persons individually. Pooling is a standard proposal to stretch the supply of test kits. We show that it can also be used to control the false …

Comparing Generic And Community-Situated Crowdsourcing For Data Validation In The Context Of Recovery From Substance Use Disorders, Sabirat Rubya, Joseph Numainville, Svetlana Yarosh

Computer Science Faculty Research and Publications

Targeting the right group of workers for crowdsourcing often achieves better quality results. One unique example of targeted crowdsourcing is seeking community-situated workers whose familiarity with the background and the norms of a particular group can help produce better outcome or accuracy. These community-situated crowd workers can be recruited in different ways from generic online crowdsourcing platforms or from online recovery communities. We evaluate three different approaches to recruit generic and community-situated crowd in terms of the time and the cost of recruitment, and the accuracy of task completion. We consider the context of Alcoholics Anonymous (AA), the largest peer …