Computer Sciences Commons^™

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

All Dissertations

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman

Graduate Theses and Dissertations

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …

Achieving Causal Fairness In Recommendation, Wen Huang

Graduate Theses and Dissertations

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …

Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks

Faculty Publications

Machine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Neutrosophic Mcdm Methodology For Evaluation Onshore Wind For Electricity Generation And Sustainability Ecological, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

The conviction in the necessity of renewable energy has been prompted by both the constantly increasing need for power production and the ecological issues of recent years. When it comes to generating power in a sustainable manner, wind is among the most essential renewable energy sources. This research attempts to present a structural technique for assessing the performance of operational onshore wind facilities with respect to the three dimensions of sustainability. The energy production, environmental effect, and practicability of onshore wind facilities are all dependent on accurate assessments. Wind resources, site accessibility, environmental impact, permitting and regulatory requirements, turbine technology, …

Neutrosophic Mcdm Methodology For Evaluation Onshore Wind For Electricity Generation And Sustainability Ecological, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

The conviction in the necessity of renewable energy has been prompted by both the constantly increasing need for power production and the ecological issues of recent years. When it comes to generating power in a sustainable manner, wind is among the most essential renewable energy sources. This research attempts to present a structural technique for assessing the performance of operational onshore wind facilities with respect to the three dimensions of sustainability. The energy production, environmental effect, and practicability of onshore wind facilities are all dependent on accurate assessments. Wind resources, site accessibility, environmental impact, permitting and regulatory requirements, turbine technology, …

Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju

Doctor of Data Science and Analytics Dissertations

Natural Language Processing (NLP) systems are included everywhere on the internet from search engines, language translations to more advanced systems like voice assistant and customer service. Since humans are always on the receiving end of NLP technologies, it is very important to analyze whether or not the Large Language Models (LLMs) in use have bias and are therefore unfair. The majority of the research in NLP bias has focused on societal stereotype biases embedded in LLMs. However, our research focuses on all types of biases, namely model class level bias, stereotype bias and domain bias present in LLMs. Model class …

Analysis Impact Of Intrinsic And Extrinsic Motivation On Job Satisfaction In Logistics Service Sector: An Intelligent Neutrosophic Model, Zenat Mohamed, Mahmoud M. Ismail, Amal F. Abd El-Gawad

Neutrosophic Systems with Applications

The success of every company relies heavily on the happiness of its workforce, and the logistics service sector is no exception. The capacity of logistics suppliers to satisfy the demands of their clients depends on the efficiency and efficacy of operations, which in turn is affected by the level of employee satisfaction. This paper aims to analyze the factors of employee satisfaction in the logistic service industry to achieve productivity and a satisfied workforce. This paper used the multi-criteria decision-making (MCDM) methodology to handle various factors. The SWARA method is an MCDM method used to compute the importance of factors. …

Analysis Impact Of Intrinsic And Extrinsic Motivation On Job Satisfaction In Logistics Service Sector: An Intelligent Neutrosophic Model, Zenat Mohamed, Mahmoud M. Ismail, Amal F. Abd El-Gawad

Neutrosophic Systems with Applications

The success of every company relies heavily on the happiness of its workforce, and the logistics service sector is no exception. The capacity of logistics suppliers to satisfy the demands of their clients depends on the efficiency and efficacy of operations, which in turn is affected by the level of employee satisfaction. This paper aims to analyze the factors of employee satisfaction in the logistic service industry to achieve productivity and a satisfied workforce. This paper used the multi-criteria decision-making (MCDM) methodology to handle various factors. The SWARA method is an MCDM method used to compute the importance of factors. …

Analysis The Role Of The Internet Of Things And Industry 4.0 In Healthcare Supply Chain Using Neutrosophic Sets, Ahmed M. Abdelmouty, Ahmed Abdel-Monem, Samah Ibrahim Abdel Aal, Mahmoud M. Ismail

Neutrosophic Systems with Applications

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes …

Digital Game-Based Approach To Math Learning For Students, Gul Ayaz, Katherine Smith

Modeling, Simulation and Visualization Student Capstone Conference

Mathematics is an important subject that is pervasive across many disciplines. It is also a subject that has proven to be challenging to both teach and learn. Students face many challenges with learning math such as a lack of motivation and anxiety. To address these challenges, game-based learning has become a popular approach to stimulate students and create a more positive classroom environment. It can serve as an alternative or supplement to traditional teaching and can better engage students while developing a positive attitude toward learning. The use of games in a classroom can create a more exciting and engaging …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …

Automatic Generation Of Virtual Work Guide For Complex Procedures: A Case, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Practical work guides for complex procedures are significant and highly affect the efficiency and accuracy of on-site users. This paper presents a technique to generate virtual work guides automatically for complex procedures. Firstly, the procedure information is extracted from the electronic manual in PDF format. And then, the extracted procedure steps are mapped to the virtual model parts in preparation for animation between adjacent steps. Next, smooth animations of the procedure are generated based on a 3D natural cubic spline curve to improve the spatial ability of the work guide. In addition, each step's annotation is automatically adjusted to improve …

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

Modeling, Simulation and Visualization Student Capstone Conference

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

Modeling, Simulation and Visualization Student Capstone Conference

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

Assessing Frustration Towards Venezuelan Migrants In Columbia: Path Analysis On Newspaper Coded Data, Brian Llinás, Guljannat Huseynli, Erika Frydenlund, Katherine Palacia, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This study analyzes the impact of Venezuelan migrants on local frustration levels in Colombia. The study found a relationship between the influx of Venezuelan migrants and the level of frustration among locals towards migrants, infrastructure, government, and geopolitics. Additionally, we identified that frustration types have an impact on other frustrations. The study used articles from a national newspaper in Colombia from 2015 to 2020. News articles were coded during a previous study qualitatively and categorized into frustration types. The code frequencies were then used as variables in this study. We used path modeling to statistically study the relationship between dependent …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Analysis The Role Of The Internet Of Things And Industry 4.0 In Healthcare Supply Chain Using Neutrosophic Sets, Ahmed M. Abdelmouty, Ahmed Abdel-Monem, Samah Ibrahim Abdel Aal, Mahmoud M. Ismail

Neutrosophic Systems with Applications

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes …

From Policy Promotion To Research Output: Brief Analysis Of Technical Challenges Of Hospital-Led Artificial Intelligence Research, Yu Zhuang, Cheng Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, artificial intelligence has become a key direction of medical and health-related research and a hot spot of international competition. In order to investigate the current situation and challenges in hospital-led artificial intelligence researched, this study selects 14 national pilot hospitals to promote the high-quality development of public hospitals as samples, adopts a combination of quantitative and qualitative methods, analyzes the research articles related to artificial intelligence published by the sample hospitals in recent years, and analyzes the technical challenges in the hospital-led artificial intelligence research. The results show that although the number of hospital-led artificial intelligence research …

Counterventions: A Reparative Reflection On Interventionist Hci, Rua Mae Williams, Louanne E. Boyd, Juan E. Gilbert

Engineering Faculty Articles and Research

Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a “counterventional approach” to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that …

From Point Estimates To Predictive Distributions In Machine Learning Models - A Statistical Importance Sampling Framework, Giuseppina Carannante

Theses and Dissertations

In this thesis, we leverage powerful statistical frameworks for optimal sequential estimation and tracking in non-linear and non-Gaussian dynamical models, which enjoy proven (asymptotic) optimality properties. Initially, we build upon our previous work, which employed first-order Taylor series approximation to propagate the first two predictive moments, to derive Bayesian encoder-decoder networks. This work introduced the notion of dense, pixel-level uncertainty map that is crucial in fields, such as autonomous vehicles and medical segmentation. We then extended the Bayesian framework to an ensembling scheme based on ensemble Kalman Filtering (EnKF). While EnKF represents the predictive distribution with an ensemble of draws, …

Haptic Systems: Trends And Lessons Learned For Haptics In Spacesuits, Mohammad Amin Kuhail, Jose Berengueres, Fatma Taher, Mariam Alkuwaiti, Sana Z. Khan

All Works

Haptic technology uses forces, vibrations, and movements to simulate a sense of touch. In the context of spacesuits, proposals to use haptic systems are scant despite evidence of their efficacy in other domains. Existing review studies have sought to summarize existing haptic system applications. Despite their contributions to the body of knowledge, existing studies have not assessed the applicability of existing haptic systems in spacesuit design to meet contemporary challenges. This study asks, “What can we learn from existing haptic technologies to create spacesuits?”. As such, we examine academic and commercial haptic systems to address this issue and draw insights …

Designing An Equity-Centered Framework And Crosswalk For Integrated Elementary Computer Science Curriculum And Instruction, Carla Strickland, Andrea Ramírez-Salgado, Lauren Weisberg, Latoya Chandler, Jeanne Di Domenico, Elizabeth M. Lehman, Maya Israel

Journal of Computer Science Integration

As computer science (CS) education becomes more prevalent in K-12 instruction, it is critical for educators, researchers, and curriculum developers to identify culturally responsive and pedagogically inclusive approaches that can increase participation, access, and feelings of belonging for students from historically marginalized communities. In response, we developed an equity-centered curricular framework and illustrative crosswalk that synchronizes three distinct pedagogical approaches: culturally responsive pedagogy (CRP), Universal Design for Learning (UDL), and project-based learning (PBL). We describe the framework’s theoretical underpinnings and explain how this framework informed the development of an integrated elementary science+CS curricular unit and provide examples of its implementation. …