Computational Engineering Commons^™

Enhanced Shoulder-Surfing Cued Recall Graphical Password System: Sequential Passpoint, Titus D. Fofung

Cybersecurity Graduate Research Symposium

During the past two decades, many graphical passwords have been used widely as an alternative to text-based passwords. However, most graphical password systems are plagued by shoulder-surfing problems, usability, and remembering capability. This study proposed a new graphical password called SPP (Sequential PassPoint), allowing users to remember three click-points on two images in specified order and image order. When the image order changes, the click order is reversed. Two decoy images for three random clicks were introduced to enhance the security of SPP. The proposed SPP system was validated both theoretically and empirically

Quantum Computing, Geopolitics, And Latin America, Heriberto Acosta-Maestre

Cybersecurity Graduate Research Symposium

No abstract provided.

Mouasla: Integrating Iot And Ai For An Intelligent Trans-Portation Payment System, Hany El-Ghaish Dr., Haitham Darweesh

Journal of Engineering Research

Smart payment systems have emerged as vital components of global public transportation, offering passengers a more efficient and convenient fare payment method. The Mouasla system addresses traditional payment limitations through IoT devices and AI-backed backend services. Features of Mouasla It employs RFID smart card and IoT features from the device to ensure all components such as a card reader function, driver functions, charging units function, and payment are combined with this system alongside a mobile application for quick access backend services. Each passenger dataset is analyzed by an AI-powered backend service to provide insight that can be used to improve …

Importance Sampling To Learn Vasopressor Dosage To Optimize Patient Mortality In An Interpretable Manner, Anshul Rastogi

Holster Scholar Projects

Sepsis is a life-threatening organ dysfunction resulting from an improperly compensated bodily response to infection. There is a high urgency among clinicians to develop a set of real-time explainable treatment guidelines and tools to address the high mortality rate of sepsis patients. We present a reinforcement learning approach for vasopressor drug dosage in intensive care unit sepsis patients to achieve a better-than-expert treatment policy. We preserved interpretability with a prototype learning layer and learn actions in an off-policy manner with importance sampling. We evaluated our design on the MIMIC-IV deidentified electronic health record dataset with an 80%-20% training-validation split for …

Gamified Machine Embroidery, Sai Rama Balakrishnan, Vince Thanh Doan

College of Engineering Summer Undergraduate Research Program

This project aims to improve accessibility to the embroidery field by developing an application that captures the utility of a embroidery software (controlling an embroidery machine to produce embroidered output) and combines it with the playfulness of a game, to put those advanced controls in the hands of novices.

Assessing The Impact Of Femur Morphological Variations On Pediatric Hip Joint Biomechanics Using Statistical Shape Modeling, Tamara Chambers

Doctoral Dissertations and Master's Theses

This dissertation aimed to (1) quantify morphological variations in the pediatric hip joint and (2) evaluate the sensitivity of an infant musculoskeletal model (MSM) to these variations, considering hip joint center estimation errors. A shape statistical model (SSM) of decedent infant femurs from the Ortolani collection was created using ShapeWorks, capturing key morphological features, such as variations in the femoral neck-shaft and anteversion angles. Seven synthetic femurs were generated from the SSM to create SSM-informed MSMs, which were systematically evaluated through kinematics and kinetics analyses in OpenSim. Incorporating the SSM led to slight changes in the pediatric MSMs’ kinematics but …

Utilizing A Virtual Firewall Appliance For Introducing And Reinforcing The Concepts And Implementation Of Devices To Improve Security In A Computing Environment, Stanley Mierzwa, Christopher Eng

Center for Cybersecurity

The educational realm of higher education cybersecurity curriculum continues to evolve to provide more opportunities for experiential hands-on and work role-related practical applications of technology solutions. Gaining more excellent competencies is quickly becoming a standard requirement for programs with the National Security Agency Center of Academic Excellence designation. The work roles of cybersecurity include a variety of knowledge, skills, and abilities, depending on the activity category or task. Firewalls have been a staple cybersecurity, network security, and information security device and strategy to protect organization networks and computing environments. This paper will provide details and a description of the effort …

Exploring The Identification Of Autoregression Model By General Least Deviation Method, Mostafa Abotaleb, Tatiana Makarovskikh, Ramadhan, Ali J

Al-Bahir

We are considering a novel method for analyzing time series data that relies on quasi-linear recurrence relations. Unlike neural networks, this approach allows for directly formulating high-quality quasi-linear difference equations that accurately represent the studied process. Techniques for determining the parameters of a single equation have been devised and validated. This work discusses and tests a technique for identifying the parameters of a quasi-linear recurrence equation. This approach is employed to tackle the issue of regression analysis including observable variables that are mutually dependent. It enables the utilization of the Generalized Least Deviations Method (GLDM). This model was utilized in …

Development Of Message Passing-Based Graph Convolutional Networks For Classifying Cancer Pathology Reports, Hong Jun Yoon, Hilda B. Klasky, Andrew E Blanchard, J. Blair Christian, Eric B Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Linda Coyle, Lynne Penberthy, Georgia D Tourassi

School of Public Health Faculty Publications

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to …

A Random Forest Classifier Model For Predicting The Impact Of Viral Infections On Adults With Chronic Conditions, Fungai Jacqueline Kiwa, Martin Muduva

African Conference on Information Systems and Technology

This study investigates the impact of viral infections on adults with chronic illnesses, focusing on the development of a Random Forest classifier model. The research aims to predict outcomes among individuals with conditions like diabetes, cancer, and tuberculosis, analyzing severity, age groups, and travel patterns. The study aims to assist healthcare professionals in resource allocation and patient prioritization based on disease severity. It reviews literature on viral infection risks for chronic illness patients and explores machine learning applications in infectious disease management. Methodologically, the study adopts a structured approach similar to the Cross-Industry Standard Process for Data Mining (CRISP -DM) …

Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu

African Conference on Information Systems and Technology

This document emphasizes on the generation of electricity from trees and its usability in all the sectors of Zimbabwe. The research focused on positively changing the lives of citizens through the provision of uninterrupted and reliable bioelectricity. The literature review was completely and accurately performed through finding out the current news associated with the use of trees in producing electricity and the use of AI to manage the flow. The Scrum’s development model was adopted and followed during the research project to address issues like transparency, early mitigation of risks and constant feedback. The Scrum-model is one of the best …

2024 Summer Proceedings Teuscher Lab, Teuscher Group, Christof Teuscher, Chelsea Ogbede, Lauren Sanday, Sofia Vargas, Artem Arefev

altREU Projects

How will computation evolve in the coming years? What problems can be tackled using artificial intelligence, in a world increasingly driven by data? And how can that data be used to better inform our decisions as a society? In this unique collection of research projects, each chapter represents a distinct work undertaken by a single individual or a group of students as part of the altREU program led by Christof Teuscher. The projects, rooted in applications of artificial intelligence and innovative computation techniques, examine impactful solutions to numerous pressing challenges affecting communities around the world.

Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines

Research & Publications

Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …

Implementation Of Machine Learning Using Deep Neural Networks To Estimate The Failure Risk Caused By Leakage In Pressure Relief Devices, Adi Yudho Wijayanto, Yossi Andreano, M. Ali Yafi Rizky, Dedi Priadi

Journal of Materials Exploration and Findings

The primary objective of deploying Pressure Relief Device (PRD) equipment is to ensure the safety of pressure vessels within a pressurized system. Over time, PRD equipment may degrade and fail to perform its intended function, which must be identified as a failure mode. To mitigate potential risks associated with this, it is recommended that an approach such as risk-based inspection (RBI) be implemented. Despite the widespread adoption of RBI, the method relies on qualitative techniques, leading to significant variations in equipment risk assessments. This study proposes a novel risk analysis method that uses deep learning-based machine learning to develop a …

Classification Model For Discovering The Type Of Crop To Plant Using Ensemble Techniques, Uma Mahesh Addanki

Electronic Theses, Projects, and Dissertations

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, …

Exploiting Physical Side-Channel Information For Offensive And Defensive Ends, Sisheng Liang

All Dissertations

Side-channel information consists of side effects of computation that range from microarchitectural to physical phenomena. Empirical studies have demonstrated the practical exploitability of these side effects in real-world systems for malicious attacks and effective defenses. In this dissertation, we discover, analyze, and exploit certain physical side-channel information for end-to-end attacks and defense across three studies.

In the first study, we demonstrate a new DNN model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emitted from a GPU to steal DNN models several meters away from the victim machine, even with some physical obstacles in between. Using Clairvoyance, an …

Large-Scale Hpc-Empowered Power Electronics Modeling And Simulation In Photovoltaic Applications, Liwei Wang

All Dissertations

The rising popularity of renewable energy sources requires advanced, efficient power electronic systems for energy conversion, grid integration, and system management, thereby raising expectations for power electronics in the energy industry. The complexity of modern power electronic systems requires comprehensive simulations and in-depth analysis to predict performance accurately, but this process is impeded by prolonged simulation times. The primary objective of this dissertation is to develop a high-fidelity, high-speed event-driven simulator to tackle challenges related to mass data processing, uncertainty evaluation, as well as modeling and simulation issues in assessing the reliability of power electronics in large-scale Photovoltaic (PV) systems. …

Medical Imaging Dataset Management Leveraging Deep Learning Frameworks In Breast Cancer Screening, Inchan Hwang

Dissertations

In the domain of Computer-Aided Diagnosis (CADx) for breast cancer diagnosis through mammography, prevailing models have traditionally been trained and validated using old film-based mammography. However, contemporary U.S. hospital practices involve the utilization of Full Field Digital Mammography (FFDM), offering more detailed images captured at various angles than old film-scanned mammography. Despite this shift, the existing body of research predominantly focuses on old-film based datasets, the implications of FFDM for CADx systems have not been understood. This dissertation addresses the issues emerged from FFDM such as data augmentation between old film-based set and new FFDM whether they are more effective …