Computer Engineering Commons^™

Manipulating Image Luminance To Improve Eye Gaze And Verbal Behavior In Autistic Children, Louanne Boyd, Vincent Berardi, Deanna Hughes, Franceli L. Cibrian, Jazette Johnson, Viseth Sean, Eliza Delpizzo-Cheng, Brandon Mackin, Ayra Tusneem, Riya Mody, Sara Jones, Karen Lotich

Engineering Faculty Articles and Research

Autism has been characterized by a tendency to attend to the local visual details over surveying an image to understand the gist–a phenomenon called local interference. This sensory processing trait has been found to negatively impact social communication. Although much work has been conducted to understand these traits, little to no work has been conducted to intervene to provide support for local interference. Additionally, recent understanding of autism now introduces the core role of sensory processing and its impact on social communication. However, no interventions to the end of our knowledge have been explored to leverage this relationship. This work …

Machine Learning Assisted Discovery Of Shape Memory Polymers And Their Thermomechanical Modeling, Cheng Yan

LSU Doctoral Dissertations

As a new class of smart materials, shape memory polymer (SMP) is gaining great attention in both academia and industry. One challenge is that the chemical space is huge, while the human intelligence is limited, so that discovery of new SMPs becomes more and more difficult. In this dissertation, by adopting a series of machine learning (ML) methods, two frameworks are established for discovering new thermoset shape memory polymers (TSMPs). Specifically, one of them is performed by a combination of four methods, i.e., the most recently proposed linear notation BigSMILES, supplementing existing dataset by reasonable approximation, a mixed dimension (1D …

A Component-Based Analysis For Online Proctoring, Salma Roshdy Ali

Theses and Dissertations

The switch to online learning due to the COVID-19 revealed flaws in the existing learning methods, especially with online proctored assessments. Hence, online proctoring using computers was needed for a fair evaluation. Many studies develop cheating detection systems using several approaches. However, to the best of our knowledge, none of the existing studies investigated the impact of their system components in detecting cheating behaviors. Combining system components, even if they do not significantly improve the system performance in cheating detection, can cause an overload on the system. Therefore, our goal is to investigate the system components’ impact, individually and combined, …

Using Deep Neural Network And Transformers To Extract Graphene Compounds And Properties, Ayman Ibn Jaman

Computer Science Graduate Research Workshop

No abstract provided.

Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor

Senior Theses

Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Program: Senior Projects (Public)

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Efficient Information Retrieval For Software Bug Localization, Saket Khatiwada

LSU Doctoral Dissertations

Software systems are often shipped with defects. When a bug is reported, developers use the information available in the associated report to locate source code fragments that need to be modified to fix the bug. However, as software systems evolve in size and complexity, bug localization can become a tedious and time-consuming process. Contemporary bug localization tools utilize Information Retrieval (IR) methods for automated support to minimize the manual effort. IR methods exploit the textual content of bug reports to capture and rank relevant buggy source files. However, for an IR-based bug localization tool to be useful, it must achieve …

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …

A Date With Cheemis: Bullying In The Virtual Space, Nicholas Roger Nolasco

Liberal Arts and Engineering Studies

A Date With Cheemis is an alternative game mode for the social platform VRChat designed in the Unity real-time 3D development platform. The project is an experience where the player meets many non-playable characters (NPCs) and makes decisions based on different scenarios. The game tells the story of a VRChat user named Cheemis who is bullied for the avatar they use and how they interact with other characters. The player must make choices of how to react to the way the NPCs treat Cheemis, whether that be defending him or being a bystander. This experience is only available through the …

Adding Temporal Information To Lidar Semantic Segmentation For Autonomous Vehicles, Mohammed Anany

Theses and Dissertations

Semantic segmentation is an essential technique to achieve scene understanding for various domains and applications. Particularly, it is of crucial importance in autonomous driving applications. Autonomous vehicles usually rely on cameras and light detection and ranging (LiDAR) sensors to gain contextual information from the environment. Semantic segmentation has been employed to process images and point clouds that were captured from cameras and LiDAR sensors respectively. One important research direction to consider is investigating the impact of utilizing temporal information in the domain of semantic segmentation. Many contributions exist in the field with regards to utilizing temporal information for semantic segmentation …

Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed

Theses and Dissertations

The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …

Camera And Lidar Fusion For Point Cloud Semantic Segmentation, Ali Abdelkader

Theses and Dissertations

Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from …

Interpretable And Anti-Bias Machine Learning Models For Human Event Sequence Data, Zihan Zhou

LSU Doctoral Dissertations

Growing volumes and varieties of human event sequence data are available in many applications such as recommender systems, social network, medical diagnosis, and predictive policing. Human event sequence data is usually clustered and exhibits self-exciting properties. Machine learning models especially deep neural network models have shown great potential in improving the prediction accuracy of future events. However, current approaches still suffer from several drawbacks such as model transparency, unfair prediction and the poor prediction accuracy due to data sparsity and bias. Another issue in modeling human event data is that data collected from real word is usually incomplete, and even …

A Neural Network Based Proportional Hazard Model For Iot Signal Fusion And Failure Prediction, Yuxin Wen, Xingxin Guo, Junbo Son, Jianguo Wu

Engineering Faculty Articles and Research

Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this paper, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and …

Eye-Gaze-Controlled Hmds And Mfd For Military Aircraft, Lrd Murthy, Abhishek Mukhopadhyay, Somnath Arjun, Varshith Yelleti, Peter Thomas, Dilli Babu Mohan, Pradipta Biswas

Journal of Aviation Technology and Engineering

Eye-gaze-controlled interfaces allow the direct manipulation of a graphical user interface by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots’ hands are occupied with flying the aircraft. This paper reports studies on analyzing the accuracy of eye-gaze-controlled interfaces inside aircraft undertaking representative flying missions. We report that using eye-gaze-controlled interfaces, pilots can undertake representative pointing and selection tasks at less than two seconds on average in a transport aircraft. Further, we analyzed the accuracy of eye-gaze-tracking glasses under various G load factors and analyzed the failure modes. We …

Icu Liberation: Early Mobility And Exercise, Leann Volkers, Holly Kockler, Kristi Patterson

Nursing Posters

The aim of this project was to streamline and standardize the delivery of the follow up Important Message from Medicare (IMM) for IP admissions across CC and Carris-RWF in compliance with regulatory standards of care.

Key drivers identified:

• Site specific variation
• Underutilization of Epic functionality
• Use of data to understand performance

A Systematic Review Of How Cloud Infrastructure And Gdpr Have Affected Digital Investigations In A Multinational Business Context, Stuart Fraser, A.Omar Portillo-Dominguez

Other

With cloud infrastructure becoming an ever more popular platform for business network implementations, and with ever-tightening data protection regulation, the ability to carry out digital investigations has become more difficult. This has led to areas of research that have looked to restore the balance to digital investigations in this environment. These areas include the use of blockchain, data tracing, and digital forensics as a service. With so many methods to consider, this article looks at how each method aims to return the balance and make it possible to carry out an investigation that complies with new privacy regulations (e.g., the …

Dielectrophoretic Trapping Of Carbon Nanotubes For Temperature Sensing, Kaylee Burdette

Theses, Dissertations and Capstones

Conventional sensors are rapidly approaching efficiency limitations at their current size. In designing more efficient sensors, low dimensional materials such as carbon nanotubes (CNTs), quantum dots, and DNA origami can be used to enable higher degrees of sensitivity. Because of the high atomic surface to core ratio, these materials can be used to detect slight changes in chemical composition, strain, and temperature. CNTs offer unique advantages in different types of sensors due to their electromechanical properties. In temperature sensing, the high responsiveness to temperature and durability can be used to produce an accurate, reliable sensor in even extreme temperatures. This …

Penetration Testing In A Small Business Network, Lee Kandle

Williams Honors College, Honors Research Projects

Penetration testing on a business network consisting of three routers, one switch, and one computer. Access Control Lists (ACLs) on the routers act as the firewall(s) for the network. 10 of the twelve ACLs do not deny any form of traffic to reflect the lax security standards common in small networks. Router 1 acts as the primary router of the Attacker/Pen Tester. Router 2 represents the edge router for the business and Router 3 is the inner router closest to end-user devices. Switch 1 is connected to Router 3 with one computer connected to the switch acting as an end-user. …

Modeling Document Classification To Automate Mental Health Diagnosis, William M. Tadlock

EWU Masters Thesis Collection

The objective of this study is to determine if diagnosis documents can be used with document classification to automatically diagnose mental health conditions. Document classification allows text documents to be analyzed and organized into their appropriate classes based on the features and words presented in the text. One application of this is within the medical field to automatically classify different patient diagnosis based on medical or patient notes. This research applied mental health diagnosis documents to automatically diagnose a group of patients with a mental health condition based on text-based survey data. This classification was approached through several feature engineering …

Deepfakes, Shallowfakes, And The Need For A Private Right Of Action, Eric Kocsis

Dickinson Law Review (2017-Present)

For nearly as long as there have been photographs and videos, people have been editing and manipulating them to make them appear to be something they are not. Usually edited or manipulated photographs are relatively easy to detect, but those days are numbered. Technology has no morality; as it advances, so do the ways it can be misused. The lack of morality is no clearer than with deepfake technology.

People create deepfakes by inputting data sets, most often pictures or videos into a computer. A series of neural networks attempt to mimic the original data set until they are nearly …

Book Review: This Is How They Tell Me The World Ends: The Cyberweapons Arms Race (2020) By Nicole Perlroth, Amy C. Gaudion

Dickinson Law Review (2017-Present)

No abstract provided.

Intelligent Voice Guidance In Vr: Understanding The Value Of Nlp In Virtual Environments, Zhiyu Xiao

Dartmouth College Master’s Theses

Virtual assistants such as Google Assistant, Alexa and Siri emerged because of the growth of NLP(natural language processing) technology. At the same time, virtual reality has developed rapidly in recent years and has become a crucial tool in engineering product development procedures. However, people feel overwhelmed in some complicated VR environments. Thus, this thesis tries to incorporate NLP technology into the VR environment and explores the value of intelligent voice guidance in VR environments. In this thesis, a car repair training system with intelligent voice guidance is designed: users can utilize voice to perform various tasks in this system, such …

An Automated Framework To Debug System-Level Concurrency Failures, Tarannum Shaila Zaman

Theses and Dissertations--Computer Science

The ever-increasing parallelism in computer systems has made software more prone to concurrency failures, causing problems during both pre- and post-development. Debugging concurrent programs is difficult because of the non-deterministic behavior and the specific sequences of interleaving in the execution flow. Debugging is a technique where programmers reproduce the bug, identify the root cause, and then take necessary steps to remove the bug from the system. The failure information may come from the bug reports of the testing phase or the production runs. In both cases, there should be steps taken to reproduce and localize the failure. However, reproducing and …

An Experimental Study Towards Underwater Propulsion System Using Structure Borne Traveling Waves, Shreyas Suhas Gadekar

Dissertations, Master's Theses and Master's Reports

The method of generating steady-state structure-borne traveling waves underwater in an infinite media creates abundant opportunities in the field of propulsive applications, and they are gaining attention from several researchers. This experimental study provides a framework for harnessing traveling waves in a 1D beam immersed under quiescent water using two force input methods and providing a motion to an object floating on the surface of the water.

In this study, underwater traveling waves are tailored using structural vibrations at five different frequencies in the range of 10Hz to 300Hz. The resulting fluid motion provides a propulsive thrust that moves a …

Reflecting On Recurring Failures In Iot Development, Dharun Anandayuvaraj, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate …

Reflections On Software Failure Analysis, Paschal C. Amusuo, Aishwarya Sharma, Siddharth R. Rao, Abbey Vincent, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Failure studies are important in revealing the root causes, behaviors, and life cycle of defects in software systems. These studies either focus on understanding the characteristics of defects in specific classes of systems or the characteristics of a specific type of defect in the systems it manifests in. Failure studies have influenced various software engineering research directions, especially in the area of software evolution, defect detection, and program repair.

In this paper, we reflect on the conduct of failure studies in software engineering. We reviewed a sample of 52 failure study papers. We identified several recurring problems in these studies, …

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …