Computer Engineering Commons^™

Human-Ai Teaming For Dynamic Interpersonal Skill Training, Xavian Alexander Ogletree

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In almost every field, there is a need for strong interpersonal skills. This is especially true in fields such as medicine, psychology, and education. For instance, healthcare providers need to show understanding and compassion for LGBTQ+ and BIPOC (Black, Indigenous, and People of Color), or individuals with unique developmental or mental health needs. Improving interpersonal skills often requires first-person experience with expert evaluation and guidance to achieve proficiency. However, due to limited availability of assessment capabilities, professional standardized patients and instructional experts, students and professionals currently have inadequate opportunities for expert-guided training sessions. Therefore, this research aims to demonstrate leveraging …

Stellar Classification Of Folded Spectra Using The Mk Classification Scheme And Convolutional Neural Networks, John Magee

Dissertations

The year 1943 saw the introduction of the Morgan-Keenan (MK) classification scheme and this replaced the existing Harvard Classification scheme. Both stellar classification scheme are fundamentally grounded in the field of spectroscopy. The Harvard Classification scheme classified stars based on stellar surface temperature. The MK Classification scheme introduced the concept of a luminosity class that is intrinsically linked to the surface gravity of a star. Temperature and luminosity class values are estimated directly from the stellar spectrum.

Machine learning is a well-established technique in astronomy. Traditionally, a spectrum is treated as a one-dimensional sequence of data. Techniques such as artificial …

A Deep Understanding Of Structural And Functional Behavior Of Tabular And Graphical Modules In Technical Documents, Michail Alexiou

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The rapid increase of published research papers in recent years has escalated the need for automated ways to process and understand them. The successful recognition of the information that is contained in technical documents, depends on the understanding of the document’s individual modalities. These modalities include tables, graphics, diagrams and etc. as defined in Bourbakis’ pioneering work. However, the depth of understanding is correlated to the efficiency of detection and recognition. In this work, a novel methodology is proposed for automatic processing of and understanding of tables and graphics images in technical document. Previous attempts on tables and graphics understanding …

Electroosmotic Mixing Of Non-Newtonian Fluid In A Microchannel With Obstacles And Zeta Potential Heterogeneity, Lanju Mei, Defu Cui, Jiayue Shen, Diganta Dutta, Willie Brown, Lei Zhang, Ibibia K. Dabipi

Computational Modeling & Simulation Engineering Faculty Publications

This paper investigates the electroosmotic micromixing of non-Newtonian fluid in a microchannel with wall-mounted obstacles and surface potential heterogeneity on the obstacle surface. In the numerical simulation, the full model consisting of the Navier–Stokes equations and the Poisson–Nernst–Plank equations are solved for the electroosmotic fluid field, ion transport, and electric field, and the power law model is used to characterize the rheological behavior of the aqueous solution. The mixing performance is investigated under different parameters, such as electric double layer thickness, flow behavior index, obstacle surface zeta potential, obstacle dimension. Due to the zeta potential heterogeneity at the obstacle surface, …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam

Dissertations

Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

Covid-19 Prediction Using Lstm Algorithm: Gcc Case Study, Kareem Kamal A. Ghany, Hossam Zawbaa, Heba M. Sabri

Articles

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates …

Abn: Agent-Aware Boundary Networks For Temporal Action Proposal Generation, Khoa Vo, Kashu Yamazaki, Sang Truong, Minh-Triet Tran, Akihiro Sugimoto, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding. Despite the great achievement in TAPG, most existing works ignore the human perception of interaction between agents and the surrounding environment by applying a deep learning model as a black-box to the untrimmed videos to extract video visual representation. Therefore, it is beneficial and potentially improves the performance of TAPG if we can capture these interactions between agents and the environment. In this paper, we propose a novel …

Design Of Lower Legs Of Mithra, A High-Performance Backdrivable Humanoid Robot, Drake Taylor

Electronic Theses and Dissertations

This thesis presents the design of the knee and ankle of Mithra, a new humanoid robot that aims to be an energy-efficient and highly agile machine. Mithra makes use of new optimization metrics for legged robots to develop a system capable of mimicking human movement. A series of low-impedance, high-torque actuator systems were developed with the goal of creating lightweight, powerful, and robust motion. The structure of Mithra's legs mimics the human structure in leg segment length and weight proportions. Detailed design and analysis were conducted in order to allow Mithra to be a robust and maintainable system. Mithra will …

Design, Manufacture, And Test Of A Hybrid Aerial-Ground Robotic Platform, William Garrett Willmon

Electronic Theses and Dissertations

A hybrid aerial-ground robotic platform allows for enhanced functionality combining most of the operational profiles of an aerial and ground vehicle with applications to intelligence, surveillance, reconnaissance (ISR), infrastructure inspection, emergency response, photography, etc. Motivated by this challenge, we designed, developed, and tested a prototype hybrid aerial-ground robotic vehicle capable of guidance, navigation, and control in the air and on the ground. The thesis focus is on the system design. As such, at first, we designed and analyzed the mechanical component to ensure durability. We then designed the electrical component to reduce overall weight and maximize battery life. We developed …

Leveraging Sequential Nature Of Conversations For Intent Classification, Shree Gotteti

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Conversations are more than just a sequence of text, it is where two or more participants interact in order to achieve their goals. Conversation Understanding (CU) requires all participants to understand each others intent. In the past decade, CU has been extended from automated human-human text processing to build automated conversational agents for human-machine interactions. Despite their popularity, these automated conversational agents (like Siri, Alexa, etc) can't handle more than one or two utterances, and they don't recognize conversations as intents. The development of approaches that extract intents behind an utterance is essential for the advancements of Question Answering (QA) …

Content Adaption And Design In Mobile Learning Of Wind Instruments, Neha Priyadarshani

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People in today's world seek things that are simple to use. Learning is one of the most crucial aspects of the ongoing digital transformation. Everything is now accessible with a single click on mobile devices, making access to instructional materials faster, easier, and more comfortable. It takes time and effort to build abilities and become an expert in the fields of learning, training, and teaching; and music learning demands a great deal of both practice and mentoring. Initially, music teachers and band directors must maintain a steady attention and devote a significant amount of time to manually teaching materials. This …

Complex Interactions Between Multiple Goal Operations In Agent Goal Management, Sravya Kondrakunta

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A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated …

A Deep Understanding Of Structural And Functional Behavior Of Tabular And Graphical Modules In Technical Documents, Michail Alexiou

Browse all Theses and Dissertations

The rapid increase of published research papers in recent years has escalated the need for automated ways to process and understand them. The successful recognition of the information that is contained in technical documents, depends on the understanding of the document’s individual modalities. These modalities include tables, graphics, diagrams and etc. as defined in Bourbakis’ pioneering work. However, the depth of understanding is correlated to the efficiency of detection and recognition. In this work, a novel methodology is proposed for automatic processing of and understanding of tables and graphics images in technical document. Previous attempts on tables and graphics understanding …

Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani

Electronic Theses and Dissertations

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:

First, a Convolutional Neural Network (CNN)-based method for …

Annual Report 2020-2021, Depaul University College Of Computing And Digital Media

CDM Annual Reports

LETTER FROM THE DEAN

As I write this letter during the beginning of the 2021–22 academic year, we have started to welcome the majority of our students to campus— many for the very first time, and some for the first time in a year and a half. It has been wonderful to be together, in-person, again. Four quarters of learning and working remotely was challenging, to be sure, but I have been consistently amazed by the resilience, innovation, and hard work of our students, faculty, and staff, even in the most difficult of circumstances. This annual report, covering the 2020–21 …

Shopping For An Ir: The Search, Adoption, And Implementation Of The University Of Louisiana At Lafayette’S Institutional Repository Platform, Zachary G. Stein

Faculty Scholarship

Institutions wishing to disseminate their scholarship on a wide scale have influenced the need for institutional repositories (IR). These repositories provide institutions the opportunity to display and preserve what they have to offer in an open access environment. The University of Louisiana at Lafayette (UL Lafayette) had attempted to adopt an IR for years and finally achieved this goal. Many issues impacted the adoption, including funding, deciding on the appropriate platform solutions, and marketing the need for contributions from faculty and university administration. There is much optimism for the IR, as it is also included in the university’s Quality Enhancement …

Clustering Hypergraphs Via The Mapequation, Matthew Swan, Justin Zhan

Computer Science and Computer Engineering Faculty Publications and Presentations

A hypergraph is a generalization of a graph in that the restriction of pairwise affinity scores is lifted in favor of affinity scores that can be evaluated between an arbitrary number of inputs. Hypergraphs clustering is the process of finding groups in which members of a given hypergraph exhibit a high similarity and dissimilarity with members outside their group. In this paper, we generalize the well-known MapEquation, an optimization equation used in the clustering of nonhypergraphs, for hypergraphs. We develop an agglomerative algorithm, Hypergraph Random Walks (HRW), to find an approximate solution to the generalized MapEquation. Our algorithm requires neither …

Multidimensional Demographic Profiles For Fair Paper Recommendation, Reem Alsaffar, Susan Gauch

Computer Science and Computer Engineering Faculty Publications and Presentations

Despite double-blind peer review, bias affects which papers are selected for inclusion in conferences and journals. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI …

Complex Interactions Between Multiple Goal Operations In Agent Goal Management, Sravya Kondrakunta

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A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated …

Adaptive Two-Stage Edge-Centric Architecture For Deeply-Learned Embedded Real-Time Target Classification In Aerospace Sense-And-Avoidance Applications, Nicholas A. Speranza

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With the growing number of Unmanned Aircraft Systems, current network-centric architectures present limitations in meeting real-time and time-critical requirements. Current methods utilizing centralized off-platform processing have inherent energy inefficiencies, scalability challenges, performance concerns, and cyber vulnerabilities. In this dissertation, an adaptive, two-stage, energy-efficient, edge-centric architecture is proposed to address these limitations. A novel, edge-centric Sense-and-Avoidance architecture framework is presented, and a corresponding prototype is developed using commercial hardware to validate the proposed architecture. Instead of a network-centric approach, processing is distributed at the logical edge of the sensors, and organized as Detection and Classification Subsystems. Classical machine vision algorithms are …

A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz

Electrical and Computer Engineering Publications

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …

Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir

Electrical and Computer Engineering Publications

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load …

‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette

Electrical and Computer Engineering Publications

Hand telerehabilitation currently has limitations for accurate and remote assessment of range of motion (ROM) in small finger joints. ‘DIGITS’ application utilises the front smartphone camera to measure finger ROM in a reliable and rapid assessment protocol. Our initial beta-phase testing examined the consistency of our software measurements to in-person goniometry. 6 to 9 degrees of difference existed between the smartphone application recorded data versus the in-person measurements. This range is within acceptable 7 to 9 degree tolerance for interrater goniometry measurements. The effect of environmental factors such as hand distance, lightings and hand orientation was evaluated. The intraclass correlation …

A 3d Point Cloud Deep Learning Approach Using Lidar To Identify Ancient Maya Archaeological Sites, Heather Richards-Rissetto, David Newton, Aziza Al Zadjali

Department of Anthropology: Faculty Publications

Airborne light detection and ranging (LIDAR) systems allow archaeologists to capture 3D data of anthropogenic landscapes with a level of precision that permits the identification of archaeological sites in difficult to reach and inaccessible regions. These benefits have come with a deluge of LIDAR data that requires significant and costly manual labor to interpret and analyze. In order to address this challenge, researchers have explored the use of state-of-the-art automated object recognition algorithms from the field of deep learning with success. This previous research, however, has been limited to the exploration of deep learning processes that work with only 2D …

Recommending Collaborations Using Link Prediction, Nikhil Chennupati

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Link prediction in the domain of scientific collaborative networks refers to exploring and determining whether a connection between two entities in an academic network may emerge in the future. This study aims to analyze the relevance of academic collaborations and identify the factors that drive co-author relationships in a heterogeneous bibliographic network. Using topological, semantic, and graph representation learning techniques, we measure the authors' similarities w.r.t their structural and publication data to identify the reasons that promote co-authorships. Experimental results show that the proposed approach successfully infer the co-author links by identifying authors with similar research interests. Such a system …

End-Effector Contact And Force Detection For Miniature Autonomous Robots Performing Lunar And Expeditionary Surgery, Eric T. Psota, Jay Carlson, Priscila Rodrigues Armijo, Laura Flores, Ka-Chun Siu, Dmitry Oleynikov, Shane Farritor, Nathan Bills

Department of Electrical and Computer Engineering: Faculty Publications

Introduction: The U.S. Space Force was stood up on December 20, 2019 as an independent branch under the Air Force consisting of about 16,000 active duty and civilian personnel focused singularly on space. In addition to the Space Force, the plans by NASA and private industry for exploration-class long-duration missions to the moon, near-earth asteroids, and Mars makes semi-independent medical capability in space a priority. Current practice for space-based medicine is limited and relies on a “life-raft” scenario for emergencies. Discussions by working groups on military space-based medicine include placing a Role III equivalent facility in a lunar surface station. …