Computer Sciences Commons^™

Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole

Computer Science and Engineering Theses - Archive

Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is …

Easemarks: Using Secondary Sketch Marks To Author And Communicate Motion Interpolation, Hadrien Nguyen

Computer Science and Engineering Theses - Archive

Motion interpolation is a process where an animator transforms jerky frame transitions into rich motions that communicate anticipation, urgency, hysteresis, and even calmness. Animators leverage mathematical functions known as easing curves to modify the rate at which in-betweens are added to keyframes. While effective, easing curves are tedious to tune since they fundamentally lack the ability to encode spatial information. Inspired by timing charts and other standards from traditional cel animation, we introduce a motion animation technique where secondary marks, which we term EaseMark (e.g., hatches, loops), are used to denote motion interpolation decisions. We synthesize an EaseMark Sketching Language …

Hemln-Sd: Substructure Discovery In Heterogeneous Multilayer Networks, Kiran Bolaj

Computer Science and Engineering Theses - Archive

Graph mining analyzes the real-world graphs for finding core substructures in chemical compounds (e.g., Benzene), identify the structure that occurs frequently in a given graph or forest. These identified structures are important as they reveal an inherent feature or property in the given graph or forest. Substructures represent interesting and repeating patterns found within an application, offering insights into hidden regularities. Therefore, the process of finding these interesting and frequent patterns in an unsupervised manner is known as substructure discovery. SUBDUE was the first main-memory algorithm developed for substructure discovery. Since then, for scalability, the algorithm has been extended to …

Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh

Computer Science and Engineering Theses - Archive

Substructure discovery is a process in data analysis and data mining that involves identifying and extracting meaningful patterns, structures, or components within a larger dataset. These substructures can be of various types, such as frequent patterns, motifs, or any other relevant features within the data. The growth of the internet and the proliferation of mobile devices have led to the generation of enormous amounts of data. Companies like Facebook and Twitter can generate large datasets from user interactions on their websites, such as connections between users and user generated content. Moreover, advances in processing power and storage capacity have made …

Feasibility Study Of Off-The-Shelf Components On A Split-Cycle Motor And Esc Testbed, Hayden C. Lotspeich

Computer Science and Engineering Theses - Archive

This project aims to create a testbed for split-cycle flapping wing systems that allows for testing of different motors and ESC protocols to find a suitable set for a flapping wing system. In order for a flapping-wing drone to be able to maneuver, it has to be able to flap its wings at different speeds when flapping forward and flapping backwards. The arching back-and-forth motion is what the output wing would be connected to, so this system is used to calculate the maximum split-cycle time ratio that can be achieved when set up with different motors and ESC protocols.

Design Of Single Precision Floating Point Unit (32-Bit Numbers) According To Ieee 754 Standard Using Verilog, And Creation Of An Education Model For Advanced Digital Logic And Design Courses, Kartikey Sharan

Computer Science and Engineering Theses - Archive

In today’s day and age of arithmetic, Floating Point Arithmetic is by far the most industry sanctioned way of approximating real number arithmetic for making numerical calculations on all computers used by industries on an everyday basis. In the year 1985, IEEE 754 standard was established that defined a single universal standard for all different arithmetic formats [1]. Before this, for a long period each computer had a different arithmetic format and size for bases, significand, and exponents. This format allowed industries all around the world to compute floating point arithmetic in a universal way and facilitated open communication between …

Enhancing Biomedical Imaging With Ai: Compression, Prediction, And Multi-Modal Integration For Clinical Advancement, Mohammad Sadegh Nasr

Computer Science and Engineering Dissertations - Archive

This dissertation delves into the enhancement of biomedical image analysis through the deployment of artificial intelligence methodologies, focusing on the transition from theoretical innovation to practical clinical utility. Spanning four cornerstone projects, the work encapsulates the development of predictive models for spatial transcriptomics, efficient image compression for cancer pathology slides, and critical evaluations of histopathology slide search engines. The first project employs Random Forest Regression and spatial point processes to forecast cell distribution patterns, thereby offering a novel perspective on gene expression in embryogenesis at a single-molecule resolution. The second venture introduces a Variational Autoencoder (VAE) that sets a new …

Parameterized Complexity Of Feature Selection For Categorical Data Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Kirill Simonov

Computer Science Faculty Publications and Presentations

We develop new algorithmic methods with provable guarantees for feature selection in regard to categorical data clustering. While feature selection is one of the most common approaches to reduce dimensionality in practice, most of the known feature selection methods are heuristics. We study the following mathematical model. We assume that there are some inadvertent (or undesirable) features of the input data that unnecessarily increase the cost of clustering. Consequently, we want to select a subset of the original features from the data such that there is a small-cost clustering on the selected features. More precisely, for given integers ℓ (the …

Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar

Computer Science Faculty Publications and Presentations

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To …

The Underrepresentation Of Black Females In Cybersecurity, Makendra Latrice Crosby

Cybersecurity Undergraduate Research Showcase

The significance of cybersecurity methods, strategies, and programs in protecting computers and electronic devices is crucial throughout the technological infrastructure. Despite the considerable growth in the cybersecurity field and its expansive workforce, there exists a notable underrepresentation, specifically among Black/African American females. This study examines the barriers hindering the inclusion of Black women in the cybersecurity workforce such as socioeconomic factors, limited educational access, biases, and workplace culture. The urgency of addressing these challenges calls for solutions such as education programs, mentorship initiatives, creating inclusive workplace environments, and promoting advocacy and increased awareness within the cybersecurity field. Additionally, this paper …

Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow

Cybersecurity Undergraduate Research Showcase

This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.

Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …

New Paths Of Attacks: Revealing The Adaptive Integration Of Artificial Intelligence In Evolving Cyber Threats Targeting Social Media Users And Their Data, Larry Teasley

Cybersecurity Undergraduate Research Showcase

The intersection between artificial intelligence tools and social media has opened doors to numerous opportunities and risks. This research delves into the escalating threat landscape in a society heavily dependent on social media. Despite the efforts by social media companies and cybersecurity professionals to mitigate cyber-attacks, the constant advancements of new technologies render social media platforms increasingly vulnerable. Malicious actors exploit generative AI to collect user data, enhancing cyber threats on social media. Notably, generative AI amplifies phishing attacks, disseminates false information, and propagates propaganda, posing substantial challenges to platform security. Ease access to large language models (LLMs) further complicates …

Lip(S) Service: A Socioethical Overview Of Social Media Platforms’ Censorship Policies Regarding Consensual Sexual Content, Sage Futrell

Cybersecurity Undergraduate Research Showcase

The regulation of sexual exploitation on social media is a pressing issue that has been addressed by government legislation. However, laws such as FOSTA-SESTA has inadvertently restricted consensual expressions of sexuality as well. In four social media case studies, this paper investigates the ways in which marginalized groups have been impacted by changing censorship guidelines on social media, and how content moderation methods can be inclusive of these groups. I emphasize the qualitative perspectives of sex workers and queer creators in these case studies, in addition to my own experiences as a content moderation and social media management intern for …

Privacy Concerns And Proposed Solutions With Iot In Wearable Technology, Hyacinth Abad

Cybersecurity Undergraduate Research Showcase

This paper examines the dynamic relationship between IoT cybersecurity and privacy concerns associated with wearable devices. IoT, with its exponential growth, presents both opportunities and challenges in terms of accessibility, integrity, availability, scalability, confidentiality, and interoperability. Cybersecurity concerns arise as diverse attack surfaces exploit vulnerabilities in IoT systems, necessitating robust defenses. In the field of wearable technology, these devices offer benefits like health data tracking and real-time communication. However, the adoption of these devices raises privacy concerns. The paper explores proposed solutions, including mechanisms for user-controlled data collection, the implementation of Virtual Trip Line (VTL) and virtual wall approaches, and …

A Review Of Threat Vectors To Dna Sequencing Pipelines, Tyler Rector

Cybersecurity Undergraduate Research Showcase

Bioinformatics is a steadily growing field that focuses on the intersection of biology with computer science. Tools and techniques developed within this field are quickly becoming fixtures in genomics, forensics, epidemiology, and bioengineering. The development and analysis of DNA sequencing and synthesis have enabled this significant rise in demand for bioinformatic tools. Notwithstanding, these bioinformatic tools have developed in a research context free of significant cybersecurity threats. With the significant growth of the field and the commercialization of genetic information, this is no longer the case. This paper examines the bioinformatic landscape through reviewing the biological and cybersecurity threats within …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Enhancing Indoors Robotic Traversability Estimation With Sensor Fusion, Christos Sevastopoulos

Computer Science and Engineering Dissertations - Archive

Generally speaking, traversability estimation illustrates the ability to navigate or move through a particular environment (indoors or outdoors). Indoor environments are governed by uncertainty and stochasticity arising from their complex structures encapsulating both static elements like furniture and walls, as well as entities such as moving humans. In our research, we underline the importance of blending semantic and spatial information for ensuring secure navigation for a mobile robot. We show that RGB sensors suffer from constrained situational awareness of the surroundings, thus highlighting the need to incorporate spatial and geometric data, which can collaborate synergistically to enhance overall perception and …

An Intelligent Multi-Modal Framework Towards Assessing Human Cognition, Ashish Jaiswal

Computer Science and Engineering Dissertations - Archive

Cognition is the mental process of acquiring knowledge and understanding through thought, experience, and senses. Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This thesis primarily focuses on building an experimental setup that induces cognitive fatigue (CF) …

External Debt Pass-Through To Inflation In Nigeria, Emmanuel A. Asue, James V. Ikyaator

CBN Journal of Applied Statistics (JAS)

This study examines external debt pass-through to inflation in Nigeria using annual data from 1981 to 2020 based on structural vector autoregressive (SVAR) model. The results reveal that an increase in external debt service leads to a significant depreciation of the exchange rate, which leads to a contemporaneous increase in inflation, while the direct response of inflation to external debt is statistically not significant. The impulse response confirms these results. The forecast error variance decomposition depicts that future values of official exchange rate depend on external debt, inflation and external debt service. The study recommends that the Nigerian government should …

Demystifying Artificial Intelligence (Ai) For Early Childhood And Elementary Education: A Case Study Of Perceptions Of Ai Of State Of Missouri Educators, Kathryn Arnone, James Hutson, Karen Woodruff

Faculty Scholarship

Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. However, with regard to education, adopting in disciplines remains limited largely to Computer Science and Information Technology in postsecondary education. Recent advances with technology are especially promising for their potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

How Chatgpt Can Be Used As A Defense Mechanism For Cyber Attacks, Michelle Ayaim

Cybersecurity Undergraduate Research Showcase

The powers of OpenAI's groundbreaking AI language model, ChatGPT, startled millions of users when it was released in November. But for many, the tool's ability to further accomplish the goals of evil actors swiftly replaced their initial excitement with significant concerns. ChatGPT gives malicious actors additional ways to possibly compromise sophisticated cybersecurity software. Leaders in a sector that is currently suffering from a 38% global spike in data breaches in 2022 must acknowledge the rising influence of AI and take appropriate action. Cybercriminals are writing more complex and focused business email compromise (BEC) and other phishing emails with the assistance …

Resilient, Sustainable, And Secure Systems Support For Ultra-Low-Power Computational Things, Nicole Tobias

All Dissertations

Wireless battery-free and energy-harvesting devices are expanding the reach and vision of the Internet of Things, where trillions of embedded computational things interconnect ubiquitously around us and inform many different aspects of our everyday lives. Designing these systems without batteries and interconnecting wires lowers maintenance, environmental, and economic costs while also extending device lifetime and deployment opportunities. Over the last decade, research on these ultra-low-power embedded sensors and systems has dramatically increased — enabling new and exciting prospects in many different scientific fields, from smart building and health monitoring applications to animal and activity tracking.

These systems are not without …

Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha

Computer Science and Engineering Dissertations - Archive

In several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation, MATLAB/Simulink has emerged as the de-facto standard tool for system modeling and analysis, model compilation into executable code, and code deployment onto embedded hardware. Within the context of cyber-physical system (CPS) development, it is imperative to both rigorously test the development tools, such as MathWorks’ Simulink, and understand modeling practices and model evolution. The existing body of work faces limitations primarily stemming from two factors: (1) contemporary testing methodologies often prove inefficient in identifying critical toolchain bugs due to a paucity of explicit toolchain specifications and (2) there …

Essence As Algorithm: Public Perceptions Of Ai-Powered Avatars Of Real People, James Hutson, Jay Ratican, Colleen Biri

Faculty Scholarship

This paper investigates the intersection of generative AI, Large Language Models (LLM), and robotics. Exemplified by systems like ChatGPT and technological marvels such as Ameca the Robot, the combination of technologies will allow humans to transcend the limitations of death. Through digital necromancy, a practice encompassing the technological resurrection of deceased individuals, the ability to not only passively see recordings of loved ones but to interact with them is made possible, leading to ethical and psychological considerations. Therefore, examining these trends extends into the motives underlying engagement with both incorporeal and corporeal reproductions of individuals, with reasons ranging from memory …

Evaluating Digital Creativity Support For Children: A Systematic Literature Review, Marte Hoff Hagen, Daniela Soares Cruzes, Letizia Jaccheri, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Creativity, the process of creating something new and valuable, benefits children by improving their skills and development, encouraging interaction and engagement, and enabling the generation and expression of novel ideas. In recent years, interactive digital tools have emerged to support the user’s creativity in the open-ended creation of new artifacts. However, the question of evaluating the creativity happening in the interplay between children, digital tools, and products is still open. This systematic literature review investigated the evaluations of digital creativity support tools for children and identified 81 peer-reviewed relevant articles from the last 10 years. This research contributes to practitioners …

Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings

Computer Science and Engineering Dissertations - Archive

In the field of computer vision, learning representations of images is an important task. This dissertation introduces deep generative sculpting models (DGSM), deep learning models that learn 3D representations of objects from 2D images. DGSMs use convolutional networks combined with a differentiable renderer to attempt to "sculpt" a base 3D mesh, such as a sphere, to faithfully represent an object in the scene, and render it to reconstruct the input image. The core methodology revolves around the encoding of the input image into latent variables. These variables are decoded into interpretable scene parameters, describing the object's translation, rotation, scale, texture, …

Assessing Open Source Tools For Enhanced Forensic Analysis Of Unmanned Aerial Vehicles (Uavs), Nura Shifa Hamed

Theses

The widespread applications of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has given rise to significant national security threats due to their illicit activities. Consequently, the domain of UAV forensics is rapidly evolving, presenting a substantial knowledge deficit among forensic experts. Open source tools provide accessible and affordable resources, making it easier for investigators to bridge this gap by gaining expertise in the use of these tools. This helps ensure that forensic professionals can keep up with the ever-changing UAV technology landscape. This thesis undertakes the mission of navigate the complex field of drone forensics and conducting a …

Deepaco: Neural-Enhanced Ant Systems For Combinatorial Optimization, Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li

Research Collection School Of Computing and Information Systems

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework leveraging deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization …

Explorelah: Personalised And Smart Trip Planner For Mobile Tourism, Aldy Gunawan, Siu Loon Hoe, Xun Yi Lim, Linh Chi Tran, Dang Viet Anh Nguyen

Research Collection School Of Computing and Information Systems

Various recommender systems for mobile tourism have been developed over the years. However, most of these recommender systems tend to overwhelm users with too much information and may not be personalised to user preferences. In this paper, we introduce ExploreLah, a personalised and smart trip planner for exploring Point of Interests (POIs) in Singapore. The user preferences are categorised into five groups: shopping, art & culture, outdoor activity, adventure, and nightlife. The problem is considered as the Team Orienteering Problem with Time Windows. The algorithm is developed to generate itineraries. Simulated experiments using test cases were performed to evaluate and …