Other Computer Sciences Commons^™

Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott

Electronic Theses and Dissertations

The Internet of Vehicles (IoV) holds immense potential for revolutionizing transporta- tion systems by facilitating seamless vehicle-to-vehicle and vehicle-to-infrastructure communication. However, challenges such as congestion, pollution, and security per- sist, particularly in rural areas with limited infrastructure. Existing centralized solu- tions are impractical in such environments due to latency and privacy concerns. To address these challenges, we propose a decentralized clustering algorithm enhanced with Federated Deep Reinforcement Learning (FDRL). Our approach enables low- latency communication, competitive packet delivery ratios, and cluster stability while preserving data privacy. Additionally, we introduce a trust-based security framework for IoV environments, integrating a central authority …

Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, Xuan Wang

Dissertations, Theses, and Capstone Projects

Contextual information has been widely used in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. However, existing approaches design specific contextual information mechanisms for different detection tasks.

In this research, we first present a comprehensive survey of context understanding in computer vision, with a taxonomy to describe context in different types and levels. Then we proposed MultiCLU, a new multi-stage context learning and utilization framework, …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam

Electronic Theses, Projects, and Dissertations

Accidents pose a significant risk to both individual and property safety, requiring effective detection and response systems. This work introduces an accident detection system using a convolutional neural network (CNN), which provides an impressive accuracy of 86.40%. Trained on diverse data sets of images and videos from various online sources, the model exhibits complex accident detection and classification and is known for its prowess in image classification and visualization.

CNN ensures better accident detection in various scenarios and road conditions. This example shows its adaptability to a real-world accident scenario and enhances its effectiveness in detecting early events. A key …

An Exploration Of Procedural Methods In Game Level Design, Hector Salinas

Computer Science and Computer Engineering Undergraduate Honors Theses

Video games offer players immersive experiences within intricately crafted worlds, and the integration of procedural methods in game level designs extends this potential by introducing dynamic, algorithmically generated content that could stand on par with handcrafted environments. This research highlights the potential to provide players with engaging experiences through procedural level generation, while potentially reducing development time for game developers.

Through a focused exploration on two-dimensional cave generation techniques, this paper aims to provide efficient solutions tailored to this specific environment. This exploration encompasses several procedural generation methods, including Midpoint Displacement, Random Walk, Cellular Automata, Perlin Worms, and Binary Space …

Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas

<strong> Theses and Dissertations </strong>

This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu

Undergraduate Honors Theses

Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …

Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa

Electronic Theses, Projects, and Dissertations

A novel technique for remote sensing image scene classification is employed using the Compact Vision Transformer (CVT) architecture. This model strengthens the power of deep learning and self-attention algorithms to significantly intensify the accuracy and efficiency of scene classification in remote sensing imagery. Through extensive training and evaluation of the RSSCNN7 dataset, our CVT-based model has achieved an impressive accuracy rate of 87.46% on the original dataset. This remarkable result underscores the prospect of CVT models in the domain of remote sensing and underscores their applicability in real-world scenarios. Our report furnishes an elaborate account of the model's architecture, training …

Exploring Binding Pockets In The Conformational States Of The Sars-Cov-2 Spike Trimers For The Screening Of Allosteric Inhibitors Using Molecular Simulations And Ensemble-Based Ligand Docking, Grace Gupta, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and …

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Monero: Powering Anonymous Digital Currency Transactions, Jake Braddy

Theses/Capstones/Creative Projects

Cryptocurrencies rely on a distributed public ledger (record of transactions) in order to perform their intended functions. However, the public’s ability to audit the network is both its greatest strength and greatest weakness: Anyone can see what address sent currency, and to whom the currency was sent. If cryptocurrency is ever going to take some of the responsibility of fiat currency, then there needs to be a certain level of confidentiality. Thus far, Monero has come out on top as the preferred currency for embodying the ideas of privacy and confidentiality. Through numerous cryptographic procedures, Monero is able to obfuscate …

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Poster Presentations

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Graph-Based Learning, Jason Gronn

Honors Projects

An educational approach to teaching students based on prerequisite knowledge they may or may not have is presented. This approach represents educational content in the form of a graph, where edges link each topic to the prerequisites of that topic. A proof-of-concept website is created based on this approach, where qualitative results are observed and a number of conclusions are drawn. Some of the findings are that, while it can prevent users from being confused by lacked prior knowledge, the users may instead be confused by the presentation of the graph structure. The work finds that the approach is workable, …

A Survey Of The Murray State University Csis Department Of Student And Instructor Attitudes In Relation To Earlier Introduction Of Version Control Systems, Gavin Johnson

Honors College Theses

Over the previous 20 years, the software development industry has overseen an evolution in application of Version Control Systems (VCS) from a Centralized Version Control System (CVCS) format to a Decentralized Version Control Format (DVCS). Examples of the former include Perforce and Subversion whilst the latter of the two include Github and BitBucket. As DVCS models allow software contributors to maintain their respective local repositories of relevant code bases, developers are able to work offline and maintain their work with relative fault tolerance. This contrasts to CVCS models, which require software contributors to be connected online to a main server. …

A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes

Honors Thesis

Frequency shift keying (FSK) is an old but powerful form of modulation that powered much of the early modems of the 1960’s, and the author felt inspired to make his own version of audio binary FSK modulation. He researched the general history and legacy of the Bell 103, a modem using FSK that defined telecommunication for the next few decades. Using research of the most common English characters of recent emails to determine which English characters should have the shortest bit length, a novel character encoding standard was created using variable bit rate. In addition, he has created a modulation …

Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq

Pharmacy Faculty Articles and Research

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols …

Factors Influencing The Perceptions Of Human-Computer Interaction Curriculum Developers In Higher Education Institutions During Curriculum Design And Delivery, Cynthia Augustine, Salah Kabanda

The African Journal of Information Systems

Computer science (CS) and information systems students seeking to work as software developers upon graduating are often required to create software that has a sound user experience (UX) and meets the needs of its users. This includes addressing unique user, context, and infrastructural requirements. This study sought to identify the factors that influence the perceptions of human-computer interaction (HCI) curriculum developers in higher education institutions (HEIs) in developing economies of Africa when it comes to curriculum design and delivery. A qualitative enquiry was conducted and consisted of fourteen interviews with HCI curriculum developers and UX practitioners in four African countries. …

Data Profits Vs. Privacy Rights: Ethical Concerns In Data Commerce, Amiah Armstrong

Cybersecurity Undergraduate Research Showcase

In today’s digital age, the collection and sale of customer data for advertising is gaining a growing number of ethical concerns. The act of amassing extensive datasets encompassing customer preferences, behaviors, and personal information raises questions of its true purpose. It is widely acknowledged that companies track and store their customer’s digital activities under the pretext of benefiting the customer, but at what cost? Are users aware of how much of their data is being collected? Do they understand the trade-off between personalized services and the potential invasion of their privacy? This paper aims to show the advantages and disadvantages …

Image De‑Photobombing Benchmark, Vatsa S. Patel, Kunal Agrawal, Samah Baraheem, Amira Yousif, Tam Nguyen

Computer Science Faculty Publications

Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index …

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …