Other Computer Engineering Commons^™

Deepfake It Til You Make It: How To Make A Short Film, Adam G. Lee

ELAIA

A recent development in the realm of computer technology is the deepfake. Deepfakes, which train a computer model to digitally superimpose one person’s face onto another body in a separate video, has its uses for good and for ill, with the unfortunate tendency to the latter. The vast majority of deepfakes are used for pornography, most commonly depicting female celebrities as the subjects. At the less notable level, it is also often used for revenge pornography. These aspects of deepfake technology are rarely discussed in mainstream media, which tends to focus on the less harmful uses, such as those for …

Text Summarization, Varun Gottam, Anusha Vunnam, Purna Sarovar Puvvada

Symposium of Student Scholars

The current era is known as the information era. Every day, millions of gigabytes of data are being transferred from one point to another. As the creation of data became easy, it became hard to keep track of the important points and the gist of data especially in areas such as research and news. To solve this conundrum, text summarization is introduced. This is a process of summarizing text from across different documents or large datasets such that it can be read and understood easily by both humans and machines.

Life During Wartime: Proactive Cybersecurity Is A Humanitarian Imperative, Stanley Mierzwa, Diane Rubino

Center for Cybersecurity

In brief:

• Humanitarian agencies responding to conflict face massive challenges in distributing aid. Cyberattacks add to that burden.
• This short overview, tailored for non-technical leaders, demystifies the process and equips clouds security experts to proactively champion cloud security at non-profits, and non-governmental organizations.

Proactive Cybersecurity is a Humanitarian Imperative | CSA (cloudsecurityalliance.org)

Multi-Perspective Analysis For Derivative Financial Product Prediction With Stacked Recurrent Neural Networks, Natural Language Processing And Large Language Model, Ethan Lo

Dissertations, Theses, and Capstone Projects

This study developed a multi-perspective, AI-powered model for predicting E-Mini S&P 500 Index Futures prices, tackling the challenging market dynamics of these derivative financial instruments. Leveraging FinBERT for analysis of Wall Street Journal data alongside technical indicators, trader positioning, and economic factors, my stacked recurrent neural network built with LSTMs and GRUs achieves significantly improved accuracy compared to single sub-models. Furthermore, ChatGPT generation of human-readable analysis reports demonstrates the feasibility of using large language models in financial analysis. This research pioneers the use of stacked RNNs and LLMs for multi-perspective financial analysis, offering a novel blueprint for automated prediction and …

The Role Of Artificial Intelligence In Determining The Criminal Fingerprint, Saeed Al Matrooshi

Journal of Police and Legal Sciences

The research aimed to identify the motives and justifications for the use of artificial intelligence in predicting crimes, to explain the challenges of artificial intelligence algorithms, the risks of bias and their ethical rules, and to highlight the role of artificial intelligence in identifying the criminal fingerprint during the detection of crimes. The research relied on the analytical approach, for the purpose of identifying the motives and justifications for the use of intelligence. Artificial intelligence in crime detection, explaining the challenges of artificial intelligence algorithms, their risks of bias, and ethical rules, and exploring how artificial intelligence technology can hopefully …

The Aim To Decentralize Economic Systems With Blockchains And Crypto, Mary Lacity

Arkansas Law Review

As an information systems (“IS”) professor, I wrote this Article for legal professionals new to blockchains and crypto. This target audience likely is most interested in crypto for its legal implications—depending on whether it functions as currencies, securities, commodities, or properties; however, legal professionals also need to understand crypto’s origin, how transactions work, and how they are governed.

Securing Edge Computing: A Hierarchical Iot Service Framework, Sajan Poudel, Nishar Miya, Rasib Khan

Posters-at-the-Capitol

Title: Securing Edge Computing: A Hierarchical IoT Service Framework

Authors: Nishar Miya, Sajan Poudel, Faculty Advisor: Rasib Khan, Ph.D.

Department: School of Computing and Analytics, College of Informatics, Northern Kentucky University

Abstract:

Edge computing, a paradigm shift in data processing, faces a critical challenge: ensuring security in a landscape marked by decentralization, distributed nodes, and a myriad of devices. These factors make traditional security measures inadequate, as they cannot effectively address the unique vulnerabilities of edge environments. Our research introduces a hierarchical framework that excels in securing IoT-based edge services against these inherent risks.

Our secure by design approach prioritizes …

Dp-Smote: Integrating Differential Privacy And Oversampling Technique To Preserve Privacy In Smart Homes, Amr Tarek Elsayed, Almohammady Sobhi Alsharkawy, Mohamed Sayed Farag, Shaban Ebrahim Abu Yusuf

Al-Azhar Bulletin of Science

Smart homes represent intelligent environments where interconnected devices gather information, enhancing users’ living experiences by ensuring comfort, safety, and efficient energy management. To enhance the quality of life, companies in the smart device industry collect user data, including activities, preferences, and power consumption. However, sharing such data necessitates privacy-preserving practices. This paper introduces a robust method for secure sharing of data to service providers, grounded in differential privacy (DP). This empowers smart home residents to contribute usage statistics while safeguarding their privacy. The approach incorporates the Synthetic Minority Oversampling technique (SMOTe) and seamlessly integrates Gaussian noise to generate synthetic data, …

Promise And Limitations Of Supervised Optimal Transport-Based Graph Summarization Via Information Theoretic Measures, Sepideh Neshatfar

Electronic Theses and Dissertations

Graph summarization is a fundamental problem in the field of data analysis, aiming to distill extensive graph datasets into more manageable, yet informative representations. The challenge lies in creating compressed graphs that faithfully retain crucial structural information for downstream tasks. A recent advancement in this domain introduces an optimal transport-based framework that enables the incorporation of a priori knowledge regarding the importance of nodes, edges, and attributes during the graph summarization process. However, the statistical properties of this innovative framework remain largely unexplored. This master's thesis embarks on a comprehensive exploration of the field of graph summarization, with a particular …

Turnstile File Transfer: A Unidirectional System For Medium-Security Isolated Clusters, Mark Monnin, Lori L. Sussman

Journal of Cybersecurity Education, Research and Practice

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Lung Lesion Segmentation Using Deep Learning Approaches, Sree Snigdha Tummala

Electronic Theses, Projects, and Dissertations

The amount of data generated in the medical imaging field, especially in a modern context, is growing significantly. As the amount of data grows, it's prudent to make use of automated techniques that can leverage datasets to solve problems that are error-prone or have inconsistent solutions.

Deep learning approaches have gained traction in medical imaging tasks due to their superior performance with larger datasets and ability to discern the intricate features of 3D volumes, a task inefficient if done manually. Specifically for the task of lung nodule segmentation, several different methods have been tried before such as region growing etc. …

Automated Medical Notes Labelling And Classification Using Machine Learning, Akhil Prabhakar Thota

Electronic Theses, Projects, and Dissertations

The amount of data generated in medical records, especially in a modern context, is growing significantly. As the amount of data grows, it is very useful to classify the data into relevant classes for further interventions. Different methods that are not automated are very time-consuming and require manual effort have been tried for this before.

Recently deep learning has been used for this task but due to the complexity of the dataset, specifically due to inter-class similarities in the dataset and specific terminology having different meanings in medical contexts has caused significant problems in having a definitive approach to medical …

Docai, Riley Badnin, Justin Brunings

Computer Science and Software Engineering

DocAI presents a user-friendly platform for recording, transcribing, summarizing, and classifying doctor-patient consultations. The application utilizes AssemblyAI for conversational transcription, and the user interface allows users to either live-record consultations or upload an existing MP3 file. The classification process, powered by 'ml-classify-text,' organizes the consultation transcription into SOAP (Subjective, Objective, Assessment, and Plan) format – a widely used method of documentation for healthcare providers. The result of this development is a simple yet effective interface that effectively plays the role of a medical scribe. However, the application is still facing challenges of inconsistent summarization from the AssemblyAI backend. Future work …

Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

Quiz Web Application, Dipti Rathod

Electronic Theses, Projects, and Dissertations

The Quiz web application is designed to facilitate the process of quiz creation and participation. This web application mainly consists of three roles: Admin, Instructor, and Student. Each role has specific features, functionalities, and permissions. With a user-friendly interface, the admin role can handle the departments, courses, and instructors. This web application also ensures smooth quiz management, allowing the instructors to schedule the upcoming quizzes, create the questions, and manage the students with ease. Student roles have features like taking quizzes and seeing their results. Additionally, this web application includes a significant feature to prevent cheating during online tests, ensuring …

Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa

Electronic Theses, Projects, and Dissertations

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …