Computer Engineering Commons^™

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.

Using Satellite Images Datasets For Road Intersection Detection In Route Planning, Fatmaelzahraa Eltaher Ph.D, Susan Mckeever, Ayman Taha, Jane Courtney

Datasets

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the …

On The Dimensionality And Utility Of Convolutional Autoencoder’S Latent Space Trained With Topology-Preserving Spectral Eeg Head-Maps, Arjun Vinayak Chikkankod, Luca Longo

Articles

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent Component Analysis (ICA) require human intervention to remove noise and artifacts. Autoencoders have automatized artifact detection and removal by representing inputs in a lower dimensional latent space. However, little research is devoted to understanding the minimum dimension of such latent space that allows meaningful input reconstruction. Person-specific convolutional autoencoders are designed by manipulating the size of their latent space. A sliding window technique with overlapping is employed …

Development And Implementation Of Robot Operating Systems For Undergraduates, Chelsey Spitzner

Dissertations, Master's Theses and Master's Reports

The purpose of this project was to create an undergraduate junior lab to teach students about Robotic Operating System (ROS). The labs were designed to highlight the usefulness of ROS and the process used. Designing algorithms, how to send/receive messages, and the hierarchy of how nodes work with each other are emphasized. Taking packages that are open-source then modifying them is also emphasized. This is done so that students can transfer their knowledge from this course to other robot operating systems.

Off Road Autonomous Vehicle Modeling And Repeatability Using Real World Telemetry Via Simulation, Matthew Paul Spencer

Dissertations, Master's Theses and Master's Reports

One approach to autonomous control of high mobility ground vehicle platforms operating on challenging terrain is with the use of predictive simulation. Using a simulated or virtual world, an autonomous system can optimize use of its control systems by predicting interaction between the vehicle and ground as well as the vehicle actuator state. Such a simulation allows the platform to assess multiple possible scenarios before attempting to execute a path. Physically realistic simulations covering all of these domains are currently computationally expensive, and are unable to provide fast execution times when assessing each individual scenario due to the use of …

Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei

Dissertations, Master's Theses and Master's Reports

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.

Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …

C2 Microservices Api: Ch4rl3sch4l3m4gn3, Thai H. Nguyễn

School of Computer Science & Engineering Undergraduate Publications

In the 21^st century, cyber-based attackers such as advance persistent threats are leveraging bots in the form of botnets to conduct a plethora of cyber-attacks. While there are several social engineering techniques used to get targets to unknowingly download these bots, it is the command-and-control techniques advance persistent threats use to control their bots that is of critical interest to the author. In this research paper, the author aims to develop a command-and-control microservice application programming interface infrastructure to facilitate botnet command-and-control attack simulations. To achieve this the author will develop a simple bot skeletal framework, utilize the latest …

Improving Network Policy Enforcement Using Natural Language Processing And Programmable Networks, Pinyi Shi

Theses and Dissertations--Computer Science

Computer networks are becoming more complex and challenging to operate, manage, and protect. As a result, Network policies that define how network operators should manage the network are becoming more complex and nuanced. Unfortunately, network policies are often an undervalued part of network design, leaving network operators to guess at the intent of policies that are written and fill in the gaps where policies don’t exist. Organizations typically designate Policy Committees to write down the network policies in the policy documents using high-level natural languages. The policy documents describe both the acceptable and unacceptable uses of the network. Network operators …

Design, Development And Benchmarking Of Machine Learning Algorithms In Biomedical Applications, Qi Sun

Theses and Dissertations--Computer Science

Machine learning algorithms are becoming the most effective methods for knowledge discovery from high dimensional datasets. Machine learning seeks to construct predictive models through the analysis of large-scale heterogeneous data. While machine learning has been widely used in many domains including computer vision, natural language processing, product recommendation, its application in biomedical science for clinical diagnosis and treatment is only emerging. However, the wealthy amount of data in the biomedical domain offers not only challenges but also opportunities for machine learning. In this dissertation, we focus on three biomedical applications from vastly different domains to understand the opportunities and challenges …

Protocols And Architecture For Privacy-Preserving Authentication And Secure Message Dissemination In Vehicular Ad Hoc Networks, Shafika Showkat Moni

Theses and Dissertations--Computer Science

The rapid development in the automotive industry and wireless communication technologies have enhanced the popularity of Vehicular ad hoc networks (VANETs). Today, the automobile industry is developing sophisticated sensors that can provide a wide range of assistive features, including accident avoidance, automatic lane tracking, semi-autonomous driving, suggested lane changes, and more. VANETs can provide drivers a safer and more comfortable driving experience, as well as many other useful services by leveraging such technological advancements. Even though this networking technology enables smart and autonomous driving, it also introduces a plethora of attack vectors. However, the main issues to be sorted out …

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 …

Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent

Dissertations

Automatic classification and segmentation of land use land cover(LULC) is extremely important for understanding the relationship between humans and nature. Human pressures on the environment have drastically accelerated in the last decades, risking biodiversity and ecosystem services. Remote sensing via satellite imagery is an excellent tool to study LULC. Research has shown that deep learning encoder-decoder architectures have achieved worthy results in the area of LULC, however the application of an ensemble approach has not been well quantified. Studies have shown it to be useful in the area of medical imaging. Ensembling by pooling together predictions to produce better predictions …

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

An Investigation Of The Relationship Between Subjective Mental Workload And Objective Indicators Of User Activity, Greg Byrne

Dissertations

Whilst the concept of physical workload is intuitively understood and readily applicable in system design, the same cannot be said of mental workload (MWL), despite its importance in our increasingly technological society. Despite its origin in the mid 20th century, the very concept of ”mental workload” is still a topic of debate in the literature, although it can be loosely defined as “the amount of mental work necessary for a person to complete a task” (Miller, 1956; Longo, 2014). Several methods have been utilized to measure of MWL, including physiological methods such as neuro-imagery, performance-based metrics, and subjective measures via …

Evaluating The Performance Impact Of Fine-Tuning Optimization Strategies On Pre-Trained Distilbert Models Towards Hate Speech Detection In Social Media, Aidan Mcgovern

Dissertations

Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation. Hate speech has gravitated towards social media as its primary platform, and its propagation represents profound risks to both the mental well-being and physical safety of targeted groups. Countermeasures to moderate hate speech face challenges due to the volumes of data generated in social media, leading companies, and the research community to evaluate methods to automate its detection. The emergence of BERT and other pre-trained transformer-based models for transfer learning in the …

Examining The Effects Of Disabilities On Vr Usage And Accessibility Issues For Persons With Disabilities, Sean Williams

Dissertations

Virtual Reality (VR) is an emerging technology that’s popularity has been increasing at a yearly rate. Despite this, concerns about the accessibility of VR devices are ever-growing as many users struggle to use the technology, especially users with disabilities. This study analyses how different types of disabilities affect how often a user uses VR and any associated re-occurring difficulties that are related to specific types of disability. To do this, a previous survey regarding VR accessibility run by Disability Visibility Project and ILMxLAB is examined. In this survey, 79 participants who identify as having a disability answered questions related to …

Exploring 3d Data Reuse And Repurposing Through Procedural Modeling, Rachel Opitz, Heather Richards-Rissetto, Karin Dalziel, Jessica Dussault, Greg Tunink

Department of Anthropology: Faculty Publications

Most contemporary 3D data used in archaeological research and heritage management have been created through ‘reality capture,’ the recording of the physical features of extant archaeological objects, structures, and landscapes using technologies such as laser scanning and photogrammetry (Garstki 2020, ch.2; Magnani et al. 2020). A smaller quantity of data are generated by Computer Aided Design (CAD) and Building Information Modeling (BIM) projects, and even fewer data are generated through procedural modeling, the rapid prototyping of multi-component threedimensional (3D) models from a set of rules (Figure 8.1.). It is unsurprising therefore that in archaeology and heritage, efforts around digital 3D …

A Reynolds Number Based Sampling Technique For 3-D Vector Fields In Computational Fluid Dynamic Environments, Trent James Schweitzer

Graduate Student Theses, Dissertations, & Professional Papers

Effective visualization of unsteady, time-dependent vector fields in a virtual environment is not a trivial task. This is due to the fact that most visualization techniques require the user to have a prior understanding of how the vector field will behave to set the parameters used to create the visualization. In this thesis we will take air flow data from a computational fluid dynamic simulations to calculate the amount of turbulence (represented as Reynolds numbers) to identify regions of interest. We then calculate wind pathlines that will intersect with these points sampled from these regions. We address the issue of …