Computer Engineering Commons^™

Domain Specific Analysis Of Privacy Practices And Concerns In The Mobile Application Market, Fahimeh Ebrahimi Meymand

LSU Doctoral Dissertations

Mobile applications (apps) constantly demand access to sensitive user information in exchange for more personalized services. These-mostly unjustified-data collection tactics have raised major privacy concerns among mobile app users. Existing research on mobile app privacy aims to identify these concerns, expose apps with malicious data collection practices, assess the quality of apps' privacy policies, and propose automated solutions for privacy leak detection and prevention. However, existing solutions are generic, frequently missing the contextual characteristics of different application domains. To address these limitations, in this dissertation, we study privacy in the app store at a domain level. Our objective is to …

Leveraging Artificial Intelligence And Machine Learning For Enhanced Cybersecurity: A Proposal To Defeat Malware, Emmanuel Boateng

Cybersecurity Undergraduate Research Showcase

Cybersecurity is very crucial in the digital age in order to safeguard the availability, confidentiality, and integrity of data and systems. Mitigation techniques used in the industry include Multi-factor Authentication (MFA), Incident Response Planning (IRP), Security Information and Event Management (SIEM), and Signature-based and Heuristic Detection.

MFA is employed as an additional layer of protection in several sectors to help prevent unauthorized access to sensitive data. IRP is a plan in place to address cybersecurity problems efficiently and expeditiously. SIEM offers real-time analysis and alerts the system of threats and vulnerabilities. Heuristic-based detection relies on detecting anomalies when it comes …

Blockchain Accelerators And An Application On Covid-19 Contact Tracing, Tao Lu

LSU Doctoral Dissertations

Blockchain technology has been an emerging technology in recent years. Its nature of decentralization and anonymity enables many applications to be built in a trustless environment but can still be validated and agreed upon by all the participants. Ever since the invention of blockchain technology, its concepts and forms are explored extensively, and it has shown the potential to be used in many industries. However, the low efficiency and the poor performance have prevented the technology to be adopted in practical largescale usage. In this dissertation, we present three major works around blockchain technology. The first one is BPU, an …

Assessing The Ability Of Arduino-Based Sensor Systems To Monitor Changes In Water Quality, Josiah Hacker

Honors College Theses

Access to safe water is vital to public health. While developed countries like the United States are recognized as having reliable and safe water, many small water utilities struggle with supplying consistent water quality. Technicians of these utilities will periodically test water samples from the influent and throughout the distribution system. However, this laborious and costly process does not capture sudden changes in influent water quality due to environmental conditions or pipes breaking in the distribution system. Here I show how an Arduino-based sensor can be used as a real-time, low-cost monitor of water quality parameters. Specifically, I developed a …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

The Fashion Visual Search Using Deep Learning Approach, Smita V. Bhoir, Sunita R. Patil

Library Philosophy and Practice (e-journal)

In recent years, the World Wide Web (WWW) has established itself as a popular source of information. Using an effective approach to investigate the vast amount of information available on the internet is essential if we are to make the most of the resources available. Visual data cannot be indexed using text-based indexing algorithms because it is significantly larger and more complex than text. Content-Based Image Retrieval, as a result, has gained widespread attention among the scientific community (CBIR). Input into a CBIR system that is dependent on visible features of the user's input image at a low level is …

Beirut Arab University - Faculty Of Engineering - Newsletter Issue 0, Faculty Of Engineering, Beirut Arab University

Engineering Newsletters

No abstract provided.

Reactive Particle Swarm Control Architecture And Application For Scalar Field Adaptive Navigation, Shae Taylor Hart

Engineering Ph.D. Theses

Adaptive navigation is a subcategory of navigation techniques that attempts to identify goal locations that satisfy specific criteria in an unknown area. In 2D scalar field adaptive navigation (SFAN), primitives navigate to or along features of interest in an unknown, possibly time-varying, planar scalar field. Features include extrema, contours, and fronts. This work solves the 2D SFAN problem using swarm robotic techniques. Robotic swarms are a subset of multi-robot systems that use decentralized control of simple interchangeable robots to perform collective actions. A subgroup of swarms is the Reactive Particle Swarm (RPS), characterized based on its simplicity, reactivity to its …

Comparison Of P4 Programmable Software Switches As Wifi Access Points, Kaustubh Pimparkar

Computer Science and Engineering Master's Theses

WiFi wireless access points must adapt to complex network operations as connected devices and their bandwidth requirements continue to rise. The use of software switches supporting Programming Protocol-independent Packet Processors (P4) in wireless access points has drawn significant attention due to their versatility, flexibility, and potential to overcome the limitations of conventional network designs. T4P4S and BmV2 are the two most widely used P4 programmable software switches, and comparing the performance of these switches allows network operators to select the most suitable switch for specific use cases, such as minimizing latency or maximizing throughput while reducing resource consumption of the …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

[Archive] Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

A Semantic Web Approach To Fault Tolerant Autonomous Manufacturing, Fadi El Kalach

Theses and Dissertations

The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future Factories are required to be agile, allowing for more customized production, and resistant to disturbances. Such production lines would have the capability to reallocate resources as needed and eliminate downtime while keeping up with market demands. These systems must be capable of complex decision making based on different parameters such as machine status, sensory data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low level decision making on the machine level without utilizing the generated data …

A Survey Of Text Representation And Embedding Techniques In Nlp, Rajvardhan Patil, Sorio Boit, Venkat Gudivada, Jagadeesh Nandigam

Open Access Publishing Support Funded Articles

Natural Language Processing (NLP) is a research field where a language in consideration is processed to understand its syntactic, semantic, and sentimental aspects. The advancement in the NLP area has helped solve problems in the domains such as Neural Machine Translation, Name Entity Recognition, Sentiment Analysis, and Chatbots, to name a few. The topic of NLP broadly consists of two main parts: the representation of the input text (raw data) into numerical format (vectors or matrix) and the design of models for processing the numerical data. This paper focuses on the former part and surveys how the NLP field has …

Nautilus Rov Robot Manipulator, Dana Stefanides, Jenny Huynh, Andrew Stewart, Steven Reimer, Andrew Nguyen, Rebecca Walters, Matt Hayes

Interdisciplinary Design Senior Theses

Global warming and climate change are prevalent issues in today’s society. As a result, research in the ocean, our world’s biggest ecosystem, is imperative in efforts to protect the environment. Santa Clara University’s Robotic Systems Lab contributes to this field through work and developments on remotely operated vehicles (ROVs). An existing ROV system called Nautilus consists of a robot arm, end effector, and storage system in order to collect various types of sediments at a depth of 300 feet. However, the previous system does not meet that requirement. In direct collaboration with researchers within the Monterey Bay Aquarium Research Institute, …

Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith

Theses and Dissertations

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …

Run Toward The Incident: Collaboration Between Academia And Law Enforcement For Cybersecurity, Center For Cybersecurity, Stanley Mierzwa

Center for Cybersecurity

Collaboration and partnership between academia and law enforcement can bring about positive contributions for future research and activities in cybersecurity.

A Study Of Issues And Mitigations On Ddos And Medical Iot Devices, Jing-Chiou Liou, Robin Singh

Center for Cybersecurity

The Internet of Things (IoT) devices are being used heavily as part of our everyday routines. Through improved communication and automated procedures, its popularity has assisted users in raising the quality of work. These devices are used in healthcare in order to better collect the patient's data for their treatment. .

Research Days Poster: Cyberbullying Detection Utilizing Artificial Intelligence And Machine Learning, Annaliese Watson, Aysha Gardner, Dahana Moz Ruiz

Center for Cybersecurity

Technology is a tool that can be used to gain knowledge and for advancements in areas like medicine, machinery, and everyday tasks. It can be used to connect with friends, work from home, and to improve quality of life. But some social media users can use it to hurt others. Cyberbullying is a major issue that has been steadily growing over the past few years. Cyberbullying has also steadily increased the rates of stress, anxiety, depression, violent behavior, low self-esteem and may cause suicide. Cyberbullying is an ongoing problem for social media users, and it is urgent that a solution …

Research Days Poster: Security Operation Center, Jaineel A. Shah, Jing-Chiou Liou

Center for Cybersecurity

A Security Operations Center (SOC) is an organizational framework for cybersecurity, staffed by cybersecurity professionals who monitor an organization's security, analyze potential or current breaches, and respond accordingly. The SOC's goal is to diagnose, evaluate, and respond to cybersecurity events using technology solutions and established procedures. SOCs mainly operate 24/7, with security analysts monitoring environmental data for emerging threats and responding as needed. The SOC manages and enhances an organization's overall security posture.

Research Days Poster: A Study On Ransomware And Possible Mitigations, Robin Singh, Jing-Chiou Liou

Center for Cybersecurity

Ransomware is a quite violent attack that has been persistent throughout the industry for so many years. This malware doesn’t only affect the regular computer users but it has been targeting the big organizations and businesses also. Ransomware (ransom software) is a subset of malware designed to restrict access to a system or data until a requested ransom amount from the attacker is satisfied [2].In this poster, we will go over our findings on ransomware spreads, its actions, exploited vulnerabilities and few mitigation to reduce the impact of infection.

Research Days Poster: An Analysis Of Sql Injection Techniques, Uko Ebreso, Pankati Patel, Jing-Chiou Liou

Center for Cybersecurity

Storing information in a database allows web applications to operate with users who have their information stored online. ● The retrieval of information from the database is done using Structured Query Language (SQL) - a language used by the database that allows for data manipulation. ● SQL Injection (SQLIi) is an attack on a susceptible system using SQl which results in a loss of confidentiality, authentication, authorization and integrity of the software/system.

Data-Driven Modeling Of Student Performance In The Time Of Distance Learning, Iman Saad Megdadi

Theses

One of the important aspects that all academic institutions work towards improving is Student Performance. It is obviously the primary indicator of success or failure of institutions. Student performance predictions are vital to instructors and educational decision makers to help, across all levels, tailor learning according to the students’ needs. Therefore, it is essential for Higher Education Institutions to predict student performance in distance learning which has been, and remains, the primary method of learning in some countries due to Corona Virus pandemic. For this reason, this research is going to predetermine a fitting definition of student performance in time …

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter

Electronic Theses and Dissertations

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant …

Enhancing Cyberspace Monitoring In The United States Aviation Industry: A Multi-Layered Approach For Addressing Emerging Threats, Matthew Janson

Doctoral Dissertations and Master's Theses

This research project examined the cyberspace domain in the United States (U.S.) aviation industry from many different angles. The research involved learning about the U.S. aviation cyberspace environment, the landscape of cyber threats, new technologies like 5G and smart airports, cybersecurity frameworks and best practices, and the use of aviation cyberspace monitoring capabilities. The research looked at how vulnerable the aviation industry is from cyber-attacks, analyzed the possible effects of cyber-attacks on the industry, and suggests ways to improve the industry's cybersecurity posture. The project's main goal was to protect against possible cyber-attacks and make sure that the aviation industry …

The Exigency And How To Improve And Implement International Humanitarian Legislations More Advantageously In Times Of Both Cyber-Warfare And Cyberspace, Shawn J. Lalman

Doctoral Dissertations and Master's Theses

This study provides a synopsis of the following topics: the prospective limiters levied on cyber-warfare by present–day international legislation; significant complexities and contentions brought up in the rendering & utilization of International Humanitarian Legislation against cyber-warfare; feasible repercussions of cyber-warfare on humanitarian causes. It is also to be contended and outlined in this research study that non–state actors can be held accountable for breaches of international humanitarian legislation committed using cyber–ordnance if sufficient resources and skill are made available. It details the factors that prosecutors and investigators must take into account when organizing investigations into major breaches of humanitarian legislation …

Assessing High Dynamic Range Imagery Performance For Object Detection In Maritime Environments, Erasmo Landaeta

Doctoral Dissertations and Master's Theses

The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. …

Neural Network Fusion Of Multi-Modal Sensor Data For Autonomous Surface Vessels, David J. Thompson

Doctoral Dissertations and Master's Theses

Autonomous surface vessels (ASV) can potentially improve the safety of vessels traditionally operated by humans. Despite advancements in autonomous on-road vehicles, many of these advancements have yet to be realized for ASVs. This is primarily due to lacking ASV sensing platforms and public datasets for ASV-based perception research. To that end, this dissertation demonstrates the design of a synchronized multi-modal sensing platform for ASVs utilizing GPS/INS, LiDAR, LWIR cameras, HDR camera, and high-resolution cameras. The sensing platform is designed to maximize the overlap of sensors for multi-modal research and provides accurate intrinsic and extrinsic calibration between each sensor. Furthermore, the …

Para Cima Y Pa’ Abajo: Building Bridges Between Hci Research In Latin America And In The Global North, Pedro Reynolds-Cuéllar, Marisol Wong-Villacres, Karla A. Badillo-Urquiola, Mayra Donaji Barrera-Machuca, Franceli L. Cibrian, Marianela Ciolfi Felice, Carolina Fuentes, Laura Sanely Gaytán-Lugo, Vivian Genaro Motti, Monica Perusquía-Hernández, Oscar A. Lemus

Engineering Faculty Articles and Research

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced …

Nftdisk: Visual Detection Of Wash Trading In Nft Markets, Xiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu, Min Zhu

Research Collection School Of Computing and Information Systems

With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with …

Secure Reconfigurable Computing Paradigms For The Next Generation Of Artificial Intelligence And Machine Learning Applications, Brooks Olney

USF Tampa Graduate Theses and Dissertations

The fields of artificial intelligence (AI) and machine learning (ML) have been popular tools for data analysis at the edge, particularly through complex deep and convolutional neural networks (DNNs/CNNs), which can learn to parameterize a function given a labeled dataset. Indeed, these technologies have enabled significant progress across a wide range of fields and are becoming ubiquitous. However, training the best model for an application and subsequently using them to evaluate data in real-time requires an immense amount of computational power. Typically, ``smart" sensors at the edge rely on the cloud to accelerate this computation due to power and compute …

A Human-In-The-Loop Robot Grasping System With Grasp Quality Refinement, Tian Tan

USF Tampa Graduate Theses and Dissertations

The goal of this dissertation is to develop a grasping system for assistive robots that can help people with disabilities and the elderly to perform tasks of daily living. In developing this robot grasping system, we maximize its reliability, accuracy, and autonomy. High reliability and accuracy are required for robots to perform tasks around human users and to safely interact with objects that might be fragile or have contents that could spill. High autonomy is desired as users with disabilities are usually not dexterous enough to directly operate the robot. In this dissertation, a human-in-the-loop (HitL) robot grasping system is …