Computer Engineering Commons^™

Sim-To-Real Reinforcement Learning Framework For Autonomous Aerial Leaf Sampling, Ashraful Islam

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

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS …

Leveraging Aruco Fiducial Marker System For Bridge Displacement Estimation Using Unmanned Aerial Vehicles, Mohamed Aly

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

The use of unmanned aerial vehicles (UAVs) in construction sites has been widely growing for surveying and inspection purposes. Their mobility and agility have enabled engineers to use UAVs in Structural Health Monitoring (SHM) applications to overcome the limitations of traditional approaches that require labor-intensive installation, extended time, and long-term maintenance. One of the critical applications of SHM is measuring bridge deflections during the bridge operation period. Due to the complex remote sites of bridges, remote sensing techniques, such as camera-equipped drones, can facilitate measuring bridge deflections. This work takes a step to build a pipeline using the state-of-the-art computer …

Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer

School of Cybersecurity Faculty Publications

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …

Modeling And Visualization Of Competing Escalation Dynamics: A Multilayer Multiagent Network Approach, Josh Allen

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

Recent advances in military technology, such as hypersonic missiles, which can travel at more than five times the speed of sound and descend quickly into the atmosphere, give world nuclear superpowers a new edge. These advances up the game for nuclear superpowers with an extremely rapid, intense burst of military striking capability to secure upfront gains before encountering potentially overwhelming military confrontation. However, this so-called fait accompli has not been systematically studied by the United States in the perspective of the escalation philosophies of nuclear power competitors, or the mathematical modeling and visualization of multi-modal escalation dynamics. This gap may …

Source Code Plagiarism Detection Using Jplag & Stack Overflow Data, Sudheer Yetthapu

Masters Theses & Specialist Projects

Advancements in computer technology and internet services have led to the availability of vast amounts of information like videos, articles, research papers, and code samples. Free online information will increase the possibility of plagiarism and collusion among students. People can commit plagiarism in both text and code [1], as tools used to detect plagiarism between texts and between codes are distinct. Traditionally plagiarism in code is detected using manual inspection, which is a tedious process and misses to compare code from previous submissions and external sources. To overcome this issue, systems that can automatically detect plagiarism in code were developed …

Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …

Enhancing Neural Text Detector Robustness With Μattacking And Rr-Training, Gongbo Liang, Jesus Guerrero, Fengbo Zheng, Izzat Alsmadi

Computer Science Faculty Publications

With advanced neural network techniques, language models can generate content that looks genuinely created by humans. Such advanced progress benefits society in numerous ways. However, it may also bring us threats that we have not seen before. A neural text detector is a classification model that separates machine-generated text from human-written ones. Unfortunately, a pretrained neural text detector may be vulnerable to adversarial attack, aiming to fool the detector into making wrong classification decisions. Through this work, we propose µAttacking, a mutation-based general framework that can be used to evaluate the robustness of neural text detectors systematically. Our experiments demonstrate …

Region-Specified Inverse Design Of Absorption And Scattering In Nanoparticles By Using Machine Learning, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced …

Study Of Lipophilicity And Adme Properties Of 1,9-Diazaphenothiazines With Anticancer Action, Beata Morak-Młodawska, Małgorzata Jeleń, Emilia Martula, Rafał Korlacki

Department of Electrical and Computer Engineering: Faculty Publications

Lipophilicity is one of the key properties of a potential drug that determines the solubility, the ability to penetrate through cell barriers, and transport to the molecular target. It affects pharmacokinetic processes such as adsorption, distribution, metabolism, excretion (ADME). The 10-substituted 1,9-diazaphenothiazines show promising if not impressive in vitro anticancer potential, which is associated with the activation of the mitochondrial apoptosis pathway connected with to induction BAX, forming a channel in MOMP and releasing cytochrome c for the activation of caspases 9 and 3. In this publication, the lipophilicity of previously obtained 1,9-diazaphenothiazines was determined theoretically using various computer programs …

Study Of Lipophilicity And Adme Properties Of 1,9-Diazaphenothiazines With Anticancer Action, Beata Morak-Młodawska, Małgorzata Jeleń, Emilia Martula, Rafał Korlacki

Department of Electrical and Computer Engineering: Faculty Publications

Lipophilicity is one of the key properties of a potential drug that determines the solubility, the ability to penetrate through cell barriers, and transport to the molecular target. It affects pharmacokinetic processes such as adsorption, distribution, metabolism, excretion (ADME). The 10-substituted 1,9-diazaphenothiazines show promising if not impressive in vitro anticancer potential, which is associated with the activation of the mitochondrial apoptosis pathway connected with to induction BAX, forming a channel in MOMP and releasing cytochrome c for the activation of caspases 9 and 3. In this publication, the lipophilicity of previously obtained 1,9-diazaphenothiazines was determined theoretically using various computer programs …

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 …

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 …

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

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 …

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: 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.

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: 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: 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.

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 …

Artificial Neural Network-Based Prediction Assessment Of Wire Electric Discharge Machining Parameters For Smart Manufacturing, Itagi Vijayakumar Manoj, Sannayellappa Narendranath, Peter Madindwa Mashinini, Hargovind Soni, Shanay Rab, Shadab Ahmad, Ahatsham Hayat

Department of Electrical and Computer Engineering: Faculty Publications

Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce “smart manufacturing,” which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5mm, have been cut on Nickelvac-HX. …

Deep Reinforcement Learning For Articulatory Synthesis In A Vowel-To-Vowel Imitation Task, Denis Shitov, Elena Pirogova, Tadeusz A. Wysocki, Margaret Lech

Department of Electrical and Computer Engineering: Faculty Publications

Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Low-Power Redundant-Transition-Free Tspc Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer, Zisong Wang, Peiyi Zhao, Tom Springer, Congyi Zhu, Jaccob Mau, Andrew Wells, Yinshui Xia, Lingli Wang

Engineering Faculty Articles and Research

In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% …

Cognitive Software Defined Networking And Network Function Virtualization And Applications, Sachin Sharma, Avishek Nag

Articles

The emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the Internet. Using SDN, network devices can be controlled from a centralized, programmable control plane that is decoupled from their data plane, whereas with NFV, network functions (such as network address translation, firewall, and intrusion detection) can be virtualized instead of being implemented on proprietary hardware. In addition, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key to automating network operations and enhancing customer service. Many of the challenges behind SDN and NFV are currently being investigated in several projects all over the world using …

A Highly Efficient Broadband Multi-Functional Metaplate, Azhar Javed Satti, Muhammad Ashar Naveed, Isma Javed, Nasir Mahmood, Muhammad Zubair, Muhammad Qasim Mehmood, Yehia Massoud

Department of Electrical and Computer Engineering: Faculty Publications

Due to the considerable potential of ultra-compact and highly integrated meta-optics, multi-functional metasurfaces have attracted great attention. The mergence of nanoimprinting and holography is one of the fascinating study areas for image display and information masking in meta-devices. However, existing methods rely on layering and enclosing, where many resonators combine various functions effectively at the expense of efficiency, design complication, and complex fabrication. To overcome these limitations, a novel technique for a tri-operational metasurface has been suggested by merging PB phase-based helicity-multiplexing and Malus's law of intensity modulation. To the best of our knowledge, this technique resolves the extreme-mapping issue …

Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha

Faculty Publications

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …

Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq

All Works

Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus …

Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …

Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton *, Richard Dill, Michael R. Grimaila, Douglas Hodson

Faculty Publications

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of …