Computer Engineering Commons^™

Human Characteristics Impact On Strategic Decisions In A Human-In-The-Loop Simulation, Andrew J. Collins, Shieda Etemadidavan

Engineering Management & Systems Engineering Faculty Publications

In this paper, a hybrid simulation model of the agent-based model and cooperative game theory is used in a human-in-the-loop experiment to study the effect of human demographic characteristics in situations where they make strategic coalition decisions. Agent-based modeling (ABM) is a computational method that can reveal emergent phenomenon from interactions between agents in an environment. It has been suggested in organizational psychology that ABM could model human behavior more holistically than other modeling methods. Cooperative game theory is a method that models strategic coalitions formation. Three characteristics (age, education, and gender) were considered in the experiment to see if …

Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases, Adrian Gheorghe, Farinaz Sabz Ali Pour, Unal Tatar, Omer Faruk Keskin

Engineering Management & Systems Engineering Faculty Publications

In the modern acquisition, it is unrealistic to consider single entities as producing and delivering a product independently. Acquisitions usually take place through supply networks. Resiliency, efficiency, and effectiveness of supply networks directly contribute to the acquisition system's resiliency, efficiency, and effectiveness. All the involved firms form a part of a supply network essential to producing the product or service. The decision-makers have to look for new methodologies for supply chain management. Blockchain technology introduces new methods of decentralization and delegation of services, which can transform supply chains and result in a more resilient, efficient, and effective supply chain. This …

Analog & Digital Remote Synthesizer, Adam Brunner, Andrew Cihon-Scott, Scott Grisso, Linus Wright

Williams Honors College, Honors Research Projects

The purpose of this project is to develop and design an analog synthesizer musical instrument that integrates embedded digital hardware into the design to enable control from a remote source. The use of digital hardware enables the potential for a wide range of convenient features such as sound profile saving and loading, output recording functionality, and the ability to accept digital input from another musical instrument utilizing the Musical Instrument Digital Interface (MIDI). In addition to the synthesizer itself, this project also includes the design of a companion application that can be hosted on a wide variety of consumer computing …

On The (Im)Practicality Of Adversarial Perturbation For Image Privacy, Arezoo Rajabi, Rakesh B. Bobba, Mike Rosulek, Charles Wright, Wu-Chi Feng

Computer Science Faculty Publications and Presentations

Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy.

Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation based approaches as a privacy defense against automated face recognition. Specifically, we first …

Fpga-Augmented Secure Crash-Consistent Non-Volatile Memory, Yu Zou

Electronic Theses and Dissertations, 2020-2023

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this thesis, we summarize and expand upon two of our innovative works, ARES and HERMES, to design …

On The Usage And Vulnerabilities Of Api Systems, Conner D. Yu

Cybersecurity Undergraduate Research Showcase

To some, Application Programming Interface (API) is one of many buzzwords that seem to be blanketed in obscurity because not many people are overly familiar with this term. This obscurity is unfortunate, as APIs play a crucial role in today’s modern infrastructure by serving as one of the most fundamental communication methods for web services. Many businesses use APIs in some capacity, but one often overlooked aspect is cybersecurity. This aspect is most evident in the 2018 misuse case by Facebook, which led to the leakage of 50 million users’ records.1 During the 2018 Facebook data breach incident, threat actors …

Appointment Scheduling System, Guzaloi Noori

Undergraduate Honors Theses

Appointment Scheduling System designed for universities so that students can schedule appointments with their university’s faculty and staff regarding academic, employment, immigration, or personal issues. The scheduling system has the ability of being embedded to the Computer Science Capstone project iAcademic, which I took part in as a front-end developer.

Improving Performance And Flexibility Of Fabric-Attached Memory Systems, Vamsee Reddy Kommareddy

Electronic Theses and Dissertations, 2020-2023

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage …

Are We In The Digital Dark Times? How The Philosophy Of Hannah Arendt Can Illuminate Some Of The Ethical Dilemmas Posed By Modern Digital Technologies, Damian Gordon, Anna Becevel

Conference Papers

Philosophers are not generally credited with being clairvoyant, and yet because they recognise, record and reflect on trends in their society, their observations can often appear prescient. In the field of the ethics of technology, there is, perhaps, no philosopher whose perspective on these issues is worth examining in detail more than that of Hannah Arendt, who can offer real perspective on the challenges we are facing with technologies in the twenty-first century. Arendt, a thinker of Jewish-German origin, student of Martin Heidegger and Karl Jaspers, encountered her life turning point when she was forced into becoming a refugee as …

A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar

Turkish Journal of Electrical Engineering and Computer Sciences

The present study aims to introduce a robust model for distribution network expansion planning considering system uncertainties. The proposed method determines optimal size and placement of distributed generation resources, as well as installation and reinforcement of feeders and substations. This model is designed to minimize cost and to determine the best time for the installation of equipment in the expansion planning. In the proposed expansion planning, the fuzzy logic theory is employed to model uncertainties of loads and energy price. Also, since the proposed model is a nonlinear and nonconvex optimization problem, a tri-stage algorithm is developed to solve it. …

Utilizing Resonant Scattering Signal Characteristics Via Deep Learning For Improvedclassification Of Complex Targets, Tuğçe Toprak, Mustafa Alper Selver, Mustafa Seçmen, Emi̇ne Yeşi̇m Zoral

Turkish Journal of Electrical Engineering and Computer Sciences

Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and …

The Effect Of Demand Response Control On Stability Delay Margins Of Loadfrequency Control Systems With Communication Time-Delays, Deni̇z Kati̇poğlu, Şahi̇n Sönmez, Saffet Ayasun, Ausnain Naveed

Turkish Journal of Electrical Engineering and Computer Sciences

This paper studies the effect of dynamic demand response (DR) control on stability delay margins of load frequency control (LFC) systems including communication time-delays. A DR control loop is included in each control area, called as LFC-DR system and Rekasius substitution is utilized to identify stability margins for various proportionalintegral (PI) gains and participation ratios of the secondary and DR control loops. The purpose of Rekasius substitution technique is to obtain purely complex roots on the imaginary axis of the time-delayed LFC-DR system. This substitution first converts the characteristic equation of the LFC-DR system including delay-dependent exponential terms into an …

An Improved Version Of Multi-View K-Nearest Neighbors (Mvknn) For Multipleview Learning, Eli̇fe Öztürk Kiyak, Derya Bi̇rant, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved …

Classification Of Neonatal Jaundice In Mobile Application With Noninvasive Imageprocessing Methods, Firat Hardalaç, Mustafa Aydin, Uğurhan Kutbay, Kubi̇lay Ayturan, Anil Akyel, Ati̇ka Çağlar, Bo Hai̇, Fati̇h Mert

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims a mobile support system to aid health care professionals in hospitals or in regions far away from hospitals to utilize noninvasive image processing methods for classification of neonatal jaundice. A considerably low processing cost is aimed to be attained by developing an algorithm that could work on a mobile device with low-end camera and processor capabilities within this study. In this context, an algorithm with low cost is developed performing detection of most meaningful parameters by a multiple input single output regression model and correlation.The advantage of the proposed method is that it can estimate bilirubin with …

Arkhe - Unmanned Detection Robot, Hannah Klooster

Senior Honors Theses and Projects

Our project is an Unmanned Thermal Detection Robot. Our robot contains a visual camera, a sonar sensor, and a thermal sensor. This project involves hardware building and coding of the sensors and robot itself. The purpose of this project is to create an accessible and affordable unmanned robot with sensors capable of being able to detect human life forms in disaster areas, or areas inaccessible to humans, and to protect the lives of rescuers, find people in disaster areas quicker, and to help save lives. Our project has successfully created an affordable robot that uses sensors to detect and avoid …

Effect Of Digital Advertising On Website Traffic At A Kentucky Comprehensive Regional University, Brandon Lee Moore

Online Theses and Dissertations

The purpose of this research is to quantify differences in key performance indicators between paid and organic (not paid) website traffic over a one year period of time at a regional comprehensive university in Kentucky, which is located in the southeastern United States. Two distinct sources of website traffic can be measured: paid traffic and organic traffic. Using data from website traffic analytics, this study employed multiple linear regression analysis and time series methods to understand the similarities and differences between key performance indicators of paid traffic and organic traffic as they relate to key performance indicators. Data from Google …

Scalable Approaches For Auditing The Completeness Of Biomedical Ontologies, Fengbo Zheng

Theses and Dissertations--Computer Science

An ontology provides a formalized representation of knowledge within a domain. In biomedicine, ontologies have been widely used in modern biomedical applications to enable semantic interoperability and facilitate data exchange. Given the important roles that biomedical ontologies play, quality issues such as incompleteness, if not addressed, can affect the quality of downstream ontology-driven applications. However, biomedical ontologies often have large sizes and complex structures. Thus, it is infeasible to uncover potential quality issues through manual effort. In this dissertation, we introduce automated and scalable approaches for auditing the completeness of biomedical ontologies. We mainly focus on two incompleteness issues -- …

Case Study: Transition To A Vegan Diet In An Elite Male Gaelic Football Player, Daniel Davey, Shane Malone, Brendan Egan

Articles

Vegan diets are increasingly of interest to athletes, but require a well-planned approach in order to mitigate the risk of potential adverse effects on nutrient intakes, and consequently performance. This case study reports the process of an elite male Gaelic football player (age 25 years; height, 1.88 m; body mass, 87.8 kg; lean body mass, 73.26 kg; body fat, 11.3%) transitioning from an omnivorous diet to a vegan diet at the beginning of a competitive season. The report encompasses key considerations in the planning and provision of nutrition support in this context, in addition to iterations needed based on challenges …

Fedoram: A Federated Oblivious Ram Scheme, Alexandre Pujol, Liam Murphy, Christina Thorpe

Articles

Instant messaging (IM) applications, even with end-to-end encryption enabled, pose privacy issues due to metadata and pattern leakage. Our goal is to develop a model for a privacy preserving IM application, by designing an IM application that focuses on hiding metadata and discussion patterns. To solve the issue of privacy preservation through the obfuscation of metadata, cryptographic constructions like Oblivious Random Access Machines (ORAM) have been proposed in recent years. However, although they completely hide the user access patterns, they incur high computational costs, often resulting in excessively slow performance in practice. We propose a new federated model, FedORAM, which …

A Monte-Carlo Analysis Of Monetary Impact Of Mega Data Breaches, Mustafa Canan, Omer Ilker Poyraz, Anthony Akil

Engineering Management & Systems Engineering Faculty Publications

The monetary impact of mega data breaches has been a significant concern for enterprises. The study of data breach risk assessment is a necessity for organizations to have effective cybersecurity risk management. Due to the lack of available data, it is not easy to obtain a comprehensive understanding of the interactions among factors that affect the cost of mega data breaches. The Monte Carlo analysis results were used to explicate the interactions among independent variables and emerging patterns in the variation of the total data breach cost. The findings of this study are as follows: The total data breach cost …

Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth

Publications

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …

Analysis Of Classifier Weaknesses Based On Patterns And Corrective Methods, Nicholas Skapura

Browse all Theses and Dissertations

Classification is an important branch of machine learning that impacts many areas of modern life. Many classification algorithms (classifiers for short) have been developed. They have highly different levels of sophistication and classification accuracy. Classification problems often have highly different levels of hardness and complexity. Practitioners of classification modeling need better understanding of those algorithms in order to select the optimal algorithm for given classification problems. Researchers of classification need new insight on how given classifiers are weak and how they can be improved by correcting their classification errors. This dissertation introduces new tools and concepts to analyze classifier weakness …

Orientation And Social Influences Matter: Revisiting Neutralization Tendencies In Information Systems Security Violation, Frank Curtis King

CCAC Theses and Dissertations

It is estimated that over half of all information systems security breaches are due directly or indirectly to the poor security practices of an organization’s employees. Previous research has shown neutralization techniques as having influence on the intent to violate information security policy. In this study, we proposed an expansion of the neutralization model by including the effects of business and ethical orientation of individuals on their tendencies to neutralize and compromise with information security policy. Additionally, constructs from social influences and pressures have been integrated into this model to measure the impact on the intent to violate information security …

Long Short-Term Memory With Spin-Based Binary And Non-Binary Neurons, Meghana Reddy Vangala

Electronic Theses and Dissertations, 2020-2023

Research in the field of neural networks has shown advancement in the device technology and machine learning application platforms of use. Some of the major applications of neural network prominent in recent scenarios include image recognition, machine translation, text classification and object categorization. With these advancements, there is a need for more energy-efficient and low area overhead circuits in the hardware implementations. Previous works have concentrated primarily on CMOS technology-based implementations which can face challenges of high energy consumption, memory wall, and volatility complications for standby modes. We herein developed a low-power and area-efficient hardware implementation for Long Short-Term Memory …

Spectral And Latent Representation Distortion For Tts Evaluation, Thananchai Kongthaworn

Chulalongkorn University Theses and Dissertations (Chula ETD)

One of the main problems in the development of text-to-speech (TTS) systems is its reliance on subjective measures, typically the Mean Opinion Score (MOS). MOS requires a large number of people to reliably rate each utterance, making the development process slow and expensive. Recent research on speech quality assessment tends to focus on training models to estimate MOS, which requires a large number of training data, something that might not be available in low-resource languages. We propose an objective assessment metric based on the DTW distance using the spectrogram and the high-level features from an Automatic Speech Recognition (ASR) model …

Improved Secure And Low Computation Authentication Protocol For Wireless Body Area Network With Ecc And 2d Hash Chain, Soohyeon Choi

Electronic Theses and Dissertations

Since technologies have been developing rapidly, Wireless Body Area Network (WBAN) has emerged as a promising technique for healthcare systems. People can monitor patients’ body condition and collect data remotely and continuously by using WBAN with small and compact wearable sensors. These sensors can be located in, on, and around the patient’s body and measure the patient’s health condition. Afterwards sensor nodes send the data via short-range wireless communication techniques to an intermediate node. The WBANs deal with critical health data, therefore, secure communication within the WBAN is important. There are important criteria in designing a security protocol for a …

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

Energy-Aware Real-Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing, Ashik Ahmed Bhuiyan

Electronic Theses and Dissertations, 2020-2023

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform's computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time …

A Comparison Of Artificial Neural Networks And The Statistical Methods In Predicting Mba Student’S Academic Performance, Ojoung Kwon, Harry Hui Xia, Serin Zhang

Journal of International Technology and Information Management

MBA has become one of the most popular and vital professional degrees internationally. The MBA program admission process’s essential task is to choose the best analysis tools to accurately predict applicants’ academic performance potential based on the evaluation criteria in making admission decisions. Prior research finds that the Graduate Management Admission Test (GMAT) and undergraduate grade point average (UGPA) are common predictors of MBA academic performance indicated by graduate grade point average (GGPA). Using a sample of 250 MBA students enrolled in a state university with AACSB accreditation from Fall 2010 to Fall 2017, we test and compare the effectiveness …

An Integrated View Of Knowledge Management Enablers, Components, And Benefits: Comprehensive Literature Review, Shuaibu Hassan Usman, Jigish Zaveri, Aminu Hamza

Journal of International Technology and Information Management

The key to success in the 21^st century, knowledge-based economy, will be the effective and efficient management of knowledge. Knowledge Management (KM) has a direct impact on organizational success. KM in a knowledge-based economy entails growth, value, and improving living standards through knowledge creation, storage, and dissemination. This study aims to integrate KM enablers, components, and benefits into a single view through a comprehensive literature search. The integrative view of these three factors would facilitate the understanding of KM initiatives within organizations. This paper performs a comprehensive literature review to explain the KM enablers, components, and benefits. From the …