Computer Sciences Commons^™

Parameter-Free Outlier Scoring Using Mass Ratio Variance For Static And Streaming Data, Phichapop Changsakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

Outlier detection is a significant problem that has been studied in a variety of research and real-world applications. However, little research has been conducted on unsupervised parameter-free outlier scoring. This thesis proposes Mass ratio variance-based Outlier Factor, or MOF, which is unsupervised parameter-free outlier scoring for static data. This algorithm calculates outlier scores based on the variance of mass ratio. The data points with high outlier scores are associated with outliers while the data points with low outlier scores are associated with normal data points. This thesis also proposes Streaming Mass ratio variance-based Outlier Factor or SMOF. This algorithm calculates …

Natural Language Processing For Digital Advertising, Yiping Jin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Advertising is not only a marketing or sales activity but a particular form of two-way communication. In this thesis, we propose to apply the two main subtasks of natural language processing (NLP), namely natural language understanding (NLU) and natural language generation (NLG), to digital advertising to enhance the effectiveness of advertising. We apply weakly-supervised text classification to rapidly build text classifiers for contextual advertising (Jin et al. 2022). The method requires a handful of labeled keywords instead of a large corpus of labeled documents and can be easily transferred to new domains. We further evaluate the weakly-supervised models using unsupervised …

Computing Competencies For Undergraduate Data Science Curricula: Acm Data Science Task Force, Andrea Danyluk, Paul Leidig

Peer-Reviewed Publications

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level.

There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science …

Constructing And Validating Feature Models Using Relational, Document, And Graph Databases, Hazim Shatnawi

Electronic Theses and Dissertations

Building a software product line (SPL) is a systematic strategy for reusing software within a family of related systems from some application domain. To define an SPL, a domain analyst must identify the common and variable aspects of a family of systems and capture them for later use in construction of specific products. To do so, Feature-Oriented Domain Analysis (FODA) introduced the feature model as an abstraction to represent the common and variable aspects, using a feature diagram to depict the model visually. However, this abstraction is often difficult for developers to use because most tools rely on specialized theories, …

A Hybrid Decision Tree - Neural Network (Dt-Nn) Model For Predictive Maintenance Applications In Aircraft, Jarrod Carson

Honors Theses

As the Age of Information has evolved over the last several decades, the demand for technology which stores, analyzes, and utilizes data has increased substantially. For countless industries such as the medical, retail, and aircraft industries, such technology is crucial to their operation. This project proposes a hybrid machine learning model consisting of Decision Trees and Neural Networks which is able to classify data of varying volume and variety effectively and efficiently. The model’s structure consists of a decision tree with each node of the tree containing a neural network trained to classify a specific category of the output using …

A Gpu Parallelized Application To Study Artificial Spin Systems Emulating The Random Bond Ising Model, Joseph Latessa

Wayne State University Theses

In a collaboration between researchers in the physics and computer science departments at Wayne State University, we have developed and implemented a GPU-accelerated application to study artificial spin systems emulating the Random Bond Ising model. To emulate the Random Bond Ising Model, we generate quadrupolar lattices with both ferroquadrupolar and antiferroquadrupolar ordered phases. We also introduce structural disorder into the system by randomly removing a percentage of individual spins. The presence of structural disorder gives rise to phase transitions that can be observed and mapped using our application. Our algorithm implements a Monte Carlo simulation based on the Metropolis model …

From The Editors, Michael E. Whitman, Herbert J. Mattord, Hossain Shahriar

Journal of Cybersecurity Education, Research and Practice

A commentary from the editors, with an overview of the articles contained in this issue of the Journal.

Sentiment Analysis Of Long-Term Social Data During The Covid-19 Pandemic, Sophanna Ek, Marco Curci, Xiaokun Yang, Beiyu Lin, Pinchao Liu, Hailu Xu

Computer Science Faculty Publications

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards …

Question Answering By Bert, Suman Karanjit

Student Academic Conference

No abstract provided.

The Introduction Of Big Data In Cloud Computing, Austin Gruenberg

Student Academic Conference

One of the fastest-growing technologies that many people are unaware of is the world of cloud computing. Having started in 2006, it is a relatively new technological advancement in the computer industry. The major branch of cloud computing that I decided to focus on was big data. I decided to research this topic to better understand what its current uses are, to see what the future holds for Big Data and cloud computing and because it is a growing, significant piece of technology being used in our society today. Big data and cloud computing are very important industries and have …

Integrating The Bullet Physics Engine Into Minecraft, Ethan Johnson

Student Academic Conference

During the past fall semester, I started a programming project called Rayon which is designed to be a realistic physics engine implementation that runs alongside the videogame Minecraft. It is a library which Minecraft mod developers can use to implement realistic entity movement into their own mods. Rayon, being entirely written in the Java programming language, currently uses a port of the Bullet physics engine called JBullet which is very outdated and no longer being maintained. To find a more performant solution, I have set out to replace JBullet with an alternative library called LibBulletJME which is designed to interface …

Automatic Subtyping Of Individuals With Primary Progressive Aphasia, Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk B. Den Ouden, Argye Hillis, Kyrana Tsapkini

Communication Sciences and Disorders Faculty Articles and Research

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Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff

All Graduate Theses, Dissertations, and Other Capstone Projects

Multiprocessor System-on-Chip (MPSoC) integrating heterogeneous processing elements (CPU, GPU, Accelerators, memory, I/O modules ,etc.) are the de-facto design choice to meet the ever-increasing performance/Watt requirements from modern computing machines. Although at consumer level the number of processing elements (PE) are limited to 8-16, for high end servers, the number of PEs can scale up to hundreds. A Network-on-Chip (NoC) is a microscale network that facilitates the packetized communication among the PEs in such complex computational systems. Due to the heterogeneous integration of the cores, execution of diverse (serial and parallel) applications on the PEs, application mapping strategies, and many other …

Reviving Mozart With Intelligence Duplication, Jacob E. Galajda

Honors Undergraduate Theses

Deep learning has been applied to many problems that are too complex to solve through an algorithm. Most of these problems have not required the specific expertise of a certain individual or group; most applied networks learn information that is shared across humans intuitively. Deep learning has encountered very few problems that would require the expertise of a certain individual or group to solve, and there has yet to be a defined class of networks capable of achieving this. Such networks could duplicate the intelligence of a person relative to a specific task, such as their writing style or music …

Examining Everyday Literacies: An Autoethnographic Analysis Of Mundane Textualities, Kyle J. Mauter

Honors Undergraduate Theses

As a way of extending perspectives of writing and learning, this thesis explores everyday literacy activities and their role in function in shaping people's activities. Taking up an autoethnographic approach to studying the mundane literacies of everyday life, this thesis offers a fine-grained analysis of the processes and practices involved in two specific literate activities I have engaged in over the two years: creating a mixtape for a friend and streaming my participation in online video games. As key findings, the analysis of these everyday literate activities suggests that the interactions between people and social contexts figure prominently in the …

Recapture: A Virtual Reality Interactive Narrative Experience Concerning Perspectives And Self-Reflection, Indira Avendano

Honors Undergraduate Theses

This project presents a virtual reality (VR) Interactive Narrative aiming to leave users reflecting on the perspectives one chooses to view life through. The narrative is driven by interactions designed using the concept of procedural rhetoric, which explores how rules and mechanics in games can persuade people about an idea, and Shin's cognitive model, which presents a dynamic view of immersion in VR. The persuasive nature of procedural rhetoric in combination with immersion techniques such as tangible interfaces and first-person elements of VR can effectively work together to immerse users into a compelling narrative experience with an intended emotional response …

A Deep Learning Approach For Learning Human Gait Signature, Alexander Matasa

Electronic Theses and Dissertations, 2020-2023

With advancements in biometric securities, focus has increased on utilizing gait as a means of recognition. Gait describes the unique walking pattern present in humans and has shown promising results in person re-identification tasks. Unlike other biometric features, gait is unique in that it is a subconscious behavior minimizing the risk of purposeful obfuscation. In this research, we first cover supervised approaches showing that current methods fail to learn a unique signature that describes the motion of a subject. Rather they extract frame-based feature information which is then aggregated. While these methods have shown to be effective, they do not …

Analyzing The Blockchain Attack Surface: A Top-Down Approach, Muhammad Saad

Electronic Theses and Dissertations, 2020-2023

Blockchains enable secure asset exchange in a distributed system, thereby facilitating innovative applications such as cryptocurrencies and smart contracts. Although the cryptographic constructs of blockchains are highly secure, however, their practical deployments are vulnerable to various attacks due to their application-specific policies, and their peer-to-peer (P2P) network intricacies. In this work, we take a top-down approach towards exploring those attacks, starting with the application-specific abuse of blockchain-based cryptocurrencies and concluding with the network conditions that violate the blockchain consistency. In the top-down approach, we first analyze the application-specific abuse of blockchain-based cryptocurrencies by uncovering (1) covert cryptocurrency mining in the …

Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri

Electronic Theses and Dissertations, 2020-2023

The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …

Evaluating Pmo Sync Implementation For Persistent Memory Object, Faishal Wahiduddin

Electronic Theses and Dissertations, 2020-2023

Persistent Memory, in the form of byte-addressable Non-Volatile Memories (NVMs), provides a low-cost and high-capacity main memory, and provides the ability to store and retain data even when the system is powered off, along with improved performance over traditional storage. Persistent Memory Direct Access (DAX) enables applications to perform byte-addressable operations such as load and store. Filesystem-DAX can store persistent data in NVMs with system call overheads. In order to reduce filesystem overheads, this study utilizes Persistent Memory Object (PMO) as an abstraction for persistent data containers on Non-Volatile Memory (NVM). Persisting data in Persistent Memory Object requires that the …

Towards Improving The Robustness Of Neural Abstractive Summarization, Kaiqiang Song

Electronic Theses and Dissertations, 2020-2023

Recent deep learning and sequence-to-sequence learning technology have produced impressive results on automatic summarization. However, the models have limited insights on the underlying language and it remains challenging for system-generated summaries to be truthful to the original input or cover the most important information. This is especially the case for generating abstractive summaries using neural models. My work aims for a flexible and controllable summarization system that can be adapted to cater to different scenarios. It is designed to incorporate linguistic structure information into deep neural networks, have the capability to produce abstracts by re-using a varying amount of source …

Awareness Of Blockchain Usage, Structure, & Generation Of Platform’S Energy Consumption: Working Towards A Greener Blockchain, Loreen Marie Powell, Michalina Hendon, Andrew Mangle, Hayden Wimmer

Information Technology: Faculty Publications

Blockchain is a disruptive information technology innovation with energy consumption. As more organizations look to implement or embrace blockchain innovations, research must focus on making the blockchain greener. This research explores the current innovative blockchain usage, structure, generations, and energy consumption. An energy consumption comparison for consensus protocols is provided along with a list of recommendations for implementing green blockchains. This paper provides a significant impact upon previous literature and aids organizations considering implementing a green blockchain.

Network Function Virtualization Technology Adoption Strategies, Abdlrazaq Ayodeji Adeyi Shittu

Walden Dissertations and Doctoral Studies

Network function virtualization (NFV) is a novel system adopted by service providers and organizations, which has become a critical organizational success factor. Chief information officers (CIOs) aim to adopt NFV to consolidate and optimize network processes unavailable in conventional methods. Grounded in the diffusion of innovation theory (DOI), the purpose of this multiple case research study was to explore strategies chief information officers utilized to adopt NFV technology. Participants include two CIOs, one chief security information officer (CSIO), one chief technical officer (CTO), and two senior information technology (IT) executives. Data were collected through semi-structured telephone interviews and eight organizational …

An Acceptable Cloud Computing Model For Public Sectors, Eswar Kumar Devarakonda

Walden Dissertations and Doctoral Studies

Cloud computing enables information technology (IT) leaders to shift from passive business support to active value creators. However, social economic-communication barriers inhibit individual users from strategic use of the cloud. Grounded in the theory of technology acceptance, the purpose of this multiple case study was to explore strategies IT leaders in public sector organizations implement to utilize cloud computing. The participants included nine IT leaders from public sector organizations in Texas, USA. Data were collected using semi-structured interviews, field notes, and publicly available artifacts documents. Data were analyzed using thematic analysis: five themes emerged (a) user-centric and data-driven cloud model; …

Strategies To Protect Against Security Violations During The Adoption Of The Internet Of Things By Manufacturers, Sixtus Anayochukwu Ekwo

Walden Dissertations and Doctoral Studies

Security violations have been one of the key factors affecting manufacturers in adopting the Internet of Things (IoT). The corporate-level information technology (IT) leaders in the manufacturing industry encounter issues when adopting IoT due to security concerns because they lack strategies to protect against security violations. Grounded in Roger’s diffusion of innovations theory, the purpose of this qualitative multiple case study was to explore strategies corporate-level IT leaders use in protecting against security violations while adopting IoT for manufacturers. The participants were senior IT leaders in the eastern region of the United States. The data collection process included interviews with …

User Awareness And Knowledge Of Cybersecurity And The Impact Of Training In The Commonwealth Of Dominica, Jermaine Jewel Jean-Pierre

Walden Dissertations and Doctoral Studies

The frequency of cyberattacks against governments has increased at an alarming rate and the lack of user awareness and knowledge of cybersecurity has been considered a contributing factor to the increase in cyberattacks and cyberthreats. The purpose of this quantitative experimental study was to explore the role and effectiveness of employee training focused on user awareness of cyberattacks and cybersecurity, with the intent to close the gap in understanding about the level of awareness of cybersecurity within the public sector of the Commonwealth of Dominica. The theoretical framework was Bandura’s social cognitive theory, following the idea that learning occurs in …

Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad

Theses and Dissertations--Civil Engineering

Next-generation smart cities are the key feature in the next chapter of human life. Cities that employ innovative and technology-driven solutions to improve the sustainability, resilience, prosperity, and amenity of the community are considered smart cities. Development of smart cities requires fundamental innovations in many technical and technological aspects including those contributing to smart structures. Smart technologies improve the structural performance against natural disasters like earthquakes, hurricanes, tornados, and promote the sustainability of structural systems. Next-generation smart structures encompass a variety of technologies including Structural Control (SC) and Structural Health Monitoring (SHM). SC covers methodologies and technologies that modify the …

Markov Decision Processes With Embedded Agents, Luke Harold Miles

Theses and Dissertations--Computer Science

We present Markov Decision Processes with Embedded Agents (MDPEAs), an extension of multi-agent POMDPs that allow for the modeling of environments that can change the actuators, sensors, and learning function of the agent, e.g., a household robot which could gain and lose hardware from its frame, or a sovereign software agent which could encounter viruses on computers that modify its code. We show several toy problems for which standard reinforcement-learning methods fail to converge, and give an algorithm, `just-copy-it`, which learns some of them. Unlike MDPs, MDPEAs are closed systems and hence their evolution over time can be treated as …

Multi-Stream Longitudinal Data Analysis Using Deep Learning, Sajjad Fouladvand

Theses and Dissertations--Computer Science

Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in …