Computer Sciences Commons^™

Petersheim Academic Exposition 2020 Math & Computer Science Abstracts, Seton Hall University

Petersheim Academic Exposition

No abstract provided.

Machine Learning Prediction Of Glioblastoma Patient One-Year Survival, Andrew Du '20, Warren Mcgee, Jane Y. Wu

Student Publications & Research

Glioblastoma (GBM) is a grade IV astrocytoma formed primarily from cancerous astrocytes and sustained by intense angiogenesis. GBM often causes non-specific symptoms, creating difficulty for diagnosis. This study aimed to utilize machine learning techniques to provide an accurate one-year survival prognosis for GBM patients using clinical and genomic data from the Chinese Glioma Genome Atlas. Logistic regression (LR), support vector machines (SVM), random forest (RF), and ensemble models were used to identify and select predictors for GBM survival and to classify patients into those with an overall survival (OS) of less than one year and one year or greater. With …

Social Media Based Algorithmic Clinical Decision Support Learning From Behavioral Predispositions, Radhika V. Medury

Doctoral Dissertations

Behavioral disorders are disabilities characterized by an individual’s mood, thinking, and social interactions. The commonality of behavioral disorders amongst the United States population has increased in the last few years, with an estimated 50% of all Americans diagnosed with a behavioral disorder at some point in their lifetime. AttentionDeficit/Hyperactivity Disorder is one such behavioral disorder that is a severe public health concern because of its high prevalence, incurable nature, significant impact on domestic life, and peer relationships. Symptomatically, in theory, ADHD is characterized by inattention, hyperactivity, and impulsivity. Access to providers who can offer diagnosis and treat the disorder varies …

Development Of Machine Learning Tutorials For R, John Pintar

All Undergraduate Theses and Capstone Projects

Machine learning (ML) techniques developed in computer science have revolutionized nearly every sector of industry. Despite the prevalence and usefulness of ML, students outside of computer science rarely receive training in ML. Students frequently receive training in statistical analysis, often using the software package R, which is free, open source, and has additional downloadable modules. A popular module is the ML package caret, which contains 238 different ML algorithms, each with 0-9 hyperparameters. caret is powerful, flexible, and provides consistent syntax across algorithms. In the hands of an experienced practitioner, this tunability is welcomed and can increase accuracy. However, when …

Using Cuda To Enhance Data Processing Of Variant Call Format Files For Statistical Genetic Analysis, Heather Mckinnon

All Graduate Projects

Utilizing the power of GPU parallel processing with CUDA can speed up the processing of Variant Call Format (VCF) files and statistical analysis of genomic data. A software package designed toward this purpose would be beneficial to genetic researchers by saving them time which they could spend on other aspects of their research. A data set containing genetics from a study of trichome production in Mimulus guttatus, or yellow monkey flower, was used to develop a package to test the effectiveness of GPU parallel processing versus serial executions. After a serial version of the code was generated and benchmarked, OpenACC …

Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework, Sakib Mahmud Khan, Mashrur Chowdhury, Linh B. Ngo, Amy Apon

Computer Science Faculty Publications

This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed …

Machine Learning Methods For The Analysis Of Metagenomes, Vito Adrian Cantu Alessio Robles

CGU Theses & Dissertations

As of October 2020, there are 18.6 × 1015 DNA base pairs publicly available in the Sequence Read Archive and this number is growing at an exponential rate. As DNA sequencing prices continue to drop, many research groups around the world have incorporated high throughput sequencing in their research, giving us access to sequences from many distinct ecosystems. This has revolutionized the field of metagenomics, which aims to fully characterize all organisms and their interactions in a particular system. Nevertheless, the plethora of available data has made its analysis difficult as traditional techniques such as genome assembly or sequence alignment …

Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh

Publications and Research

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. ics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions and …

On Basing One-Way Permutations On Np-Hard Problems Under Quantum Reductions, Nai-Hui Chia, Sean Hallgren, Fang Song

Computer Science Faculty Publications and Presentations

A fundamental pursuit in complexity theory concerns reducing worst-case problems to average-case problems. There exist complexity classes such as PSPACE that admit worst-case to average-case reductions. However, for many other classes such as NP, the evidence so far is typically negative, in the sense that the existence of such reductions would cause collapses of the polynomial hierarchy(PH). Basing cryptographic primitives, e.g., the average-case hardness of inverting one-way permutations, on NP-completeness is a particularly intriguing instance. As there is evidence showing that classical reductions from NP-hard problems to breaking these primitives result in PH collapses, it seems unlikely to base cryptographic …

Selectivity And Robustness Of Sparse Coding Networks, Dylan M. Paiton, Charles Frye, Sheng Y. Lundquist, Joel D. Bowen, Ryan Zarcone, Bruno A. Olshausen

Computer Science Faculty Publications and Presentations

We investigate how the population nonlinearities resulting from lateral inhibition and thresholding in sparse coding networks influence neural response selectivity and robustness. We show that when compared to pointwise nonlinear models, such population nonlinearities improve the selectivity to a preferred stimulus and protect against adversarial perturbations of the input. These findings are predicted from the geometry of the single-neuron iso-response surface, which provides new insight into the relationship between selectivity and adversarial robustness. Inhibitory lateral connections curve the iso-response surface outward in the direction of selectivity. Since adversarial perturbations are orthogonal to the iso-response surface, adversarial attacks tend to be …

Computer Science For Equity: Teacher Education, Agency, And Statewide Reform, Joanna Goode, Max Skorodinsky, Jill Hubbard, James Hook

Computer Science Faculty Publications and Presentations

This paper reports on a statewide “Computer Science for All” initiative in Oregon that aims to democratize high school computer science and broaden participation in an academic subject that is one of the most segregated disciplines nationwide, in terms of both race and gender. With no statewide policies to support computing instruction, Oregon's legacy of computer science education has been marked by both low participation and by rates of underrepresented students falling well-below the already dismal national rates. The study outlined in this paper focuses on how teacher education can support educators in developing knowledge and agency, and impacting policies …

Values Of Artificial Intelligence In Marketing, Yingrui Xi

Masters Theses

“Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

Data And Artificial Intelligence: Mismatch Between Expectations And Uses, Diana Garcia

Cybersecurity Undergraduate Research Showcase

People like to hide behind their phones when it comes to social media. Not every user has their real name or their own photo on display in their social media account. To obfuscate their identities, some users use unusual usernames and profile photos that are divorced from their true identity.

Accessibility Of Deepfakes, Andrew L. Collings

Cybersecurity Undergraduate Research Showcase

The danger posed by falsified media, commonly referred to as deepfakes, has been well researched and documented. The software Faceswap to was used to swap the faces of two politician (Joe Biden and Donald Trump). The testing was performed using an affordable consumer GPU (an AMD Radeon RX 570) over 100,000 iterations. The process and results for the two attempts with the best results (and largest differences) were recorded. The result was ultimately unconvincing, while the software was able to recreate the facial structure the lighting and skin tone did not blend at all.

The Interdisciplinary Impacts Of Technology Semantics And Communicational Bypassing In The Cybersecurity Field, Brooke Nixon

Cybersecurity Undergraduate Research Showcase

In 2012, former director of the Federal Bureau of Investigation (FBI) Robert Mueller shocked many when he warned, “[t]here are only two types of companies: those that have been hacked and those that will be.” As technology and information systems develop rapidly, security has become a topic of increasing interest and concern. In 2018, it was noted that the total cost to account for cybercrime on a global scale surpassed US$1 billion (Milkovich, 2020). In the United States, a hacker attack occurs every 39 seconds, and each year, 1 in 3 Americans are personally impacted by a form of hacking …

Learning Transferable Representations For Visual Recognition, Yang Zhang

Electronic Theses and Dissertations, 2020-2023

In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable …

On Patching Learning Discrepancies In Neural Network Training, Mohamed Elfeki

Electronic Theses and Dissertations, 2020-2023

Neural network's ability to model data patterns proved to be immensely useful in a plethora of practical applications. However, using the physical world's data can be problematic since it is often cluttered, crowded with scattered insignificant patterns, contain unusual compositions, and widely infiltrated with biases and imbalances. Consequently, training a neural network to find meaningful patterns in seas of chaotic data points becomes virtually as hard as finding a needle in a haystack. Specifically, attempting to simulate real-world multi-modal noisy distributions with high precision leads the network to learn an ill-informed inference distribution. In this work, we discuss four techniques …

Infrastructure For Performance Monitoring And Analysis Of Systems And Applications, Ramin Izadpanah

Electronic Theses and Dissertations, 2020-2023

The growth of High Performance Computer (HPC) systems increases the complexity with respect to understanding resource utilization, system management, and performance issues. HPC performance monitoring tools need to collect information at both the application and system levels to yield a complete performance picture. Existing approaches limit the abilities of the users to do meaningful analysis on actionable timescale. Efficient infrastructures are required to support largescale systems performance data analysis for both run-time troubleshooting and post-run processing modes. In this dissertation, we present methods to fill these gaps in the infrastructure for HPC performance monitoring and analysis. First, we enhance the …

Algorithms And Applications Of Novel Capsule Networks, Rodney Lalonde

Electronic Theses and Dissertations, 2020-2023

Convolutional neural networks, despite their profound impact in countless domains, suffer from significant shortcomings. Linearly-combined scalar feature representations and max pooling operations lead to spatial ambiguities and a lack of robustness to pose variations. Capsule networks can potentially alleviate these issues by storing and routing the pose information of extracted features through their architectures, seeking agreement between the lower-level predictions of higher-level poses at each layer. In this dissertation, we make several key contributions to advance the algorithms of capsule networks in segmentation and classification applications. We create the first ever capsule-based segmentation network in the literature, SegCaps, by introducing …

Cybersecurity Using Risk Management Strategies Of U.S. Government Health Organizations, Ian Cornelius Wilkinson

Walden Dissertations and Doctoral Studies

Seismic data loss attributed to cybersecurity attacks has been an epidemic-level threat currently plaguing the U.S. healthcare system. Addressing cyber attacks is important to information technology (IT) security managers to minimize organizational risks and effectively safeguard data from associated security breaches. Grounded in the protection motivation theory, the purpose of this qualitative multiple case study was to explore risk-based strategies used by IT security managers to safeguard data effectively. Data were derived from interviews of eight IT security managers of four U.S. government health institutions and a review of relevant organizational documentation. The research data were coded and organized to …

Exploring Software Testing Strategies Used On Software Applications In The Government, Angel Diane Cross

Walden Dissertations and Doctoral Studies

Developing a defect-free software application is a challenging task. Despite many years of experience, the intense development of reliable software remains a challenge. For this reason, software defects identified at the end of the testing phase are more expensive than those detected sooner. The purpose of this multiple case study is to explore the testing strategies software developers use to ensure the reliability of software applications in the government contracting industry. The target population consisted of software developers from 3 government contracting organizations located along the East Coast region of the United States. Lehman’s laws of software evolution was the …

Organization Global Software Development Challenges Of Software Product Quality, Patrick Enabudoso

Walden Dissertations and Doctoral Studies

Leaders of global software development (GSD) processes in organizations have been confronting low software product quality. Managers of these processes have faced challenges that have been affecting customer satisfaction and that have resulted in negative social impacts on public safety, business financial performance, and global economic stability. The purpose of this qualitative exploratory multiple case study was to discover a common understanding shared by managers in Canadian GSD organizations of how to meet software product quality goals and enhance customer satisfaction. The conceptual framework for the study was based on Deming's 14 principles of quality management. The purposeful sample included …

Uncertainty-Aware Brain Lesion Visualization, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Karl T. Hoffman, Hans Hagen, Ross Maciejewski, Gerik Scheuermann

Computer Science and Engineering Faculty Publications

A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed forbrain lesions. Our method is based on an uncertainty …

The Cyberworld And Human Trafficking: A Double-Edged Sword, Bridget Dukes

Cybersecurity Undergraduate Research Showcase

This report examines the advantages and disadvantages associated with the growth of technology within the United States, specifically how technology, digital literacy, and cybersecurity can be used to both facilitate and combat sex trafficking and sexual exploitation on the Internet. The first part of the report addresses trafficking statistics in the United States, as well as legal intervention the country has taken against this epidemic, including an explanation of the Trafficking Victims Prevention Act and the FOSTA-SESTA bill. The second part of the report addresses the online recruitment of buyers and sellers, as well as how the use of open-source …

Understanding The Use Of Malware And Encryption, Eva M. Castillo

OUR Journal: ODU Undergraduate Research Journal

The main objective of this research project is understanding malware and encryption.

Topical Review Of Vulnerability Management For Local Hampton Roads Industry, Gregory W. Hubbard Jr., Matthew Eunice

OUR Journal: ODU Undergraduate Research Journal

The progress towards an interconnected digital world offers an exciting level of advancement for humanity. Unfortunately, this “online” connection is not safe from the threats and dangers typically associated with physical operations. With the foundation of Cyber Command of DoD cyberspace, the United States Government is taking a prominent stance in cyberspace operations. Like the federal government, both industries and individuals are not immune and are oftentimes unknowingly at risk to cyberattack. This report hopes to bring awareness to common vulnerabilities in multi-user networks by describing a historical background on cyber security as well as outlining current methods of vulnerability …

Automatic Distinction Between Twitter Bots And Humans, Jeremiah Stubbs

All Undergraduate Theses and Capstone Projects

Weak artificial intelligence uses encoded functions of rules to process information. This kind of intelligence is competent, but lacks consciousness, and therefore cannot comprehend what it is doing. In another view, strong artificial intelligence has a mind of its own that resembles a human mind. Many of the bots on Twitter are only following a set of encoded rules. Previous studies have created machine learning algorithms to determine whether a Twitter account was being run by a human or a bot. Twitter bots are improving and some are even fooling humans. Creating a machine learning algorithm that differentiates a bot …

Gröbner Bases And Systems Of Polynomial Equations, Rachel Holmes

All Graduate Theses, Dissertations, and Other Capstone Projects

This paper will explore the use and construction of Gröbner bases through Buchberger's algorithm. Specifically, applications of such bases for solving systems of polynomial equations will be discussed. Furthermore, we relate many concepts in commutative algebra to ideas in computational algebraic geometry.

Off-Policy Q-Learning For Anti-Interference Control Of Multi-Player Systems, Jinna Li, Zhenfei Xiao, Tianyou Chai, Frank L. Lewis, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally …

Using Prosody To Spot Location Mentions, Gerardo Cervantes

Open Access Theses & Dissertations

Identifying location mentions in speech is important for many information retrieval and information extraction tasks; here I explore the use of prosody for location spotting. While previous work has explored the use of prosody for spotting named entities, including locations, the specific value of prosody for finding locations in spontaneous speech has not been measured. Using the Switchboard corpus and LSTM modeling I obtain results indicating that prosody is useful in spotting location mentions. Further, I identify specific prosodic

features that tend to mark locations in American English.