Computer Science Masters Theses

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Understanding the impact of COVID-19 on online eating disorder communities on Reddit
(North Dakota State University, 2024) Al Amin, Md
Like precedent disease outbreaks, COVID-19 has immense implications for health challenges. The social restrictions and disruption in daily activities pose a psychological burden for humans worldwide and may be particularly detrimental for individuals with mental disorders. Psychological stressors stemming from the COVID-19 pandemic and resultant stay-at-home orders may exacerbate Eating Disorder(ED)- related triggers and present a challenging environment for individuals with anorexia nervosa, bulimia nervosa, and binge eating disorder. In this research, we aim to comprehend how COVID-19 has affected individuals with eating disorders through a comparative analysis of data obtained from online communities. We developed a tool for extracting information from well-known social media communities such as Reddit. We collected data spanning two years before and after the declaration of the pandemic from the subreddits r/AnorexiaNervosa, r/BingeEatingDisorder, and r/EatingDisorders. The research presents multi-faceted tasks where we analyze the content of each of the subreddits by applying a strategy that combines topic modeling, social network analysis, and time series modeling for a better understanding of these communities on both content and network levels. Through a comparative analysis, we address the discussion topic changes based on users’ content and determine how COVID-19 leads to changes in communication patterns within the communities. Finally, we implement time series models like ARIMA, Prophet, LSTM, and Transformer on daily posts and comments count to forecast users’ activities within the subreddit and establish a performance comparison of these time series models. The findings indicate that both the content of users’ discussions and the level of communication and online support-seeking related to eating disorders on Reddit underwent significant changes during the pandemic.
Augmented Reality and Cross-Device Interaction for Seamless Integration of Physical and Digital Scientific Papers
(North Dakota State University, 2024) Miah, Md Ochiuddin
Researchers face the challenge of efficiently navigating vast scientific literature while valuing printed papers in the digital age. Printed materials facilitate deeper engagement and comprehension, leading to superior exam performance and enhanced retention. However, existing digital tools often need to pay more attention to the needs of researchers who value the tactile benefits of printed documents. In response to this gap, we introduce AR-PaperSync, a transformative solution that leverages Augmented Reality (AR) and cross-device interaction technology. AR-PaperSync seamlessly integrates the physical experience of printed papers with the interactive capabilities of digital tools. Researchers can effortlessly navigate inline citations, manage saved references, and synchronize reading notes across mobile, desktop, and printed paper formats. Our user-centric approach, informed by in-depth interviews with six researchers, ensures that AR-PaperSync is tailored to its target users' needs. A comprehensive user study involving 28 participants evaluated AR-PaperSync's significantly enhanced efficiency, accuracy, and system usability and reduced cognitive load in academic reading tasks compared to conventional methods. These findings suggest that AR-PaperSync enhances the reading experience of printed scientific papers and provides a seamless integration of physical and digital reading environments for researchers.
Recruiting Through Identification: Identification Strategies and Tactics Used in External Higher Education Strategic Communication
(North Dakota State University, 2010) Anderson, Dana Raye
This exploratory study examines higher education strategic communication for identification inducement strategies and tactics using Cheney's (1983) typology as a guiding framework. The study also explores the differences in identification inducement strategies and tactics used between traditional public universities and for-profit online educators. The results of a deductive textual analysis indicate that identification inducement strategies are used in higher education strategic communication designed to recruit students, an external audience with a reverse paid relationship with the organization. A new strategy and two tactics are added to Cheney's typology to fully account for the identification appeals identified.
Optimization of Mobile Sensor Movement in Self-Healing Sensor Networks
(North Dakota State University, 2010) Basu, Samidip
This paper moves forward the key idea as proposed in past research works - a self-healing deployment approach for sensor networks, where a small percentage of mobile sensors are deployed along with the static sensors into a field of concern. Mobile sensors can move to make-up for a coverage holes or sensor failure and significantly boost network performance. However, since there are energy constraints on each individual mobile sensor, potentially receiving multiple requests from network holes, the decision to move a mobile sensor has to be optimum, one that maximizes network benefit. In this paper, I propose a hybrid distributed & central decision making algorithm to facilitate optimal moves by each mobile sensor. The algorithm uses several layered techniques like Rough Set analysis, sorting & multi-level auction to provide the best possible decision, given the network scenario and the approach is robust to incompleteness of information. The proposed solution also safeguards against network deadlocks and extensive simulations & statistical analysis have demonstrated superior performance of the algorithm when compared to its peers. Some traits of the algorithm proposed derive inspiration for decision support from Ants' swarm intelligence.
Resource and Bandwidth Allocation in Hybrid Wireless Mobile Networks
(North Dakota State University, 2010) Bengfort, Benjamin John
In the lead up to the implementation of 802.16 and 4G wireless networks, there have been many proposals for addition of multi-hop MANET zones or relay stations in order to cut the cost of building a new backbone infrastructure from the ground up. These types of Hybrid Wireless Networks will certainly be a part of wireless network architecture in the future, and as such, simple problems such as resource allocation must be explored to maximize their potential. This study explores the resource allocation problem in three distinct ways. First, this study highlights two existing backbone architectures: max-coverage and max-resource, and how hybridization will affect bandwidth allocation, with special emphasis on OFDM-TMA wireless networks. Secondly, because of the different goals of these types of networks, the addition of relay stations or MANET zones will affect resource availability differently, and I will show how the addition of relay stations impacts the backbone network. Finally, I will discuss specific allocation algorithms and policies such as top-down, bottom-up, and auction-based allocation, and how each kind of allocation will maximize the revenue of both the backbone network as well as the mobile subscribers while maintaining a minimum Quality of Service (or fairness). Each of these approaches has merit in different hybrid wireless systems, and I will summarize the benefits of each in a study of a network system with a combination of the elements discussed in the previous chapters.
Athletic Fundraising and University Development Offices: A Structurational Relationship
(North Dakota State University, 2010) Dickhudt, Keith Michael
This case study examines the working relationship between an athletic department and a central development office within a university. This study focuses primarily on the coordination of fundraising efforts between the two offices. A qualitative approach, using a structuration theoretical framework, presents the working relationship at Midwestern State University (MWSU) through in-depth interviews. Results suggest the two offices could improve the coordination of fundraising efforts. Recommendations, based on the results and theoretical framework, are given.
An Architecture for the Implementation and Distribution of Multiuser Virtual Environments.
(North Dakota State University, 2010) Dischinger, Benjamin James
JavaMOO is an architecture for creating multiuser virtual environments focusing on domain-specific design and rapid development. JavaMOO components use best practices and extensible design for system configuration, client-server communication, event handling, object persistence, content delivery, and agent control. Application dependencies such as database and web servers are embedded, promoting wide dissemination by decreasing management overhead. The focus of this thesis is the design and implementation of the JavaMOO architecture and how it helps improve the state of multiuser virtual environments.
Post-Deployment Key Management in Heterogeneous Wireless Sensor Networks
(North Dakota State University, 2010) Loree, Paul Edward
Many wireless sensor network applications require secure communication between nodes in the network. However, establishing pair-wise keys between nodes to provide security is challenging due to the limited resources in sensor nodes and the hostile environments in which they are deployed. Many key establishment schemes have been previously proposed for wireless sensor networks. However, most of these schemes were designed to work in a homogeneous network environment in which the nodes all have similar capabilities. Our work establishes that better performance can be achieved in a heterogeneous sensor network environment. We present a key management scheme for establishing pair-wise keys after deployment in a heterogeneous wireless sensor network. We take advantage of the more powerful nodes present in a heterogeneous network to reduce the communication overhead and ultimately the power consumption necessary to perform these services to the network. Additionally, by taking advantage of these nodes we are able to increase the overall network connectivity and resiliency against node capture attacks.
Dynamic Algorithms for Sensor Scheduling and Adversary Path Prediction
(North Dakota State University, 2010) Pandey, Shivendushital Pyarelal
In this thesis we describe three new dynamic, real time and robust sensor scheduling algorithms for intruder tracking and sensor scheduling. We call them Tactic Association Based Algorithm (TABA), Tactic Case Based Algorithm (TCBA) and Tactic Weight Based Algorithm (TWBA). The algorithms are encoded, illustrated visually, validated, and tested. The aim of the algorithms is to efficiently track an intruder or multiple intruders while minimizing energy usage in the sensor network by using real time event driven sensor scheduling. What makes these intrusion detection schemes different from other intrusion detection schemes in the literature is the use of historical data in path prediction and sensor scheduling. The TABA uses sequence pattern mining to generate confidences of movement of an intruder from one location to another location in the sensor network. TCBA uses the Case-based reasoning approach to schedule sensors and track intruders in the wireless sensor network. TWBA uses weighted hexagonal representation of the sensor network to schedule sensors and track intruders. In this research we also introduce a novel approach to generate probable intruder paths which are strong representatives of the paths intruders would take when moving through the sensor network.
Anonymity and Hostile Node Identification in Wireless Sensor Networks
(North Dakota State University, 2010) Reindl, Phillip Steven
In many secure wireless network attack scenarios, the source of a data packet is as sensitive as the data it contains. Existing work to provide source anonymity in wireless sensor networks (WSN) are not frugal in terms of transmission overhead. We present a set of schemes to provide secure source anonymity. As the state of the art in WSN advances, researchers increasingly look to heterogeneous network topologies. We leverage high powered cluster head nodes to further reduce transmission overhead and provide excellent scalability. A significant threat to WSN is the insider attack due to the ease of tampering with low-cost sensors. Should a node become compromised and start making malicious collisions, it is desirable to identify the corrupt node and revoke its keys. We present schemes to identify the source of an arbitrary transmission in a reliable and distributed fashion.
Implementation of Parallel Programming to Improve Transaction Speed and Scalability in Blockchain Systems
(North Dakota State University, 2021) DeNio, Joshua
This thesis presents a parallel mining architecture model intended to be used in blockchain systems to improve transaction speed and network scalability while maintaining decentralization. Typical blockchain validation times are significantly slower than traditional digital transaction systems. The model presented is intended to allow devices with limited computational power to make meaningful contributions to the blockchain system by introducing parallel proof of work, managed by automated manager nodes. This will allow blockchain systems to be integrated into cloud environments and the internet of things. The presented model is also intended to address and reduce power consumption problems current blockchain systems face, by allowing the network to validate transactions without the need of high-powered specialty mining machines. Automation and virtualization of network nodes is intended to utilize hardware already online to preform parallel proof of work together in contrast to nodes all competing against each other and ultimately wasting electrical power.
A Closed Form Optimization Model for The Conflict Neutralization Problem
(North Dakota State University, 2010) Wang, Yan
In this study, we proposed a novel closed form optimization model for the Conflict Neutralization Problem (CKP) and implemented an efficient algorithm for solving the problem. A novel tableau representation of the CNP model was presented and described in detail. We implemented a special structured branch and bound algorithm to solve the problem. Key components of the implementation were described. To test the computation performance of our algorithm, we designed and conducted three sets of experiments. The experiment results were reported and analyzed in this report. The test results showed the efficiency of the algorithm for solving the Conflict Neutralization Problem.
Distance-Aware Relay Placement and Scheduling in Wireless Networks.
(North Dakota State University, 2011) Bai, Shi
The WiMAX technology and cognitive radio have been active topics in wireless networks. A WiMAX mesh network is able to provide larger wireless coverage, higher network capacity and Non-Line-Of-Sight (NLOS) communications. Cognitive radios enable dynamic spectrum access over a large frequency range. These characteristics make WiMAX mesh networks and cognitive radio networks able to provide users with low-cost, high-speed and long-range wireless communications, as well as better Quality of Service. However, there are still several challenges and problems to be solved in this area, such as relay station placement problems and scheduling problems. In this thesis, I studied a distance-aware relay placement problem and max-min fair scheduling problem in WiMAX mesh networks. To solve these problems, approximation algorithms and heuristic algorithms are proposed. Theoretical analysis and simulation results are provided to evaluate the solutions. I also studied a scheduling problem adopting the idea of cognitive radio technique in wireless networks over water. Two heuristics are presented to solve this unique problem. I provide the numerical results to justify the performance and efficiency of our proposed scheduling algorithms.
Uses and Gratifications of Amputees' Online Social Networks
(2011) Frohlich, Dennis Owen
The following thesis examined amputee support groups on Facebook. Having an 111 amputation can be a life-changing experience. and social support may be necessary to understand one's amputation. Hundreds of amputee groups exist on Face book, and many of them foster active onlinc communities. This study sought to accomplish two goals: describe how amputees use these groups, and uncover the predictors that lead to engagement in socially supportive behaviors, particularly providing informational support and emotional support. While not all groups are active, this study found that amputees do connect with others through Facebook, sometimes quite frequently. Posting on group walls significantly predicted use of informational support behaviors, and age, overall heal th condition, and posting on group walls significantly predicted use of emotional support behaviors.
Frequent Substring-Based Sequence Classification Using Reduced Alphabets
(North Dakota State University, 2011) Chitraranjan, Charith Devinda
In recent years, various disciplines have generated large quantities of sequence data which has necessitated automated techniques for classifying these sequences into different categories of interest. Especially with the rapid rate at which biological sequence data has been emerging out of high throughput sequencing efforts, the need to interpret these large quantities of raw sequence data and gain deeper insights into them has become an essential part of modern biological research. Understanding the functions, localization and structure of newly identified protein sequences in particular has become a major challenge and is seeking the aid of computational techniques to keep up with the pace. In this thesis, we1 evaluate frequent pattern-based algorithms for predicting aforementioned attributes of proteins from their primary structure ( amino acid sequence). \Ve also apply our algorithms to datasets containing wheat Expressed Sequence Tags (ESTs) as an attempt to predict ESTs that are likely to be located near the centromere of their respective chromosomes. \Ve use frequent substrings mined from the training sequences as features to train a classifier. Our evaluation includes SVM and association rule-based classifiers. Some amino acids have similar properties and may substitute one another without altering the topology or function of a protein. Therefore, we use a combination of reduced amino acid alphabets in an attempt to capture patterns that may contain such substitutions. Frequent substrings mined from different alphabets are treated as features resulting from multiple sources and we evaluate both feature fusion and classifier fusion approaches towards multiple source prediction. 'We compare the performance of the different approaches using protein sub-cellular location, protein function and EST chromosomal location datasets. Pair-wise sequence-alignment-based Nearest Neighbor and basic SVM k-gram classifiers are also included as baseline algorithms in the comparison. Results show that frequent pattern-based SVM classifiers demonstrate better performance compared to other classifiers on the sub-cellular location datasets and they perform competitively with the nearest neighbor classifier on the protein function datasets. Our results also show that the use of reduced alphabets provides statistically significant performance improvements for the SVM-based classifier fusion algorithm, for half of the classes studied.
POMDP Planning in Service Composition
(North Dakota State University, 2011) Min, Chen
Automated vVeb service composition is becoming an increasingly important research topic. It describes the automatic process of composing atomic services into a chain of services that provide a specific functionality that could not be achieved by atomic services alone. A service-oriented environment is dynamic in nature, meaning that services come online and go offiine, services change their functionality, etc. Current classical AI planning techniques suffer from the assumption of deterministic behavior of \Veb services and require execution monitoring for service failures. To address this concern, Partially Observable Markov Decision Processes (POMDP) in workflow composition has been used. POMDPs provide a powerful mathematical framework for planning and decision making in noisy and/ or dynamic environments. PO:\1DPs have been widely used to model many real-world problems. This thesis develops, implements and analyzes the suitability of the POMDP approach to the Web service composition problem. iii
An Evaluation Framework for Business Intelligence Visualization
(North Dakota State University, 2022) Zhang, Hankuan
Nowadays, data visualization is becoming an essential part of data analysis. Business Intelligence Visualization (BIV) is a powerful tool that helps modern business flows faster and smoother than ever before. However, studies on BIV evaluation are severely lacking; most evaluation studies for BIV is guided by general principles of usability, which have limited aspects covered for customers’ needs. The purpose of this research is to develop a framework that evaluates BIV, including decision-making experience. First, we did a literature review for good understanding of research progress on related fields, and established a conceptual framework. Second, we performed a user study that implemented this framework with a set of questionnaires to demonstrate how our framework can be used in real business. Our result proved that this framework can catch differences among different designs of BIV from the users’ standpoints. This can help design BIV and promote better decision-makings on business affairs.
Analysis of SDR to Detect Long Range RFID Badge Cloners
(North Dakota State University, 2022) Knecht, Brett
This thesis proposes a way of detecting when radio frequency identification (RFID) badge credentials are being captured through the use of software defined radio (SDR). A method for using SDR to detect when badge cloning technologies are in use on the premises is presented, tested, and analyzed. This thesis presents an overview of the problem with badge systems and a background literature review. Next, the proposed method of detection and its workings are presented. Then, the strategy for evaluating the methods performance. This is discussed by discussion and evaluation of the results. Finally, the thesis concludes with a discussion of the method’s potential benefits and proposed future work.
Multi-Teacher Knowledge Distillation Using Teacher's Domain Expertise
(North Dakota State University, 2022) Hossain, Arafat Bin
Large BERT models cannot be used with low computing power and storage capacity. Knowledge Distillation solves this problem by distilling knowledge into a smaller BERT model while retaining much of the teacher’s accuracy in student. A teacher expert in predicting one class should be chosen, by student, over others for that class - we used the teacher’s domain expertise like this to train the student. We calculated per-class accuracy for the Student and the Teacher and recorded the difference between the student from the teacher for all k classes. With k differences, we calculated the median of the differences to quantify the student's overall deviation from the teacher over all k classes. Student trained using our approach eventually outperformed all its teachers for the MIND dataset where it was 1.3% more accurate than its teacher, BERT-base-uncased, and 2.6% more accurate than its teacher, RoBERTA, in predicting k classes.
Analysis of Java's Common Vulnerabilities and Exposures in GitHub's Open-Source Projects
(North Dakota State University, 2022) Akanmu, Semiu
Java developers rely on code reusability because of its time and effort reduction advantage. However, they are exposed to vulnerabilities in publicly available open-source software (OSS) projects. This study employed a multi-stage research approach to investigate the extent to which open-source Java projects are secured. The research process includes text analysis of Java’s Common Vulnerabilities and Exposures (CVE) descriptions and static code analysis using GitHub’s CodeQL. This study found (a) cross-site scripting, (b) buffer overflow (though analyzed as array index out of bounds), (c) data deserialization, (d) input non-validation for an untrusted object, and (e) validation method bypass as the prevalent Java’s vulnerabilities from the MITRE CVEs. The static code analysis of the compatible seven (7) Java projects out of the 100 top projects cloned from GitHub revealed a 71.4% presence of the array index out-of-bounds vulnerability.

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