Computer Science Masters Theses

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Adapting Web Page Tables on Mobile Web Browsers: Results from Two Controlled Empirical Studies
(North Dakota State University, 2014) Annadi, Ramakanth Reddy
Displaying web page content in mobile screens is a challenging task and users often face difficulty retrieving the relevant data. It can force them to adopt a time-consuming hunt-and-peck strategy. Application of design principles can improve the view of the webpage data content and reduce the time consumption in viewing it. This is especially true with HTML tabular data content. This thesis discusses the background and applications of the gestalt design principle techniques to HTML tabular data content. An empirical study was performed to investigate the usability of two types of the adaptive styles namely, single and multi-layout. This thesis also compared the adaptive styles that use gestalt principles with simple HTML tabular data on mobile screens. A controlled study which involved university students was performed showed that the adaptive layout styles improved the efficiency of finding information in the website by gestalt principles usage and eliminating horizontal scroll.
Adaptive Regression Testing Strategy: An Empirical Study
(North Dakota State University, 2012) Arafeen, Md. Junaid
When software systems evolve, different amounts of code modifications can be involved in different versions. These factors can affect the costs and benefits of regression testing techniques, and thus, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but no research has been done on the problem of helping practitioners systematically choose appropriate techniques on new versions as systems evolve. To address this problem, we propose adaptive regression testing (ART) strategies that attempt to identify the regression testing techniques that will be the most cost-effective for each regression testing session considering organization’s situations and testing environment. To assess our approach, we conducted an experiment focusing on test case prioritization techniques. Our results show that prioritization techniques selected by our approach can be more cost-effective than those used by the control approaches.
Alexa for Health Practitioners
(North Dakota State University, 2020) Bhatt, Vidisha Nareshkumar
Many industries, including healthcare, are trying to take advantage of voice assistant systems by incorporating their technology into the industries’ environment. However, not many companies or researchers have successfully integrated this technology into the daily practice of healthcare practitioners. Doctors, nurses and other healthcare practitioners spend much of their interaction time with patients clicking on the Electronic Medical Record (EMR) screen trying to access and update data. An important contribution of this research is to analyze this healthcare need for this technology in the healthcare practitioner’s workflow. This research developed an Alexa chatbot skill, “Doctor’s Assistant,” as a generic application to help healthcare practitioners access and update EMR data via speech, while reducing data entry time and providing better patient care. The evaluation of this application illustrates that the “Doctor’s Assistant” skill is both effective and accurate.
Alexa, What Should I Eat? A Personalized Virtual Nutrition Coach for Native American Diabetes Patients Using Amazon's Smart Speaker Technology
(North Dakota State University, 2020) Maharjan, Bikesh
Among all other ethnic groups in the USA, native American’s have higher chances of developing diabetes. A lot of tools have been developed to address this issue and help them in managing diabetes. However, these tools fail to address two major issues, first, the focus on research and need of Native Americans, and second, the intuitive user interface to use the functions available in these tools without requiring a complex knowledge of technology. This project focuses on reducing these two gaps. The project uses the underlying knowledge base to provide personalized recommendations and leverages the benefit of smart speakers to deliver the service to the user’s which is highly intuitive and less demanding technologically. This project utilizes Ontology as a knowledge base and Amazon’s Alexa platform for the initial experiment to provide personalized recommendations to the users.
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.
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.
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.
Application of Memory-Based Collaborative Filtering to Predict Fantasy Points of NFL Quarterbacks
(North Dakota State University, 2019) Paramarta, Dienul Haq Ambeg
Subjective expert projections have been traditionally used to predict points in fantasy football, while machine prediction applications are limited. Memory-based collaborative filtering has been widely used in recommender system domain to predict ratings and recommend items. In this study, user-based and item-based collaborative filtering were explored and implemented to predict the weekly statistics and fantasy points of NFL quarterbacks. The predictions from three seasons were compared against expert projections. On both weekly statistics and total fantasy points, the implementations could not make significantly better predictions than experts.However, the prediction from the implementation improved the accuracy of other regression models when used as additional feature.
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.
An Artificial Immune System Heuristic in a Smart Electrical Grid
(North Dakota State University, 2014) Chowdhury, Md. Minhaz
The immune system of the human body follows a process that is adaptive and learns via experience. Some algorithms are designed to take advantage of this process to determine solutions for complex problem domains. The collection of these algorithms is known as Artificial Immune Systems. Among this collection, one important algorithm is "The Danger Theory." In this thesis, an application of the algorithm has been implemented to solve an electrical grid problem. This problem of interest is the automatic detection of faulty and failure conditions in the electrical grid. A novel application of the Artificial Immune System algorithm is presented to solve this problem (i.e., to find faults in electrical-grid data in an automated fashion). The methodology treats streams of electrical-grid data as artificial antigens, and uses artificial antibodies to identify and locate potentially harmful conditions in the grid. The results demonstrate that the approach is promising. I believe this approach has a good contribution for the emerging field of Smart Grids.
Association Rule Mining of Biological Field Data Sets
(North Dakota State University, 2017) Shrestha, Anuj
Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.
Attacking the Messenger: Exploring the Security of Big Data Messenger Apache Kafka
(North Dakota State University, 2021) Losinski, Tyler
As technology becomes faster, cheaper, and more compact, a higher volume of data must be processed. This demand drives the need to process high volumes of data in near real time. As a result, technologies such as Kafka have been created as high throughput messaging bus systems. Utilizing these new technologies could vastly improve the way we look at data processing, especially when that data is coming from IoT or distributed systems. Kafka provides multiple methods of encryption and authentication with its brokers, however, securing the producers and consumers is the responsibility of the application owner. This paper focuses on this key aspect in order to consider how an attacker could exploit and compromise the flow and integrity of the data. After access to the producers and consumers has been compromised, examples of data manipulation are performed in order to demonstrate real world consequence of breaches such as these.
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.
An Automated Approach for Discovering Functional Risk-Inducing Flaws in Software Designs
(North Dakota State University, 2015) Hassan, Amro Salem Salem
For safety critical applications, it is necessary to ensure that risk-inducing flaws do not exist in the final product. To date, many risk-based testing techniques were proposed. The majority of these techniques address flaws in the implementation. However, since the overhead of software flaws increases the later they are discovered in the development process, it is important to test for these flaws earlier in the development process. Few approaches have addressed the problem of testing for risk-inducing flaws in the design phase. These approaches are manual approaches, which makes them hard to apply on large complicated software designs. To address this problem, we propose an automated approach for testing designs for risk-inducing flaws. To evaluate our approach, we performed an experiment focusing on specifications of safety critical systems. Our results show that the proposed approach could be effective in discovering functional flaws in behavioral designs that is exposing a risk.
Blood Glucose Prediction Models for Personalized Diabetes Management
(North Dakota State University, 2018) Fernando, Warnakulasuriya Chandima
Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.
Building Plant 3D Genome Computing Resources
(North Dakota State University, 2021) Bulathsinghalage, Chanaka Sampath Cooray
Chromatin interactions play increasingly important roles in three-dimensional genome organization and long-range gene regulation. Analyzing the three-dimensional structure of the plants is currently a growing field and we noticed that there is lack of computing resources on chromatin interactions for the plants. So, we are introducing a database of statistically significant chromatin interactions processed using Hi-C experimental approach. The users can search in the database using a set of genes or regions for a selected plant organism through a web browser and it lists down all the statistically significant chromatin interactions involved those genes or regions with the confidence scores, Gene Ontology information and pathway information. It serves as a computing resource for biologists and scientists who want to study plant genomes under the context of three-dimensional structure without any programming experience.
Chemical Compound Classification Ensemble
(North Dakota State University, 2013) Zhu, Ya
In the research of health science, scientists often need to screen numerous chemical compounds to find drugs that can treat a disease. The process of testing the functionality of these compounds in the laboratory is very time-consuming. Computational methods have been used to accelerate this process. These computational methods are implemented based on the principle that chemical compounds with similar structure often have similar function. Thus, these methods maintain a database of chemical compounds whose function has been verified using laboratory experiments. The database contains the chemical structural formula of a compound, the 3D coordinate of every atom, and whether it has a certain function, e.g. it can kill a virus. Then, for a new compound, the programs compare its structure with those in the database and predict if it has the function based on the structure similarity. Thus, predicting the function of a compound is a two-class classification problem. In this project, we try to address this two-class classification problem using global and local similarity between compounds. The global similarity measures the overall structural resemblance between two compounds. When a group of compounds have the same function, they usually share some common sub-structures. These common sub-structures may correspond to their functional sites. Local similarity is computed based on the occurrences of common sub-structures between compounds. We built several classification models based on global and local similarity. To improve the classification result, we used an ensemble of those models to predict the function compounds in NCI cancer data sets. We predict whether a compound can inhibit cancer cell growth or not, obtaining AUC higher than 80% for five datasets. We compare our results with other state-of-the-art methods. Our classification result is the best in all five datasets. Our results show that local similarity is more useful than global similarity in predicting compound function. An ensemble method integrating global and local similarity achieves much better performance than single predicting models.
Classification of LiDar Data Using Window-Based Techniques
(North Dakota State University, 2016) Li, Shuhang
Given LiDAR maps, we focus on identifying anthropologically relevant ditches automatically on the map. Archeologists can identify these features visually at the site, but approaches based on remotely sensed data would be preferable. This paper proposes an algorithm that uses window-based technique to read the characteristics of each region from maps, whose ditches are already identified, regressively, and then builds histograms to represent the different characters of each region. A classification model is then built based on the histograms and used to predict future data. The goal is to produce a large training data set using window-based technology and use it to classify future data. We demonstrated our algorithm successfully identifies target regions efficiently on real LiDAR maps.
Classifying Gene Coexpression Networks Using Discrimination Pattern Mining
(North Dakota State University, 2016) Qormosh, Bassam M M
Several algorithms for graph classi cation have been proposed. Algorithms that map graphs into feature vectors encoding the presence/absence of speci c subgraphs, have shown excellent performance. Most of the existing algorithms mine for subgraphs that appear frequently in graphs belonging to one class label and not so frequently in the other graphs. Gene coexpression networks classi cation attracted a lot of attention in the recent years from researchers in both biology and data mining because of its numerous useful applications. The advances in high-throughput technologies that provide an easy access to large microarray datasets necessitated the development of new techniques that can scale well with large datasets and produce a very accurate results. In this thesis, we propose a novel approach for mining discriminative patterns. We propose two algorithms for mining discriminative patterns and then we use these patterns for graph classi cation. Experiments on large coexpression graphs show that the proposed approach has excellent performance and scales to graphs with millions of edges. We compare our proposed algorithm to two baseline algorithms and we show that our algorithm outperforms the baseline techniques with a very high accurate graph classi cation. Moreover, we perform topological and biological enrichment analysis on the discriminative patterns reported by our mining algorithm and we show that the reported patterns are signi cantly enriched.
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.

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Browsing Computer Science Masters Theses by browse.metadata.department "Computer Science"