Computer Sciences Commons^™

Community Detection And Ranking In Big Data Graphs, Matin Pirouz Nia

UNLV Theses, Dissertations, Professional Papers, and Capstones

Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about the network. A community is defined by determining a lower density of relations between groups comparing to higher density among every group. Such knowledge can be utilized for grouping similar, yet distinct, nodes with applications in health, marketing, and many more. The ever-growing variety of social networks necessitates detection of minute and scattered communities, which are important problems across different research fields including biology, social studies, physics, etc. As a result, analyzing complex networks has become very popular among researchers in academia and …

Application Of Machine Learning In Cancer Research, Mandana Bozorgi

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation revisits the problem of five-year survivability predictions for breast cancer using machine learning tools. This work is distinguishable from the past experiments based on the size of the training data, the unbalanced distribution of data in minority and majority classes, and modified data cleaning procedures. These experiments are also based on the principles of TIDY data and reproducible research. In order to fine-tune the predictions, a set of experiments were run using naive Bayes, decision trees, and logistic regression.

Of particular interest were strategies to improve the recall level for the minority class, as the cost of misclassification …

Performance Analysis Of Blockchain Platforms, Pradip Singh Maharjan

UNLV Theses, Dissertations, Professional Papers, and Capstones

Blockchain technologies have drawn massive attention to the world these past few years mostly because of the burst of cryptocurrencies like Bitcoin, Etherium, Ripple and many others. A Blockchain, also known as distributed ledger technology, has demonstrated huge potential in saving time and costs. This open-source technology which generates a decentralized public ledger of transactions is widely appreciated for ensuring a high level of privacy through encryption and thus sharing the transaction details only amongst the participants involved in the transactions. The Blockchain is used not only for cryptocurrency but also by various companies to meet their business ends, such …

A Deep Learning Approach To Recognizing Bees In Video Analysis Of Bee Traffic, Astha Tiwari

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified.

This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further …

Word Recognition In Nutrition Labels With Convolutional Neural Network, Anuj Khasgiwala

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Nowadays, everyone is very busy and running around trying to maintain a balance between their work life and family, as the working hours are increasing day by day. In such hassled life people either ignore or do not give enough attention to a healthy diet. An imperative part of a healthy eating routine is the cognizance and maintenance of nourishing data and comprehension of how extraordinary sustenance and nutritious constituents influence our bodies. Besides in the USA, in many other countries, nutritional information is fundamentally passed on to consumers through nutrition labels (NLs) which can be found in all packaged …

Teaching Landscape Construction Using Augmented Reality, Arshdeep Singh

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis describes the design, development, and evaluation of an interactive Microsoft HoloLens application that projects landscape models in Augmented Reality. The application was developed using the Unity framework and 3D models created in Sketchup. Using the application, students can not only visualize the models in real space but can also interact with the models using gestures. The students can interact with the models using gaze and air-tap gestures.

Application testing was conducted with 21 students from the Landscape Architecture and Environmental Planning department at Utah State University. To evaluate the application, students completed a usability survey after using the …

A Study Of The Genetic Algorithm Parameters For Solving Multi-Objective Travelling Salesman Problem, Romit S. Beed, Sunita Sarkar, Arindam Roy, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

The objective of this work is to present a solution to a multiple-objective optimization problem using genetic algorithms (GA). Generally, the objectives (minimizing cost, maximizing performance, reducing carbon footprints, maximizing profit) are conflicting for multiple-objective problems, hindering concurrent optimization of each objective. A bi-objective traditional combinatorial optimization of Travelling Salesman Problem is undertaken named as the Multi-Objective Travelling Salesman Problem (MTSP). The two objectives are minimization of the distance travelled by the salesman and minimization of the time taken to travel. The purpose of this paper is to model the problem as a single objective optimization problem using the weighted …

Lightweight Call-Graph Construction For Multilingual Software Analysis, Anne-Marie Bogar, Damian Lyons, David Baird

Faculty Publications

Analysis of multilingual codebases is a topic of increasing importance. In prior work, we have proposed the MLSA (MultiLingual Software Analysis) architecture, an approach to the lightweight analysis of multilingual codebases, and have shown how it can be used to address the challenge of constructing a single call graph from multilingual software with mutual calls. This paper addresses the challenge of constructing monolingual call graphs in a lightweight manner (consistent with the objective of MLSA) which nonetheless yields sufficient information for resolving language interoperability calls. A novel approach is proposed which leverages information from …

Mac Layer Misbehavior Detection Using Time Series Analysis, Maggie X. Cheng, Yi Ling, Wei Biao Wu

Computer Science Faculty Research & Creative Works

This paper presents a solution to the real-time detection of MAC layer misbehaviors in IEEE 802.11 networks. Among the wide range of misbehaviors, we focus on the sender side selfish behavior that creates a channel- capturing effect by using favorable parameters, and the receiver side selfish behavior that does not respond with CTS and ACK upon receiving RTS and data packets, which clears the channel for itself and causes its sender to waste resources. These misbehaviors are subtle to detect and yet can undermine the performance of the well-behaved nodes significantly. This paper shows a powerful real-time detection method that …

Lightweight Multilingual Software Analysis, Damian Lyons, Anne Marie Bogar, David Baird

Faculty Publications

Large software systems can often be multilingual – that is, software systems are written in more than one language. However, many popular software engineering tools are monolingual by nature. Nonetheless, companies are faced with the need to manage their large, multilingual codebases to address issues with security, efficiency, and quality metrics. This paper presents a novel lightweight approach to multilingual software analysis – MLSA. The approach is modular and focused on efficient static analysis computation for large codebases. One topic is addressed in detail – the generation of multilingual call graphs to identify language boundary problems in multilingual code. The …

An Application Of Game Theory In Distributed Collaborative Decision Making, Angran Xiao

Publications and Research

In a distributed product realization environment, new paradigms and accompanying software systems are necessary to support the collaborative work of geographically dispersed engineering teams from different disciplines who have different knowledge, experience, tools and resources. To verify the concept of collaboration by separation, we propose a generic information communication medium to enable knowledge representation and exchange between engineering teams, a digital interface. Across digital interfaces, each engineering team maintains its own perspective towards the product realization problem, and each controls a subset of design variables and seeks to maximize its own payoff function subject to individual constraints. Hence, we postulate …

Computer Organization With Mips, Seth D. Bergmann

Open Educational Resources

This book is intended to be used for a first course in computer organization, or computer architecture. It assumes that all digital components can be constructed from fundamental logic gates. The book begins with number representation schemes and assembly language for the MIPS architecture, including assembler directives, pseudo-operations, and floating point instructions. It then describes the machine language instruction formats, and shows the student how to translate an assembly language program to machine language. This is followed by a chapter which describes how to construct an assembler for MIPS. This chapter may be omitted without loss of continuity. This is …

An Exploratory Study Of Organizational Security Risk Management For Improved Effectiveness, Angela Jackson-Summers

Doctor of Business Administration Dissertations

Information technology executives continue to have concerns about information security (Kappelman, McLean, Johnson, & Torres, 2016) and the increasing rate of information security threats facing organizations (Ponemon Institute, 2017). This exploratory study aimed to take a closer look at the organizational security risk management process for improved effectiveness, and its integral role among other organizational processes. Two research questions were addressed by identifying organizational drivers to security risk management effectiveness, and by determining challenges to achieving organizational security risk management effectiveness. Using the resource-based view theory (RBV), and Carnegie Mellon University’s Software Engineering Institute Capability Maturity Model Integration …

Query-Constraint-Based Mining Of Association Rules For Exploratory Analysis Of Clinical Datasets In The National Sleep Research Resource, Rashmie Abeysinghe, Licong Cui

Computer Science Faculty Publications

Background: Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics.

Methods: We introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on …

An Algorithmic Approach To Creating Effective Study Groups Using A Smart Phone App, Kelvin J. Rosado-Ayala

Honors College Theses

For many students entering college, meeting new people and studying are a common struggle. Study groups are generally recommended, especially if the groups are comprised of members with complementary personality traits. But the challenge still remains, how do freshmen or transfer students find and form these heterogeneous study groups. In order to help alleviate this issue, an Android application was developed to automatically create study groups for students. Using basic information provided by students upon registration, the algorithm is able to automatically find matching group members. The application was designed using an agile life cycle model over the course of …

Mining Association Rules For Low-Frequency Itemsets, Jimmy Ming-Tai Wu, Justin Zhan, Sanket Chobe

Computer Science Faculty Research

High utility itemset mining has become an important and critical operation in the Data Mining field. High utility itemset mining generates more profitable itemsets and the association among these itemsets, to make business decisions and strategies. Although, high utility is important, it is not the sole measure to decide efficient business strategies such as discount offers. It is very important to consider the pattern of itemsets based on the frequency as well as utility to predict more profitable itemsets. For example, in a supermarket or restaurant, beverages like champagne or wine might generate high utility (profit), but also sell less …

Reconstructability And Dynamics Of Elementary Cellular Automata, Martin Zwick

Complex Systems Faculty Publications and Presentations

Reconstructability analysis (RA) is a method to determine whether a multivariate relation, defined set- or information-theoretically, is decomposable with or without loss into lower ordinality relations. Set-theoretic RA (SRA) is used to characterize the mappings of elementary cellular automata. The decomposition possible for each mapping w/o loss is a better predictor than the λ parameter (Walker & Ashby, Langton) of chaos, & non-decomposable mappings tend to produce chaos. SRA yields not only the simplest lossless structure but also a vector of losses for all structures, indexed by parameter τ. These losses are analogous to transmissions in information-theoretic RA (IRA). IRA …

Data Center Application Security: Lateral Movement Detection Of Malware Using Behavioral Models, Harinder Pal Singh Bhasin, Elizabeth Ramsdell, Albert Alva, Rajiv Sreedhar, Medha Bhadkamkar

SMU Data Science Review

Data center security traditionally is implemented at the external network access points, i.e., the perimeter of the data center network, and focuses on preventing malicious software from entering the data center. However, these defenses do not cover all possible entry points for malicious software, and they are not 100% effective at preventing infiltration through the connection points. Therefore, security is required within the data center to detect malicious software activity including its lateral movement within the data center. In this paper, we present a machine learning-based network traffic analysis approach to detect the lateral movement of malicious software within the …

Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis

SMU Data Science Review

A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.

Supervised Machine Learning Bot Detection Techniques To Identify Social Twitter Bots, Phillip George Efthimion, Scott Payne, Nicholas Proferes

SMU Data Science Review

In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns. Detecting bot activity is complex because many bots are actively trying to avoid detection. We present a novel, complex machine learning algorithm utilizing a range of features including: length of user names, reposting rate, temporal patterns, sentiment expression, followers-to-friends ratio, …

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …

Case Study: Using Crime Data And Open Source Data To Design A Police Patrol Area, Brent Allen

SMU Data Science Review

This case study examines how to use existing crime data augmented with open source data to design a patrol area. We used the a demand signal of "calls for service" vice reports which summarize calls for service. Additionally, we augmented our existing data with traffic data from Google Maps. Traffic delays did not correspond to traffic incidents reported in the area examined. These data were plotted geographically to aid in the determination of the new patrol area. The new patrol area was created around natural geographic boundaries, the density of calls for service and police operational experience.

Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.

SMU Data Science Review

In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we use …

Towards Distributed Cyberinfrastructure For Smart Cities Using Big Data And Deep Learning Technologies, Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure …

Social Engineering In Non-Linear Warfare, Bill Gardner

Journal of Applied Digital Evidence

This paper explores the use of hacking, leaking, and trolling by Russia to influence the 2016 United States Presidential Elections. These tactics have been called “the weapons of the geek” by some researchers. By using proxy hackers and Russian malware to break into the email of the Democratic National Committee and then giving that email to Wikileaks to publish on the Internet, the Russian government attempted to swing the election in the favor of their preferred candidate.

The source of the malware used in the DNC hack was determined to be of Russian in nature and has been used on …

A New Functional-Logic Compiler For Curry: Sprite, Sergio Antoy, Andy Jost

Computer Science Faculty Publications and Presentations

We introduce a new native code compiler for Curry codenamed Sprite. Sprite is based on the Fair Scheme, a compilation strategy that provides instructions for transforming declarative, non-deterministic programs of a certain class into imperative, deterministic code. We outline salient features of Sprite, discuss its implementation of Curry programs, and present benchmarking results. Sprite is the first-to-date operationally complete implementation of Curry. Preliminary results show that ensuring this property does not incur a significant penalty.

Experiential Learning Builds Cybersecurity Self-Efficacy In K-12 Students, Abdullah Konak

Journal of Cybersecurity Education, Research and Practice

In recent years, there have been increased efforts to recruit talented K-12 students into cybersecurity fields. These efforts led to many K-12 extracurricular programs organized by higher education institutions. In this paper, we first introduce a weeklong K-12 program focusing on critical thinking, problem-solving, and igniting interest in information security through hands-on activities performed in a state-of-the-art virtual computer laboratory. Then, we present an inquiry-based approach to design hands-on activities to achieve these goals. We claim that hands-on activities designed based on this inquiry-based framework improve K-12 students’ self-efficacy in cybersecurity as well as their problem-solving skills. The evaluation of …

Student Misconceptions About Cybersecurity Concepts: Analysis Of Think-Aloud Interviews, Julia D. Thompson, Geoffrey L. Herman, Travis Scheponik, Linda Oliva, Alan Sherman, Ennis Golaszewski, Dhananjay Phatak, Kostantinos Patsourakos

Journal of Cybersecurity Education, Research and Practice

We conducted an observational study to document student misconceptions about cybersecurity using thematic analysis of 25 think-aloud interviews. By understanding patterns in student misconceptions, we provide a basis for developing rigorous evidence-based recommendations for improving teaching and assessment methods in cybersecurity and inform future research. This study is the first to explore student cognition and reasoning about cybersecurity. We interviewed students from three diverse institutions. During these interviews, students grappled with security scenarios designed to probe their understanding of cybersecurity, especially adversarial thinking. We analyzed student statements using a structured qualitative method, novice-led paired thematic analysis, to document patterns in …

"Think Before You Click. Post. Type." Lessons Learned From Our University Cyber Security Awareness Campaign, Rachael L. Innocenzi, Kaylee Brown, Peggy Liggit, Samir Tout, Andrea Tanner, Theodore Coutilish, Rocky J. Jenkins

Journal of Cybersecurity Education, Research and Practice

This article discusses the lessons learned after implementing a successful university-wide cyber security campaign. The Cyber Security Awareness Committee (CyberSAC), a group comprised of diverse units across campus, collaborated together on resources, talent, people, equipment, technology, and assessment practices to meet strategic goals for cyber safety and education. The project involves assessing student learning and behavior changes after participating in a Cyber Security Password Awareness event that was run as a year-long campaign targeting undergraduate students. The results have implications for planning and implementing university-wide initiatives in the field of cyber security, and more broadly, higher education at large.

Voice Hacking: Using Smartphones To Spread Ransomware To Traditional Pcs, Bryson R. Payne, Leonardo I. Mazuran, Tamirat Abegaz

Journal of Cybersecurity Education, Research and Practice

This paper presents a voice hacking proof of concept that demonstrates the ability to deploy a sequence of hacks, triggered by speaking a smartphone command, to launch ransomware and other destructive attacks against vulnerable Windows computers on any wireless network the phone connects to after the voice command is issued. Specifically, a spoken, broadcast, or pre-recorded voice command directs vulnerable Android smartphones or tablets to a malicious download page that compromises the Android device and uses it as a proxy to run software designed to scan the Android device’s local area network for Windows computers vulnerable to the EternalBlue exploit, …