Computer Sciences Commons^™

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

Application Development For Goods Management System Towards Waste Minimization Options, Wan Ahmad Nadzim

Student Works (2020-2029)

Waste is a global concern. Increasing waste generation levels are causing the increase of waste management costs. In fact, the greatest costs of waste are measured by its impacts to the environment, society and economy. Prevention is better than cure—since all wastes come from goods, thus the best way to manage wastes is to manage goods. An application model of goods management system has been developed using GIS (Geographic Information System) and app prototyping. The primary purpose of goods management is to maximize goods and minimize waste by promoting reduction and reuse. GIS was used to perform spatial analyses on …

The Vden: An Ipt Designed For Usability, Alexander Jaeger

Theses and Dissertations

Currently, headsets are the emphasis of Virtual Reality and the primary reason is that they are easy to develop and demo, lightweight, simple, and aordable. Historically, IPTs have required dedicated rooms due to their large size and experts in order to run and maintain them. These devices are expensive and can be complicated due to mirrors, the number of displays, and the overwhelming size of the infrastructure and computing clusters.The research in minimial IPTs have led up to the use of front-projection and dual GPU gaming computers, however this only works in a few cases. This thesis presents new work …

Heuristic One-Time Pad Encryption, James B. Joyce

Theses and Dissertations

Heuristic One-Time Pad (HOP) represents a methodology that is compliant with the requirements as established by Claude Shannon for a globally scalable and permanent encryption solution. The technique is not limited to encryption, rather, it also encompasses dynamic hashing and authentication. The two key Shannon-identified problems of continuous random number generation and secure distribution of encryption keys have been solved and are integral to the method. At each step of the algorithms, entropy is ‘1’ and unicity is ‘infinite’. All encryption key generation is independent of the value of the data being encrypted, and is dependent upon a combination of …

Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas

SMU Data Science Review

In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …

A Data Science Approach To Defining A Data Scientist, Andy Ho, An Nguyen, Jodi L. Pafford, Robert Slater

SMU Data Science Review

In this paper, we present a common definition and list of skills for a Data Scientist using online job postings. The overlap and ambiguity of various roles such as data scientist, data engineer, data analyst, software engineer, database administrator, and statistician motivate the problem. To arrive at a single Data Scientist definition, we collect over 8,000 job postings from Indeed.com for the six job titles. Each corpus contains text on job qualifications, skills, responsibilities, educational preferences, and requirements. Our data science methodology and analysis rendered the single definition of a data scientist: A data scientist codes, collaborates, and communicates – …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

A Data Driven Approach To Forecast Demand, Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, Brent Allen

SMU Data Science Review

Abstract. In this paper, we present a model and methodology for accurately predicting the following quarter’s sales volume of individual products given the previous five years of sales data. Forecasting product demand for a single supplier is complicated by seasonal demand variation, business cycle impacts, and customer churn. We developed a novel prediction using machine learning methodology, based upon a Dense neural network (DNN) model that implicitly considers cyclical demand variation and explicitly considers customer churn while minimizing the least absolute error between predicted demand and actual sales. Using parts sales data for a supplier to the oil and gas …

Ordinal Hyperplane Loss, Bob Vanderheyden

Doctor of Data Science and Analytics Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning …

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin

Master of Science in Computer Science Theses

This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …

Towards Interpretable Machine Learning With Applications To Clinical Decision Support, Zhicheng Cui

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning models have achieved impressive predictive performance in various applications such as image classification and object recognition. However, understanding how machine learning models make decisions is essential when deploying those models in critical areas such as clinical prediction and market analysis, where prediction accuracy is not the only concern. For example, in the clinical prediction of ICU transfers, in addition to accurate predictions, doctors need to know the contributing factors that triggered the alert, which factors can be quickly altered to prevent the ICU transfer. While interpretable machine learning has been extensively studied for years, challenges remain as among …

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

On The Human Factors Impact Of Polyglot Programming On Programmer Productivity, Phillip Merlin Uesbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Polyglot programming is a common practice in modern software development. This practice is often considered useful to create software by allowing developers to use whichever language they consider most well suited for the different parts of their software. Despite this ubiquity of polyglot programming there is no empirical research into how this practice affects software developers and their productivity. In this dissertation, after reviewing the state of the art in programming language and linguistic research pertaining to the topic, this matter is investigated by way of two empirical studies with 109 and 171 participants solving programming tasks. Based on the …

On Algebraic Structures And Automaton For Optimal Computer Network Design And Performance Study, Xiangrong Ma

UNLV Theses, Dissertations, Professional Papers, and Capstones

For decades, study of computer networks has been concentrated on the use of the well-established OSI or TCP/IP reference models that have found tremendous success in the actual implementation of various network structures and protocols. Lack of theoretical foundation, this implementation-driven, empirical approach, however, is experiencing insurmountable difficulties in delivering, tuning for, and sustaining the promised peak performance of a computer system that is so heavily dependent on the performance of the underline computer networks. This issue is becoming particularly prevalent in today’s cloud-based high-performance computing environment where CPU-time-extensive computation loads need to get distributed among computing machines through high-speed …

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us …

Learning Nearest Neighbor Graphs From Noisy Distance Samples, Blake Mason, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and …

Maxgap Bandit: Adaptive Algorithms For Approximate Ranking, Sumeet Katariya, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm's gap requires …

Research On Simulation Platform For Equipment System Analysis, Yuping Li, Shaojie Mao, Zhenqi Ju, Zhou Fang, Guoqiang Yan

Journal of System Simulation

Abstract: With the development of equipment construction from platform-centric to network-centric, simulation analysis of equipment system is an important means and tool to support the transformation and development of equipment construction under the condition of multi-task joint operation. Starting from the requirement of system simulation analysis, a cloud-based equipment system simulation architecture is proposed to realize flexible and configurable simulation environment according to task requirements; and a high-performance simulation framework is conducted, which provides strong support for different application modes, such as parallel hyper-real-time and distributed simulation deduction. The unified description, organization and management method of model and data resources …

Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan

Journal of System Simulation

Abstract: To solve the problem of unbalanced resource requirements and low resource utilization when the same type of tasks are executed in parallel in the cloud manufacturing system, a task resource scheduling model with the goal of minimizing cost, minimizing time, maximizing reliability and optimizing quality is established. A non-dominated sorting genetic algorithm based on reference points (NSGA-III) is adopted to solve the model by combining real number matrix coding and crossover and mutation based on real number coding instead of common evolutionary strategy. And an optimal decision strategy based on combination of analytic hierarchy process and entropy value method …

Research On Source Seeking Methods Of Harmful Gas Leakage In Chemical Industry Parks, Zhao Yong, Bin Chen, Xiaodong Wang, Zhengqiu Zhu, Rongxiao Wang, Xiaogang Qiu

Journal of System Simulation

Abstract: Chemical production safety accidents often lead to harmful gas leakage, causing serious environmental damage and casualties. Mastering the information of leaking source quickly can assist in emergency disposal decisions and reduce the harm of accidents. In this paper, Entrotaxis algorithm is used to guide the ground source seeking equipment to move to the vicinity of the leaking source quickly and autonomously in a chemical park scene, and to master the source information. According to the characteristics of the chemical industry park scene, this paper applies intermittent search module into Entrotaxis algorithm and proposes a robust and suitable algorithm (Entrotaxis-Jump …

Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren

Journal of System Simulation

Abstract: Multi-objective optimization of aerodynamic layout is a key technology in the design of vehicles. The overall configuration of the shape parameters is optimized with a double swept-shaped wave shape as the basic configuration. We use NSGA-Ⅱ multi-objective genetic algorithm, take the aircraft double sweep angle as the design variable, consider the maximum takeoff weight, range, volume ratio and other performance indicators, use Elman neural network to establish the relationship between shape parameters and performance parameters, and establish constraints based on mission planning requirements. The Pareto optimal solution set is obtained by using optimized design and the individuals with …

Fast Simulation Of Yacht In Calm Water Based On Improved Savitsky Method, Xiaochen Li, Yin Yong

Journal of System Simulation

Abstract: An improved Savitsky method is proposed to increase the accuracy of mathematical model of yacht in fast simulation. Three degrees of freedom mathematical model is established to simulate the motion in displacement regime. warped hull forms of yachts are simplified into prismatic hull and improved Savitsky method is used to calculate resistance. The simulation is carried out in pre-planing regime and planing regime. The validity of the proposed method has been assessed by comparing results with previous experimental data, which shows good agreements. The improved method runs well and the result is accurate when it is added to the …

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi

Master's Projects

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different …

Point Cloud Processing With Neural Networks, Stephanie Miller, Jiahao Li

All Computer Science and Engineering Research

In this project, we explore new techniques and architectures for applying deep neural networks when the input is point cloud data. We first consider applying convolutions on regular pixel and voxel grids, using polynomials of point coordinates and Fourier transforms to get a rich feature representation for all points mapped to the same pixel or voxel. We also apply these ideas to generalize the recently proposed "interpolated convolution", by learning continuous-space kernels as a combination of polynomial and Fourier basis kernels. Experiments on the ModelNet40 dataset demonstrate that our methods have superior performance over the baselines in 3D object recognition.

Study On Optimization Of Shore Bridge Operator Scheduling Considering The Influence Of Ambient Temperature, Yibin Wang, Haihong Yu, Danlan Xie

Journal of System Simulation

Abstract: The refinement of port management puts forward new requirements for its service capacity, and human factors are the key factors affecting the port service capacity. It is a new and feasible research direction to improve port operation efficiency by adjusting human factors. Based on the theoretical model of temperature affecting efficiency, this paper uses FlexSim system simulation software to analyze the change of employee's working efficiency with time, and studies the optimal scheduling strategy of shore bridge operators under different temperatures. Finally, it is concluded that when the ambient temperature is suitable for human body, the eight-hour scheduling …

Synergic Production Scheduling Method For Distributed Multi-Plants Based On Fusion Decision Tree, Wang Yan, Tianlun Jiang

Journal of System Simulation

Abstract: In the synergic production scheduling optimization problem of distributed multi-plants, it is necessary to consider the two stages of job allocation between factories and job scheduling in factories at the same time. This paper first establishes a distributed multi-plant scheduling model with total cost and advance/delay as the optimization objectives, and then proposes a nested optimization algorithm framework integrating ID3 decision tree with Gauss particle swarm optimization. In this framework, independent scheduling optimization within the factory is nested in the process of inter factory allocation optimization, and elite retention strategy is introduced to improve the algorithm optimization. In …