Computer Sciences Commons^™

Modeling And Simulation Of Traction System In Hybrid Shunting Locomotive, Kun Shen, Wang Ling, Wang Jian, Xiaoyang Yao

Journal of System Simulation

Abstract: The structure of traction system in hybrid shunting locomotive was analyzed, and the working principles of traction system under multiform power models were introduced. Based on which, the models of traction system and power accumulators of this hybrid shunting locomotive were built by MATLAB/Simulink, then the simulation experiments on hybrid shunting locomotive with the power models of hybrid power, and pure diesel generator power or pure accumulator power were done respectively. The simulation results show that hybrid shunting locomotive can achieve reliable operation in diflFerent conditions by the designed main circuit structure, parameters and system energy management strategy.

Impact Of Number Of Solar Cells In Parallel/Series And Temperature On Junction Capacitance, Zhigang Zhao, Chunjie Zhang, Gao Pu, Hutang Sang, Xiaoqian Li

Journal of System Simulation

Abstract: The study to the dynamic parameters of the photovoltaic cell is of crucial importance for the design of the corresponding afterward stage controller in the photovoltaic power generation system. Beginning with the analysis to physical mechanism of the photovoltaic cell, more accurate equivalent formulation of the junction voltage and bias voltage of the photovoltaic cells /module/array was deduced, and the explicit formulation between the bias voltage and output voltage was received by using the Lambert W function based on equivalent series resistance and the saturation current. The formulation was adopted which combined the engineering mathematics model and intrinsic carrier …

Evaluation And Analysis Of New Method Of Measurement Of Target Scattering Matrix, Zhenyu Huang, Zhao Bo, Huanyao Dai, Liandong Wang, Xuequan Zhou

Journal of System Simulation

Abstract: The basic theory of radar polarization signal processing is measurement of the target scattering matrix. The current algorithm of measurement of the target scattering matrix is gotten through orthogonal dual polarization channel in time-sharing or simultaneously, which demands orthogonal polarization measurement signals with complex coding. The measuring accuracy is higher. The complexity and the cost of the system are relatively higher. Scattering matrix can also be obtained by making use of the spatial polarization characteristics of the antenna to processing the radar echo without structure reformation of the radar. It only. needs to renewal measurement technology. The comparison and …

Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Graduate Student Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules In Breast Cancer, Marzieh Ayati, Mark R. Chance, Mehmet Koyuturk

Computer Science Faculty Publications

Motivation Protein phosphorylation is a ubiquitous mechanism of post-ranslational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.

Results We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this …

Identify Rna-Associated Subcellular Localizations Based On Multi-Label Learning Using Chou’S 5-Steps Rule, Hao Wang, Yijie Ding, Jijun Tang Ph.D., Quan Zou, Fei Guo

Faculty Publications

Background: Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem.

Results: In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to …

Discovery And Characterization Of Circular And Chimeric Rnas In Complex Diseases, Xiaoxin Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Non-co-linear RNAs, including circular and chimeric RNAs, have been investigated in a broad range of cellular processes and various diseases. The current biogenesis models of circular and chimeric RNAs depend on RNA splicing mechanisms. We hypothesized that some of the non-co-linear RNAs are products of an alternative mechanism other than RNA splicing. To test this hypothesis, we developed a method, which is independent of genome annotations and splicing signals, to identify Circular and Chimeric RNAs of All Types (C2AT or CAT for simplicity) by analyzing the RNA species profiled by whole genome RNA sequencing. Statistical analysis and experimental validation revealed …

Mapping Transcription Factor Networks And Elucidating Their Biological Determinants, Yiming Kang

McKelvey School of Engineering Graduate Student Theses & Dissertations

A central goal in systems biology is to accurately map the transcription factor (TF) network of a cell. Such a network map is a key component for many downstream applications, from developmental biology to transcriptome engineering, and from disease modeling to drug discovery. Building a reliable network map requires a wide range of data sources including TF binding locations and gene expression data after direct TF perturbations. However, we are facing two roadblocks. First, rich resources are available only for a few well-studied systems and cannot be easily replicated for new organisms or cell types. Second, when TF binding and …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

Designing A Relational Model To Identify Relationships Between Suspicious Customers In Anti-Money Laundering (Aml) Using Social Network Analysis (Sna), Abdul Khalique Shaikh, Malik Al-Shamli, Amril Nazir

All Works

The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge …

Molecule Optimization By Explainable Evolution, Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Machine Learning Faculty Publications

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science, and drug discovery. This paper develops a novel algorithm for optimizing molecular properties via an Expectation-Maximization (EM) like explainable evolutionary process. The algorithm is designed to mimic human experts in the process of searching for desirable molecules and alternate between two stages: the first stage on explainable local search which identifies rationales, i.e., critical subgraph patterns accounting for desired molecular properties, and the second stage on molecule completion which explores the larger space of molecules containing good rationales. We test our approach against various baselines …

Multi-Modal Classification Using Images And Text, Stuart J. Miller, Justin Howard, Paul Adams, Mel Schwan, Robert Slater

SMU Data Science Review

This paper proposes a method for the integration of natural language understanding in image classification to improve classification accuracy by making use of associated metadata. Traditionally, only image features have been used in the classification process; however, metadata accompanies images from many sources. This study implemented a multi-modal image classification model that combines convolutional methods with natural language understanding of descriptions, titles, and tags to improve image classification. The novelty of this approach was to learn from additional external features associated with the images using natural language understanding with transfer learning. It was found that the combination of ResNet-50 image …

Sars-Cov-2 Pandemic Analytical Overview With Machine Learning Predictability, Anthony Tanaydin, Jingchen Liang, Daniel W. Engels

SMU Data Science Review

Understanding diagnostic tests and examining important features of novel coronavirus (COVID-19) infection are essential steps for controlling the current pandemic of 2020. In this paper, we study the relationship between clinical diagnosis and analytical features of patient blood panels from the US, Mexico, and Brazil. Our analysis confirms that among adults, the risk of severe illness from COVID-19 increases with pre-existing conditions such as diabetes and immunosuppression. Although more than eight months into pandemic, more data have become available to indicate that more young adults were getting infected. In addition, we expand on the definition of COVID-19 test and discuss …

Gophish: Implementing A Real-World Phishing Exercise To Teach Social Engineering, Andy Luse, Jim Burkman

Journal of Cybersecurity Education, Research and Practice

Social engineering is a large problem in our modern technological world, but while conceptually understood, it is harder to teach compared to traditional pen testing techniques. This research details a class project where students implemented a phishing exercise against real-world targets. Through cooperation with an external corporate partner, students learned the legal, technical, behavioral, analysis, and reporting aspects of social engineering. The outcome provided both usable data for a real-world corporation as well as valuable educational experience for the students.

Applying High Impact Practices In An Interdisciplinary Cybersecurity Program, Brian K. Payne, Lisa Mayes, Tisha Paredes, Elizabeth Smith, Hongyi Wu, Chunsheng Xin

Journal of Cybersecurity Education, Research and Practice

The Center for Cybersecurity Education and Research at Old Dominion University has expanded its use of high impact practices in the university’s undergraduate cybersecurity degree program. Strategies developed to promote student learning included learning communities, undergraduate research, a robust internship program, service learning, and electronic portfolios. This paper reviews the literature on these practices, highlights the way that they were implemented in our cybersecurity program, and discusses some of the challenges encountered with each practice. Although the prior literature on high impact practices rarely touches on cybersecurity coursework, the robust evidence of the success of those practices provides a sound …

An Assessment Of Internet Use And Cyber-Risk Prevalence Among Students In Selected Nigerian Secondary Schools, Adeola O. Opesade Dr, Abiodun O. Adetona Mr

Journal of Cybersecurity Education, Research and Practice

The use of the Internet has become highly pervasive among adolescents. While these people derive numerous benefits from their use of this technology, they are also faced with a challenge of being exposed to many cyber risks. Nigeria is a developing country with a teeming population of adolescents who are regular users of the Internet, but with inadequate research on adolescent Internet safety. There is therefore, a need to conduct studies on child online risks in Nigeria, to help evaluate the enormity of child online abuses. The present study investigated Internet use and cyber-risk prevalence among four hundred secondary school …

Automation Of Crawling Blogosphere Based On Pattern Recognition, Anal Kanti Roy

Theses and Dissertations

Social media plays an important role in the propagation and dissemination of ideas and thoughts. Compared to other social media platforms, blogs provide a convenient platform for users to post detailed information, engage in active discussions and share the content on other social media sites, such as Facebook and Twitter. Thus, the blogosphere has been an enormous and ever-growing part of the open-source intelligence. In order to track and monitor online social behavior particularly from blogs, the first challenging part is to mine the vast pool of unstructured data. To scale up this process and cope with the continuously changing …

Challenges When Identifying Migration From Geo-Located Twitter Data, Caitrin Armstrong, Ate Poorthuis, Matthew Zook, Derek Ruths, Thomas Soehl

Geography Faculty Publications

Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code …

Landlords Of The Digital World: How Territoriality And Social Identity Predict Playing Intensity In Location-Based Games, Samuli Laato, Bastian Kordyaka, A.K.M. Najmul Islam, Konstantinos Papangelis

Presentations and other scholarship

Popular location-based games (LBGs) such as Pokemon GO have been downloaded hundreds of millions of times and have been shown to have a positive impact on mild exercise and social well-being of their players. Several currently popular LBGs introduce a gamified implementation of territorial conflict, where players are divided into teams that battle for the ownership of geographically distributed points of interest. We investigate how social factors and territoriality influence playing intensity in the context of Pok´emon GO. Using reasoning from social identity theory, we propose a structural model connecting territoriality, sociality and playing intensity. To test the model, we …

Adverse Health Effects Of Kratom: An Analysis Of Social Media Data, Abdullah Wahbeh, Tareq Nasralah, Omar El-Gayar, Mohammad A. Al-Ramahi, Ahmed El Noshokaty

Computer Information Systems Faculty Publications (Archived)

This study investigates the adverse healthcare effects associated with the use of kratom. Using machine learning techniques, we analyzed a total of 36,516 users’ posts related to kratom. The results and analysis showed that social media could help identify important insights related to the use of kratom. The sentiment and emotion analyses showed that the kratom experience was negative and largely associated with anger, fear, disgust, and sadness. The results from. topic modeling showed that kratom is associated with a number of healthcare issues such as rashes and itching, urination, constipation, loss of appetite/weight, dry mouth, seizures, nausea, heartburn, dehydration, …

Low Light Image Enhancement Via Global And Local Context Modeling, Aditya Arora, Muhammad Haris, Syed Waqas Zamir, Munawar Hayat, Fahad Shahbaz Khan, Ling Shao, Ming-Hsuan Yang

Computer Vision Faculty Publications

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives to model dependencies, existing data-driven deep models do not exploit the contexts at various spatial scales to address low-light image enhancement. These contexts can be crucial towards inferring several image enhancement tasks, e.g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent. To this end, we introduce a context-aware deep network for low-light image enhancement. First, …

Vehicle-Life Interaction In Fog-Enabled Smart Connected And Autonomous Vehicles, Bushra Feroz, Amjad Mehmood, Hafsa Maryam, Sherali Zeadally, Carsten Maple, Munam Ali Shah

Information Science Faculty Publications

Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, …

Collaborative Behavior, Performance And Engagement With Visual Analytics Tasks Using Mobile Devices, Lei Chen, Hai-Ning Liang, Feiyu Lu, Konstantinos Papangelis, Ka Lok Man, Yong Yue

Articles

Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visu‐ alizations. This research investigates the use of co‐located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non‐Shared) and how being placed at various position arrangements (Corner‐to‐Corner, Face‐to‐Face, and Side‐by‐Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 partici‐ pants divided into 6 groups (2 modes×3 positions) using a tool that we developed to support the exploration of 3D visual …

Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …

A Computational Approach Within Medical Research, Ryan Christopher Hogan

Theses and Dissertations

Within the context of medical image diagnosis, we explore novel computational models to facilitate the detection of two medical conditions that burden our society. In particular, this research focuses on the use of deep learning models for the detection of Alzheimer’s Disease in Magnetic Resonance images (MRI) scans, as well as the detection of heart arrhythmias from electrocardiogram (ECG) recordings. We propose a novel architecture that depends on the 3D-CNN model to classify between MRI scans of cognitively healthy individuals and AD patients. Moreover, we explore the use of LSTM deep learning models to detect abnormal heart arrhythmias that present …

Cassie Dibenedetti's Portfolio, Cassie Dibenedetti

Honors College Portfolios

The Data Science B.S. curriculum surpasses bare collection and interpretation of data. An expansion of these applications are foundational courses that include critical thinking, articulate translation, and data immersion. The mathematical pillar of the Data Science program teaches students fundamental calculus and statistical skills, and the computer science pillar teaches students the utilization of software when working with data. Most importantly, the data science pillar intertwines the two and teaches students to provide practical solutions to problems involving data.

This portfolio serves as a glimpse into the Data Science student’s curriculum. Viewers can observe the varying skills required of a …

How To Guarantee Fairness Of Grading Without Sacrificing Privacy?, Vladik Kreinovich, Olga Kosheleva, Christian Servin

Departmental Technical Reports (CS)

Everyone -– instructors and students –- want to make sure that grading of each test is fair, that the only thing that determines the students’ grade is their level of knowledge, that different students get the same penalty for the same mistake, irrespective of their gender, of their past grades, of their behavior in the class, of how many classes they missed, etc. How to help instructors achieve this goal? How to make sure that students are convinced that grading was indeed fair? In this paper, we describe possible measures: anonymous submissions, forming (and posting for all the student to …

Fireeye: Cybersecurity In Action, Singapore Management University

Perspectives@SMU

FireEye built its success on its ‘Human + AI’ philosophy. But can a cybersecurity firm get ahead of the attackers and predict an attack…on itself?

Evaluating Grasping Visualizations And Control Modes In A Vr Game, Alex Adkins, Lorraine Lin, Aline Normoyle, Ryan Canales, Yuting Ye, Sophie Jörg

Computer Science Faculty Research and Scholarship

A primary goal of the Virtual Reality(VR) community is to build fully immersive and presence-inducing environments with seamless and natural interactions. To reach this goal, researchers are investigating how to best directly use our hands to interact with a virtual environment using hand tracking. Most studies in this field require participants to perform repetitive tasks. In this article, we investigate if results of such studies translate into a real application and game-like experience. We designed a virtual escape room in which participants interact with various objects to gather clues and complete puzzles. In a between-subjects study, we examine the effects …