Computer Sciences Commons^™

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …

Applications Of Parallel Discrete Event Simulation, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents three applications of parallel discrete event simulation (PDES), which describe the motivation for and the benefits of using PDES, the kinds of synchronization algorithms that are used, and scaling behavior with these different synchronization algorithms.

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …

Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization, Isurumali Neththikumara, Todd Satogata, Alex Bogacz, Ryan Bodenstein, Arthur Vandenhoeke

College of Sciences Posters

Beamline optics design for recirculatory linear accelerators requires special attention to suppress beam instabilities arising due to collective effects. The impact of these collective effects becomes more pronounced with the addition of energy recovery (ER) capability. Jefferson Lab’s multi-pass, multi-GeV ER proposal for the CEBAF accelerator, ER@CEBAF, is a 10- pass ER demonstration with low beam current. Tighter control of the beam parameters at lower energies is necessary to avoid beam break-up (BBU) instabilities, even with a small beam current. Optics optimizations require balancing both beta excursions at high-energy passes and overfocusing at low-energy passes. Here, we discuss an optics …

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections, Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti, Sorawich Maichum

College of Sciences Posters

We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on …

Medical Devices And Cybersecurity, Hilary Finch

School of Cybersecurity Posters

I begin by looking at the role of cybersecurity in the medical world. The healthcare industry adopted information technology quite quickly. While the advancement was obviously beneficial and necessary to keep up with an ever-growing demand, the healthcare industry did not place any kind of pointed focus on the security of their IT department, or the sensitive information housed therein.

When rapid advancements of technology outpaced the gradual advancement of hospital cybersecurity, security concerns became a difficult issue to control. There is a serious need for more advancements in hospital security. Each interconnected medical device has its own unique security …

A Technological Skills Gap: What Can We Do About It?, Will Moore

Cybersecurity Undergraduate Research Showcase

In the last thirty years, education has transformed faster than ever before. As a society we have developed new classroom technologies that allow for better communication and access to more information than ever before. However, at the same time, education seems rockier than ever before. Several major companies, including Tesla, Apple, Google and Netflix, no longer require a college degree for employment, and many companies, such as Amazon, Google, and Microsoft, have begun programs to train their own employees in skills and create their own certification programs to help employees learn necessary skills (Akhtar, O’Donnell). Why do these major corporations …

Application Of U.S. Sanction Laws And Ransomware Payments, Trinity Woodbury

Cybersecurity Undergraduate Research Showcase

Ransomware is a major threat that widely affects individuals and organizations, including businesses. Ransomware victims face the situation of potentially paying ransom payments to threat actors, some of whom might be foreign-based criminals. Ransomware affects victims from all sectors and industries.

Cova Cci Undergrad Cyber Research, Nana Jeffrey

Cybersecurity Undergraduate Research Showcase

Is your digital assistant your worst enemy? Modern technology has impacted our lives in a positive way making tasks that were once time consuming become more convenient. For example a few years ago writing down your grocery list with a paper and pen was a norm, now with technology we have access to IoT devices such as smart fridges that can inform us on what items are low in stock, send a message to our digital assistants such as iOS Siri and Amazon's Alexa to remind us to buy those groceries. Although these digital assistants have helped make our daily …

How Online Platforms Are Used By Child Predators And What Are The Effective Preventive Measures?, Kayla Macpherson

Cybersecurity Undergraduate Research Showcase

As technology has evolved greatly in the twenty-first century alone, younger generations have had an opportunity to grow up with devices at their fingertips none have ever before. This accessibility has strengthened their ability of quick use, skill, and a strong feeling of comfort with using and having access to technology. There seems to be more children with access to the internet than there are without, but there is also an ongoing issue behind the screens.

A Super Fast Algorithm For Estimating Sample Entropy, Weifeng Liu, Ying Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

: Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexity of a time series, which is essentially defined as − log(B/A), where B denotes the number of matched template pairs with length m and A denotes the number of matched template pairs with m + 1, for a predetermined positive integer m. It has been widely used to analyze physiological signals. As computing sample entropy is time consuming, the box-assisted, bucket-assisted, x-sort, assisted sliding box, and kd-tree-based algorithms were proposed to accelerate its computation. These algorithms require O(N²) or …

Understanding The Mechanism Of Deep Learning Frameworks In Lesion Detection For Pathological Images With Breast Cancer, Wei-Wen Hsu, Chung-Hao Chen, Chang Hao, Yu-Ling Hou, Xiang Gao, Yun Shao, Xueli Zhang, Jingjing Wang, Tao He, Yanhong Tai

Electrical & Computer Engineering Faculty Publications

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end …

Diabetic Foot Exam System, Stephanie Trusty

Undergraduate Research Symposium

The diabetic foot exam system aims to perform certain aspects of the dermatological and musculoskeletal assessments that are typical to a 3-minute diabetic foot exam. Utilizing the RaspberryPi computer and camera module, the system seeks to capture a series of images of the patient’s foot. It then evaluates these images for calluses, blisters, and three types of deformities: claw toe deformities, hammertoe deformities, and bunions. This evaluation is performed using a trained TensorFlow image classification model, which categorizes the image as a callus, blister, or deformity. The system was tested using six different images: four callus images, a hammertoe deformity …

Covid-19 Classroom Occupancy Detection System, Stephanie Trusty

Undergraduate Research Symposium

The classroom occupancy detection system aims to limit the spread of COVID-19 and support mitigation efforts advised by national and international health organizations by enforcing social distancing in classroom environments. Utilizing the RaspberryPi computer and its compatible camera module, the system accomplishes this by capturing an overhead image of a classroom and assessing the image for violations. Here, violations are defined as the presence of adjacent occupied seats. As such, for an acceptable state to be detected, there must be at least one vacant seat between all students seated in the classroom. The system communicates the classroom’s state with two …

Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna

Undergraduate Research Symposium

Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …

Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin

Undergraduate Research Symposium

Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …

Did They Really Tweet That?, Caleb Bradford, Michael L. Nelson (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Networks Of Disinformation: The Proliferation Of Hate Speech In Chile And Colombia During The Venezuelan Migration Crisis, Isabelle Valdes, Erika Frydenlund (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Module 1: Introduction To Technology Foresight, Risk Management, And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 1 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

Module 2: Case Studies Of Ai And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 2 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

Module 3: Technology Foresight And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 3 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

An Assessment Of Scientific Claim Verification Frameworks: Final Presentation, Ethan Landers, Jian Wu (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Insurtech And Distribution, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions regarding InsurTech and distribution.

Disinformation About Mental Health On Tiktok, Dani Graber, Anne Perrotti (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Protecting Blind Screen-Reader Users From Deceptive Content, Ash Dobrenen, Vikas Ashok (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

Visually impaired people who want to use a computer rely on screen readers to independently do this. This research focuses on beginning to build a chrome extension in order to help users more safely navigate the internet using a screen reader. to begin collecting the data, a screen reader was used to help determine items in the website that might take the user somewhere they did not mean to go since the link or image was not sufficiently able to be described by the screen reader. Next, those items were tagged with ’data-attribute=”deceptive”’. After, those data-attributes were extracted and tagged …

Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Point Cloud-Based Mapper For Qcd Analysis, Tareq Alghamdi, Yasir Alanazi, Manal Almaeen, Nobuo Sato, Yaohang Li

The Graduate School Posters

In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mapping. Data that connects parameters to detector level events is used to train the proposed model. A point cloud is used to describe a series of events that keeps the permutation invariant property and geometric correlations of the events while being flexible with the number of events in the input. The trained Point Cloud-based Variational Autoencoder functions as an effective inverse function from detector level events to …

Insurtech And Actuarial, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions regarding InsurTech and actuarial work.