Computer Sciences Commons^™

Two Kinds Of Average Approximation Accuracy, Qingzhao Kong, Wanting Wang, Dongxiao Zhang, Wenbin Zhang

Michigan Tech Publications, Part 1

Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is …

Creating And Evaluating Dimensional Analysis Software For University Students, Ac Hýbl

Campus Research Month

Most scientific disciplines depend on mathematics to varying degrees. Real-world problems often include quantifiable measurements with units. For example, chemistry, physics, and pharmacology require flawless unit conversions and dimensional homogeneity to obtain acceptable results. Students often choose to ignore units until the end of the problem-solving process, but this introduces errors and robs students of a deep understanding of units. Current tools for teaching dimensional analysis are limited both in scope and accessibility. Unit Playground addresses this issue by providing an interactive interface to experiment with units and their relationships.

A Study Of A Collaborative Task Management Application Built On React Native Using The Basic Ux Framework, Andrei Modiga

Campus Research Month

Many times it can be difficult to accomplish all this is proposed in a meeting. This project aimed to build a simple planner application using React Native that allows groups of people to collaborate and stay organized. The application was built using the BASIC Framework as a guide, and featured a collaboration feature that enabled users to share tasks, projects, and communicate with one another in order to stay coordinated and productive. The user interface was designed for easy use, allowing for quick and efficient task management within a group. The goal of the application was to provide a useful …

Web Repository Of Southern’S Research Projects, Rebecca Zaldivar, Siegwart Mayr

Campus Research Month

A research repository was created so that Southern Adventist University has a central place for all past, current, and future research projects. This repository is a web application created with the use of the Yii framework that utilizes PHP and SQL. The repository has a user-friendly interface to let authorized users upload the information about their projects. Also, professors and students from different departments can see the list of projects per department.

Creating Characters In A Game Based Learning System, Jaehyun Park, Siegwart Mayr

Campus Research Month

Gamification is the idea of adding video game elements into a non-gaming context, such as for educational or business purposes. Game-based learning takes that idea one step further by not only adding elements of video games, but turning the whole process into a game-like structure.

The Center for Innovation and Research in Computing (CIRC) is currently developing a game-based learning system that can be used in place of a traditional classroom setting. The focus of this research work is on the relationship between users and characters within a course in the Character Module. The Character Module encompasses everything to do …

Interactive Dashboard Of Diabetes In The Us, Marc Butler

Campus Research Month

The contribution of this research project is the construction and interactive dashboard in order to facilitate the visualization of diabetes-related data to the public

Generating University Course Catalogs Via A Php Based Module, Chileleko Chileya, Siegwart Mayr

Campus Research Month

Catalogs are a requirement at any university. The current academic system at Southern Adventist University entails creating catalogs for the school by hand. The current model is very outdated and time consuming especially considering the database of all related school records available. The solution is a module using the PHP based framework called Yii to construct a module that will be integrated into the Southern Adventist University website that will automatically generate formatted catalogs through the frontend of the website. The previous module uses Kuali to store academic data as well as provide an API for third-party applications but was …

Game-Based Learning Activities And Assignments, Samuel Rivera, Siegwart Mayr

Campus Research Month

The Center for Innovation and Research in Computing (CIRC) is creating a web application that uses game-based learning to help students be engaged and collaborate, as an adventure-based quest.

In this research project, the activity module was created for this web application. This module contains assignment creation, completion, and grading. These assignments can be included in the quests and courses. The seamless connection between the activity module and the course module was possible with the Yii framework.

Visualizing Literary Narratives With A Graph-Centered Approach., Meg Ermer

Campus Research Month

The art of storytelling is multifaceted and nonlinear, involving multiple characters, themes, and symbols while often jumping between the present and past. While media forms such as novels can encapsulate these complexities, it is often difficult to visualize a narrative in an easy-to-understand format. Our contribution is a graph-based system to let users organize and visualize those narratives. Events and characters are represented as nodes and their relationships are represented as edges. Neo4J is used as a database management system to store the graph and to run queries on it, and Streamlit and Pyvis are used to represent the database …

Analysis The Role Of The Internet Of Things And Industry 4.0 In Healthcare Supply Chain Using Neutrosophic Sets, Ahmed M. Abdelmouty, Ahmed Abdel-Monem, Samah Ibrahim Abdel Aal, Mahmoud M. Ismail

Neutrosophic Systems with Applications

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes …

Extracting Information From Twitter Screenshots, Tarannum Zaki, Michael L. Nelson, Michele C. Weigle

Modeling, Simulation and Visualization Student Capstone Conference

Screenshots are prevalent on social media as a common approach for information sharing. Users rarely verify before sharing screenshots whether they are fake or real. Information sharing through fake screenshots can be highly responsible for misinformation and disinformation spread on social media. There are services of the live web and web archives that could be used to validate the content of a screenshot. We are going to develop a tool that would automatically provide a probability whether a screenshot is fake by using the services of the live web and web archives.

Digital Game-Based Approach To Math Learning For Students, Gul Ayaz, Katherine Smith

Modeling, Simulation and Visualization Student Capstone Conference

Mathematics is an important subject that is pervasive across many disciplines. It is also a subject that has proven to be challenging to both teach and learn. Students face many challenges with learning math such as a lack of motivation and anxiety. To address these challenges, game-based learning has become a popular approach to stimulate students and create a more positive classroom environment. It can serve as an alternative or supplement to traditional teaching and can better engage students while developing a positive attitude toward learning. The use of games in a classroom can create a more exciting and engaging …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Assessing The Frequency And Severity Of Malware Attacks: An Exploratory Analysis Of The Advisen Cyber Loss Dataset, Ahmed M. Abdelmagid, Farshid Javadnejad, C. Ariel Pinto, Michael K. Mcshane, Rafael Diaz, Elijah Gartell

Modeling, Simulation and Visualization Student Capstone Conference

In today's business landscape, cyberattacks present a significant threat that can lead to severe financial losses and damage to a company's reputation. To mitigate this risk, it is essential for stakeholders to have an understanding of the latest types and patterns of cyberattacks. The primary objective of this research is to provide this knowledge by utilizing the Advisen cyber loss dataset, which comprises over 137,000 cyber incidents that occurred across various industry sectors from 2013 to 2020. By using text mining techniques, this paper will conduct an exploratory data analysis to identify the most common types of malware, including ransomware. …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …

Automatic Generation Of Virtual Work Guide For Complex Procedures: A Case, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Practical work guides for complex procedures are significant and highly affect the efficiency and accuracy of on-site users. This paper presents a technique to generate virtual work guides automatically for complex procedures. Firstly, the procedure information is extracted from the electronic manual in PDF format. And then, the extracted procedure steps are mapped to the virtual model parts in preparation for animation between adjacent steps. Next, smooth animations of the procedure are generated based on a 3D natural cubic spline curve to improve the spatial ability of the work guide. In addition, each step's annotation is automatically adjusted to improve …

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

Modeling, Simulation and Visualization Student Capstone Conference

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

Modeling, Simulation and Visualization Student Capstone Conference

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

Assessing Frustration Towards Venezuelan Migrants In Columbia: Path Analysis On Newspaper Coded Data, Brian Llinás, Guljannat Huseynli, Erika Frydenlund, Katherine Palacia, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This study analyzes the impact of Venezuelan migrants on local frustration levels in Colombia. The study found a relationship between the influx of Venezuelan migrants and the level of frustration among locals towards migrants, infrastructure, government, and geopolitics. Additionally, we identified that frustration types have an impact on other frustrations. The study used articles from a national newspaper in Colombia from 2015 to 2020. News articles were coded during a previous study qualitatively and categorized into frustration types. The code frequencies were then used as variables in this study. We used path modeling to statistically study the relationship between dependent …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

From Policy Promotion To Research Output: Brief Analysis Of Technical Challenges Of Hospital-Led Artificial Intelligence Research, Yu Zhuang, Cheng Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, artificial intelligence has become a key direction of medical and health-related research and a hot spot of international competition. In order to investigate the current situation and challenges in hospital-led artificial intelligence researched, this study selects 14 national pilot hospitals to promote the high-quality development of public hospitals as samples, adopts a combination of quantitative and qualitative methods, analyzes the research articles related to artificial intelligence published by the sample hospitals in recent years, and analyzes the technical challenges in the hospital-led artificial intelligence research. The results show that although the number of hospital-led artificial intelligence research …

Analysis The Role Of The Internet Of Things And Industry 4.0 In Healthcare Supply Chain Using Neutrosophic Sets, Ahmed M. Abdelmouty, Ahmed Abdel-Monem, Samah Ibrahim Abdel Aal, Mahmoud M. Ismail

Neutrosophic Systems with Applications

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes …

Novel Paradigm For Ai-Driven Scientific Research: From Ai4s To Intelligent Science, Feiyue Wang, Qinghai Miao

Bulletin of Chinese Academy of Sciences (Chinese Version)

No abstract provided.

Investigating The Comprehension Of Gpt's Interpretation Of Word Meanings, Longge Yuan

Research & Creative Achievement Day

ChatGPT is a large-scale language model developed by OpenAI. Its purpose is to start a conversation with people and provide them with information ranging from simple facts to more complex topics. Trained on a large amount of text data, ChatGPT has shown us that it can understand human language and respond in a meaningful way. In this paper, we show a way to judge whether ChatGPT can accurately understand the various meanings of words and use them based on the answers output by ChatGPT. The five dimensions, each scored up to 5 points, are word explanation, etymology, synonyms and antonyms, …

Ecomves: Enhancing Comves Using Data Piggybacking For Resource Discovery At The Network Edge, Sanzida Hoque

Theses

Over the past few years, Augmented Reality (AR) and Virtual Reality (VR) have emerged as highly popular technologies that demand rapid and efficient processing of data with low latency and high bandwidth, in order to enable seamless real-time interaction between users and the virtual environment. This presents challenges for network infrastructure design, which can be addressed through edge computing. However, edge computing also presents challenges, such as selecting the appropriate edge server for computing tasks in dynamic networks with rapidly changing resource availability. Named Data Networking (NDN) is a potential future Internet architecture that could provide a balanced distribution of …

Nviz: Unraveling Neural Networks Through Visualization, Kevin Hoffman

Mathematics, Computer Science & Statistics Presentations

The growing utility of artificial intelligence (AI) is attributed to the development of neural networks. These networks are a class of models that make predictions based on previously observed data. While the inferential power of neural networks is great, the ability to explain their results is difficult because the underlying model is automatically generated. The AI community commonly refers to neural networks as black boxes because the patterns they learn from the data are not easily understood. This project aims to improve the visibility of patterns that neural networks identify in data. Through an interactive web application, NVIZ affords the …