Proceedings of the 2023 ACM Southeast Conference最新文献

Visual Analytics in Software Maintenance: A Systematic Literature Review 软件维护中的可视化分析:系统的文献综述

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587022

Kaihua Liu, S. Reddivari

引用次数: 0

Cyberattack Repository: A Web Application for the Selection and Composition of Cyberattack Models 网络攻击存储库:网络攻击模型选择和组合的Web应用

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587012

Katia P. Maxwell, Levi Seibert

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Success with S-STEM: The Appalachian High Achievers in STEM S-STEM的成功:阿巴拉契亚人在STEM方面的高成就者

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587017

R. Tashakkori, Cindy Norris, Jennifer R. McGee

{"title":"Success with S-STEM: The Appalachian High Achievers in STEM","authors":"R. Tashakkori, Cindy Norris, Jennifer R. McGee","doi":"10.1145/3564746.3587017","DOIUrl":"https://doi.org/10.1145/3564746.3587017","url":null,"abstract":"Since 1999, the National Science Foundation has supported a program that provides scholarships to low income students who pursue degrees in computer science, engineering, or mathematics. Originally, this program was called \"Computer Science, Engineering, and Mathematics Scholarships\" (CSEMS). In 2004, the program was renamed \"Scholarships for Science, Technology, Engineering, and Mathematics\" (S-STEM) and modified to include students from physical and life sciences. Appalachian State University (App State) has been the recipient of five CSEMS/S-STEM awards since 2001. Nearly all of the students in these programs experienced high levels of financial need and the majority were first generation college students. Our CSEMS and S-STEM programs have consistently maintained high rates of retention, significantly higher than national retention rates for these majors. Our current S-STEM program incorporates students from chemistry, geology, and physics and astronomy in addition to computer science and mathematics. We have also been able to maintain high rates of retention, over 88 percent, for this more diverse group of majors. We attribute this success to addressing financial, academic, and social barriers to success in STEM. This paper discusses the components of our current S-STEM program, The Appalachian High Achievers in STEM.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing a GUI Application: GPU-Accelerated Malicious Domain Detection 开发GUI应用程序:gpu加速恶意域检测

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587105

Trevor Rice, Dae Wook Kim, Mengkun Yang

引用次数: 0

Mobility-based Optimal Relay Node Selection for IoT-oriented SDWSN 面向物联网的SDWSN基于移动性的中继节点优化选择

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587026

Poornima M R, Vimala H S, J Shreyas

引用次数: 0

Exploring Node Classification Uncertainty in Graph Neural Networks 探索图神经网络中节点分类的不确定性

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587019

Md. Farhadul Islam, Sarah Zabeen, Fardin Bin Rahman, Md. Azharul Islam, Fahmid Bin Kibria, Meem Arafat Manab, Dewan Ziaul Karim, Annajiat Alim Rasel

{"title":"Exploring Node Classification Uncertainty in Graph Neural Networks","authors":"Md. Farhadul Islam, Sarah Zabeen, Fardin Bin Rahman, Md. Azharul Islam, Fahmid Bin Kibria, Meem Arafat Manab, Dewan Ziaul Karim, Annajiat Alim Rasel","doi":"10.1145/3564746.3587019","DOIUrl":"https://doi.org/10.1145/3564746.3587019","url":null,"abstract":"In order to represent and investigate interconnected data, Graph Neural Networks (GNN) offer a robust framework that deftly combines Graph theory with Machine learning. Most of the studies focus on performance but uncertainty measurement does not get enough attention. In this study, we measure the predictive uncertainty of several GNN models, to show how high performance does not ensure reliable performance. We use dropouts during the inference phase to quantify the uncertainty of these transformer models. This method, often known as Monte Carlo Dropout (MCD), is an effective low-complexity approximation for calculating uncertainty. Benchmark dataset was used with five GNN models: Graph Convolutional Network (GCN), Graph Attention Network (GAT), Personalized Propagation of Neural Predictions (PPNP), PPNP's fast approximation (APPNP) and GraphSAGE in our investigation. GAT proved to be superior to all the other models in terms of accuracy and uncertainty both in node classification. Among the other models, some that fared better in accuracy fell behind when compared using classification uncertainty.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"13 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Performance Evaluation Between Virtual/Native Nodes Running on ARM-based SBCs Using KVM as Hypervisor 基于arm的sbc虚拟/本机节点网络性能评估(KVM为Hypervisor

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587015

Eric Gamess

{"title":"Network Performance Evaluation Between Virtual/Native Nodes Running on ARM-based SBCs Using KVM as Hypervisor","authors":"Eric Gamess","doi":"10.1145/3564746.3587015","DOIUrl":"https://doi.org/10.1145/3564746.3587015","url":null,"abstract":"Single-Board Computers (SBCs) are increasingly being used due to their small form factor, reduced energy consumption, versatility, affordability, and increasing computational power. Therefore, they are now used in projects where they were not initially contemplated, such as running a Virtual Machine Manager (VMM). In this work, two ARM-based SBCs were selected (Raspberry Pi 4 Model B and ODROID-N2+) and an empirical evaluation was carried out to evaluate the network performance between two nodes running in the same SBC, or in different SBCs directly connected through WiFi or Ethernet. Although several hypervisors are suitable for these SBCs, Kernel-based Virtual Machine (KVM) was chosen since it seems to be the most active project that is developed for the ARM-based architecture. The metrics reported in this study include the TCP latency, UDP latency, TCP throughput, and HTTP latency. In general, the network performance of the ODROID-N2+ exceeded the Raspberry Pi 4 Model B. However, the latter has an indisputable advantage over the former with a much larger and more active community, making the development and deployment of applications much faster and straightforward. Hence, selecting the suitable SBCs should be done cautiously, considering the required software and additional hardware that the project is planning to connect to the SBCs.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding College Level Student Learning of Basic Programming at an Open Access Institution 了解大学水平的学生在开放存取机构学习基本程式设计

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587007

Cindy Robertson, Anca Doloc-Mihu

引用次数: 0

A Quantum Computing Arithmetic-logic Unit 量子计算算术逻辑单元

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587005

Bryson Phillip, Ethan Butler, Ben Ulrich, David Carroll

引用次数: 1

Robust Efficient License Plate and Character Detection System Based on Simplified CNN 基于简化CNN的稳健高效车牌特征检测系统

Proceedings of the 2023 ACM Southeast Conference Pub Date : 2023-04-12 DOI: 10.1145/3564746.3587108

Selena He, Tu N. Nguyen, Kun Suo

引用次数: 0