Computer Sciences Commons^™

Examining Information Systems Use To Facilitate The Workplace Accommodation Process, Shiya Cao

Statistical and Data Sciences: Faculty Publications

BACKGROUND: The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes.

OBJECTIVE: Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process.

METHODS: Thirty-six interviews were conducted with disabled employees from various organizations. …

Questions (And Answers) For Incorporating Nontraditional Grading In Your Statistics Courses, Brenna Curley

Statistical and Data Sciences: Faculty Publications

Nontraditional grading methods have recently become more common, and as with any large pedagogical shift, there are a number of questions to consider when applying a new grading scheme to a course. This article summarizes four types of nontraditional grading and shares experiences from the authors who have applied them to a variety of courses in statistics. This article is structured as a set of questions and answers, seeking to address many of the concerns and considerations that one may face as they transition a course’s grading structure. Supplementary materials for this article are available online.

Identifying Fact-Checks Helpful For Vetting Factual Claims, Theodora Toutountzi

Computer Science and Engineering Dissertations - Archive

Recent efforts to combat misinformation have increasingly focused on automating the fact-checking process. For fact-checking systems to be automated, they need to recognize claims worth checking, match them with previously fact-checked claims, and determine their truthfulness. We refer to the task of matching unvetted claims with previously fact-checked claims as claim-matching. To be precise, the task is defined as follows: given a factual claim, identifying the fact-checks from a repository that could be helpful, or partially helpful, for vetting the given claim. A solution to this task is useful in practice since claimants often repeat the same claims even if …

Enhancing The Efficiency And Scalability Of Cloud Networking Systems, Jiaxin Lei

Computer Science and Engineering Dissertations - Archive

Overlay networks are the de facto network virtualization technique for providing flexible and customized connectivity among distributed containers in the cloud. Despite their widespread adoption, overlay networks incur significant overhead due to their complexity, resulting in notable performance degradation compared to physical networks.

In this dissertation, I present our three-stage solutions aimed at addressing the challenges of efficiency and scalability in cloud-based container overlay networks: Firstly, we conduct a comprehensive empirical performance study of container overlay networks, identifying crucial parallelization bottlenecks within the kernel network stack. Our observations and root cause analysis uncover that these inefficiencies primarily arise from the …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Differences In Student-Ai Interaction Process On A Drawing Task: Focusing On Students' Attitude Towards Ai And The Level Of Drawing Skills, Jinhee Kim, Yoonhee Ham, Sang-Soog Lee

STEMPS Faculty Publications

Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in …

The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi

STEMPS Faculty Publications

This study employs a synthesis of Intrinsic Motivation Theory with three shopping orientations, namely “adventure,” “idea,” and “personalized” shopping, in order to examine their potential influence on individuals' motivation towards shopping. We proposed that consumers’ experiential value of intrinsic enjoyment is an indispensable mediator that affects their willingness to follow EAI’s advice. The study offers novel insights into the way that consumers’ characteristics of influencing others’ clothing consumption affect their shopping motivations to find adventure and stimulation, keep up with new fashion trends and products information, and their preference to patronize stores and interact with store staff on a personal …

Higher Education Faculty Perceptions Of Chatgpt And The Influencing Factors: A Sentiment Analysis Of X, Yoseph Mamo, Helen Crompton, Diane Burke, Christine E. Nickel

STEMPS Faculty Publications

ChatGPT, an AI chatbot developed by OpenAI, was released in November 2022, sparking a significant surge in global awareness and utilization of generative AI across various domains. Although recent studies have acknowledged the significance of ChatGPT in the education sector, they have yet to focus on exploring faculty attitudes toward ChatGPT. We gathered a comprehensive corpus of tweets containing “#ChatGPT” and “#highered” between November 30th, 2022, and April 30th, 2023. We analyzed data by triangulating VADER, NRC lexicon, and ground coding. Findings suggest that 40% of the expressed sentiments were positive, 51% were neutral, and 9% were negative. The study …

Exploring Students' Perspectives On Generative Ai-Assisted Academic Writing, Jinhee Kim, Seongryeong Yu, Rita Detrick, Na Li

STEMPS Faculty Publications

The rapid development of generative artificial intelligence (GenAI), including large language models (LLM), has merged to support students in their academic writing process. Keeping pace with the technical and educational landscape requires careful consideration of the opportunities and challenges that GenAI-assisted systems create within education. This serves as a useful and necessary starting point for fully leveraging its potential for learning and teaching. Hence, it is crucial to gather insights from diverse perspectives and use cases from actual users, particularly the unique voices and needs of student-users. Therefore, this study explored and examined students' perceptions and experiences about GenAI-assisted academic …

The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future, Aras Bozkurt, Junhong Xiao, Robert Farrow, John Y. H. Bai, Chrissi Nerantzi, Stephanie Moore, Jon Dron, Christian M. Stracke, Lenandlar Singh, Helen Crompton, Apostolos Koutropoulos, Evgenii Terentev, Angelica Pazurek, Mark Nichols, Alexander M. Sidorkin, Eamon Costello, Steven Watson, Dónal Mulligan, Sarah Honeychurch, Charles B. Hodges, Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J. Slagter Van Tryon, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurnova, Thomas K. F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino

STEMPS Faculty Publications

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that …

Ung: A Diagnostic Standard C Library, Jakob Knute Kaivo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Undefined behavior in C programs is a major source of unreliable software. Many of the most common exploitable software vulnerabilities can be traced directly to undefined behavior. In the increasingly connected world, a successful attack can cost the victim millions of dollars to recover from. While static program analysis aids in identifying undefined behavior, testing indicates that even the best static analysis tools correctly identifies about 35% of these defects. This dissertation introduces UNG’s Not GNU (UNG), a standard C library designed to mitigate undefined behavior. Where others have seen opportunities for optimization, UNG makes every effort to identify undefined …

Foraging Economies: A Market Based Methodology For Robotic Swarm Foraging, John A. Little

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach …

Effect Of Resin Bleed Out On Compaction Behavior Of The Fiber Tow Gap Region During Automated Fiber Placement Manufacturing, Von Clyde Jamora, Virginia Rauch, Sergii G. Kravchenko, Oleksandr G. Kravchenko

Mechanical & Aerospace Engineering Faculty Publications

Automated fiber placement is a state-of-the-art manufacturing method which allows for precise control over layup design. However, AFP results in irregular morphology due to fiber tow deposition induced features such as tow gaps and overlaps. Factors such as the squeeze flow and resin bleed out, combined with large non-linear deformation, lead to morphological variability. To understand these complex interacting phenomena, a coupled multiphysics finite element framework was developed to simulate the compaction behavior around fiber tow gap regions, which consists of coupled chemo-rheological and flow-compaction analysis. The compaction analysis incorporated a visco-hyperelastic constitutive model with anisotropic tensorial prepreg viscosity, which …

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

A Secure And Privacy-Preserving Signature Protocol Using Quantum Teleportation In Metaverse Environment, Pankaj Kumar, Vivek Bharmaik, Sunil Prajapat, Garima Thakur, Ashok Kumar Das, Sachin Shetty, Joel J. P. C. Rodrigues

VMASC Publications

The burgeoning concept of the metaverse as an interconnected virtual space represents the forefront of the next-generation internet. Quantum teleportation, known for its prowess in ensuring secure and reliable communications, stands poised to revolutionize interactions within this immersive digital realm. In this context, we propose a comprehensive interaction protocol tailored for the metaverse environment. The designed protocol entails two fundamental components: first, the interaction between a user and their avatar, facilitated by a secure seven-qubit entangled state; and second, the interaction between two avatars, enabled through an efficient two-qubit entangled state. To fortify the protocol’s resilience, quantum key distribution (QKD) …

Designing A Blockchain-Empowered Telehealth Artifact For Decentralized Identity Management And Trustworthy Communication: Interdisciplinary Approach, Xueping Liang, Nabid Alam, Tahmina Sultana, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

Objective: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

Methods: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype …

A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …

Classification Of Instructional Activities In Classroom Videos Using Neural Networks, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos have become an integral part of classroom observation and instructional quality studies. Researchers encounter substantial financial and time-consuming costs gathering trained personnel to catalogue and analyze a large quantity (i.e., hundreds of hours) of classroom videos. In this chapter, we argue that deep-learning neural networks have potential to help with these tasks by detecting features of instruction in videos. We evaluated the performance of three neural networks in detecting instructional activities in classroom videos. The video dataset included a total of forty-six hours, which was evenly split between elementary mathematics and English language arts lesson recordings. Results showed …

Efficiently Learning An Encoder That Classifies Token Replacements And Masked Permuted Network-Based Bigru Attention Classifier For Enhancing Sentiment Classification Of Scientific Text, Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

VMASC Publications

The exponential growth of scientific literature in digital repositories poses challenges in interpreting complex attitudes within academic texts. Traditional sentiment analysis methods often struggle with nuanced word meanings due to contextual variations. To address this, we propose the Electra-MPNet-based BiGRU attention classifier that extracts the high-level semantic features from citation sentences using the combined strength of Electra and MPNet encoder layers. These features are then combined to extract long-range dependencies through a stacked BiGRU layer. A linear attention mechanism is imposed to estimate the attention weights and context vector which enables the model to selectively focus on relevant information. The …

Automatic Classification Of Activities In Classroom Videos, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Reducing The Uncertainty In Estimating Soil Microbial-Derived Carbon Storage, Han Hu, Chao Qian, Ke Xue, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni, Jixian Ding, Weigen Huang, Jingdong Mao, Rong-Xi Tan, Jizhong Zhou, Thomas W. Crowther, Zhi-Hua Zhou, Jiabao Zhang, Yuting Liang

Chemistry & Biochemistry Faculty Publications

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors …

Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach, Hongyu Guo, Mehdi Keyvan-Ekbatani, Kun Xie

Civil & Environmental Engineering Faculty Publications

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with …

Implications Of Alternative Communications And Sensing Technologies For Implementing Variable Speed Limit Control Through Connected Vehicles: Sag Curve As A Case Study, Reza Vatani Nezafat, Mecit Cetin, Elizabeth Williams, George F. List

Civil & Environmental Engineering Faculty Publications

Connected vehicles (CVs) will enable various applications to improve traffic flow. This paper's focus is to investigate how the potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs makes it possible to mitigate congestion at a sag curve bottleneck. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) options for CVs are investigated when implementing the VSL control strategy in a simulation environment. Also, for measuring traffic density …

Enhancing Decision-Making In Higher Education: Exploring The Integration Of Chatgpt And Data Visualization Tools In Data Analysis, Tristan Jiang, Elina Liu, Tasawar Baig, Qingrong Li

University Administration Publications

This chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data-informed decision-making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.

Toward Attention-Based Feature Fusion For Medical Image Analysis, Anup Khanal

Dissertations and Theses

The field of medical image analysis has benefited significantly from developments in deep learning (DL), especially in convolution neural networks (CNNs). Even after these advances, there remains a need for more robust and accurate methods for various tasks such as classification, segmentation, captioning, and object detection, particularly in medical diagnostics. Inspired by the principle of the wisdom of the crowd, feature fusion is a common process used, especially in tasks related to classification. This thesis introduces a novel attention-based feature fusion architecture designed to enhance classification performance by integrating the Convolution Block Attention Mechanism (CBAM) before fusion. This approach selectively …

‘I Know It When I See It’– Developing Quality Schedules Considering Subjective Or Unspecified Criteria, Douglas L. Moody

Publications and Research

Most timetabling problems have a given objective function to measure the quality of a solution. However, users may have a “I know it when I see it” recognition of a quality schedule, without specifying the complete basis for their judgment. In this situation, the objective function cannot be exclusively used as a solution quality measurement. This work presents an AI based approach to aid in categorizing the solution’s quality when the users have not explicitly defined all factors used in their criteria.

Generative Adversarial Networks For Music Generation, Harry Berman

Pomona Senior Theses

In this paper, we aim to harness a machine learning model called Genera- tive Adversarial Networks (GAN) in order to produce AI generated musical strands. The “Generative” part of the model’s name implies that its goal is to create something – music in the case of this paper – and the “Adversarial Networks” refer to the fact that there are two neural networks that learn from each other. One network attempts to trick the other one by creating music that it believes sounds real while the other tries to discern the real from the fake music. After training for long …

Multiview Outlier Filtered Pediatric Heart Sound Classification, Sagnik Dakshit

Computer Science Faculty Publications and Presentations

The advancements in deep learning has generated a large-scale interest in development of black-box models for various use cases in different domains such as healthcare, in both at-home and critical setting for diagnosis and monitoring of various health conditions. The use of audio signals as a view for diagnosis is nascent and the success of deep learning models in ingesting multimedia data provides an opportunity for use as a diagnostic medium. For the widespread use of these decision support systems, it is prudent to develop high performing systems which require large quantities of data for training and low-cost method of …

Concept Of Multifactor Method And Non-Functional Requirements Solution To Increase Resilience Through Functional Safety With Cybersecurity Analysis, Emilian Piesik, Marcin Sliwinski, Nary Subramanian, Janusz Zalewski

Computer Science Faculty Publications and Presentations

In the process of designing safety systems, an integrated approach in safety and cybersecurity analysis is necessary. The paper describes a new technique of increasing resilience through integrated analysis of functional safety and cybersecurity. It is a modeling methodology based on the combination of the multifactor method utilizing modified risk graphs, used previously for Safety Integrity Level (SIL) assessment, and the Non-Functional Requirements (NFR) approach. The NFR approach, based on the analysis of graphical representation of conceptual and physical components of the system, contributes a technique to include cybersecurity through the Softgoal Interdependency Graph. The assessment methodology is outlined in …