Computer Sciences Commons^™

Code Of Faith: Programming Spirituality In The Digital Age, Caleb Martin

Senior Honors Theses

With the increasing pervasiveness of technology in our daily lives, it is critical to consider the potential effects on an individual's religious practices and beliefs from software applications developed within a religious framework. This thesis delves into the relationship between software development and religious experiences, examining the manner in which the creation, operation, and application of religious technology can mold and impact an individual's spiritual development. This thesis aims to inform the implementation of technologies with a nonsecular application, ensuring that they are developed with a deep respect for the nuances of religious experience. The findings of this study will …

The Impact Of Ai Usage On Employee Work Outcomes: The Mediating Roles Of Personal Control And Job Insecurity And The Moderating Role Of Ai Trust, Tiantian Wang

Dissertations and Theses Collection (Open Access)

The widespread application of artificial intelligence (AI) technology in the workplace offers significant potential for process optimization andperformance improvement. However, the psychological mechanisms throughwhich AI usage affects employee outcomes remain underexplored. To address this gap, the present study investigated a sample of 170 employees froma media company in China, utilizing a three-wave longitudinal survey design. Specifically, this study examined how AI usage influenced employee creativity and task performance improvement through two mediatingmechanisms: the enhancement of personal control in problem-solving and the elicitation of job insecurity. Furthermore, the moderating role of trust in AI inthe relationship between AI usage and job …

What Is A Digital Twin Anyway? Deriving The Definition For The Built Environment From Over 15,000 Scientific Publications, Abdelrahman Mahmoud, Edgardo Macatulad, Binyu Lei, Matias Quintana, Clayton Miller, Filip Biljecki

Research Collection College of Integrative Studies

The concept of Digital Twins (DT) has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners.We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of DTs from a corpus of more than 15,000 full-text articles spanning diverse disciplines. The study compares these findings with insights from an expert survey that included 52 experts. The …

Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems, Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu

Research Collection School Of Computing and Information Systems

Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals …

Scuzer: A Scheduling Optimization Fuzzer For Tvm, Xiangxiang Chen, Xingwei Lin, Jingyi Wang, Jun Sun, Jiashui Wang, Wenhai Wang

Research Collection School Of Computing and Information Systems

The concept of Deep Learning (DL) compiler was proposed to deploy DL models more efficiently on diverse hardware through optimization techniques. As one of the most popular DL compilers, TVM incorporates three levels (high-level, schedule, and low-level) of optimizations, which can inadvertently introduce code logic bugs and build failure bugs. Among these optimizations, scheduling optimization is the core component of DL compilers, which ensures the acceleration of models on all devices. However, the existing works only focus on the testing of high-level and low-level optimizations in TVM, fail to take the most important and challenging intermediate scheduling optimization layer into …

Global Crossroads Of Cybercrime: Youth, Enterprise And State Vulnerabilities In The Digital Age, Christopher S. Kayser, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Cybercrime As A Threat To The Banking Sector: A Perspective From Commercial Banks In Bangladesh, Hasibul Hossain, Rezaul Karim Shohag, Nikhil Chandra Nath, Sushmita Das Dalia

International Journal of Cybersecurity Intelligence & Cybercrime

Cyber and technology related crimes are gradually increasing all over the world due to rapid transitions and transactions in the digital world and cyberspace. Cyber related threats are increasingly becoming universal, multi-faceted, sophisticated and transnational in this tech-driven age. Governments, law enforcement agencies, IT professionals, scholars, and researchers worldwide have been concerned about digital deviance and crime. The transition to this widespread cybercrime is particularly difficult for developing countries. Recently, the banking sectors in Bangladesh have seen the emerging threats to its system and reserves through cyberspace, e. g. cyber-attacks or taking illegal access. Cybercrime is becoming a threat to …

Need Of Paradigm Shift In Cybersecurity Implementation For Small And Medium Enterprises (Smes), Shekhar Pawar, Hemant Palivela

International Journal of Cybersecurity Intelligence & Cybercrime

The increasing digitization of small and medium enterprises (SMEs) has significantly increased their attack surface, creating opportunities for various cyberthreats. In the global market, there are various cybersecurity standards and frameworks available, but there are still many cyber news stories from each corner of the world talking about increasing sophisticated cyber-attacks among organizations. According to recent studies, one out of five cyberattacks is targeting SMEs. Even though SMEs are relatively smaller as individuals, they are responsible for maximum contribution towards the betterment of the global economy, including the highest role in GDP and various employment opportunities. As compared to large …

Neural Network-Based Low-Level 3d Point Cloud Processing, Pingping Cai

Theses and Dissertations

3D computer vision is a promising research field with the potential to revolutionize future lifestyles. Among various 3D representation formats, point clouds stand out for their efficiency in depicting 3D objects using a set of coordinates, enabling advancements in fields such as autonomous driving, virtual reality, and robotics. Due to the limitations of sensor fields of view and scanning trajectories, the collected point clouds are usually sparse, noisy, and incomplete, impeding the performance of many downstream applications. Thus, the tasks of low-level point cloud processing are proposed to refine and generate dense, clean, and complete point clouds. To accomplish these …

Gamescope, Jake Rankin, Luis Garza, Brain Lujan, Mauricio Rebaza Figueroa

Posters - 2025

Video games have grown exponentially since their debut in the late 20th century. Despite the widespread digitalization and advancements within the gaming community marked by a transition from physical discs to digital downloads and many more major improvements, the lack of an efficient, multipurpose application for reviews remains prevalent. When designing GameScope, we wanted to tackle the key problem of the absence of a multi-platform gaming review system. Gamers currently lack a popular platform to easily find game reviews and get personalized recommendations. Our aim is to create a space where gamers can share their experiences and explore new games …

Mi Lock Pros, Feras Rabee

Posters - 2025

Locksmith businesses often rely on inefficient communication and outdated job management methods, leading to delays, missed opportunities, and customer dissatisfaction. Mi Lock Pros was created to solve this problem. It is a mobile app designed to streamline job assignment, technician tracking, and customer communication. The solution includes secure login, job tracking, real-time messaging, GPS based navigation, and technician performance monitoring—all accessible via a simple interface on both Android and iOS. Powered by ASP.NET Core Web API and .NET MAUI, it ensures smooth backend integration with a user-friendly frontend.

Emerging Technologies In Beluga Research: Potential And Possibilities, Alejandro Zuniga-Schettino

Posters - 2025

Beluga whale face increasing threats in the Arctic, demanding effective research for conservation. Transitional methods going on field trips to collect short videos in excel, going on field trips to collect short videos, and having to rewatch the video are often time- consuming labor intensive, and limited in scope. This poster explores how engineering and AI can improve research. Engineering can provide robust tools like autonous underwater vehicles with advanced sensors for data collection in challenging environments. These technology offer an enhanced understanding of belugas behavior and ecology

Mente -Mental Health Tracking App, Vu Han

Posters - 2025

Mental health plays a crucial role in overall well-being, yet many digital tools in this space are either overly complex or lack usercentered design. Mente is a streamlined, web-based application created to support daily mental health engagement through simplicity and ease of use.

•Purpose: To provide a minimal, intuitive platform for users to reflect on their emotional well-being and develop healthier habits over time.

•Core Features:

• Mood tracking with visual trends

• Journaling for personal reflection

• Goal setting and progress tracking

• Health assessment for self-awareness

• Analytics for self-reflection •Design Focus: A clean, distraction-free interface that emphasizes …

Holdfast War Archives, Albert Mendez

Posters - 2025

Holdfast War Archives is a full-stack website designed for the competitive community of the 19th-century multiplayer roleplaying game, Holdfast Nations at War. This project caters to the North American (NA) melee competitive scene, offering tools to enhance player engagement, maintain records, track performance, and facilitate competitive matchmaking.

Cutting-Edge Deep Learning Methods For Image-Based Object Detection In Autonomous Driving: In-Depth Survey, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed

Research outputs 2022 to 2026

Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image-based object detection methods, which offer several advantages over other modalities, such as cost-effectiveness and …

A Survey On Unauthorized Uav Threats To Smart Farming, Peng Chen, Shihao Yan, Helge Janicke, Arash Mahboubi, Hang Thanh Bui, Hamed Aboutorab, Michael Bewong, Rafiqul Islam

Research outputs 2022 to 2026

The integration of Internet of Things (IoT) and unmanned aerial vehicles (UAVs) in smart farming has revolutionized agricultural practices by enhancing monitoring, automation, and decision-making to improve agricultural productivity and sustainability. However, the widespread use of these technologies has also introduced new security challenges, particularly the risk of interference from unauthorized UAVs. This survey provides an analysis of the threats posed by unauthorized UAVs to smart farms, highlighting potential vulnerabilities such as data interception, communication jamming, and physical damage. This paper first explores recent advancements in IoT and UAV technologies, which are integral to the functioning of smart farms. Then, …

Association Of Ai Derived Biomechanics And Hand Grip Strength, Theophile Nsabimana

Posters - 2025

Biomechanical analysis offers a way of better understanding the mechanism of a person's movement pattern or functional decline. Usually, motion analysis is costly and requires the purchase of a lot of equipment and software. This makes the technology out of reach of students, educators and researchers in austere settings.

Fortunately, artificial intelligence has brought affordability to motion analysis and created a whole new method of analyzing functional performance. OpenCap is an application which was produced by Stanford University and is hailed as being a future replacement to higher costing systems. Gait analysis provides an indication of a person's walking symmetry …

Hallucinations In Large Foundation Models: Characterization, Quantification, Detection, Avoidance, And Mitigation, Vipula Rawte

Theses and Dissertations

Deception is an inherent aspect of social interactions, with research indicating that most people engage in deceptive behavior at least once or twice daily . In parallel, advances in artificial intelligence have led to machines exhibiting deceptive tendencies. These deceptions can be categorized into two types: unintended and intentional. Unintended deceptions - often referred to as hallucinations - occur when generative AI systems produce plausible and convincing narratives yet are factually inaccurate. This phenomenon primarily results from the systems' architectural design, extensive parametric memory, and reliance on statistical assumptions. In this thesis, we provide a comprehensive discussion on the characterization, …

Augmenting Deep Learning For Efficient Nextg Wireless Communication And Sensing Systems, Hem Kanta Regmi

Theses and Dissertations

Wireless networks have become an integral aspect of our daily lives. Over the years, earlier generations of wireless networks have enabled some innovative applications, such as wireless gaming, fast internet browsing, and home automation, which were previously unattainable. However, to support emerging technologies like autonomous driving, virtual reality, telemedicine, and intelligent manufacturing, which require high data throughput and low latency, there is a need for advanced wireless networks. Legacy networks like WiFi/LTE, which operate below 6 GHz, have limited bandwidth and are insufficient to fulfill the high data throughput demands of several applications. Millimeter-wave (mmWave) networks, operating between 30 GHz …

Pulsesight: Ai-Powered Smartphone Solution For Non-Invasive Oxygen Saturation, Respiration Monitoring & Emr Integration, Kazi Zawad Arefin

Dissertations (1934 -)

The utilization of non-invasive, contactless methods to detect physiological parameters such as oxygen saturation (SpO2) and respiration rate has the potential to significantly improve healthcare delivery. This dissertation suggests a new system that utilizes photoplethysmography (PPG) signals extracted from facial and fingertip video recordings. These video recordings are captured using a standard smartphone. The system accomplishes real-time, contactless health monitoring without the necessity of specialized medical equipment by employing advanced image processing and signal analysis techniques. This method addresses critical health challenges, particularly for vulnerable populations, by facilitating continuous monitoring in resource-constrained environments. The development of a context-aware mobile application …

User Perception For Usability And Security On New Technology And Online User Privacy In Real-Time Bidding, Subhash Rajapaksha

Dissertations (1934 -)

The digital advertising capabilities to reach users with personalized ads have been steadily improving over the last couple of decades. Automated mechanisms use efficient algorithms and a wealth of data to complete transactions between websites/apps and potential advertisers as part of what is known as programmatic advertising. Among the most prevalent protocols is Real Time Bidding (RTB), which selects ads for a user visiting a website in real-time through a series of messages within online ad exchanges. Such communications have the potential to carry detailed, personal information about users without their knowledge and have raised privacy concerns. RTB also poses …

Applying Software Engineering Black-Box Methods For Testing Machine Learning Models, Timothy Elvira

Doctoral Dissertations and Master's Theses

This dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system's code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. …

Framework For Integrating Industry Knowledge Into A Large Language Model To Assist Construction Cost Estimation, Prashnna Ghimire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The construction industry generates a large amount of data across projects produced by digital devices, tools, and methods, and this volume is rapidly increasing. However, the industry lags behind in adopting data-driven technologies. On the other hand, the rapid advancement of generative AI (GenAI) in recent years, especially state-of-the-art large language models (LLMs), shows great potential and has been increasingly adopted in many industries; however, the construction industry is behind in adoption. While academic studies have proposed various machine learning applications for construction, industry implementation has lagged due to a disconnect between these proof-of-concept developments and practical industry needs. Also, …

Context-Aware Representation: Jointly Learning Item Features And Selection From Triplets, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

In areas of machine learning such as cognitive modeling or recommendation, user feedback is usually context-dependent. For instance, a website might provide a user with a set of recommendations and observe which (if any) of the links were clicked by the user. Similarly, there is growing interest in the so-called “odd-one-out” learning setting, where human participants are provided with a basket of items and asked which is the most dissimilar to the others. In both of those cases, the presence of all the items in the basket can influence the final decision. In this article, we consider a classification task …

Digital Transformation And The Future Of Work: Closing The Digital Skills Gap, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the near future digital technology landscape and propose several specific in-demand digital skills for organizations and individuals in the next few years. This article reviews some recent publications from representative inter-governmental, governmental, non-governmental, and commercial organizations on the rise of digital technologies and corresponding growth in digital jobs. Within this context, several specific in-demand skills are proposed by the author who has written a book on the topic of digital transformation. Rapid advancements in digital technologies continue to shape organizational practices and the future of work. To take advantage of emerging digital …

Verifying Timed Properties Of Programs In Iot Nodes Using Parametric Time Petri Nets, Étienne André, Jean-Luc Béchennec, Sudipta Chattopadhyay, Sebastien Faucou, Didier Lime, Dylan Marinho, Olivier H. Roux, Jun Sun

Research Collection School Of Computing and Information Systems

The analysis of timed properties of programs is a complex task, as it is highly dependent on both the software and the hardware. In this work, we propose a framework for modeling with timed formal models the execution of programs, taking into account the micro-architecture of the machine on which it executes. We model both the program, at the instruction set architecture level, and the hardware, including the processor micro-architecture, using time Petri nets. Our implementation uses the ARM Cortex-M instruction set architecture and a hardware architecture representative of microcontrollers used in IoT nodes. The whole translation is fully automated …

Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li

Research Collection School Of Computing and Information Systems

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into …

Frame-Voyager: Learning To Query Frames For Video Large Language Models, Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin, Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, Hao Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leading to sub-optimal performance. In this paper, we propose Frame-Voyager that learns to query informative frame combinations, based on the given textual queries in the task. To train Frame-Voyager, we introduce a new data collection and labeling pipeline, by …

A Selective Vehicle Routing Problem For The Bloodmobile System, Aldy Gunawan, Samuel Alan Darmasaputra, Sy Hoang Do, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Mobile blood collection has the advantage of greater reach compared to blood drives at fixed donation sites and is preferable for individuals with limited time or means of transportation. Bloodmobiles are widely used in healthcare logistics to increase the number of donors and donation frequency and to better match blood demand with collection. Bloodmobiles are stationed at predetermined locations, while shuttles are assigned to visit these locations to collect the donated blood. This problem is formulated as the Selective Vehicle Routing Problem under the Bloodmobile System (SVRP-BM). This research extends the Selective Vehicle Routing Problem with Integrated Tours problem (SVRPwIT) …

Can Llms Replace Manual Annotation Of Software Engineering Artifacts?, Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel

Research Collection School Of Computing and Information Systems

Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies in our field are challenging, due to the cost and difficulty of finding and employing suitable subjects, ideally, professional programmers with varying degrees of experience. Meanwhile, large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. We study this idea by applying six …