Digital Commons Network^™

Building Trustable Methods For Group Recommendations: Advancing Fairness And Robustness Across Domains, Siva Likitha Valluru

Theses and Dissertations

On the internet, where the number of available choices for information is exponentially growing, there is a need to prioritize and deliver relevant results to users efficiently, on demand. Recommendation systems (RSs) address that need by searching through and filtering large amounts of dynamically generated information and providing users with recommendations tailored to them. These systems have primarily focused on (1) single-user models, where recommendations are tailored towards a specific individual, or (2) single-item models, where items are recommended based on a broader appeal to users and similarities in item metadata, in the past. In many real-world scenarios, however, recommendation …

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, …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa

Dissertations

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), enable direct communication between the brain and external devices without the involvement of peripheral nerves or muscles. Among various BCI paradigms, motor imagery (MI)–based BCIs are particularly appealing due to their intuitive, cue-independent nature, allowing users to issue control commands at will. MI–BCIs hold substantial promise for improving the quality of life of individuals with motor impairments, as well as enhancing hands-free control for healthy users. However, their widespread adoption remains limited by challenges such as low signal-to-noise ratio, inter- and intra-subject variability, and the need for frequent calibration. These challenges …

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, …

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 …

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 …

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 …

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, …

The Role Of Artificial Intelligence In Workforce Learning And Development: A Systematic Review, Mildred V. Jones

Educational Leadership & Workforce Development Theses & Dissertations

The purpose of this study is to investigate how artificial intelligence (AI) is currently employed in workforce learning and development. The study examined the types of AI employed and the affordances realized for organizations and employees. A PRISMA systematic review methodology was utilized to address the overarching problem statement and answer the three questions guiding the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta Analysis for Protocols was used to direct each phase of the research. In addition, the Preferred Reporting Items for Systematic Reviews and Meta Analysis was used to conduct the article selection process. …

Exploring Transfer Learning For Deep Learning Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr., Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare …

Towards Testing, Detecting, And Debloating Insecure Components In Android Applications, Zicheng Zhang

Dissertations and Theses Collection (Open Access)

The Android ecosystem’s openness and extensibility have fueled its dominance in the mobile market, but they also broaden the attack surface of applications by introducing insecure or redundant methods. Vulnerabilities arise from various sources, including insecure API usage, code cloning, and feature bloat, especially from unneeded components introduced during development. To address these challenges, this dissertation presents a systematic, three-phase pipeline that transitions seamlessly from vulnerability discovery to clone-based detection and, ultimately, to dynamic mitigation through runtime debloating. Each phase builds upon the insights and limitations of the previous, collectively forming a practical approach to improving Android app security.

In …

Network-Based Crypto Asset Analysis, Ling Cheng

Dissertations and Theses Collection (Open Access)

The rise of cryptocurrency, particularly Bitcoin (BTC), has revolutionized the financial landscape, enabling decentralized, peer-to-peer transactions without the need for intermediaries such as banks or financial institutions. Since its inception in 2009, Bitcoin has grown exponentially, not only in terms of market value but also in its impact on global finance. However, together with this popularity comes a wide range of cybercrimes including hacking, Ponzi schemes, wash trading, extortion, and money laundering. As noted in recent research, the volume of illicit cryptocurrency activities has grown significantly, with billions of dollars in crypto assets being stolen or used for illegal purposes …

Assessing Readiness For Transformation From Rulebased To Ai-Based Chatbot In Uae Healthcare: A Case Study Of A Rehabilitation Hospital In Abu Dhabi, Mubarak Alketbi

Theses

This study investigates the readiness for transforming rule-based chatbots to AI-based chatbots in UAE healthcare, examining a rehabilitation hospital in Abu Dhabi through quantitative research involving healthcare professionals (N=96) and technical analysis. Findings revealed positive perceptions of the current system alongside enhancement opportunities through AI capabilities, with perceived usefulness strongly correlating with behavioural intention, high service quality ratings for empathy and responsiveness, midcareer professionals demonstrating the highest AI acceptance levels, and system integration identified as the highest priority implementation area.

The research contributes to healthcare technology transformation knowledge in the UAE by providing a structured implementation framework addressing technical requirements, …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Theses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …

On Unraveling Student Resilience And Academic Performance In Higher Education, Aldy Gunawan, Ee-Peng Lim, Audrey Tedja Widjaja, William Tov, James Foo, Lieven Lode E. Demeester

Research Collection School Of Computing and Information Systems

The transition period from pre-tertiary to higher education levels is critical. We explore the role of resilience by conducting a survey to investigate students’ resilience and the relationship with overall academic performance, learning experience, and well-being. This effort is part of an initiative to develop strategies for better student engagement in the academic program, enhance their resilience, and prepare them for a competitive job market. We conclude that (i) high-resilience students are associated with better life satisfaction and are likely to perform well academically, (ii) a favorable learning environment supports students to study and perform well in the university, and …

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 …

Weapons Of Mass Disruption: How Small States Use Cyber To Resist Larger Powers, Russell Alexander Korb

Graduate Program in International Studies Theses & Dissertations

This paper examines the ways in which small states can engage larger actors using cyber- attacks. Since the end of both World Wars, small states have increased in both numbers and relevance, with strong international institutions and norms against military aggression allowing small states to gain legitimacy by the very act of participating in the international system. However, although small states can now do more than simply choose a larger, stronger benefactor to ward off their enemies, they still cannot defy larger powers outright due to the still- dramatic difference in capabilities between them. Those small states interested in confronting …

Integrating Computational Thinking Into K–12 Classroom Instruction, Jorge Valenzuela

STEMPS Theses & Dissertations

In 2016, legislation mandated the integration of computer science (CS) and computational thinking (CT) skills across K–12 instruction in the Commonwealth of Virginia. CT integration is also a focus in many schools throughout the United States. Educators must understand CT/CS core concepts and practices, looking for practical ways to integrate CT across the K–12 curricula. To address this, the current study was conducted. The study compares the effects of the Jigsaw technique and teacher-directed instruction on the participants by surveying their self-efficacy for integrating CT in their teaching. This study uses a pre- and post-test design, and data collection took …

Algorithms For Order Statistics In Farey Sequences: A Computational Study, Connor Weyers

School of Computing: Dissertations, Theses, and Student Research

Farey sequences are the sets of irreducible fractions in increasing order with denominator less or equal to some integer n. They are a well-known concept in number theory problems and are related to many other concepts in number theory including integer factoring, Fibonacci sequences, and Riemann’s Zeta function. In this paper, we investigate some known algorithms to solve certain problems in Farey sequences from a computational perspective. In particular, we implement established algorithms that have not been previously implemented with the goal of creating a package that can be used more broadly. We also develop a new algorithm for rational …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

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 …

A Study Of Conceptual Primitive Elimination: Embedding Ingest Into Ptrans, Jamie C. Macbeth, Alexis Kilayko

Computer Science: Faculty Publications

In cognitive systems and cognitive linguistics, primitive decomposition systems attempt to explain cognitive phenomena by breaking things down into conceptual building blocks and provide rich and flexible representations for systems. A prime example is the Schank–Minsky Conceptual Dependency Trans-frames system, which maintains a commitment to keeping the number of primitives small and allowing them to be combined in complex ways in representing meaning, knowledge, and dynamic episodic memory. Motivated by the desire to keep the set of primitives small, this paper describes an effort to eliminate the Conceptual Dependency INGEST primitive and reconstitute its uses through combinations of the CD …

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 …

Ada-Gen: Iterative And Incremental Generation Of Full-Stack Apps For Learning Agile/Devops Software Development Practices, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly …

Capo: Cooperative Plan Optimization For Efficient Embodied Multi-Agent Cooperation, Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees Snoek, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where discussion and cooperative plan are crucial. To solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance the cooperation efficiency of LLM-based embodied agents. Inspired by human cooperation schemes, CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and …

Ivyapc: Auditable Generalized Payment Channels, Ming Li, Yuxian Li, Jian Weng, Yingjiu Li, Jiasi Weng, Junzuo Lai, Robert H. Deng

Research Collection School Of Computing and Information Systems

Payment channels are a cornerstone of a scalable blockchain infrastructure that enables transacting parties to lock assets on the blockchain and perform rapid off-chain updates with minimal latency and overhead. These protocols dramatically reduce on-chain interaction and improve throughput, with blockchain consensus only invoked in the event of disputes or final closure. While widely adopted in single-chain settings—such as in the Lightning Network for Bitcoin—existing constructions have several limitations, in particular they suffer from at least one of the following limitations: 1. No cross-chain. They do not enable fast trading of assets that reside on multiple isolated blockchains. 2. Non-optimal …

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 …