Digital Commons Network^™

Parallelized Quadtrees For Image Compression In Cuda And Mpi, Aidan Jones

Senior Honors Theses

Quadtrees are a data structure that lend themselves well to image compression due to their ability to recursively decompose 2-dimensional space. Image compression algorithms that use quadtrees should be simple to parallelize; however, current image compression algorithms that use quadtrees rarely use parallel algorithms. An existing program to compress images using quadtrees was upgraded to use GPU acceleration with CUDA but experienced an average slowdown by a factor of 18 to 42. Another parallelization attempt utilized MPI to process contiguous chunks of an image in parallel and experienced an average speedup by a factor of 1.5 to 3.7 compared to …

Shutting Out Noise And Understanding Artificial Intelligence, Lauren J. Yu

Michigan Law Review

A review of Noise: A Flaw in Human Judgment. By Daniel Kahneman, Olivier Sibony and Cass R. Sunstein, and You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place. By Janelle Shane.

Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo

Research Collection School Of Computing and Information Systems

Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, …

Stealthy Backdoor Attack For Code Models, Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with backdoor attacks. To this end, we propose A (dversarial eature as daptive Back). A achieves stealthiness …

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Study Of Deep Learning Models To Classify Nasa’S Kepler Light Curves, Heena Minnich

Computer Science Theses & Dissertations

The search for exoplanets has been an ongoing effort since the first discoveries of planets beyond our solar system in the 1990s. Finding a potentially habitable planet outside our solar system could provide key insights on life elsewhere in the universe. NASA Missions such as the Kepler, launched in 2009 and completed in 2018, have provided a massive amount of data in this goal by using the transit method to discover repetitive and periodic dips in visible light around a star. The transit method has been used to measure flux, the brightness of a star over time. These flux time …

A Trustworthy Self-Sovereign Data And Identity Management Framework, Efat Fathalla

Electrical & Computer Engineering Theses & Dissertations

Data is a fundamental building block in the digital world, providing a basis for decision making and growth across numerous applications. In our modern world, we have become accustomed to collecting data on everything, including devices, machines, and people. The increased value of such data has led to aggressive harvesting mechanisms that prioritize data collection, storage, and pervasiveness while often disregarding security, privacy concerns, and compliance with regulations and standards. Such a pervasive attitude towards data has resulted in a loss of control, prompting concerns among individuals and mobilizing the scientific community towards advocating for data self-sovereignty.

Self-Sovereign Identity (SSI) …

Toward Cyber-Attack Detection For Industrial Internet Of Things Systems Using Machine Learning, Cayden Cather

Honors Theses

In recent years, the way we interact with daily life and industrial environments has changed dramatically due to the rise of the Internet of Things (IoT), from smart homes to industrial control systems. In particular, industry devices, sensors, and actuators can be connected to form various Industrial Internet of Things (IIoT) networks and controlled using operational technologies, such as ICS and SCADA. However, the vulnerabilities and limited security features pose significant challenges in preserving IIoT systems' security as cyber-attacks increase and become more frequent, dynamic, and complex. Additionally, the lack of IIoT testbeds has made it more difficult to find …

Digital Frontiers In Aesthetics: Applying Dewey's Insights To Generative Ai, Malcolm F. Lathrop-Allen

Student Publications

The last few years have seen the emergence of ‘artificially intelligent’ systems en masse, which perform tasks which had previously only been possible by human intelligence. Arguably, the impact of ‘AI 2.0’ has been felt most prominently in the art world — artists have panicked as DALL-E, Midjourney, and other image generation algorithms manufacture pieces which previously required weeks of painstaking labor to create. This project seeks to develop a more critical framework for this novel mode of artistic creation and propose better ways of thinking about, using, and “becoming with” artificial intelligence in the domain of artistry. The first …

Combine Shapelets, Zeng Qingwen

LMU Theses and Dissertations

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. I was a candidate in an activity classification thesis. It collected 4 activities, which included walking on the sidewalk for a set distance, walking up and down a set of stairs, walking on the treadmill at 2.5 mph for 2 minutes, and jogging on the treadmill at 5.5 mph for 1 minute. It took 30 minutes to collect one candidate data. If …

Intelligent Tutoring System Ontology, Wael Mohamed Hassan

Theses

The integration of pedagogical rules into Intelligent Tutoring Systems (ITS) using semantic web technologies, particularly the Web Ontology Language (OWL), holds great promise for enhancing the capabilities of these systems. However, a significant challenge arises from the labor-intensive process of manually constructing ontologies, which can consume valuable time and resources. While ontologies offer numerous advantages, including robust knowledge inference and scalability, the limitations of manual ontology creation are evident in terms of time and flexibility. Therefore, the primary objective of this research is to develop an efficient and automated solution that harnesses the benefits of ontologies while reducing the time …

Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi

Theses

In recent years, Artificial Intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …

An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan

Dissertations

Autonomous Vehicles (AVs) are transforming next-generation autonomous mobility. These vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance overall mobility. They achieve higher levels of Autonomous Driving (AD) by integrating sensors, communication technologies, computation, and Artificial Intelligence (AI). Apart from these advancements, significant gaps remain in integrating heterogeneous technologies and disciplines essential for optimizing AD. Therefore, the current solution approaches lack the capability to exploit intelligent road infrastructures and effectively orchestrate perceptual services for complex driving scenarios. The main objective of this dissertation is to address these challenges by proposing a novel end-to-end intelligent framework …

Implementation And Evaluation Of Ai-Based Citizen Question-Answer Recommender (Acqar) To Enhance Citizen Service Delivery In Singapore Public Sector: A Case Study, Hui Shan Lee

Dissertations and Theses Collection (Open Access)

Government agencies prioritize citizen service delivery to foster trust with the public. Technological advancements, particularly in Artificial Intelligence (AI), hold promise for improving service provision and aligning government operations with citizens' needs. Yet the inherent inflexibility of Service Level Agreements (SLAs) often overlooks the nuances of human emotions and the varied nature of citizen inquiries, exacerbated by a lack of tools to guide appropriate responses. This dissertation aims to address the gaps of overlook of human emotions and non-support for appropriate responses, by exploring the following questions: (1) Can a predictive model incorporating both numeric and textual data effectively forecast …

Can Organizational Focus On Responsible Ai Lead To Improved Ai Adoption By Employees?, Seema Chokshi

Dissertations and Theses Collection (Open Access)

The duality inherent in Artificial Intelligence technology entails that while AI has the potential to bring about transformative benefits to organizations, unintended consequences of AI applications could lead to biased and discriminatory outcomes, which could have negative consequences for the organization and society in general. Concerns about such unintended consequences are an impediment to AI adoption where unwilling employees and practitioners often fear ethical breaches, thereby, negatively impacting their engagement with AI driven applications. In response to these concerns various organizations and regulatory bodies have developed governing frameworks broadly known as Responsible AI standards, that set guidelines to design, …

Sequential Recommendation: From Representation Learning To Reasoning, Lei Wang

Dissertations and Theses Collection (Open Access)

The recommender system is a crucial component of today's online services. It helps users navigate through an overwhelmingly large number of items and discovering those that interest them. Unlike general recommender systems, which recommend items based on the user's overall preferences, sequential recommender systems consider the order of user-item interactions. Sequential recommendations aim to predict the next item a user will interact with, given a sequence of previously interacted items, while considering the short-term and long-term dependencies among items.

In this thesis, we focus on sequential recommendation methods: from representation learning to large language model (LLM)-based reasoning. On the one …

Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Test migration, which enables the reuse of test cases crafted with knowledge and creativity by testers across various platforms and programming languages, has exhibited effectiveness in mobile app testing. However, unit test migration at the source code level has not garnered adequate attention and exploration. In this paper, we propose a novel cross-language and cross-platform test migration methodology, named MUT, which consists of four modules: code mapping, test case filtering, test case translation, and test case adaptation. MUT initially calculates code mappings to establish associations between source and target projects, and identifies suitable unit tests for migration from the source …

Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language …

Scaled And Graduated Learning In Deep Relu Networks And Reconstructing Depp Inelastic Scattering Kinematics, Abdullah Ayar Farhat

Mathematics & Statistics Theses & Dissertations

To address computational challenges in learning deep neural networks, properties of deep RELU networks were studied to develop a multi-scale learning model. The multi-scale model was compared to the multi-grade learning models. Unlike the deep neural network learned from the standard single-scale, single-grade model, the multi-scale neural networks use low scale information from all hidden layers, and thusly provide a robust approximation method that requires fewer parameters, lower computational time, and is resistant to noise. It is shown that the multiscale method is not subject to issues arising from the vanishing gradient problem. This allows very deep multi-scale networks to …

Measuring Jury Perception Of Explainable Machine Learning And Demonstrative Evidence, Rachel Edie Sparks Rogers

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

Subjective pattern comparison has been subject to increased scrutiny by the courts and by the general public, resulting in an increased interest in pattern comparison algorithms that provide quantitative assessments of similarity for use by forensic scientists. While these algorithms would mark an improvement over current subjective comparison methods, individuals without a statistical background may struggle with the statistical concepts and language necessary for describing algorithmic methods. If algorithms are to be used, examiners must be able to testify about their use in a way that is accessible to the jury. In a series of studies, we conduct an assessment …

Design, Analysis, And Drop Assembly Of Interlocking Rigid Bodies, Amy K. Sniffen

Dartmouth College Ph.D Dissertations

This work presents a system of interlocking blocks that can be used to build a wide variety of structures. The blocks slide together to form structures that interlock geometrically like a puzzle to form semi-permanent structures without the need for cement or friction lock. The blocks are designed to be easy to fabricate, assemble, and disassemble. Contributions of the block designs include a novel interlocking joint structure; the joints are wedge-shaped, allowing for error mitigation during assembly and allowing structures to be assembled without jamming even if there is manufacturing error. We introduce planar, 3D, and volumetric designs using these …

Development Of On-The-Fly Quasi-Steady State Approximation For Chemical Kinetics In Cfd, Abhinav Balamurugan

Doctoral Dissertations and Master's Theses

This study analyzes the feasibility of On-The-Fly Quasi-Steady-State Approximation (OTF-QSSA) application for solving chemical kinetics within Computational Fluid Dynamics (CFD) simulations, aiming to reduce the computational demand of detailed mechanisms. An algorithm that dynamically identifies and designates Quasi-Steady-State (QSS) species at specific grid locations and instances during the simulation was developed. With this information, our method pseudo-delays the advancement of concentrations for these QSS species—effectively setting their rate of concentration change to zero for a set number iteration before updating using the detailed mechanism and thereby omitting the computationally intensive processes typically required for their calculation during those skipped iteration. …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen

Research Collection School Of Computing and Information Systems

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …

W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …

Improving Automated Code Reviews: Learning From Experience, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet

Research Collection School Of Computing and Information Systems

Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to automate the process, teaching large language models to provide reviews on submitted code, just as a human would. A recent approach pre-trained and fine-tuned the code intelligent language model on a large-scale code review corpus. However, such techniques did not fully utilise quality reviews …

Beyond A Joke: Dead Code Elimination Can Delete Live Code, Haoxin Tu, Lingxiao Jiang, Debin Gao, He Jiang

Research Collection School Of Computing and Information Systems

Dead Code Elimination (DCE) is a fundamental compiler optimization technique that removes dead code (e.g., unreachable or reachable but whose results are unused) in the program to produce smaller or faster executables. However, since compiler optimizations are typically aggressively performed and there are complex relationships/interplay among a vast number of compiler optimizations (including DCE), it is not known whether DCE is indeed correctly performed and will only delete dead code in practice. In this study, we open a new research problem to investigate: can DCE happen to erroneously delete live code? To tackle this problem, we design a new approach …

Unveiling Memorization In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of lines of code from both open-source and private repositories. A code model memorizes and produces source code verbatim, which potentially contains vulnerabilities, sensitive information, or code with strict licenses, leading to potential security and privacy issues.This paper investigates an important problem: to what extent do code models memorize their training data? We conduct an empirical study to explore memorization in large pre-trained code models. Our study highlights that simply extracting …

Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of …

Exploiting Library Vulnerability Via Migration-Based Automated Test Generation, Zirui Chen, Xing Hu, Xin Xia, Yi Gao, Tongtong Xu, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclosed, project maintainers need to determine whether their projects are affected by the vulnerability, which requires developers to invest substantial effort in assessment. However, existing tools face a series of issues: static analysis tools produce false alarms, dynamic analysis tools require existing tests and test generation tools have low success rates when facing complex vulnerabilities.Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability …