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Identifying Social Media Users That Are Susceptible To Phishing Attacks, Zoe Metzger
Identifying Social Media Users That Are Susceptible To Phishing Attacks, Zoe Metzger
Undergraduate Honors Theses
Phishing scams are a billion-dollar problem. According to Threatpost, in 2020, business email compromise phishing attacks cost the US economy $ 1.8 billion. Social media phishing scams are also on the rise with 74% of companies experiencing social media attacks in 2021 according to Proofpoint. Educating users about phishing scams is an effective strategy for reducing phishing attacks. Despite efforts to combat phishing, the number of attacks continues to rise, likely indicative of a reticence of users to change online behaviors. Existing research into predicting vulnerable social media users that are susceptible to phishing mostly focuses on content analysis of …
Kfactorvae: Self-Supervised Regularization For Better A.I. Disentanglement, Joseph S. Lee
Kfactorvae: Self-Supervised Regularization For Better A.I. Disentanglement, Joseph S. Lee
Undergraduate Honors Theses
Obtaining disentangled representations is a goal sought after to make A.I. models more interpretable. Studies have proven the impossibility of obtaining these kinds of representations with just unsupervised learning, or in other words, without strong inductive biases. One strong inductive bias is a regularization term that encourages the invariance of factors of variations across an image and a carefully selected augmentation. In this thesis, we build upon the existing Variational Autoencoder (VAE)-based disentanglement literature by utilizing the aforementioned inductive bias. We evaluate our method on the dSprites dataset, a well-known benchmark, and demonstrate its ability to achieve comparable or higher …
A Reevaluation Of Why Crypto-Detectors Fail: A Systematic Revaluation Of Cryptographic Misuse Detection Techniques, Scott Marsden
A Reevaluation Of Why Crypto-Detectors Fail: A Systematic Revaluation Of Cryptographic Misuse Detection Techniques, Scott Marsden
Dissertations, Theses, and Masters Projects
The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of their effectiveness at finding crypto-API misuse in practice. The original paper presents the MASC framework, which enables a systematic and data-driven evaluation of crypto-detectors using mutation testing. MASC was grounded in a comprehensive view of the problem space by developing a data-driven …
Domain-Specific Optimization For Machine Learning System, Yu Chen
Domain-Specific Optimization For Machine Learning System, Yu Chen
Dissertations, Theses, and Masters Projects
The machine learning (ML) system has been an indispensable part of the ML ecosystem in recent years. The rapid growth of ML brings new system challenges such as the need of handling more large-scale data and computation, the requirements for higher execution performance, and lower resource usage, stimulating the demand for improving ML system. General-purpose system optimization is widely used but brings limited benefits because ML applications vary in execution behaviors based on their algorithms, input data, and configurations. It's difficult to perform comprehensive ML system optimizations without application specific information. Therefore, domain-specific optimization, a method that optimizes particular types …
Appearance Driven Reflectance Modeling, James Christopher Bieron
Appearance Driven Reflectance Modeling, James Christopher Bieron
Dissertations, Theses, and Masters Projects
Creating realistic computer generated imagery is essential for modern movies and video games. Recreating the appearance of materials is integral to generating such photo-realistic images. While the problem of how to model materials is well studied, here we will focus on the question of how to recreate the appearance of specific materials found in the real world. In this dissertation we will begin with a short introduction to rendering, followed by a discussion of various material models, techniques for measuring reflectance, and strategies for fitting these models to reflectance data. We will then introduce a novel two-stage process for fitting, …
Exploring Software Licensing Issues Faced By Legal Practitioners, Nathan James Wintersgill
Exploring Software Licensing Issues Faced By Legal Practitioners, Nathan James Wintersgill
Dissertations, Theses, and Masters Projects
Most modern software products incorporate open source components, which requires compliance with each component’s licenses. As noncompliance can lead to significant repercussions, organizations often seek advice from legal practitioners to maintain license compliance, address licensing issues, and manage the risks of noncompliance. While legal practitioners play a critical role in the process, little is known in the software engineering community about their experiences within the open source license compliance ecosystem. To fill this knowledge gap, a joint team of software engineering and legal researchers designed and conducted a survey with 30 legal practitioners and related occupations and then held 16 …
Program Analysis For Software Engineers And Students, Jialiang Tan
Program Analysis For Software Engineers And Students, Jialiang Tan
Dissertations, Theses, and Masters Projects
Software inefficiencies are inevitable in computer systems. At the code level, software packages have become increasingly complex, they are comprised of a large amount of source code, sophisticated control and data flow, and growing levels of abstraction. This complexity often introduces inefficiencies across software stacks, leading to performance degradation. At the resource level, the evolution of hardware outpaces the performance optimization of software, leading to resource wastage and energy dissipation in emerging architecture. To better understand program behaviors, software developers take advantage of performance profiling tools. Existing profiling techniques, whether fine-grained profilers or coarse-grained profilers focus on identifying hotspots, which …
Learning-Based Ubiquitous Sensing For Solving Real-World Problems, Woosub Jung
Learning-Based Ubiquitous Sensing For Solving Real-World Problems, Woosub Jung
Dissertations, Theses, and Masters Projects
Recently, as the Internet of Things (IoT) technology has become smaller and cheaper, ubiquitous sensing ability within these devices has become increasingly accessible. Learning methods have also become more complex in the field of computer science ac- cordingly. However, there remains a gap between these learning approaches and many problems in other disciplinary fields. In this dissertation, I investigate four different learning-based studies via ubiquitous sensing for solving real-world problems, such as in IoT security, athletics, and healthcare. First, I designed an online intrusion detection system for IoT devices via power auditing. To realize the real-time system, I created a …
Matfusion: A Generative Diffusion Model For Svbrdf Capture, Samuel Lee Sartor
Matfusion: A Generative Diffusion Model For Svbrdf Capture, Samuel Lee Sartor
Dissertations, Theses, and Masters Projects
We formulate SVBRDF estimation from photographs as a diffusion task. To model the distribution of spatially varying materials, we first train a novel unconditional SVBRDF diffusion backbone model on a large set of 312,165 synthetic spatially varying material exemplars. This SVBRDF diffusion backbone model, named MatFusion, can then serve as a basis for refining a conditional diffusion model to estimate the material properties from a photograph under controlled or uncontrolled lighting. Our backbone MatFusion model is trained using only a loss on the reflectance properties, and therefore refinement can be paired with more expensive rendering methods without the need for …
Intelligent Software Tooling For Improving Software Development, Nathan Allen Cooper
Intelligent Software Tooling For Improving Software Development, Nathan Allen Cooper
Dissertations, Theses, and Masters Projects
Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc. The success of Deep Learning (DL) over the past decade has shown huge advancements in automation across many domains, including Software Development processes. One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces …
Efficient Parallelization Of Irregular Applications On Gpu Architectures, Qihan Wang
Efficient Parallelization Of Irregular Applications On Gpu Architectures, Qihan Wang
Dissertations, Theses, and Masters Projects
With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with a computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to a coarse-grained kernel; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. …
A Comprehensive Study Of Bills Of Materials For Software Systems, Trevor Stalnaker
A Comprehensive Study Of Bills Of Materials For Software Systems, Trevor Stalnaker
Dissertations, Theses, and Masters Projects
Software Bills of Materials (SBOMs) have emerged as tools to facilitate the management of software dependencies, vulnerabilities, licenses, and the supply chain. Significant effort has been devoted to increasing SBOM awareness and developing SBOM formats and tools. Despite this effort, recent studies have shown that SBOMs are still an early technology not adequately adopted in practice yet, mainly due to limited SBOM tooling and lack of industry consensus on SBOM content, tool usage, and practical benefits. Expanding on previous research, this paper reports a comprehensive study that first investigates the current challenges stakeholders encounter when creating and using SBOMs. The …
Recoverable Memory Bank For Class-Incremental Learning, Jiangtao Kong
Recoverable Memory Bank For Class-Incremental Learning, Jiangtao Kong
Dissertations, Theses, and Masters Projects
Incremental learning aims to enable machine learning systems to sequentially learn new tasks without forgetting the old ones. While some existing methods, such as data replay-based and parameter isolation-based approaches, achieve remarkable results in incremental learning, they often suffer from memory limits, privacy issues, or generation instability. To address these problems, we propose Recoverable Memory Bank (RMB), a novel non-exemplar-based approach for class incremental learning (CIL). Specifically, we design a dynamic memory bank that stores only one aggregated memory representing each class of the old tasks. Next, we propose a novel method that combines a high-dimensional space rotation matrix and …
Quantum Federated Learning: Training Hybrid Neural Networks Collaboratively, Anneliese Brei
Quantum Federated Learning: Training Hybrid Neural Networks Collaboratively, Anneliese Brei
Undergraduate Honors Theses
This thesis explores basic concepts of machine learning, neural networks, federated learning, and quantum computing in an effort to better understand Quantum Machine Learning, an emerging field of research. We propose Quantum Federated Learning (QFL), a schema for collaborative distributed learning that maintains privacy and low communication costs. We demonstrate the QFL framework and local and global update algorithms with implementations that utilize TensorFlow Quantum libraries. Our experiments test the effectiveness of frameworks of different sizes. We also test the effect of changing the number of training cycles and changing distribution of training data. This thesis serves as a synoptic …
Exploring Multi-Level Parallelism For Graph-Based Applications Via Algorithm And System Co-Design, Zhen Peng
Exploring Multi-Level Parallelism For Graph-Based Applications Via Algorithm And System Co-Design, Zhen Peng
Dissertations, Theses, and Masters Projects
Graph processing is at the heart of many modern applications where graphs are used as the basic data structure to represent the entities of interest and the relationships between them. Improving the performance of graph-based applications, especially using parallelism techniques, has drawn significant interest both in academia and industry. On the one hand, modern CPU architectures are able to provide massive computational power by using sophisticated memory hierarchy and multi-level parallelism, including thread-level parallelism, data-level parallelism, etc. On the other hand, graph processing workloads are notoriously challenging for achieving high performance due to their irregular computation pattern and unpredictable control …
Techniques For Accelerating Large-Scale Automata Processing, Hongyuan Liu
Techniques For Accelerating Large-Scale Automata Processing, Hongyuan Liu
Dissertations, Theses, and Masters Projects
The big-data era has brought new challenges to computer architectures due to the large-scale computation and data. Moreover, this problem becomes critical in several domains where the computation is also irregular, among which we focus on automata processing in this dissertation. Automata are widely used in applications from different domains such as network intrusion detection, machine learning, and parsing. Large-scale automata processing is challenging for traditional von Neumann architectures. To this end, many accelerator prototypes have been proposed. Micron's Automata Processor (AP) is an example. However, as a spatial architecture, it is unable to handle large automata programs without repeated …
Flexible And Robust Iterative Methods For The Partial Singular Value Decomposition, Steven Goldenberg
Flexible And Robust Iterative Methods For The Partial Singular Value Decomposition, Steven Goldenberg
Dissertations, Theses, and Masters Projects
The Singular Value Decomposition (SVD) is one of the most fundamental matrix factorizations in linear algebra. As a generalization of the eigenvalue decomposition, the SVD is essential for a wide variety of fields including statistics, signal and image processing, chemistry, quantum physics and even weather prediction. The methods for numerically computing the SVD mostly fall under three main categories: direct, iterative, and streaming. Direct methods focus on solving the SVD in its entirety, making them suitable for smaller dense matrices where the computation cost is tractable. On the other end of the spectrum, streaming methods were created to provide an …
Communication And Computation Efficient Deep Learning, Zeyi Tao
Communication And Computation Efficient Deep Learning, Zeyi Tao
Dissertations, Theses, and Masters Projects
Recent advances in Artificial Intelligence (AI) are characterized by ever-increasing datasets and rapid growth of model complexity. Many modern machine learning models, especially deep neural networks (DNNs), cannot be efficiently carried out by a single machine. Hence, distributed optimization and inference have been widely adopted to tackle large-scale machine learning problems. Meanwhile, quantum computers that process computational tasks exponentially faster than classical machines offer an alternative solution for resource-intensive deep learning. However, there are two obstacles that hinder us from building large-scale DNNs on the distributed systems and quantum computers. First, when distributed systems scale to many nodes, the training …
Enabling Practical Evaluation Of Privacy Of Commodity-Iot, Sunil Manandhar
Enabling Practical Evaluation Of Privacy Of Commodity-Iot, Sunil Manandhar
Dissertations, Theses, and Masters Projects
There has been a massive shift towards the use of IoT products in recent years. While companies have come a long way in making these devices and services easily accessible to the consumers, very little is known about the privacy issues pertaining to these devices. In this dissertation, we focus on evaluating privacy pertaining to commodity-IoT devices by studying device usage behavior of consumers and privacy disclosure practices of IoT vendors. Our analyses consider deep intricacies tied to commodity-IoT domain, revealing insightful findings that help with building automated tools for a large scale analysis. We first present the design and …
Practical Gpgpu Application Resilience Estimation And Fortification, Lishan Yang
Practical Gpgpu Application Resilience Estimation And Fortification, Lishan Yang
Dissertations, Theses, and Masters Projects
Graphics Processing Units (GPUs) are becoming a de facto solution for accelerating a wide range of applications but remain susceptible to transient hardware faults (soft errors) that can easily compromise application output. One of the major challenges in the domain of GPU reliability is to accurately measure general purpose GPU (GPGPU) application resilience to transient faults. This challenge stems from the fact that a typical GPGPU application spawns a huge number of threads and then utilizes a large amount of potentially unreliable compute and memory resources available on the GPUs. As the number of possible fault locations can be in …
Static And Dynamic Analysis In Cryptographic-Api Misuse Detection Of Mobile Application, Kunyang Li
Static And Dynamic Analysis In Cryptographic-Api Misuse Detection Of Mobile Application, Kunyang Li
Undergraduate Honors Theses
With Android devices becoming more advanced and gaining more popularity, the number of cryptographic-API misuses in mobile applications is escalating. Numerous snippets of code in Android are from Stack Overflow and over 90% of them contain several crypto-issues. Various crypto-misuse detectors come out aiming to report vulnerabilities of apps and better secure users’ privacy. These detectors can be broadly classified into two categories based on the analysis strategies employed to catch misuses – static analysis (i.e., by scanning the code base) and dynamic analysis (i.e., by executing the code). However, there are not enough research on comparing their underlying differences, …
Low-Overhead Techniques For Secure And Reliable Gpu Computing, Gurunath Kadam
Low-Overhead Techniques For Secure And Reliable Gpu Computing, Gurunath Kadam
Dissertations, Theses, and Masters Projects
In recent years, Graphics Processing Units (GPUs) have become a de facto choice to accelerate the computations in various domains such as machine learning, security, financial and scientific computing. GPUs leverage the inherent data parallelism in the target applications to provide high throughput at superior energy efficiency. Due to the rising usage of GPUs for a large number of applications, they are facing new challenges, especially in the security and reliability domains. From the security side, recently several microarchitectural attacks targeting GPUs have been demonstrated. These attacks leak the secret information stored on GPUs, for example, the parameters of a …
Performance Optimization With An Integrated View Of Compiler And Application Knowledge, Ruiqin Tian
Performance Optimization With An Integrated View Of Compiler And Application Knowledge, Ruiqin Tian
Dissertations, Theses, and Masters Projects
Compiler optimization is a long-standing research field that enhances program performance with a set of rigorous code analyses and transformations. Traditional compiler optimization focuses on general programs or program structures without considering too much high-level application operations or data structure knowledge. In this thesis, we claim that an integrated view of the application and compiler is helpful to further improve program performance. Particularly, we study integrated optimization opportunities for three kinds of applications: irregular tree-based query processing systems such as B+ tree, security enhancement such as buffer overflow protection, and tensor/matrix-based linear algebra computation. The performance of B+ tree query …
Data-Driven Reflectance Estimation Under Natural Lighting, Victoria Cooper
Data-Driven Reflectance Estimation Under Natural Lighting, Victoria Cooper
Dissertations, Theses, and Masters Projects
Bidirectional Reflectance Distribution Functions, (BRDFs), describe how light is reflected off of a material. BRDFs are captured so that the materials can be re-lit under new while maintaining accuracy. BRDF models can approximate the reflectance of a material, but are unable to accurately represent the full BRDF of the material. Acquisition setups for BRDFs trade accuracy for speed with the most accurate methods, gonioreflectometers, being the slowest. Image-based BRDF acquisition approaches range from using complicated controlled lighting setups to uncontrolled known lighting to assuming the lighting is unknown. We propose a data-driven method for recovering BRDFs under known, but uncontrolled …
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Undergraduate Honors Theses
Density functional theory (DFT) has become a popular method for computational work involving larger molecular systems as it provides accuracy that rivals ab initio methods while lowering computational cost. Nevertheless, computational cost is still high for systems greater than ten atoms in size, preventing their application in modeling realistic atmospheric systems at the molecular level. Machine learning techniques, however, show promise as cost-effective tools in predicting chemical properties when properly trained. In the interest of furthering chemical machine learning in the field of atmospheric science, I have developed a training method for predicting cluster energetics of newly characterized nitrogen-based brown …
A Pain Free Nociceptor: Predicting Football Injuries With Machine Learning, Andrew Lyubovsky
A Pain Free Nociceptor: Predicting Football Injuries With Machine Learning, Andrew Lyubovsky
Undergraduate Honors Theses
Injuries are a significant aspect of every sport, with the ability to impact a player’s career and the success of a team in their season. As sensor data is able to pick up on a player’s physical state, recently it has been analyzed for its ability to predict player injuries. We inspect the predictive power of player stats, subjective player responses, GPS data, and training load data in forecasting game injuries from an NCAA American football team during the 2019 season. Data processing techniques are used to remove noise and decrease correlated data, and as large portions of the data …
Performance Implications Of Memory Affinity On Filesystem Caches In A Non-Uniform Memory Access Environment, Jacob Adams
Performance Implications Of Memory Affinity On Filesystem Caches In A Non-Uniform Memory Access Environment, Jacob Adams
Undergraduate Honors Theses
Non-Uniform Memory Access imposes unique challenges on every component of an operating system and the applications that run on it. One such component is the filesystem which, while not directly impacted by NUMA in most cases, typically has some form of cache whose performance is constrained by the latency and bandwidth of the memory that it is stored in. One such filesystem is ZFS, which contains its own custom caching system, known as the Adaptive Replacement Cache. This work looks at the impact of NUMA on this cache via sequential read operations, shows how current solutions intended to reduce this …
Scope: Building And Testing An Integrated Manual-Automated Event Extraction Tool For Online Text-Based Media Sources, Matthew Crittenden
Scope: Building And Testing An Integrated Manual-Automated Event Extraction Tool For Online Text-Based Media Sources, Matthew Crittenden
Undergraduate Honors Theses
Building on insights from two years of manually extracting events information from online news media, an interactive information extraction environment (IIEE) was developed. SCOPE, the Scientific Collection of Open-source Policy Evidence, is a Python Django-based tool divided across specialized modules for extracting structured events data from unstructured text. These modules are grouped into a flexible framework which enables the user to tailor the tool to meet their needs. Following principles of user-oriented learning for information extraction (IE), SCOPE offers an alternative approach to developing AI-assisted IE systems. In this piece, we detail the ongoing development of the SCOPE tool, present …
Revisiting Isolation For System Security And Efficiency In The Era Of Internet Of Things, Lele Ma
Revisiting Isolation For System Security And Efficiency In The Era Of Internet Of Things, Lele Ma
Dissertations, Theses, and Masters Projects
Isolation is a fundamental paradigm for secure and efficient resource sharing on a computer system. However, isolation mechanisms in traditional cloud computing platforms are heavy-weight or just not feasible to be applied onto the computing environment for Internet of Things(IoT). Most IoT devices have limited resources and their servers are less powerful than cloud servers but are widely distributed over the edge of the Internet. Revisions to the traditional isolation mechanisms are needed in order to improve the system security and efficiency in these computing environments. The first project explores container-based isolation for the emerging edge computing platforms. We show …
On Supporting Android Software Developers And Testers, Carlos Eduardo Bernal Cardenas
On Supporting Android Software Developers And Testers, Carlos Eduardo Bernal Cardenas
Dissertations, Theses, and Masters Projects
Users entrust mobile applications (apps) to help them with different tasks in their daily lives. However, for each app that helps to finish a given task, there are a plethora of other apps in popular marketplaces that offer similar or nearly identical functionality. This makes for a competitive market where users will tend to favor the highest quality apps in most cases. Given that users can easily get frustrated by apps which repeatedly exhibit bugs, failures, and crashes, it is imperative that developers promptly fix problems both before and after the release. However, implementing and maintaining high quality apps is …