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Full-Text Articles in Physical Sciences and Mathematics
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, …
Toponym-Assisted Map Georeferencing: Evaluating The Use Of Toponyms For The Digitization Of Map Collections, Karim Bahgat, Daniel Runfola
Toponym-Assisted Map Georeferencing: Evaluating The Use Of Toponyms For The Digitization Of Map Collections, Karim Bahgat, Daniel Runfola
Arts & Sciences Articles
A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map …
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 …
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 …
Predicting Road Quality Using High Resolution Satellite Imagery: A Transfer Learning Approach, Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Daniel Runfola
Predicting Road Quality Using High Resolution Satellite Imagery: A Transfer Learning Approach, Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Daniel Runfola
Arts & Sciences Articles
Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in …
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 …
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 …
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 …
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 …
Epidemic Spread Modeling For Covid-19 Using Hard Data, Anna Schmedding
Epidemic Spread Modeling For Covid-19 Using Hard Data, Anna Schmedding
Dissertations, Theses, and Masters Projects
We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020 ,to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and illustrate how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals. We use this characterization to parameterize agent-based simulations that capture the spread of the disease, we evaluate simulation predictions with ground truth, and we evaluate different what-if counter-measure scenarios. Although the presented agent-based model is …
Distributed Byzantine Tolerant Machine Learning, Qi Xia
Distributed Byzantine Tolerant Machine Learning, Qi Xia
Dissertations, Theses, and Masters Projects
Oftentimes, training a large-scale deep learning neural network on a single machine becomes more difficult in a complex network model. Distributed training provides an efficient solution, but opens up participating workers to Byzantine attacks. This problem emerges when some workers cheat during uploading gradients or weights to the central server, e.g., the information received by the server is not always the true result computed by workers. In order to address this problem, we investigate Byzantine problems in distributed machine learning and respectively defend against these kinds of attacks in three scenarios: i) classic distributed machine learning; ii) federated learning; and …
Combining Performance Profiling And Modeling For Accuracy And Efficiency, Hao Xu
Combining Performance Profiling And Modeling For Accuracy And Efficiency, Hao Xu
Dissertations, Theses, and Masters Projects
Modern computer systems have evolved to employ powerful parallel architectures, including multi-core processors, multi-socket chips, large memory subsystems, and fast network communication. Given such powerful hardware, developers rely on performance profiling and modeling to guide their performance optimization. However, performance optimization is facing new challenges on efficiency and accuracy with emerging computer systems. In this dissertation, we propose approaches to address these challenges. We first study memory contention in Non-Uniform Memory Access (NUMA) architectures. We present DR-BW, a new tool based on machine learning to identify bandwidth contention in NUMA architectures and provide optimization guidance. DR-BW collects performance data with …
High-Dimensional Machine Learning Models In Fintech, Qiong Wu
High-Dimensional Machine Learning Models In Fintech, Qiong Wu
Dissertations, Theses, and Masters Projects
This thesis develops several forecasting models for simultaneously predicting the prices of d assets traded in financial markets, a most fundamental problem in the emerging area of ``FinTech''. The models are optimized to address three critical challenges, C1. High-dimensional interactions between assets. Assets could interact (e.g., Amazon's disclosure of its revenue change in cloud services could indicate that revenues also could change in other cloud providers). The number of possible interactions is quadratic in d, and is often much larger than the number of observations. C2. Non-linearity of the hypothesis class. Linear models are usually insufficient to characterize the relationship …
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 …
Rethinking Cache Hierarchy And Interconnect Design For Next-Generation Gpus, Mohamed Assem Abd Elmohsen Ibrahim
Rethinking Cache Hierarchy And Interconnect Design For Next-Generation Gpus, Mohamed Assem Abd Elmohsen Ibrahim
Dissertations, Theses, and Masters Projects
To match the increasing computational demands of GPGPU applications and to improve peak compute throughput, the core counts in GPUs have been increasing with every generation. However, the famous memory wall is a major performance determinant in GPUs. In other words, in most cases, peak throughput in GPUs is ultimately dictated by memory bandwidth. Therefore, to serve the memory demands of thousands of concurrently executing threads, GPUs are equipped with several sources of bandwidth such as on-chip private/shared caching resources and off-chip high bandwidth memories. However, the existing sources of bandwidth are often not sufficient for achieving optimal GPU performance. …