Surmounting Challenges In Aggregating Results From Static Analysis Tools,
2024
Montana State University
Surmounting Challenges In Aggregating Results From Static Analysis Tools, Dr. Ann Marie Reinhold, Brittany Boles, A. Redempta Manzi Muneza, Thomas Mcelroy, Dr. Clemente Izurieta
Military Cyber Affairs
Aggregation poses a significant challenge for software practitioners because it requires a comprehensive and nuanced understanding of raw data from diverse sources. Suites of static-analysis tools (SATs) are commonly used to assess organizational security but simultaneously introduce significant challenges. Challenges include unique results, scales, configuration environments for each SAT execution, and incompatible formats between SAT outputs. Here, we document our experiences addressing these issues. We highlight the problem of relying on a single vendor's SAT version and offer a solution for aggregating findings across multiple SATs, aiming to enhance software security practices and deter threats early with robust defensive operations.
Generative Machine Learning For Cyber Security,
2024
Washington State University
Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin
Military Cyber Affairs
Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.
Factors Influencing Performance Of Students In Software Automated Test Tools Course,
2024
Western University
Factors Influencing Performance Of Students In Software Automated Test Tools Course, Susmita Haldar, Mary Pierce, Luiz Fernando Capretz
Electrical and Computer Engineering Publications
Formal software testing education is important for building efficient QA professionals. Various aspects of quality assurance approaches are usually covered in courses for training software testing students. Automated Test Tools is one of the core courses in the software testing post-graduate curriculum due to the high demand for automated testers in the workforce. It is important to understand which factors are affecting student performance in the automated testing course to be able to assist the students early on based on their needs. Various metrics that are considered for predicting student performance in this testing course are student engagement, grades on …
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities,
2024
Singapore Management University
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang
Research Collection School Of Computing and Information Systems
Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on their ability to understand code semantics. Additionally, existing analyses assume the number of edges between nodes at the abstract syntax tree (AST) is related to syntax distance, and also often require transforming the high-dimensional space of deep learning models to a low-dimensional one, which may introduce inaccuracies. To study how code models represent code syntax and semantics, we conduct a comprehensive analysis of 7 code …
Navigating Real-World Challenges: A Quadruped Robot Guiding System For Visually Impaired People In Diverse Environments,
2024
Singapore Management University
Navigating Real-World Challenges: A Quadruped Robot Guiding System For Visually Impaired People In Diverse Environments, Shaojun Cai, Ashwin Ram, Zhengtai Gou, Mohd Alqama Wasim Shaikh, Yu-An Chen, Yingjia Wan, Kotaro Hara, Shengdong Zhao, David Hsu
Research Collection School Of Computing and Information Systems
Blind and Visually Impaired (BVI) people find challenges in navigating unfamiliar environments, even using assistive tools such as white canes or smart devices. Increasingly affordable quadruped robots offer us opportunities to design autonomous guides that could improve how BVI people find ways around unfamiliar environments and maneuver therein. In this work, we designed RDog, a quadruped robot guiding system that supports BVI individuals’ navigation and obstacle avoidance in indoor and outdoor environments. RDog combines an advanced mapping and navigation system to guide users with force feedback and preemptive voice feedback. Using this robot as an evaluation apparatus, we conducted experiments …
Comparative Predictive Analysis Of Stock Performance In The Tech Sector,
2024
University of New Orleans
Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi
LSU New Orleans Theses and Dissertations
This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …
Exploring Decentralized Computing Using Solid And Ipfs For Social Media Applications,
2024
University of Arkansas, Fayetteville
Exploring Decentralized Computing Using Solid And Ipfs For Social Media Applications, Pranav Balasubramanian Natarajan
Computer Science and Computer Engineering Undergraduate Honors Theses
As traditional centralized social media platforms face growing concerns over data privacy, censorship, and lack of user control, there has been an increasing interest in decentralized alternatives. This thesis explores the design and implementation of a decentralized social media application by integrating two key technologies: Solid and the InterPlanetary File System (IPFS). Solid, led by Sir Tim Berners-Lee, enables users to store and manage their personal data in decentralized "Pods," giving them ownership over their digital identities. IPFS, a peer-to-peer hypermedia protocol, facilitates decentralized file storage and sharing, ensuring content availability and resilience against censorship. By leveraging these technologies, the …
The Quantitative Analysis And Visualization Of Nfl Passing Routes,
2024
University of Arkansas, Fayetteville
The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi
Computer Science and Computer Engineering Undergraduate Honors Theses
The strategic planning of offensive passing plays in the NFL incorporates numerous variables, including defensive coverages, player positioning, historical data, etc. This project develops an application using an analytical framework and an interactive model to simulate and visualize an NFL offense's passing strategy under varying conditions. Using R-programming and data management, the model dynamically represents potential passing routes in response to different defensive schemes. The system architecture integrates data from historical NFL league years to generate quantified route scores through designed mathematical equations. This allows for the prediction of potential passing routes for offensive skill players in response to the …
Automatic Extraction Of Vulnerability Information For Security Operators Using Gpt Models,
2024
University of Arkansas, Fayetteville
Automatic Extraction Of Vulnerability Information For Security Operators Using Gpt Models, Waeland Elder
Computer Science and Computer Engineering Undergraduate Honors Theses
Artificial intelligence has progressed rapidly in recent years, greatly revolutionizing the world and helping to automate increasingly complex tasks. However, there are still some disciplines where the problem of automation has not yet been thoroughly tackled, such as in software vulnerability management. Vulnerability management is a critical component of cybersecurity for an organization. Until recently, learning about a vulnerability required a security operator to manually search and read through online information and security advisories to find needed information. Doing so is a significantly time-consuming task, as these advisories tend to be quite lengthy and packed with information about various technologies. …
Dancetag: Using Sensors To Improve Feedback Given To Dance Students,
2024
Chapman University
Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego
Student Scholar Symposium Abstracts and Posters
The structure of dance classrooms has remained unchanged for several years. Very little, if any, technology has been incorporated to improve the quality of teaching. This has motivated our research project, whose goal is to capture dance movements with wearable sensors, to develop DANCETAG (Data Analytics and Notation with Captured Event Tagging). This is a platform that allows the gathering of data captured by Sony’s Mocopi sensors and annotating them with the dancer's movements. The Mocopi sensors make up a motion capture system. It is comprised of six small, round sensors that can be attached to velcro straps and clips. …
A Ui-Enhanced Approach To Generic Web-Based Scheduling,
2024
University of Connecticut - Storrs
A Ui-Enhanced Approach To Generic Web-Based Scheduling, Tyler Hinrichs
Honors Scholar Theses
Administrative scheduling is a key aspect of a wide variety of systems, but despite being a widespread need, it is not a straightforward task. Organizational uniqueness introduces complexity when attempting to use algorithmic methods to automate scheduling, as individual organizations often have their own ways of determining various details and constraints of a schedule. However, in this paper, we assert that there are relevant commonalities that many different schedules fundamentally possess, allowing us to create a generic scheduling application that can be productively used for as many different scenarios as possible. After devising a schema that captures this generic representation, …
Improving The Performance Of Wi-Fi Indoor Localization In Both Dense And Unknown Environments,
2024
Singapore Management University
Improving The Performance Of Wi-Fi Indoor Localization In Both Dense And Unknown Environments, Quang Truong Hai
Dissertations and Theses Collection (Open Access)
Indoor localization is important for various pervasive applications, garnering considerable research attention over recent decades. Despite numerous proposed solutions, the practical application of these methods in real-world environments with high applicability remains challenging. One compelling use case for building owners is the ability to track individuals as they navigate through the building, whether for security, customer analytics, space utilization planning, or other management purposes. However, this task becomes exceedingly difficult in environments with hundreds or thousands of people in motion. Conversely, the need to track oneself’s location is also meaningful from the perspective of individuals traversing in crowded spaces. These …
Vaid: Indexing View Designs In Visual Analytics System,
2024
Singapore Management University
Vaid: Indexing View Designs In Visual Analytics System, Lu Ying, Aoyu Wu, Haotian Li, Zikun Deng, Ji Lan, Jiang Wu, Yong Wang, Huamin Qu, Dazhen Deng, Yingcai Wu
Research Collection School Of Computing and Information Systems
Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up …
Regret-Based Defense In Adversarial Reinforcement Learning,
2024
Singapore Management University
Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo
Research Collection School Of Computing and Information Systems
Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations, which can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about traffic signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) can be fatal. Leading existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on (a) regularization approaches that make expected value objectives robust by adding adversarial loss terms; or (b) employing "maximin'' (i.e., maximizing the minimum value) notions of robustness. While regularization approaches are adept at reducing the probability …
Analyzing And Revivifying Function Signature Inference Using Deep Learning,
2024
Jinan University - China
Analyzing And Revivifying Function Signature Inference Using Deep Learning, Yan Lin, Trisha Singhal, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Function signature plays an important role in binary analysis and security enhancement, with typical examples in bug finding and control-flow integrity enforcement. However, recovery of function signatures by static binary analysis is challenging since crucial information vital for such recovery is stripped off during compilation. Although function signature recovery using deep learning (DL) is proposed in an effort to handle such challenges, the reported accuracy is low for binaries compiled with optimizations. In this paper, we first perform a systematic study to quantify the extent to which compiler optimizations (negatively) impact the accuracy of existing DL techniques based on Recurrent …
Robust Auto-Scaling With Probabilistic Workload Forecasting For Cloud Databases,
2024
Zhejiang University
Robust Auto-Scaling With Probabilistic Workload Forecasting For Cloud Databases, Haitian Hang, Xiu Tang, Jianling Sun, Lingfeng Bao, David Lo, Haoye Wang
Research Collection School Of Computing and Information Systems
Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we …
Analyzing And Revivifying Function Signature Inference Using Deep Learning,
2024
Singapore Management University
Analyzing And Revivifying Function Signature Inference Using Deep Learning, Yan Lin, Trisha Singhal, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Function signature plays an important role in binary analysis and security enhancement, with typical examples in bug finding and control-flow integrity enforcement. However, recovery of function signatures by static binary analysis is challenging since crucial information vital for such recovery is stripped off during compilation. Although function signature recovery using deep learning (DL) is proposed in an effort to handle such challenges, the reported accuracy is low for binaries compiled with optimizations. In this paper, we first perform a systematic study to quantify the extent to which compiler optimizations (negatively) impact the accuracy of existing DL techniques based on Recurrent …
Exploring The Relationship Between Anxiety And Virtual Reality Sickness,
2024
University of South Alabama
Exploring The Relationship Between Anxiety And Virtual Reality Sickness, David Wesley Woolverton
Graduate Theses and Dissertations (2019 - present)
As virtual reality (VR) becomes more commonly used in education, it is important to understand the technology’s weakness and mitigate any potential negative effects on student success. One adverse side-effect of VR use is simulation-induced motion sickness, known in the context of VR as VR sickness. Previous research by Howard and Van Zandt (2021) found that possessing a phobia had a significant positive correlation with VR sickness, but only if the phobia is triggered by the simulation, suggesting that symptoms are actually connected to the anxiety the phobia induces. This study explored the hypothesized correlation between anxiety and VR sickness, …
Vibmilk: Non-Intrusive Milk Spoilage Detection Via Smartphone Vibration,
2024
University of New South Wales
Vibmilk: Non-Intrusive Milk Spoilage Detection Via Smartphone Vibration, Yuezhong Wu, Wei Song, Yanxiang Wang, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed …
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges,
2024
Singapore Management University
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude
Research Collection School Of Computing and Information Systems
The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.
