Physical Sciences and Mathematics Commons^™

Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerful models, there remain several scenarios where developers cannot take full advantage of them, stemming from factors such as restricted or unreliable internet access, institutional privacy policies that prohibit external transmission of code to third-party vendors, and more. Therefore, developing a compact, efficient, and yet energy-saving model for deployment on developers' devices becomes essential.To this aim, we propose Avatar, a novel approach that crafts a deployable model from a large language model of code by optimizing …

Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which …

Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu

Research Collection School Of Computing and Information Systems

Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue.In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to …

Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results.In this paper, we propose a new patch presence test framework named Ppt4J …

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 …

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, …

Context-Aware Representation: Jointly Learning Item Features And Selection From Triplets, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

In areas of machine learning such as cognitive modeling or recommendation, user feedback is usually context-dependent. For instance, a website might provide a user with a set of recommendations and observe which (if any) of the links were clicked by the user. Similarly, there is growing interest in the so-called “odd-one-out” learning setting, where human participants are provided with a basket of items and asked which is the most dissimilar to the others. In both of those cases, the presence of all the items in the basket can influence the final decision. In this article, we consider a classification task …

Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.

Final Blacktail Creek Remediation And Contaminated Groundwater Hydraulic Control Site Pumping Test Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Technical Specifications - Silver Bow Creek Conservation Area - 60% Submittal April 2024, Atlantic Richfield Company

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Reflections About The Academy And Its Centennial, Marco Aldi, Woodward S. Bousquet

Virginia Journal of Science

Brief reflective essays from members of the Virginia Academy of Science were solicited as part of the Academy's centennial commemoration in 2023. The essays received demonstrate the many and varied ways in which the Academy has fostered collegiality, encouraged research, supported science education, and shaped the course of science in Virginia during the organization's 100-year history.

Historical Trends In The Astronomy, Math, And Physics Section Of The Virginia Academy Of Science, Joseph D. Rudmin

Virginia Journal of Science

A brief history of the Astronomy, Math, and Physics section of the Virginia Academy of Science is presented, noting trends in participation and content.

Hydrophobic Deep Eutectic Solvents: Liquid-Liquid Extraction Of Organic Water Pollutants, Abigail Louise Maletta

Masters Theses

Pollution is a topic of significant research within the scientific community. The massive demand for industrial processes to deliver the technology and resources we rely on has opened numerous avenues for pollutants to enter the environment, affecting air, water, soil, and the food chain. Oftentimes, these pathways are intertwined, and the interplay between water and air currents spreads pollution globally, creating a cascade of environmental impacts. Water, a vital resource for all forms of life, is particularly susceptible to industrial pollution. Among the most prevalent water pollutants are heavy metals, such as lead and mercury, and organic compounds, often released …

Combating Financial Crimes With Unsupervised Learning Techniques: Clustering And Dimensionality Reduction For Anti-Money Laundering, Ahmed N. Bakry, Almohammady S. Alsharkawy, Mohamed S. Farag, Kamal R. Raslan

Al-Azhar Bulletin of Science

Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of financial systems. One keychallenge in AML is identifying high-risk groups based on their behavior. Unsupervised learning, particularly clustering, is a promising solution for this task. However, the use of hundreds of features todescribe behavior results in a highdimensional dataset that negatively impacts clustering performance.In this paper, we investigate the effectiveness of combining clustering method agglomerative hierarchicalclustering with four dimensionality reduction techniques -Independent Component Analysis (ICA), andKernel Principal Component Analysis (KPCA), Singular Value Decomposition (SVD), Locality Preserving Projections (LPP)- to overcome the issue of high-dimensionality in AML data and …

Graph Neural Network Guided By Feature Selection And Centrality Measures For Node Classification On Homophilic And Heterophily Graphs, Asmaa M. Mahmoud, Heba F. Eid, Abeer S. Desuky, Hoda A. Ali

Al-Azhar Bulletin of Science

One of the most recent developments in the fields of deep learning and machine learning is Graph Neural Networks (GNNs). GNNs core task is the feature aggregation stage, which is carried out over the node's neighbours without taking into account whether the features are relevant or not. Additionally, the majority of these existing node representation techniques only consider the network's topology structure while completely ignoring the centrality information. In this paper, a new technique for explaining graph features depending on four different feature selection approaches and centrality measures in order to identify the important nodes and relevant node features is …

The Social Pot: A Social Media Application, Reid Long

Honors Projects

The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …

Integrating Artificial Intelligence For Automated Storytelling In Turn-Based Strategy Games, Timothy Ripper

Theses

This project is inspired by turn-based strategy games, Final Fantasy Tactics, X-Com 2, and modern turn-based strategy games. This project is structured around the use of artificial intelligence for storytelling within strategy games. The focus of this project utilizes artificial intelligence in creating a quest generation system for storytelling. The resulting quest system creates new quests dynamically after communicating with an artificial intelligence allowing players to potentially experience an ever-expanding story from quests

Exploring Tokenization Techniques To Optimize Patch-Based Time-Series Transformers, Gabriel L. Asher

Computer Science Senior Theses

Transformer architectures have revolutionized deep learning, impacting natural language processing and computer vision. Recently, PatchTST has advanced long-term time-series forecasting by embedding patches of time-steps to use as tokens for transformers. This study examines and seeks to enhance PatchTST's embedding techniques. Using eight benchmark datasets, we explore explore novel token embedding techniques. To this end, we introduce several PatchTST variants, which alter the embedding methods of the original paper. These variants consist of the following architectural changes: using CNNs to embed inputs to tokens, embedding an aggregate measure like the mean, max, or sum of a patch, adding the exponential …

Finding Identities: Identities In Video Games From A Gender, Race, And Identity Representation, Osayame Erinmwingbovo

ART 108: Introduction to Games Studies

In this paper I will bring light to the exploration of gender, race, and identity in video games. While also having a focus on how the representation crosses with social and cultural contexts. I will be researching different games from many different genres, which will show light to the way video games reflect and shape societal attitudes towards gender, race, and identity. When using close textual analysis and theoretical framework from topics that include critical race theory, media, and feminist theory. This research will help to seek to explore the nuances and complexities of representation in gaming which implicates video …

Gradual Memory Safety, Jack Phillips

All NMU Master's Theses

This paper extends the theory of Gradual Types to include memory safe Region-Types and Region-Based Memory Management. It also makes advancements in the capabilities of Region-Based systems. Lastly, it presents the Svejk language and Hasek Type System.

Introduction To The Special Centennial Issue, Woodward S. Bousquet, Christopher Osgood

Virginia Journal of Science

This special issue of the Virginia Journal of Science (VJS) is dedicated to the 2023 Centennial of the Virginia Academy of Science (VAS). It includes congratulations from Commonwealth leaders, a proclamation by the Virginia General Assembly Senate, Academy members’ personal reflections, several historical papers and summaries, and portions of the program for the May 25-26, 2023 Annual Meeting in Williamsburg on the William & Mary campus.

Virginia Academy Of Science At 100: A Presentation Given To The Chemistry Section Of The Virginia Academy Of Science, Tom Devore

Virginia Journal of Science

While I have not been an active member of the Virginia Academy of Sciences (VAS) for all of its 100 years of existence, I have seen many changes in VAS during the more than 30 years that I have been an active member. This paper, delivered at the VAS Annual Meeting at the College of William and Mary on May 25, 2023, recalls some of the outstanding scientists in Virginia that I have had the privilege of knowing and who have led and lived out the goals of VAS. They inspired me to try to live up to the high …

Attendance At Virginia Academy Of Science Meetings: A Report Of The Panel Discussion Held By The Chemistry Section Of The Virginia Academy Of Science, Tom Devore

Virginia Journal of Science

Since the attendance in the Chemistry Section at the Virginia Academy of Science (VAS) spring meetings has not returned to the numbers attending prior to the cancellation of the 2020 meeting due to the Covid pandemic, a roundtable discussion was held as part of the 2023 VAS Annual Meeting at the College of William and Mary on May 25, 2023 to discuss possible ways to increase attendance at future meetings. This is a report of the topics discussed and the suggestions made.

What Soil Is Worth: A Cost-Benefit Framework Analysis Of Syntropic Farming, Aubrey Kettley

Independent Study Project (ISP) Collection

Syntropic farming, a type of regenerative agriculture, models its farming system after a forest. This type of farming prioritizes soil health while also providing a varied yield of crops. Because it is a fairly new system globally, little research has been done on the economic impacts of syntropic farming, and therefore the feasibility of scaling up regenerative systems like this. This study aims to analyze the economic feasibility of this system through a literature review and a cost-benefit analysis framework. The results highlight the applicability, environmental advantage and economic feasibility of the system. Based on the presented framework, the short …

Ecological Monitoring Program At Vims Esl: Annual Report 2023, Paige G. Ross, Richard A. Snyder

Reports

An Ecological Monitoring Program (EMP) has been established at the Virginia Institute of Marine Science Eastern Shore Laboratory (VIMS ESL) for the coastal environment near the Wachapreague lab. The goals of the initiative are to 1) provide status and trends information to scientists who study and regulators who manage Virginia’s marine resources, 2) provide a scientific context for short-term research and grant proposals 3) provide pedagogical enrichment for educators to use in their classes, and 4) build capacity in staff expertise and training of interns and students at VIMS ESL.

The program formalizes and standardizes data collection for a long-term …

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 …

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 …

Factors Predicting Public’S Willingness To Support National Aeronautics And Space Administration’S Artemis Mission, Sean Crouse

Doctoral Dissertations and Master's Theses

NASA's Artemis program aspires to return astronauts to the moon and aims to land the first woman and person of color on the lunar surface. The endeavor symbolizes the next evolution in space exploration and serves as a testament to the human spirit of discovery. In the face of this significant undertaking, gauging public sentiment and understanding the factors driving public support becomes necessary. The current study aimed to address a critical gap in the literature by examining public support for NASA’s Artemis mission, which is essential for sustaining the program’s momentum and cultivating a culture of innovation and exploration. …

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. …

2024- The Twenty-Eighth Annual Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the 28th Annual Symposium of Student Scholars, held on April 17-19, 2024. Includes abstracts from the presentations and posters.