Software Engineering Commons^™

Development Of Software For Genetic Distances, Genotyping, And Phylogenetics Using Rna-Seq Data, Andrew C. Tapia

Theses and Dissertations--Computer Science

This dissertation concerns a new application of RNA-seq data—computation of pairwise genetic distance matrices. RNA-seq captures sequences of RNA molecules in some cells or tissues of interest. RNA-seq provides data are well-suited to studies examining gene expression, and its use for this purpose is currently widespread. A pairwise genetic distance matrix, the main topic of this dissertation, quantifies differences in the genomes of every pair of samples (e.g., individuals) in a given set. Genetic distance matrices are versatile; they can be used for various kinds of downstream analyses, including genotyping, phylogenetics, and genetic diversity measurement. Although DNA sequence data are …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia

Theses

Understanding complex three-dimensional systems and spatial relationships is a recurring difficulty in healthcare education, where students are often expected to reason about internal structures and multi-system processes from 2D diagrams, textbook figures, and static mannequins. This thesis presents the design, implementation, and mixed-methods evaluation of Systems Simulation, a reusable mixed reality (MR) application intended to help undergraduate nursing students explore human anatomy and pathophysiology using immersive 3D visualization.

Built in C# with the StereoKit framework for Microsoft HoloLens 2, Systems Simulation organizes nine anatomical body systems within a shared application. Learners can select a system, anchor the model in their …

Developing A Graphql Mesh Federated Api Gateway: Rapid Integration Of New Endpoints Into A Predefined Schema, Noah Kolczynski

Dissertations, Master's Theses and Master's Reports

The Navy’s Undersea Warfare Decision Support System (USW-DSS) uses data from an ever growing number of sensors, accessible through an equally growing number of Application Programming Interfaces (APIs). Due to the lack of standardization among these sensors and APIs, as the system has continued to grow, the challenge of collecting and using these data has become increasingly prevalent. Previous work at Michigan Tech, in collaboration with engineers at ARiA (Applied Research in Acoustics LLC), introduced a GraphQL Mesh federated API gateway. The gateway would enable the combination of diverse API sources into a predefined hierarchical structure. This report follows the …

Automatic Scoring Cornhole Board System, Eric Diffendal, Jonah Harsh, Connor Lengel, Brett Sukie

Williams Honors College, Honors Research Projects

The objective is to create a self-scoring cornhole board that can detect and calculate each team's score based on the bags thrown each round and to be created at a low cost/eventually being sold at the current cost of a normal board. When playing cornhole, the game is simple: throw a bag on the board; however, the scores are variable (deduct and add) across each round. The most common issue when playing cornhole is miscalculations of the scores and forgetting the correct scores. Thus, this invention will make gameplay easy for all to play.

Skinrisk Ai, Spencer Simms

Williams Honors College, Honors Research Projects

SkinRisk AI is an exploration of the opportunities for implementing machine learning (ML) and artificial intelligence (AI) in the medical technology field, specifically in the early detection of skin cancer. This project presents the design, development, and evaluation of a mobile application that allows users to capture images of skin lesions and receive a machine learning assisted risk assessment. The system combines a convolutional neural network (CNN) for image analysis with an intuitive mobile app built using Flutter, FastAPI, and Supabase to deliver real time screening.

Motivated by the rising skin cancer rates and importance of early detection, SkinRisk AI …

Autonomous Landscape Exploration Rover Using Imaging Surface Navigation (Alexis), Noah Jones, Dominic Salupo

Williams Honors College, Honors Research Projects

The objective is to develop a small-form-factor rover prototype that can be used to prove out a novel traversal method for use on extraterrestrial surfaces. The novel traversal method being proposed is LIDAR/CV-enhanced navigation, provided by a detachable flight vehicle that can communicate with the rover. On planets with thin atmospheres, cold gas thrusters or similar may be needed, but for the scope of this project more traditional flight/propulsion methods will be used.

The Gender Wage Gap In An Online Labor Market: The Cost Of Interruptions, Abi Adams, Kotaro Hara, Kristy Milland, Chris Callison-Burch

Research Collection School Of Computing and Information Systems

This paper analyses gender differences in working patterns and wages on Amazon Mechanical Turk, a popular online labour platform. Using information on 2 million tasks, we find no gender differences in task selection nor experience. Nonetheless, women earn 20% less per hour on average. Gender differences in working patterns are a significant driver of this wage gap. Women are more likely to interrupt their working time on the platform with consequences for their task completion speed. A follow-up survey shows that the gender differences in working patterns and hourly wages are concentrated amongst workers with children.

Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: Testing Adequacy: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. Interpretability: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which …

Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

files play an important role in providing installation-related instructions to software users and are widely used in open source software systems on platforms such as GitHub. Software projects evolve rapidly alongside their dependencies in dynamic software ecosystems, requiring frequent updates to installation instructions. These instructions are crucial for users to start with a software project. Despite their significance, there is a lack of systematic understanding regarding the documentation efforts invested in README files and the triggers behind them. To fill the research gap, we conducted a qualitative study, investigating 400 GitHub repositories with 1,163 README commits that focused on updates …

More Effective Javascript Breaking Change Detection Via Dynamic Object Relation Graph, Dezhen Kong, Jiakun Liu, Chao Ni, David Lo, Lingfeng Bao

Research Collection School Of Computing and Information Systems

JavaScript libraries are characterized by their widespread use, frequent code changes, and a high tolerance for backward incompatible changes. Awareness of such breaking changes can help developers adapt to version updates and avoid negative impacts. Several tools have been targeted to or can be used to detect breaking change detection in the JavaScript community. However, these tools detect breaking changes using different ways, and there are currently no systematic reviews of these approaches. From a preliminary study on popular JavaScript libraries, we find that existing approaches, including simple regression testing, model-based testing and type differencing cannot detect many breaking changes …

Don’T Complete It! Preventing Unhelpful Code Completion For Productive And Sustainable Neural Code Completion Systems, Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li, David Lo

Research Collection School Of Computing and Information Systems

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited and may conversely aggravate the workload of developers, as the code completions are automatically and actively generated in state-of-the-art code completion systems as developers type out once the service is enabled. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and …

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …

Openmuse: Integrating Open-Source Models Into Music Creation Workflows, Tyler K. Vergho

Dartmouth College Master’s Theses

This master's thesis introduces OpenMUSE (Open Multimodal Unified Sound Engine), a platform that demonstrates the potential of open-source AI music generation by integrating state-of-the-art deep learning models into a unified system. By unifying ten different open-source models, including MusicGen, AudioLDM2, and custom-trained text-to-symbolic music generation models, OpenMUSE aims to create a user-friendly interface that empowers artists to produce complex, adaptive musical compositions. The system enhances accessibility by providing a simple web interface and natural language controls, while improving controllability through features like melody conditioning and semantic audio editing. Specifically, OpenMUSE offers a digital audio workstation (DAW)-inspired interface that lowers the …

Procedural Terrain Generation: Noise Functions, Modern Methods, And Style Transfer, Hunter Barton

EWU Masters Thesis Collection

Procedural terrain generation, the algorithmic creation of digital terrain, finds use in multiple types of digital media. As the capabilities of modern computation increase, the ability to create more and more realistic terrains fully procedurally at scale improves. Modern methods of procedural generation have also overlapped with these advances, most notably advances in hardware. To account for this, a survey was done of modern methods for procedural terrain generation. Smooth procedural noise functions are one of the backbones of procedural terrain generation. Perlin noise, value noise, and fractal noise were explored in-depth. These noise functions were also tested for capabilities …

Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library, Anthony R. Papetti

Honors Theses and Capstones

No abstract provided.

Neural And Computational Approach To Understanding Environmental Modulation Of Behavioral Identity In Zebrafish, John W. Hageter

Graduate Theses, Dissertations, and Problem Reports (ETD)

Organisms rely on behavior for survival. Animals engage in behaviors that allow for feeding, mating, exploring and navigating their environment among others. Necessary for these behaviors to develop are the environmental factors and underlying circuitry which make behavior possible. Specifically, how the environment guides underlying neural circuitry to develop unique facets or phenotypes of a larger behavior are key to understanding why unique behaviors exist. In this thesis, I build foundational evidence for determining these mechanisms through the use of the zebrafish local search behavior. This is a behavior that zebrafish employ following the loss of environmental illumination where they …

Automated Program Refinement: Guide And Verify Code Large Language Model With Refinement Calculus, Yufan Cai, Zhe Hou, David Sanan, Xiaokun Luan, Yun Lin, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Recently, the rise of code-centric large language models (LLMs) appears to have reshaped the software engineering world with low-barrier tools like Copilot that can generate code easily. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into …

Performance Evaluation Of Newsql Databases In A Distributed Architecture, Zhiyao Zhang, Alan @ Ali Madjelisi Megargel, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the last decade, application architectures have evolved drastically, moving from monolithic architectures to distributed architectures where deployment has shifted from dedicated on-premises servers to the cloud. Distributed architectures and cloud computing has enabled businesses to scale their application components across different geographical locations. While it is easy to scale the application layer, scaling its database layer that relies on traditional SQL databases is challenging and often is a common source of bottlenecks when it comes to application performance. This paper evaluates the performance characteristics between two NewSQL databases solutions, MySQL NDB Cluster vs. TIBCO ActiveSpaces IMDG. Serving as an …

Measuring And Improving Api Usability And Quality: A Comprehensive Framework And Empirical Study, Sultan Alanazy

Computer Science and Engineering Theses and Dissertations

Cloud computing provides on-demand access to flexible computing resources, enabling rapid application deployment without substantial infrastructure investment. Application Programming Interfaces (APIs) play an important role in ensuring the success of cloud applications. The primary users of APIs are the extensive community of application programmers who search, read, and understand APIs before integrating them into their applications or systems. In addition, developers often turn to online API support when seeking help. Problems in such support can result in incorrect API usage and integration problems. There is an urgent need to measure API usability and support issues to identify, characterize, and assess …

Exploration Of The Gap Between The Secure Web Application Development Competencies Needed By Industry And Those Competencies Provided By Graduates Of U.S. Undergraduate Software Engineering Programs, Gary Allen Harris

Theses and Dissertations

Literature demonstrates that threats and attacks on computer systems and networks have been around since the beginning of computing, and the number, severity, sophistication, and costs of attacks and data breaches are continuing to grow. Several studies suggest that one of the most common causes of data breaches is insecure web applications that contain vulnerable application code. These studies suggest that poor secure web application development practices are a prime cause of the susceptible web applications. Additionally, studies suggest that higher education is not meeting industry’s secure software/web application development needs. Employers have reported that they are not getting the …

Dancetag: Using Sensors To Improve Feedback Given To Dance Students, Yanelly Mego, Franceli L. Cibrian

Student Scholar Symposium Abstracts and Posters

The structure of dance classrooms has remained largely unchanged for years, with minimal integration of technology to enhance teaching. This has motivated our research project, which aims to capture dance movements using wearable sensors and translate the information into meaningful visualizations to help dancers improve their skills. As the first step in addressing the research question—can data from commercial wearables differentiate between the movements of dancers and non-dancers?—we developed DANCETAG (Data Analytics and Notation with Captured Event Tagging), a platform designed for data collection and movement annotation. We utilized Sony’s Mocopi sensors, a motion capture system with six sensors attached …

Software Implementations And Analyses Of The Emotional Impact Of Various Binaural Beat Classifications Layered Into Music., Neil Azimi

Student Scholar Symposium Abstracts and Posters

This study explores the psychoacoustic effects of binaural beats, which are produced when sinusoidal waves of slightly differing frequencies are played into each ear, leading to brainwave entrainment. Binaural beats are categorized by frequency bands (e.g., Beta: 14–30 Hz for energy, Theta: 4–8 Hz for relaxation), each associated with different psychological effects. This research contributes to the field by empirically analyzing whether binaural beats alter the emotional impact of music. Past studies have investigated the potential benefits of binaural beats in relaxation and energy stimulation. However, their effects, when combined with music, especially regarding a song's perceived emotional quality or …

Visualization Of Paleocurrents On A Web Application Using Gplates, Anjan Sapkota

MS in Computer Science Theses

Paleocurrents are flow directions derived from features of sedimentary rocks that reveal the direction of the current of wind or water that deposited the sediment. In 2015, Brand et al. created a global database of paleocurrents, which contains over 1,000,000 measurements worldwide: North America, South America, Australia, Great Britain, parts of Western Europe, China, Africa are fairly well represented; Antarctica, Eastern Europe, and Asia are modestly represented and Russia is poorly represented. The contribution of this thesis is a web application that uses the GPlates’ Application Programming Interface (API) to visualize global paleocurrents through time in an interactive way based …

Graph Neural Networks Powered Scientific Paper Recommendation, Junhao Shen

Computer Science and Engineering Theses and Dissertations

Scientific paper recommendation systems aim to help researchers discover relevant papers amidst the vast and ever-growing body of literature. With the exponential yearly increase in scientific publications, the demand for effective paper recommendation solutions has become both critical and increasingly challenging. In recent years, deep learning techniques have revolutionized recommender systems, and scientific paper recommendations have naturally integrated these advancements. In this dissertation, we address these challenges through three progressive contributions.

First, we enhance traditional content-based methods using Graph Neural Networks (GNNs) by introducing a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) approach. This method improves upon conventional topic modeling techniques …

Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …

Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …

A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. Firstly, the absence of a unified evaluation platform for defining and describing tools’ supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity. Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose …

Mining Work Items To Streamline Software Maintenance Tasks, Salomé Perez-Rosero

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

Software engineering maintenance tasks often require associating code changes into groupings of related units of work to have as much information as possible about the developments toward addressing a specific code task. A comprehensive understanding of how a code task has evolved helps developers make better decisions about changes in the overall codebase, where a commit represents the set of code changes made to the codebase at a specific time. While the concept of work items as logically related code changes has been primarily theoretical, its impact on software maintenance tasks, such as tracing the origins of bugs or fixes …

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …