Digital Commons Network^™

Neutrosophic Model For Analyzing The Effect Of The Enterprise Resource Planning Functions On Business Processes, Samah Ibrahim Abdel Aal, Mahmoud M. A. Abdellatif

Neutrosophic Sets and Systems

No abstract provided.

From Data To Decisions Based On Ai-Driven Insights: Intelligent Evaluation Of English Translation Pedagogy In Higher Education Under Interval Complex Neutrosophic Set, Hongmiao Yuan

Neutrosophic Sets and Systems

No abstract provided.

University English Writing Teaching Quality Evaluation Driven By Artificial Intelligence In The Context Of New Liberal Arts: Linguistic Neutrosophic Multivalued Approach, Xiaoling Lyu, Feng Li, Yiming Zhao

Neutrosophic Sets and Systems

No abstract provided.

Triangular Offnorm With Multi-Criteria Decision-Making Method For College Political And Ideological Instruction On Intelligent Learning Platforms, Haiying Wang

Neutrosophic Sets and Systems

No abstract provided.

Collective Analysis, Sydney L. Reichin, Angie Benda, James C. Peters, Joel S. Elson, Samuel Hunter

Reports, Projects, and Research

This report serves as Deliverable 7: Collective Analysis, as tasked by the Department of Homeland Security (DHS). The primary goal of this effort was to review literature (both academic and practitioner-based) related to threats found in K-12 schools, and to validate findings from the literature with interviews with subject matter experts (SMEs). Interviews discussed threats and vulnerabilities SMEs face along with their needs for future risk assessments. This review provides insights within and across interviews from SMEs and resource coding, identifying common and emerging themes that schools experience. The findings will inform the development of a comprehensive K-12 risk assessment …

Phl 110e.50: Introduction To Ethics, Charles B. Hayes

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Examining The Legal Responsibilities And Key Challenges Of Personal Representatives In Malaysian Inheritance Management, Muhammad Amrullah Drs Nasrul, Anis A'Fifah Zairin Zain, Wan Noraini Wan Mohd Salim

The Indonesian Journal of Socio-Legal Studies

Personal representative connotes a person authorised under the law to manage the estate of the deceased person. The personal representative is responsible to gather all the assets belonging to the deceased and distribute the assets to the beneficiaries legally. The process of inheritance management in Malaysia requires a formal application to be made to the administrative bodies for the appointment of a personal representative, either as an executor or administrator. However, instances of misappropriation of the deceased’s estate by personal representatives have raised serious concerns, affecting the proper administration of estates and the rights of beneficiaries This study emphasises on …

Irhunter: Universal Detection Of Instruction Reordering Vulnerabilities For Enhanced Concurrency In Distributed And Parallel Systems, Guohua Xin, Guangquan Xu, Yao Zhang, Cheng Wen, Cen Zhang, Xiaofei Xie, Neal N. Xiong, Shaoying Liu, Pan Gao

Research Collection School Of Computing and Information Systems

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts …

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

Enhancing Vulnerability Detection Via Inter-Procedural Semantic Completion, Bozhi Wu, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Inspired by advances in deep learning, numerous learning-based approaches for vulnerability detection have emerged, primarily operating at the function level for scalability. However, this design choice has a critical limitation: many vulnerabilities span multiple functions, causing function-level approaches to lose the semantics of called functions and fail to capture true vulnerability patterns. To address this issue, we propose VulnSC, a novel framework designed to enhance learning-based approaches by complementing inter-procedural semantics. VulnSC retrieves the source code of called functions for datasets and leverages large language models (LLMs) with well-designed prompts to generate summaries for these functions. The datasets, enhanced with …

Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Regtrieve: Reducing System-Level Regression Errors For Machine Learning Systems Via Retrieval-Enhanced Ensemble, Junming Cao, Xuwen Xiang, Mingfei Cheng, Bihuan Chen, Xinyan Wang, You Lu, Chaofeng Sha, Xiaofei Xie, Xin Peng

Research Collection School Of Computing and Information Systems

Multiple machine learning (ML) models are often incorporated into real-world ML systems. However, updating an individual model in these ML systems frequently results in regression errors, where the new model performs worse than the old model for some inputs. While model-level regression errors have been widely studied, little is known about how regression errors propagate at system level. To address this gap, we propose RegTrieve, a novel retrieval-enhanced ensemble approach to reduce regression errors at both model and system level. Our evaluation across various model update scenarios shows that RegTrieve reduces system-level regression errors with almost no impact on system …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …

Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …

Less Is More: On The Importance Of Data Quality For Unit Test Generation, Junwei Zhang, Xing Hu, Shan Gao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open …

Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Identifying logical fallacies is essential for maintaining log-ical reasoning and reducing false information in a variety of domains, such as the media, law, and education. We present an extensive study on the use of large language models (LLMs) for logical fallacy detection and provide a comparative overview of model performance across various fallacy classes. We evaluate the logical fallacy detection capabilities of multiple state-of-the-art models (LLaMA, Qwen, Gemma, Phi) utilizing accuracy, precision, recall, and F1-score as assessment measures. Accord-ing to our findings, our models do well on simple fallacies like “circular reasoning,” but they have trouble with more interpretive reasoning …

Hth 475e.50: Legal And Ethical Issues - Health And Exercise Professons, Charles G. Palmer

University of Montana Course Syllabi, 2021-2025

No abstract provided.

Emergency Bleeding Education Fixture, Nate Colley, Kennedy K. Austin

Biomedical Engineering: Graduate Reports and Projects

Despite the deadly nature and widespread occurrence of emergency bleeding events, there are currently no training devices or demonstrations which educate users on the urgency of these events and potential methods to address them. Additionally, current models often fail to replicate physiological conditions correctly. A fixture was created to demonstrate the urgency of hemorrhaging events and educate users on potential methods for treating hemorrhaging, namely tourniquets and pressure wraps. This fixture utilized gravity to replicate systolic blood pressure, a silicone model to replicate various adult leg sizes and correct tissue properties, and a tubing system with varying compliance to replicate …

(Law) School To Prison Pipeline, Cheyenne Petrich

Minnesota Journal of Law & Inequality

No abstract provided.

From Abc To Ot: A Historical Critique Of The Flsa’S Unfair Overtime Exemption For Preschool Teachers, Anthony Alas

Minnesota Journal of Law & Inequality

No abstract provided.

Thinking Beyond Confinement: The Suspension Of Minnesota’S 48-Hour Law And The False Choice Between Incarceration Or Institutionalization, Sophie Herrmann

Minnesota Journal of Law & Inequality

No abstract provided.

Genesis Station Fire Protection And Life Safety Analysis, Mark Anthony Bautista

Fire Protection Engineering: Culminating Experience Project Reports

This fire protection and life safety analysis evaluates GENESIS Station, a nine-story Type I-B mixed-tenant building located in Daly City, California. The facility includes business, research laboratory, and assembly occupancies. A combination of prescriptive code compliance and performance-based analysis was applied to assess the building’s overall fire safety strategy.

The building utilizes a layered fire protection strategy that combines passive fire resistive construction with active systems. Fire rated construction separate tenant suites, limiting the spread of fire and smoke, defining hazardous materials control areas, and ensuring compliant separation of egress paths.

Active fire protection systems include a fully automatic sprinkler …

Cryogenic Fuel Delivery System, Carter Josef, Reilly Humphreys, Joseph O'Connor, Nick Spreen

Mechanical Engineering

This project presents the design and validation of a cryogenic fuel delivery system aimed at enabling sustainable aviation through hydrogen fuel vaporization. Developed by a team of senior mechanical engineering students at Cal Poly and sponsored by Boeing, the system utilizes atmospheric air to vaporize liquid nitrogen, used as a stand-in for hydrogen, via forced convection heat exchangers, eliminating the need for engine-supplied heat and reducing parasitic loads. A feedback loop and turbine component are integrated to regulate pressure and recover energy, respectively. A benchtop prototype was built and tested to evaluate system performance, with results closely aligning with analytical …

Conversational Social Robot, Julianna M. Christopoulos, Mackenzie Goldman, Cece E. Hujanen, Jared Hunter

Mechanical Engineering

Background: Social robots are used in various settings to reduce burden on human workers and expand opportunities for people in need of assistance.

Challenge: Design a humanoid robot head and torso capable of holding conversations and interacting with a user using a LLM and motion. Conversations are limited to discussing Cal Poly resources and opportunities with visitors to the Bently Research Center on campus.

Combustion Liner Optimization, William Weeks, Daniel Reed, Isaac Trull

Mechanical Engineering

Solar Turbines provides gas power generation solutions, focusing on gas turbine engines and compressors. The Gas Turbine Products Engineering (GTPE) team at Solar Turbines needs a way to efficiently and cost-effectively maintain the combustion liner of the Taurus model gas turbine at or below the cold-side temperature target. The current system involves a manufacturing process with ergonomic concerns and uncertain cooling effectiveness.

To address this, our team proposed a new combustion liner system featuring impingement cooling. We provided Solar Turbines with heat transfer models of both the existing and proposed designs, demonstrating improved cooling performance. Additionally, a prototype of the …

License To Drill, Gabrielle Noemi Orlando, Alexcias Olga Meeks, Dayana Guadalupe Gonzalez, Mark Dahmen

Mechanical Engineering

The goal of this project is to provide the student machine shops with an updated License to Drill (LTD) certification training, formally known as the Red Tag. The current training has two key components: a preliminary Canvas course and an in-person training with a safety tour, safety quiz, and a hands-on project. Currently, the hands-on project is outdated and unengaging. Thus, the primary focus is to replace it with an inspiring and memorable project, which includes written and visual instructional content on how to make it. The Canvas will thus be updated, a new instructional video will be created, and …