Physical Sciences and Mathematics Commons^™

Video Sentiment Analysis For Child Safety, Yee Sen Tan, Nicole Anne Huiying Teo, Ezekiel En Zhe Ghe, Jolie Zhi Yi Fong, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The proliferation of online video content underscores the critical need for effective sentiment analysis, particularly in safeguarding children from potentially harmful material. This research addresses this concern by presenting a multimodal analysis method for assessing video sentiment, categorizing it as either positive (child-friendly) or negative (potentially harmful). This method leverages three key components: text analysis, facial expression analysis, and audio analysis, including music mood analysis, resulting in a comprehensive sentiment assessment. Our evaluation results validate the effectiveness of this approach, making significant contributions to the field of video sentiment analysis and bolstering child safety measures. This research serves as a …

Designing Large-Scale Intelligent Collaborative Platform For Freight Forwarders, Pang Jin Tan, Shih-Fen Cheng, Richard Chen

Research Collection School Of Computing and Information Systems

In this paper, we propose to design a large-scale intelligent collaborative platform for freight forwarders. This platform is based on a mathematical programming formulation and an efficient solution approach. Forwarders are middlemen who procure container capacities from carriers and sell them to shippers to serve their transport requests. However, due to demand uncertainty, they often either over-procure or under-procure capacities. We address this with our proposed platform where forwarders can collaborate and share capacities, allowing one's transport requests to be potentially shipped on another forwarder's container. The result is lower total costs for all participating forwarders. The collaboration can be …

Software Architecture In Practice: Challenges And Opportunities, Zhiyuan Wan, Yun Zhang, Xin Xia, Yi Jiang, David Lo

Research Collection School Of Computing and Information Systems

Software architecture has been an active research field for nearly four decades, in which previous studies make significant progress such as creating methods and techniques and building tools to support software architecture practice. Despite past efforts, we have little understanding of how practitioners perform software architecture related activities, and what challenges they face. Through interviews with 32 practitioners from 21 organizations across three continents, we identified challenges that practitioners face in software architecture practice during software development and maintenance. We reported on common software architecture activities at software requirements, design, construction and testing, and maintenance stages, as well as corresponding …

Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee

Research Collection School Of Computing and Information Systems

Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a …

Evaluating Transfer Learning For Simplifying Github Readmes, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

Software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various stakeholders. However, software documentation can be hard to comprehend if it is written with jargon and complicated sentence structure. In this study, we explored the potential of text simplification techniques in the domain of software engineering to automatically simplify GitHub README files. We collected software-related pairs of GitHub README files consisting of 14,588 entries, aligned difficult sentences with their simplified counterparts, and trained a Transformer-based model to automatically simplify …

Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities focus on the most critical dependencies (i.e., those most depended upon within the ecosystem), little is known about whether the rest of the ecosystem suffers the same fate. Our vision is to promote and establish safer practises throughout the ecosystem. To motivate our vision, in this paper, we present preliminary data based on three representative samples from a population of 88,416 pull requests (PRs) and …

Ethical Considerations For Artificial Intelligence In Educational Assessments, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

In the vital context of education, the application of artificial intelligence (AI) to assessments necessitates a nuanced examination of the boundaries between ethically permissible and impermissible practices. In this chapter, the authors applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to different assessment phases in a triadic ontological framework. The chapter looks to provide researchers and practitioners the insights into the ethical challenges that exist across an end-to-end assessment pipeline.

Butte Priority Soils Operable Unit Uncontrolled Surface Flow Areas Draft Remedial Design Work Plan (Rdwp), Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Uncontrolled Surface Flow Areas Pre-Design Investigation Work Plan, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Bpsou 2023 Uncontrolled Surface Flow Area Soil Characterization Quality Assurance Project Plan (Qapp), Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Characterization Of The Long-Distance Dispersal Kernel Of White-Tailed Deer And Evaluating Its Impact On Chronic Wasting Disease Spread In Wisconsin, Mennatallah Gouda

All Graduate Theses and Dissertations, Fall 2023 to Present

Chronic Wasting Disease (CWD) is a fatal, untreatable neurodegenerative disease that infects deer and related species. It is highly contagious and caused by abnormal malfunction and assembly of normal cellular proteins into aggregation-prone proteins. The Centers for Disease Control and prevention report that the prevalence of CWD in free-ranging deer in the US is still relatively low. However, in several states the infection rates exceed 1 deer in 10. Deer may uptake CWD from direct interaction with infected individuals or from the environment. Infected individuals shed CWD into the environment through feces, urine, saliva or carcasses, and long-distance dispersal of …

Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data, Kevin Vulcano

All Graduate Theses and Dissertations, Fall 2023 to Present

In the realm of safeguarding digital systems, the ability to detect anomalies in log sequences is paramount, with applications spanning cybersecurity, network surveillance, and financial transaction monitoring. This thesis presents AdvSVDD, a sophisticated deep learning model designed for sequence anomaly detection. Built upon the foundation of Deep Support Vector Data Description (Deep SVDD), AdvSVDD stands out by incorporating Adversarial Reweighted Learning (ARL) to enhance its performance, particularly when confronted with limited training data. By leveraging the Deep SVDD technique to map normal log sequences into a hypersphere and harnessing the amplification effects of Adversarial Reweighted Learning, AdvSVDD demonstrates remarkable efficacy …

Co2 Storage Capacity And Injectivity Of Stacked Carbonates Of The Pennsylvanian And Permian In Western Nebraska, Lateef Lawal

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Geological carbon storage (GCS) is a critical aspect of carbon capture and storage (CCS) in which captured CO₂ from power plants and industrial processes is injected and stored securely underground. Potential subsurface rock formations include saline aquifers, depleted oil and gas reservoirs, unmineable coal seams and volcanic rocks. GCS technology has been proven in the United States and many other parts of the world as a net-zero carbon emission strategy to mitigate the current climate crisis of our planet. Unlike other states such as Wyoming, GCS projects are still in the early phases in Nebraska. The goal of this …

Characterization Of Interlayer Laser Shock Peening During Fused Filament Fabrication Of Polylactic Acid (Pla), Fabien Denise

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The field of additive manufacturing (AM) has gained a significant amount of popularity due to the increasing need for more sustainable manufacturing techniques and the adaptive development of complex product geometries. The problem is that AM parts routinely exhibit flaws or weaknesses that affect functionality or performance. Over the years, surface treatments have been developed to compensate certain flaws or weaknesses in manufactured products. Combining surface treatments with the modularity of additive manufacturing could lead to more adaptable and creative improvements of product functions in the future. The current work evaluates the feasibility of pursuing a new research axis in …

Covariance-Based Causal Debiasing For Entity And Relation Extraction, Lin Ren, Yongbin Liu, Yixin Cao, Chunping Ouyang

Research Collection School Of Computing and Information Systems

Joint entity and relation extraction tasks aim to recognize named entities and extract relations simultaneously. Suffering from a variety of data biases, such as data selection bias, and distribution bias (out of distribution, long-tail distribution), serious concerns can be witnessed to threaten the model’s transferability, robustness, and generalization. In this work, we address the above problems from a causality perspective. We propose a novel causal framework called covariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed covariance optimizing (COP) minimizes characterizing features’ covariance for alleviating the selection and distribution bias and …

A Comprehensive Evaluation Of Large Language Models On Legal Judgment Prediction, Ruihao Shui, Yixin Cao, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4’s law evaluation raise questions concerning their performance in real-world legal tasks. To systematically investigate their competency in the law, we design practical baseline solutions based on LLMs and test on the task of legal judgment prediction. In our solutions, LLMs can work alone to answer open questions or coordinate with an information retrieval (IR) system to learn from similar cases or solve simplified multi-choice questions. We show that similar cases and multi-choice options, namely label candidates, included in prompts …

Metabox: A Benchmark Platform For Meta-Black-Box Optimization With Reinforcement Learning, Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of lower-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including …

Attack Prompt Generation For Red Teaming And Defending Large Language Models, Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework …

Large Language Models As Source Planner For Personalized Knowledge-Grounded Dialogues, Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to …

Wsdms: Debunk Fake News Via Weakly Supervised Detection Of Misinforming Sentences With Contextualized Social Wisdom, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

Research Collection School Of Computing and Information Systems

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news …

Make The U In Uda Matter: Invariant Consistency Learning For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) that do not generalize to the target domain. Unfortunately, even enriched with additional unsupervised target domains, existing Unsupervised DA (UDA) methods still suffer from it. This is because the source domain supervision only considers the target domain samples as auxiliary data (e.g., by pseudo-labeling), yet the inherent distribution in the target domain—where the valuable de-correlation clues hide—is disregarded. We propose to make the U in UDA matter by giving equal status to the two domains. Specifically, we learn an …

Peer Learning In An Undergraduate Linear Algebra Course - A Social Network Analysis, Manoj Thulasidas, Kyong Jin Shim, Jonathan Teo

Research Collection School Of Computing and Information Systems

This study employs Social Network Analysis (SNA) to explore peer learning behaviors among undergraduate Linear Algebra students. By examining the relational dynamics within the classroom, SNA unveils patterns of interaction, information flow, and collaboration among students. Our analysis identifies the prevalence and evolution of peer learning, and how it influences the students' academic performance. It also unveils the attributes of the students who engage in peer helping and the formation of small communities through such interactions. The findings of the study can provide valuable insights for educators aiming to enhance peer learning and improve educational practices in Linear Algebra and …

Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Accurate detection of semantic code clones has many applications in software engineering but is challenging because of lexical, syntactic, or structural dissimilarities in code. CodeBERT, a popular deep neural network based pre-trained code model, can detect code clones with a high accuracy. However, its performance on unseen data is reported to be lower. A challenge is to interpret CodeBERT's clone detection behavior and isolate the causes of mispredictions. In this paper, we evaluate CodeBERT and interpret its clone detection behavior on the SemanticCloneBench dataset focusing on Java and Python clone pairs. We introduce the use of a black-box model interpretation …

Studies Toward A Total Synthesis Of The Pyrrole-Imidazole Alkaloid Palau'amine And Computationally Assisted Stereochemical Elucidation, Brandon Fulton

Chemistry & Biochemistry Dissertations - Archive

This thesis documents progress toward a total synthesis of the marine natural product palau'amine, a stereochemically dense hexacyclic pyrrole-imidazole alkaloid with pharmaceutically relevant anticancer, antifungal, and antibacterial activity. Also described herein are several miscellaneous synthesis projects and the application of computational NMR as an aid for solving structure elucidation problems. Chapter 1 is a continuation of our lab’s prior work utilizing vinylimidazoles in cycloaddition reactions to gain rapid access to scaffolds resembling dimeric pyrrole-imidazole alkaloids such as ageliferin or palau’amine. Mechanistic aspects of these cycloadditions for both inter- and intramolecular cases were probed via time-course NMR experiments and computational studies, …

The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz

Cybersecurity Undergraduate Research Showcase

This paper illustrates the analysis and impact of Artificial Intelligence (AI) on job loss across various industries. This paper will discuss an overview of AI technology, a brief history of AI in industry, the positive impacts of AI, the negative impacts of AI on employment, AI considerations that contribute to job loss, the future outlook of AI, and employment loss mitigation strategies Various professional source articles and reputable blog posts will be used to finalize research on this topic.

Recurrence Interval Estimates For The July 2022 Eastern Kentucky Floods, North Fork Of The Kentucky River, William C. Haneberg

Open File Reports--KGS

July 2022 flood recurrence interval calculations for three gages along the North Fork of the Kentucky River—Whitesburg, Hazard, and Jackson, Kentucky—based upon both empirical distributions and theoretical log-Pearson Type III distributions yield a range of results from 851 years for the Whitesburg gage near the headwaters of the North Fork to about 2 years for the Hazard gage to 94 years for the Jackson gage. While the log-Pearson Type III approach worked well for the Whitesburg and Hazard gages, it produced an unrealistically low estimate for the Jackson gage because the empirical and theoretical curves diverge significantly for large floods …

Radiation Exposure Calibration Of The Al2o3:C With Radium-226 And Cesium-137 Using The Osl Method, Selma Tepeli Aydin

All Theses

Optically stimulated luminescence (OSL) dosimetry was utilized to calibrate Al₂O₃:C powder dosimeters, available commercially as the nanoDot® from Landauer Inc., and compare the dosimeter response to radium-226 (²²⁶Ra) and cesium-137 (¹³⁷Cs). The signal from the OSL was quantified using a microSTARii^® OSL reader also produced by Landauer Inc. Dose-response curves were developed for ²²⁶Ra and ¹³⁷Cs experiments (5 dosimeters each) at thirteen absorbed doses. Individual dosimeter response was tracked by serial number. Linear regression analysis was performed to determine if there were significant differences between the intercepts of the …

Thermal Energy Storage Using High Temperature Borehole Heat Exchangers In Unconsolidated Materials, Kayla Bicknell

All Theses

Thermal energy storage is a potential method for storing excess energy produced when supply is greater than demand. The use of the subsurface for storing thermal energy has become more recognized as a viable alternative to conventional methods of energy storage. However, high temperature borehole thermal energy storage has yet to be researched in-depth. Therefore, the goal of this project is to determine the feasibility of using the subsurface to store thermal energy at relatively high temperatures.

The focus of this work is to determine what design elements would make a borehole thermal energy storage system most effective and produce …

Observational Uncertainty For Global Drought-Pluvial Volatility, Yichan Li, Linyin Cheng, Chiyuan Miao, Zhiyong Liu

Geosciences Faculty Publications and Presentations

Droughts and pluvials have occurred in most regions in the past. However, what calls growing attention is the additive effects of these two opposite extreme events occurring in spatial-temporal proximity to one another, sometimes beyond either of the hazards individually. This study examines the likelihood of global drought-pluvial volatility which involves both meteorological drought-to-pluvial (DTP) and pluvial-to-meteorological drought transitions; meanwhile, identifies discrepancies and agreements among the widely used observations for such events, an aspect that remains currently overlooked. Globally, we find that the observation-based data sets including Global Precipitation Climatology Center (GPCC), Climate Research Unit (CRU) and ERA5 reach a …

Deglacial Perspectives Of Future Sea Level For Singapore, Timothy A. Shaw, Tanghua Li, Trina Ng, Niamh Cahill, Stephen Chua, Jedrzej M. Majewski, Yudhishthra Nathan, Gregory G. Garner, Robert E. Kopp, Till J.J. Hanebuth, Adam D. Switzer, Benjamin P. Horton

Marine Science

Low elevation equatorial and tropical coastal regions are highly vulnerable to sea level rise. Here we provide probability perspectives of future sea level for Singapore using regional geological reconstructions and instrumental records since the last glacial maximum ~21.5 thousand years ago. We quantify magnitudes and rates of sea-level change showing deglacial sea level rose from ~121 m below present level and increased at averaged rates up to ~15 mm/yr, which reduced the paleogeographic landscape by ~2.3 million km². Projections under a moderate emissions scenario show sea level rising 0.95 m at a rate of 7.3 mm/yr by 2150 …