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Full-Text Articles in Physical Sciences and Mathematics

Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das Jun 2024

Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying K heterogeneous UAVs in the air to form a temporarily connected …

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A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi Jun 2024

A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi

All Works

This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding …

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Matching The Scales Of Planning And Environmental Risk: An Evaluation Of Community Wildfire Protection Plans In The Western Us, Matthew Hamilton, Cody Evers, Max Nielsen-Pincus, Alan Ager Jun 2024

Matching The Scales Of Planning And Environmental Risk: An Evaluation Of Community Wildfire Protection Plans In The Western Us, Matthew Hamilton, Cody Evers, Max Nielsen-Pincus, Alan Ager

Environmental Science and Management Faculty Publications and Presentations

Theory predicts that effective environmental governance requires that the scales of management account for the scales of environmental processes. A good example is community wildfire protection planning. Plan boundaries that are too narrowly defined may miss sources of wildfire risk originating at larger geographic scales whereas boundaries that are too broadly defined dilute resources. Although the concept of scale (mis)matches is widely discussed in literature on risk mitigation as well as environmental governance more generally, rarely has the concept been rigorously quantified. We introduce methods to address this limitation, and we apply our approach to assess scale matching among Community …

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Let’S Think Outside The Box: Exploring Leap-Of-Thought In Large Language Models With Multimodal Humor Generation, Shanshan Zhong, Zhongzhan Huang, Shanghua Gao, Wushao Wen, Liang Lin, Marinka Zitnik, Pan Zhou Jun 2024

Let’S Think Outside The Box: Exploring Leap-Of-Thought In Large Language Models With Multimodal Humor Generation, Shanshan Zhong, Zhongzhan Huang, Shanghua Gao, Wushao Wen, Liang Lin, Marinka Zitnik, Pan Zhou

Research Collection School Of Computing and Information Systems

Chain-of-Thought (CoT) [2, 3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs — a nonsequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus …

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Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Meng Kiat Gary Ang, Ee-Peng Lim Jun 2024

Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …

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Few-Shot Learner Parameterization By Diffusion Time-Steps, Zhongqi Yue, Pan Zhou, Richang Hong, Hanwang Zhang, Sun Qianru Jun 2024

Few-Shot Learner Parameterization By Diffusion Time-Steps, Zhongqi Yue, Pan Zhou, Richang Hong, Hanwang Zhang, Sun Qianru

Research Collection School Of Computing and Information Systems

Even when using large multi-modal foundation models, few-shot learning is still challenging—if there is no proper inductive bias, it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end, we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes, i.e., as the forward diffusion adds noise to an image at each time-step, nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent. Building on this, we propose Time-step Few-shot (TiF) …

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Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson Jun 2024

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

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Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson Jun 2024

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

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Spatial And Spectral Characterization Of The Gravitational-Wave Background With The Pta Optimal Statistic, Kyle A. Gersbach, Stephen R. Taylor, Patrick M. Meyers, Joseph D. Romano Jun 2024

Spatial And Spectral Characterization Of The Gravitational-Wave Background With The Pta Optimal Statistic, Kyle A. Gersbach, Stephen R. Taylor, Patrick M. Meyers, Joseph D. Romano

Physics and Astronomy Faculty Publications and Presentations

Pulsar timing arrays (PTAs) have made tremendous progress and are now showing strong evidence for the gravitational-wave background (GWB). Further probing the origin and characteristics of the GWB will require more generalized analysis techniques. Bayesian methods are most often used but can be computationally expensive. On the other hand, frequentist methods, like the PTA Optimal Statistic (OS), are more computationally efficient and can produce results that are complementary to Bayesian methods, allowing for stronger statistical cases to be built from a confluence of different approaches. In this work we expand the capabilities of the OS through a technique we call …

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On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov Jun 2024

On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov

Computer Science Faculty Publications and Presentations

Fair clustering is a constrained clustering problem where we need to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. The problem was recently introduced by Chierichetti et al. (2017) [1]. We propose a new construction of coresets for fair clustering for Euclidean and general metrics based on random sampling. For the Euclidean space Rd, we provide the first coreset whose size does not depend exponentially on the dimension d. The question of whether such constructions …

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Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang Jun 2024

Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang

Research Collection School Of Computing and Information Systems

Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success, the mechanisms behind SAM’s generalization enhancements remain elusive, limiting its progress in deep learning optimization. In this work, we investigate SAM’s core components for generalization improvement and introduce “Friendly-SAM” (F-SAM) to further enhance SAM’s generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation, i.e., the current minibatch gradient, which significantly influences SAM’s generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, …

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Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma Jun 2024

Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a widely embraced form of aerobic exercise, offering various physical and mental benefits. However, improper running gaits (i.e., the way of foot landing) can pose safety risks and impact running efficiency. As many runners lack the knowledge or continuous attention to manage their foot strikes during running, in this work, we present a portable and non-invasive running gait monitoring system. Specifically, we leverage the in-ear microphone on wireless earbuds to capture the vibrations generated by foot strikes. Landing with different parts of the foot (e.g., forefoot and heel) generates distinct vibration patterns, and thus we utilize machine learning …

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How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan Jun 2024

How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …

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Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang Jun 2024

Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To solve this issue, we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However, the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy, and thus is …

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Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan Jun 2024

Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …

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Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee Jun 2024

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

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Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry Jun 2024

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

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Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson Jun 2024

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

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Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson Jun 2024

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

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Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson Jun 2024

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

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Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson Jun 2024

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

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Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Katarzyna Wasielewska, Rasmus Bro, David Kotz Jun 2024

Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Katarzyna Wasielewska, Rasmus Bro, David Kotz

Dartmouth Scholarship

There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows …

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Quality And Establishment Of Some Water-Conserving Turfgrass Species For Sustainable Development And Some Ecosystem Services In Arid Urban Environments, Fatemeh Kazemi, Mahmood Reza Golzarian, Seyedeh Maliheh Rabbani Kheir Khah Jun 2024

Quality And Establishment Of Some Water-Conserving Turfgrass Species For Sustainable Development And Some Ecosystem Services In Arid Urban Environments, Fatemeh Kazemi, Mahmood Reza Golzarian, Seyedeh Maliheh Rabbani Kheir Khah

Research outputs 2022 to 2026

Turfgrasses are essential landscape plants with social, environmental, and aesthetic services for urban ecosystems. However, more is needed to know how to establish them so that they can benefit from their ecosystem services in urban environments. This research examined some quality and morphological and physiological factors for the establishment and social and environmental service assessment of three warm-season turfgrasses, including Kikuyu grass (Pennisetum clandestinum), bermuda grass (Cynodon dactylon), and buffalo grass (Buchloe dactyloides), compared to the cool-season grass of tall fescue (Festuca arundinacea Schreb.). The experiment was split-plot in time, based on a randomized complete block design with eight replications. …

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Spectroscopic Characterization, Dft Calculations, In Vitro Pharmacological Potentials, And Molecular Docking Studies Of N, N, O-Schiff Base And Its Trivalent Metal Complexes, Ikechukwu P. Ejidike, Amani Direm, Cemal Parlak, Adebayo A. Adeniyi, Mohammad Azam, Athar Ata, Michael O. Eze, Joshua W. Hollett, Hadley S. Clayton Jun 2024

Spectroscopic Characterization, Dft Calculations, In Vitro Pharmacological Potentials, And Molecular Docking Studies Of N, N, O-Schiff Base And Its Trivalent Metal Complexes, Ikechukwu P. Ejidike, Amani Direm, Cemal Parlak, Adebayo A. Adeniyi, Mohammad Azam, Athar Ata, Michael O. Eze, Joshua W. Hollett, Hadley S. Clayton

Michigan Tech Publications, Part 2

In this study, trivalent metal complexes of the category: [M(L)(H2O)nCly] obtained from the interaction of metal3+ ion salts with organic N, N, O-Schiff base (HL) (where: HL = 4-{(Z)-((2-{(E)-((2-hydroxyphenyl)methylidene)amino}ethyl)imino)methyl}-2-methoxyphenol; n, y = 1 or 2 and M = Ti(III), Fe(III), Ru(III), Cr(III) and Al(III)) were synthesized and characterized viz molar conductance, FT-IR, and UV–Vis spectroscopies, elemental analyses, thermal analyses (TGA and DTA), and UV–Vis spectroscopy, theoretical calculations. A distorted octahedral structure around the metal ions was proposed based on the obtained experimental and calculated data. Thermal examination of the complexes signposts the step-by-step disintegration to give the final decomposition product …

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New Examples Of Self-Dual Near-Extremal Ternary Codes Of Length 48 Derived From 2-(47,23,11) Designs, Sanja Rukavina, Vladimir Tonchev Jun 2024

New Examples Of Self-Dual Near-Extremal Ternary Codes Of Length 48 Derived From 2-(47,23,11) Designs, Sanja Rukavina, Vladimir Tonchev

Michigan Tech Publications, Part 2

In a recent paper (Araya and Harada, 2023), Araya and Harada gave examples of self-dual near-extremal ternary codes of length 48 for 145 distinct values of the number A12 of codewords of minimum weight 12, and raised the question about the existence of codes for other values of A12. In this note, we use symmetric 2-(47,23,11) designs with an automorphism group of order 6 to construct self-dual near-extremal ternary codes of length 48 for 150 new values of A12.

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Western Kentucky University Stormwater Utility Survey 2024, Warren Campbell Jun 2024

Western Kentucky University Stormwater Utility Survey 2024, Warren Campbell

SEAS Faculty Publications

The main goal of this survey is to identify as many U.S. Stormwater Utilities (SWUs) as possible. Because many stormwater professionals do not have the time to respond to questionnaires, our primary method of identification was Internet searches. We searched key terms such as “stormwater utility,” “stormwater fee,” and “drainage fee.” We scoured online municipal codes such as Municode, AmLegal, Sterling, LexisNexis, General Code, and others. We searched through many city web websites to find utilities. We have also had many people contact me to update fees and identify new utilities. However, the data primarily comes from Internet sources and …

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Predicting Seagrass Ecosystem Resilience To Marine Heatwave Events Of Variable Duration, Frequency And Re-Occurrence Patterns With Gaps, Paula Sobenko Hatum, Kathryn Mcmahon, Kerrie Mengersen, Kieryn Kilminster, Paul Pao Yen Wu Jun 2024

Predicting Seagrass Ecosystem Resilience To Marine Heatwave Events Of Variable Duration, Frequency And Re-Occurrence Patterns With Gaps, Paula Sobenko Hatum, Kathryn Mcmahon, Kerrie Mengersen, Kieryn Kilminster, Paul Pao Yen Wu

Research outputs 2022 to 2026

Background: Seagrass, a vital primary producer habitat, is crucial for maintaining high biodiversity and offers numerous ecosystem services globally. The increasing severity and frequency of marine heatwaves, exacerbated by climate change, pose significant risks to seagrass meadows. Aims: This study acknowledges the uncertainty and variability of marine heatwave scenarios and aims to aid managers and policymakers in understanding simulated responses of seagrass to different durations, frequencies and recurrence gaps of marine heatwaves. Materials and Methods: Using expert knowledge and observed data, we refined a global Dynamic Bayesian Network (DBN) model for a specific case study on Halophila ovalis in Leschenault …

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Trace Element Contamination In Urban Soils: Testing And Management, Melissa Chilinski, Melanie Stock, Paul R. Grossl, Eli Oliver Jun 2024

Trace Element Contamination In Urban Soils: Testing And Management, Melissa Chilinski, Melanie Stock, Paul R. Grossl, Eli Oliver

All Current Publications

Trace elements, often referred to as heavy metals, naturally occur in the soil at low levels. Certain land use histories can elevate the concentrations of trace elements to levels that present health risks. Understanding which elements and soil test values may impact human or crop health is an important aspect of gardening and micro-farming, particularly in urban environments that are at increased risk of soil contamination. This fact sheet provides instructions on interpreting soil test results for trace elements through the Total Element Composition EPA 3050B Soil Test (#S19) at Utah State University Analytical Laboratory.

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D-Hacking, Emily Black, Talia B. Gillis, Zara Hall Jun 2024

D-Hacking, Emily Black, Talia B. Gillis, Zara Hall

Faculty Scholarship

Recent regulatory efforts, including Executive Order 14110 and the AI Bill of Rights, have focused on mitigating discrimination in AI systems through novel and traditional application of anti-discrimination laws. While these initiatives rightly emphasize fairness testing and mitigation, we argue that they pay insufficient attention to robust bias measurement and mitigation — and that without doing so, the frameworks cannot effectively achieve the goal of reducing discrimination in deployed AI models. This oversight is particularly concerning given the instability and brittleness of current algorithmic bias mitigation and fairness optimization methods, as highlighted by growing evidence in the algorithmic fairness literature. …

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Enhancing Government Service Delivery: A Case Study Of Acqar Implementation And Lessons Learned From Chatgpt Integration In A Singapore Government Agency, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Jun 2024

Enhancing Government Service Delivery: A Case Study Of Acqar Implementation And Lessons Learned From Chatgpt Integration In A Singapore Government Agency, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection Lee Kong Chian School Of Business

This paper presents the pilot implementation of AI Based Citizen Question-Answer Recommender (ACQAR) as an attempt to enhance citizen service delivery within a Singaporean government agency. Drawing insights from previous studies on the Empath library's use in Service Level Agreement (SLA) prediction and the implementation of the Citizen Question-Answer system (CQAS), we redesigned the pilot system, ACQAR. ACQAR integrates the outputs from Empath X SLA predictor and CQAS as essential inputs to the ChatGPT engine, creating contextually aware responses for customer service officers to use as responses to the citizens.Empath X SLA predictor anticipates the expected service response time based …

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