Physical Sciences and Mathematics Commons^™

A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …

Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find …

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …

Phoneme Recognition For Pronunciation Improvement, Matthew Heywood

Theses/Capstones/Creative Projects

This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.

Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …

Environmental Dna Identifies Coastal Plant Community Shift 1,000 Years Ago In Torrens Island, South Australia, Nicole R. Foster, Alice R. Jones, Oscar Serrano, Anna Lafratta, Paul S. Lavery, Kor-Jent Van Dijk, Ed Biffin, Bronwyn M. Gillanders, Jennifer Young, Pere Masque, Patricia S. Gadd, Geraldine E. Jacobsen, Atun Zawadzki, Andria Greene, Michelle Waycott

Research outputs 2022 to 2026

Anthropogenic activities are causing detrimental changes to coastal plants– namely seagrass, mangrove, and tidal marshes. Looking beyond recent times to past vegetation dynamics is critical to assess the response and resilience of an environment to change. Here, we develop a high-resolution multi-proxy approach, providing a new evidence base to decipher long-term change in coastal plant communities. Combining targeted environmental DNA analysis with chemical analysis of soils, we reconstructed 4,000 years of change at a temperate wetland on Torrens Island South Australia and identified an ecosystem shift that occurred ~ 1000 years ago. What was once a subtidal seagrass system shifted …

Curbing Environmental Degradation To Balance Sustainable Development: Evidence From China, Muneza Kagzi, Vishal Dagar, Nadia Doytch, Deepika Krishnan, Manisha Raj

Ateneo School of Government Publications

To achieve the goals of sustainable development, it is crucial to check the balance of increased level of international trade along with financial development, foreign direct investment (FDI), energy consumption, and institutional advancement with the quality of the environment. This study focuses on how these variables have caused environmental degradation in China. To achieve Goal 13 of the Sustainable Development Goals (SDGs), i.e. to increase the nation's resilience to natural disasters and hazards related to climate change, to promote climate action and safeguard life as part of sustainable development, this involves the analysis of time-series data sets from 1975 to …

Energy Intensity Convergence Among Chinese Provinces: A Theil Index Decomposition Analysis, Yifan Wang, Wei Li, Nadia Doytch

Ateneo School of Government Publications

China, the world’s largest carbon emitter, has one of the most stringent provincial emissions reduction programs, incorporated into its Five-Year National Plan to reduce carbon emissions. However, the widening energy intensity gap between provinces poses a great challenge for carbon reduction. In this study, we analyze the convergence of Energy intensity (EIC), i.e., the time-dependent decrease of differences among regional energy intensity over time focusing on a data set of 30 Chinese provinces from 2000 to 2015. Our goal is to identify the provinces that are responsible for the observed divergence in energy intensity and identify the factors causing that …

Hitch Cart “Landing Gear”, Rebekah White, Jose Raygoza, Randy Hernandez, Brandon Leon

Mechanical Engineering

This report aims to allow our sponsor, to review our design process of the Hitch Cart Landing Gear Prototype. In the design overview section of this report, we discuss the primary design modifications we made to the wheel mechanism of the existing hitch cart prototype, including the addition of the ACME screws and the folding brackets. This allows our sponsor to see the intended improvements made to the past prototype and understand the primary goal of our project. Then, in the implementation section, we cover the entire manufacturing process to allow our sponsor to understand what manufacturing steps must be …

Optimizing Mobility On Demand Systems: Multiagent Reinforcement Learning Approaches To Order Assignment And Vehicle Guidance, Jiyao Li

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores ways to improve Mobility on Demand (MoD) systems, which are services like ride-sharing and autonomous taxi systems. The main goal is to make these services more efficient and reliable, benefiting both passengers and drivers by better matching the number of available vehicles with the number of people needing rides.

For ride-sharing services, a new method called T-Balance helps match riders with drivers and guides empty taxis to areas where more people need rides. This reduces wait times for passengers and increases earnings for drivers. Another method, called GRL-HM, looks at how riders and drivers behave to further …

The Mongolian Remodeling And The Structure Of Anoplocephalid Cestode Diversity, Mackenzie Grover

All NMU Master's Theses

Ecological disruption plays an important role in structuring diversity of flora, fauna, and their parasites. At the end of the Eocene, climatic change across Asia resulted in a faunal turnover (the Mongolian Remodeling) as rodents diversified and larger mammals declined. Throughout the Oligocene, the landscape in Asia was characterized by episodic climatic and landscape changes resulting in pulses of rodent diversification. The role of historical ecological disruption in Central Asia in structuring the diversity of parasites of small rodents has not been thoroughly investigated. The hyper-diverse Paranoplocephala species complex (family: Anoplocephalidae) infect rodents throughout the Holarctic and present an opportunity …

Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif

All Works

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …

Quantifying The Impact Of Rain-On-Snow Induced Flooding In The Western United States, Emma M. Watts

All Graduate Theses and Dissertations, Fall 2023 to Present

Serious flooding can happen when rain falls on snow, which we call a rain-on-snow (ROS) event. Increasing our understanding of the behavior of floods resulting from ROS events can help us design better systems to manage flood water and prevent it from causing damage. This thesis explores how ROS events affect streamflow in the Western United States by examining the weather conditions that precede a streamflow surge. We classify stream surges as ROS or non-ROS induced based on these weather conditions, which helps us separate floods caused by ROS events from those caused by other factors. By comparing these different …

Impact Of Snow Accumulation On Structural Integrity: Present And Future Perspectives, Kenneth K. Pomeyie

All Graduate Theses and Dissertations, Fall 2023 to Present

In the United States, accommodating the weight of accumulated snow on buildings is a crucial consideration in building design. Engineers are tasked with determining the design snow load, which is defined as the weight of accumulated snow that a structure should withstand to limit the risk of building collapse to an acceptably low level. Typically, this process involves analyzing historical data of the annual maximum snow accumulations for each snow season. However, accurately assessing these design snow loads entails navigating through a series of statistical challenges. This dissertation, composed of three papers, is dedicated to addressing these statistical hurdles in …

The Model Of Norm-Regulated Responsibility For Proenvironmental Behavior In The Context Of Littering Prevention, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Previous research suggests that descriptive norms positively influence proenvironmental behavior, including littering prevention. However, in some behavioral contexts, a weak descriptive norm may increase individuals’ feelings of responsibility by signaling a need for action. We examined this effect in the context of litter prevention by conducting structural equation modeling of survey data from 1400 Singapore residents. The results showed that descriptive norms negatively predicted ascription of responsibility and were negatively related to littering prevention behavior via ascription of responsibility and personal norms. It also showed that strong injunctive norms can reduce the inhibitory effect of descriptive norms on ascription of …

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 …

Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda

All Works

Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …

Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with …

Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing …

Quantitative Evaluation Of Baseflow Separation Methods Using An Integrated Hydrologic Model: A Case Study In A Snow-Dominated Watershed, Jihad Othman

All Graduate Reports and Creative Projects, Fall 2023 to Present

Baseflow, commonly referred to as the groundwater contribution to streamflow, constitutes approximately 50% of streamflow in mountainous regions of the Western United States. Accurately quantifying the amount of baseflow is critical for water management and decision-making, as it significantly impacts stream water quality, low flow availability, and ecosystem health. Traditionally, baseflow has been estimated using conceptual and automated baseflow separation methods, which are known to be both arbitrary and ambiguous, posing a challenge to validate them. In this study, we developed an integrated hydrologic model that seamlessly integrated the exchange between surface and subsurface flows to physically quantify the baseflow …

Interpreting Neural Networks For Particle Tracing In Fluid Simulation Ensembles: An Interactive Visualization Framework, Maanav Choubey

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding the internal mechanisms of neural networks, particularly Multi-Layer Perceptrons (MLP), is essential for their effective application in a variety of scientific domains. In particular, in the scientific visualization domain their adoption has recently shown to be a promising tool to predict particle trajectories in fluid dynamics simulation and aid the interactive visualization of flows. This research addresses the critical challenge of interpretability of such models.

While interpretability has been extensively explored in fields like computer vision and natural language processing, its application to time series data, particularly for particle tracing (or prediction of trajectories), has not garnered sufficient attention. …

Testing For Patterns Of Deformation From The Yellowstone Hotspot Along The Gallatin River, Sw Montana, Jack Willard

All Graduate Theses and Dissertations, Fall 2023 to Present

Yellowstone has fascinated humans for thousands of years. Geologists have addressed many of the region’s mysteries, including the underlying mantle hotspot, the chain of calderas tracking the motion of the North American Plate over this hotspot, and the risks posed by the super volcano. However, the way that the hotspot impacts regional tectonics and topography remains under-studied. Influential previous work recognized the Yellowstone Crescent of High Terrain (YCHT), an arc of high topography around the Yellowstone Plateau with limbs extending to the west and south into central Idaho and northern Utah. The YCHT is also hypothesized to be the pattern …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Jamming Precoding In Af Relay-Aided Plc Systems With Multiple Eavessdroppers, Zhengmin Kong, Jiaxing Cui, Li Ding, Tao Huang, Shihao Yan

Research outputs 2022 to 2026

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the …

Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, …

Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very questionable in real-world situations due to the influence of contextual factors. Therefore, allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable. However, existing machine learning techniques accounting for the spatially varying relationship between the dependent variable …

Toward A Globally Lunar Calendar: A Machine Learning-Driven Approach For Crescent Moon Visibility Prediction, Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk

All Works

This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, …

Methyl Jasmonate Advances Fruit Ripening, Colour Development, And Improves Antioxidant Quality Of ‘Yoho’ And ‘Jiro’ Persimmon, Mahmood Ul Hasan, Zora Singh, Hafiz Muhammad Shoaib Shah, Andrew Woodward, Eben Afrifa-Yamoah

Research outputs 2022 to 2026

Methyl jasmonate (MJ) has potential to regulate fruit ripening and quality. ‘Yoho’ and ‘Jiro’ persimmons were sprayed with MJ (0, 2, 4, and 6 mM), four weeks before anticipated harvest to evaluate its effects on fruit colour and bioactive compounds. Preharvest MJ application significantly improved fruit colour with increased a*, b*, chroma, and colour index. The MJ 6 mM application had significantly enhanced soluble solids content (SSC), reduced total chlorophyll content in peel and pulp, and soluble and total tannins in persimmons. MJ treatments exhibited higher contents of total phenolics, flavonoids, carotenoids, and antioxidant activities. Additionally, MJ treatments enhanced the …

Early Detection Of Risk Factor For Suicidal Ideation Among Senior High School Students In Jakarta: Updated Measurement, Nova R. Yusuf, Sabarinah Prasetyo, Byron J. Good

Kesmas

The key strategy to address suicide in adolescents is school-based suicidal prevention by adapting a screening instrument to the local culture and policymakers’ perception of suicide. This study aimed to develop an instrument for the early detection of risk for suicidal ideation and identify influential risk factors for suicidal ideation among high school students in Jakarta, Indonesia. This study was conducted in 2018 with a mixed-method design (quantitative and qualitative approaches). It was found that 5% of students had suicidal ideation in July–November 2018, and 13.8% had a high-risk factor for suicidal ideation. The instrument developed in this study consisted …

Development Of A Web-Based Information System For Student Leave Permission At Dar Al-Raudhah Islamic Boarding School: Iso Quality Standards Analysis, Bonita Destiana, Priyanto Priyanto, Rahmatul Irfan, Muhammad Gus Khamim, Muhammad Yusuf Ridlo, Muhammad Iqbal

Elinvo (Electronics, Informatics, and Vocational Education)

Dar Al-Raudhah Entrepreneur, Islamic Boarding School, has adopted digital technology by upgrading hardware and software also investing in reliable internet infrastructure. However, this school still faces issues with students’ leave permission process due to reliance on manual bookkeeping and Excel, which leads to potential errors. Based on those problems, this research aims to create a web-based student leave permission system called SIPERSAN. The SIPERSAN system was developed with a Waterfall development model, which includes requirements analysis, design, implementation, testing, and deployment. The database is managed with MySQL, and the system is developed using PHP with the Laravel framework. Based on …