Physical Sciences and Mathematics Commons^™

Multilingual Multifaceted Understanding Of Online News In Terms Of Genre, Framing And Persuasion Techniques, Jakub Piskorski, Nicolas Stefanovitch, Nikolaos Nikolaidis, Giovanni Da San Martino, Preslav Nakov

Natural Language Processing Faculty Publications

We present a new multilingual multifacet dataset of news articles, each annotated for genre (objective news reporting vs. opinion vs. satire), framing (what key aspects are highlighted), and persuasion techniques (logical fallacies, emotional appeals, ad hominem attacks, etc.). The persuasion techniques are annotated at the span level, using a taxonomy of 23 fine-grained techniques grouped into 6 coarse categories. The dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans of persuasion techniques. We describe the dataset and the …

Development And Reliability Analysis Of A Split-Administration Test Of The Math Epistemic Games Survey, Stephen Hackler, E. Elliott, M. Eichenlaub, Alison M. Sweeney

Physics & Astronomy Faculty Works

The increasing and diversifying student enrollments in introductory physics courses make reliable, valid, and usable instruments for measuring student skills and gains ever more important. In introductory physics, in addition to teaching facts about mechanics, we also seek to teach our students the skills of “thinking like a physicist,” or expertise in and intuition for physical problem solving. How and when these expert, intuitive problem-solving skills emerge during a STEM education, or what the most effective teaching methods might be, are not certain. A facile survey to measure students’ “physics-thinking” skills in a pretest and post-test format is therefore desirable …

Toi-2498 B: A Hot Bloated Super-Neptune Within The Neptune Desert, G. Frame, D. J. Armstrong, H. M. Cegla, J. Fernández Fernández, A. Osborn, V. Adibekyan, K. A. Collins, E. Delgado Mena, S. Giacalone, J. F. Kielkopf, N. C. Santos, S. G. Sousa, K. G. Stassun, C. Ziegler, D. R. Anderson, S. C. C. Barros, D. Bayliss, C. Briceño, D. M. Conti, C. D. Dressing, X. Dumusque, P. Figueira, W. Fong, S. Gill, F. Hawthorn, J. M. Jenkins, Eric L.N. Jensen, M. A. F. Keniger, D. W. Latham, N. Law, J. J. Lissauer, A. W. Mann, L. D. Nielsen, H. Osborn, M. Paegert, S. Seager, R. P. Schwarz, A. Shporer, G. Srdoc, P. A. Strøm, J. N. Winn, P. J. Wheatley

Physics & Astronomy Faculty Works

We present the discovery and confirmation of a transiting hot bloated super-Neptune using photometry from the Transiting Exoplanet Survey Satellite (TESS) and the Las Cumbres Observatory Global Telescope (LCOGT) and radial velocity measurements from the High Accuracy Radial velocity Planet Searcher (HARPS). The host star TOI-2498 is a V = 11.2, G-type (T_eff = 5905 ± 12 K) solar-like star with a mass of 1.12 ± 0.02 M_⊙ and a radius of 1.26 ± 0.04 R_⊙. The planet, TOI-2498 b, orbits the star with a period of 3.7 d, has a radius of …

Conformal Prediction For Federated Uncertainty Quantification Under Label Shift, Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

Machine Learning Faculty Publications

Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this …

A Convolutional Neural Network Based Approach To Study The Gravitational Waves From Core-Collapse Supernovae In Ligo's Third Observation Run: Detection Efficiency And Parameter Estimation, Bhawana Sedhai

Theses and Dissertations

Core-Collapse Supernova (CCSN) is one of the most anticipated sources of Gravitational Waves (GW) arriving at the advanced LIGO detectors during the fourth observation run (O4). CCSN are rare, weak and unmodeled having a very low rate of occurrence in our galaxy (estimated 2 per century). Thus, detection of GW from CCSN is a challenging problem. An analysis pipeline used in this study is Multi-Layer Signal Enhancement with cWB and CNN or MuLaSEcC that combines Machine Learning methods with a network of Gravitational Wave detectors to identify and reconstruct signals from core collapse supernovae, while minimizing false alarms through the …

Using Deep Learning For Encrypted Traffic Analysis Of Amazon Echo, Surendra Pathak

Theses and Dissertations

The adoption of the Amazon Echo family of devices in modern homes has become very widespread at the current time, with hundreds of millions of devices sold. Moreover, the global smart speaker market size is growing vigorously and is projected to continue to bigger. Smart speakers allow users hands-free interaction by allowing voice control, promoting human-computer interaction to greater avenues. Though smart speaker can be useful assistant, it has some serious security concerns that need to be studied. In this study, an analysis of the security and privacy concerns of smart speakers is presented along with a passive attack, namely …

Identification Of Heart Disorders With Symbolic Aggregate Approximation, Moses K. Owusu

Theses and Dissertations

This project is an application of the Symbolic Aggregate Approximation (SAX) to 1000 fragments of ECG signals for 45 patients (42% females aged between 23 and 89 years and 58% males aged 32 to 89 years) using data obtained from the MIH-BIH Arrhythmia database to recognize cardiac health disorders. Data include a normal sinus rhythm, pacemaker rhythm and ECG readings for 15 heart disorders, making 17 in total. The aim is to use SAX to classify heart disorders using ECG signal, that analyzes QRS-complexes by first splitting the time series into smaller equally sized segments using the Piecewise Aggregate Approximation …

Variability In Consumption And End Uses Of Water For Residential Users, Camilo J. Bastidas Pacheco, Jeffery S. Horsburg, Attallah A. Nour

Research Briefs

Research Objective/Summary: In most large urban water systems in the US, the residential sector consumes the majority of total supplied fresh water. In a world plagued with increasing water scarcity and climate change stresses, understanding individual home water end-uses is vital to water management and conservation. We studied the end uses of water in residential homes, both indoor and outdoor to find patterns and variations in consumption over time. Results indicate a need for more efficient water fixtures, particularly toilets, and provide an opportunity to promote conservation behavior.

Ideology Prediction From Scarce And Biased Supervision: Learn To Disregard The “What” And Focus On The “How”!, Chen Chen, Dylan Walker, Venkatesh Saligrama

Business Faculty Articles and Research

We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the fact that manual data-labeling is expensive, while self-reported labels are often scarce and exhibit significant selection bias. We propose a novel statistical model that decomposes the document embeddings into a linear superposition of two vectors; a latent neutral context vector independent of ideology, and a latent position vector aligned with ideology. We train an end-to-end model that has intermediate contextual and positional vectors as outputs. At deployment time, our model predicts labels for input documents …

Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh

Research Collection Lee Kong Chian School Of Business

This study examined business communication practices with chatbots among various Small and Medium Enterprise (SME) stakeholders in Singapore, including business owners/employees, customers, and developers. Through qualitative interviews and chatbot transcript analysis, we investigated two research questions: (1) How do the expectations of SME stakeholders compare to the conversational design of SME chatbots? and (2) What are the business reasons for SMEs to add human-like features to their chatbots? Our findings revealed that functionality is more crucial than anthropomorphic characteristics, such as personality and name. Stakeholders preferred chatbots that explicitly identified themselves as machines to set appropriate expectations. Customers prioritized efficiency, …

Managing The Creative Frontier Of Generative Ai: The Novelty-Usefulness Tradeoff, Anirban. Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

In this paper, drawing inspiration from the human creativity literature, we explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. We posit that overemphasizing either aspect can lead to limitations such as hallucinations and memorization. Hallucinations, characterized by AI responses containing random inaccuracies or falsehoods, emerge when models prioritize novelty over usefulness. Memorization, where AI models reproduce content from their training data, results from an excessive focus on usefulness, potentially limiting creativity. To address these challenges, we propose a framework that includes domain-specific analysis, data and transfer learning, user preferences and customization, custom evaluation metrics, …

Analyzing Taxi Drivers’ Decision-Making And Recommending Strategies For Enhanced Performance: A Data-Driven Approach, Mengyu Ji

Dissertations and Theses Collection (Open Access)

This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are gained into drivers’ choices regarding optimal routes, timing, and areas with high demand. Integrating real-time information sources, such as GPS data and passenger updates, allows drivers to adapt their strategies dynamically to changing traffic conditions and emerging demand patterns. Predictive analytics models, includ- ing ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and …

State Of The Fisheries: Status Reports And Aquatic Resources Of Western Australia 2021/22, Department Of Primary Industries And Regional Development, Western Australia, S J. Newman, B S. Wise, K G. Santaro, D J. Gaughan

Status reports of the fisheries and aquatic resources

Aquatic resources within Western Australia (WA) are in good condition, and this has positioned WA as a global leader in sustainable fisheries management. The sustainable fisheries of WA continue to support our strong economy and regional communities. Nonetheless, the lack of a consistent approach to build in the knowledge of Traditional Owners remains a gap in our longer-term fisheries science in Western Australia.

Climate change and climate variability continues to impact fish stocks, challenging our ability to effectively monitor, assess, and manage fish stocks. We are continually working with our stakeholders, and the broader community to be adaptive, responsive, and …

Modified Geometries, Clifford Algebras And Graphs: Their Impact On Discreteness, Locality And Symmetr, Roman Sverdlov

Mathematics & Statistics ETDs

In this dissertation I will explore the question whether various entities commonly used in quantum field theory can be “constructed". In particular, can spacetime be “constructed" out of building blocks, and can Berezin integral be “constructed" in terms of Riemann integrals.

As far as “constructing" spacetime out of building blocks, it has been attempted by multiple scientific communities and various models were proposed. But the common downfall is they break the principles of relativity. I will explore the ways of doing so in such a way that principles of relativity are respected. One of my approaches is to replace points …

Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard

Chemistry Summer Research Program

No abstract provided.

The Dynamic Shift To Green Chemistry: Investigating The Spectral Behavior Of Natural Deep Eutectic Solvents (Nades) And Their Performance As Maldi-Tof Matrices, Grayson Weavil, Lucas B. Ayres, Miguel Jose-Bueno, Rakesh Sachdeva, Carlos D. Garcia

Chemistry Summer Research Program

No abstract provided.

Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard

Chemistry Summer Research Program

No abstract provided.

Prediction Of Dislocation Densities In Pure Metals And Alloys, J. Garey Weaver, Enrique Martinez

Chemistry Summer Research Program

No abstract provided.

Measurement Of Protein And Drug Adsorption On Nanoparticles By Competition With Dyes, Ashleigh Carroll, Jason Mcneill

Chemistry Summer Research Program

No abstract provided.

Development Of A Rapid Sars-Cov-2 Nucleocapsid Assay Using Buoyant And Magnetic (Bam) Beads, Spasenija Radenovic, Zahra Karimpourkalou, Chaunlei Wang, Jeffrey N. Anker

Chemistry Summer Research Program

No abstract provided.

Isolation Of Bovine Milk-Derived Exosomes Via A Polyester Capillary- Channeled Polymer (C-Cp) Fiber Stationary Phase, Jerisa Pimentel, Carolina Mata, R. Kenneth Marcus

Chemistry Summer Research Program

No abstract provided.

Multi-Catalysis: Trifluoromethylation Of Amides, Ethan Apsley, Jason Wilt, Giovani Gutierrez, Byoungmoo Kim

Chemistry Summer Research Program

No abstract provided.

Suspicious Behavior Detection With Temporal Feature Extraction And Time-Series Classification For Shoplifting Crime Prevention, Amril Nazir, Rohan Mitra, Hana Sulieman, Firuz Kamalov

All Works

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then …

Maximum Activation 3d Cube Transition System For Virtual Emotion Surveillance, Taewoo Lee, Jalel Ben-Othman, Hyunbum Kim

All Works

The concept of barrier coverage has been utilized for with various applications of surveillance, object tracking in smart cities. In barrier coverage, it is desirable to have large number of active barriers to maximize lifetime of UAV-assisted application. Because existing studies primarily focused on the formation of barriers in two-dimensional area with limited applicability, it is indispensable to extend the barrier constructions in three-dimensional area. In this letter, a cube transition barrier system using smart UAVs is designed for three-dimensional space. Then, we formally define a problem whose goal is to maximize the number of cube transition barriers by applying …

Artificial Intelligence Tool For The Study Of Covid-19 Microdroplet Spread Across The Human Diameter And Airborne Space, Hesham H. Alsaadi, Monther Aldwairi, Faten Yasin, Sandra C.P. Cachinho, Abdullah Hussein

All Works

The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance …

Self-Healing In Cyber–Physical Systems Using Machine Learning: A Critical Analysis Of Theories And Tools, Obinna Johnphill, Ali Safaa Sadiq, Feras Al-Obeidat, Haider Al-Khateeb, Mohammed Adam Taheir, Omprakash Kaiwartya, Mohammed Ali

All Works

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures …

Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb

All Works

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the …

A Comparison Of Confidence Intervals In State Space Models, Jinyu Du

Statistical Science Theses and Dissertations

This thesis develops general procedures for constructing confidence intervals (CIs) of the error disturbance parameters (standard deviations) and transformations of the error disturbance parameters in time-invariant state space models (ssm). With only a set of observations, estimating individual error disturbance parameters accurately in the presence of other unknown parameters in ssm is a very challenging problem. We attempted to construct four different types of confidence intervals, Wald, likelihood ratio, score, and higher-order asymptotic intervals for both the simple local level model and the general time-invariant state space models (ssm). We show that for a simple local level model, both the …

Lecture Notes On Cloud Computing (Ver. Summer 2023), Jun Li

Open Educational Resources

No abstract provided.

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …