Physical Sciences and Mathematics Commons^™

Acoustic/Gravity Wave Phenomena In Wide-Field Imaging: From Data Analysis To A Modeling Framework For Observability In The Mlt Region And Beyond, Jaime Aguilar Guerrero

Doctoral Dissertations and Master's Theses

Acoustic waves, gravity waves, and larger-scale tidal and planetary waves are significant drivers of the atmosphere’s dynamics and of the local and global circulation that have direct and indirect impacts on our weather and climate. Their measurements and characterization are fundamental challenges in Aeronomy that require a wide range of instrumentation with distinct operational principles. Most measurements share the common features of integrating optical emissions or effects on radio waves through deep layers of the atmosphere. The geometry of these integrations create line-of-sight effects that must be understood, described, and accounted for to properly present the measured data in traditional …

Where2change: Change Request Localization For App Reviews, Tao Zhang, Jiachi Chen, Xian Zhan, Xiapu Luo, David Lo, He Jiang

Research Collection School Of Computing and Information Systems

Million of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users. Developers usually extract useful information from user reviews to maintain and evolve mobile apps. One of the important activities that developers need to do while reading user reviews is to locate the source code related to requested changes. Unfortunately, this manual work is costly and time consuming since: (1) an app can receive thousands of reviews, and (2) a mobile app can consist of hundreds of source code files. To address this challenge, Palomba et …

Feasibility Of Development Of Flood Resiliency Clearinghouse Program, Commonwealth Center For Recurrent Flooding Resiliency, Mujde Erten-Unal, Carol Considine, Mark W. Luckenbach, Elizabeth Armistead Andrews

Commonwealth Center for Recurrent Flooding Resiliency (CCRFR): Reports

[Introduction]

House Bill 2187ⁱ, introduced by Delegate Keith Hodges in the 2021 session of the Virginia General Assembly, directed the Commonwealth Center for Recurrent Flooding Resiliency (CCRFR), a partnership between Old Dominion University, the Virginia Institute of Marine Science (VIMS) and the William & Mary Law School’s Virginia Coastal Policy Center (VCPC) established by Virginia Chapter 440 of the 2016 Acts of Assembly (HB 903), to evaluate the development of a Flood Resiliency Clearinghouse Program (henceforth Clearinghouse). The bill stipulated that the Center should work with the Department of Conservation and Recreation (DCR) to evaluate solutions that manage …

Nudging Students To Use Stronger Passwords: A Test Of Big Five Personality-Based Messages, Shelia Kennison, Eric Chan-Tin

Computer Science: Faculty Publications and Other Works

Cybersecurity breaches can occur when one uses an easily hacked password. Prior research has investigated 1) possible steps to encourage users to use strong passwords and 2) how personality is related to users using strong passwords.

We investigated whether personality-based nudging messages based on Big Five traits could nudge people to create stronger passwords (c.f., Jones et al., 2021). We also examined how personal characteristics, such as gender, age, personality traits, password knowledge, attitudes, and behavior, and need for cognition, were related to password strength.

We tested the hypothesis that passwords created following messages matching participants’ personality would be stronger …

Tweets R Us: Predicting Personality From Language And Emoji Use On Twitter, Maxwell Meckling, Sarah Shoup, D. E. Chan-Tin, Shelia Kennison

Computer Science: Faculty Publications and Other Works

The research investigated the suggestion from prior research that language and emojis use on Twitter and other social media platforms can predict users’ personality and gender (Adali et al., 2014; Golbeck et al., 2011; Li et al., 2019; Moreno et al., 2019; Raess, 2018). Some studies have also analyzed Twitter language to identify individuals with specific health conditions (e.g., alcohol recovery, Golbeck, 2012; sleep problems, Suarez et al., 2018).

If strategies to predict Twitter users’ characteristics prove to be successful, future efforts to direct persuasive messages related to recommended practices in public health and/or cybersecurity will be possible. Commercial applications …

Fundamental Solutions For The Dirac Equation In Curved Spacetime And Generalized Euler-Poisson-Darboux Equation, Karen Yagdjian, Anahit Galstian

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We present the fundamental solutions for the spin-1/2 fields propagating in spacetimes with power type expansion/contraction and the fundamental solution of the Cauchy problem for the Dirac equation. The derivation of these fundamental solutions is based on formulas for the solutions to the generalized Euler-Poisson-Darboux equation, which are obtained by the integral transform approach.

Iowa Waste Reduction Center Newsletter, November 2021, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Inside this Issue:

--- Iowa Green Brewery Certification Celebrates Five Years
--- Calendar
--- ISGP Webinar
--- Welcome Jordan Evans, Jason Clay & Andrew Kawano!
--- Industry News

Research Information Management In The United States: Part One, Findings And Recommendations, Rebecca Bryant, Jan Fransen, Pablo De Castro, Brenna Helmstutler, David Scherer

Copyright, Fair Use, Scholarly Communication, etc.

Research information management (RIM) is a rapidly growing area of investment in US research universities. RIM systems that support the collection and use of research outputs metadata have been in place for many years. Globally, the RIM ecosystem is quite mature in locales where national research assessment exercises like the United Kingdom’s Research Excellence Framework (REF) and the Excellence in Research for Australia (ERA) require institutions to collect and report on the outputs of institutional research. A pan-European community of practice is led by euroCRIS.

This report describes six discrete RIM use cases detailed in the companion report:

• Faculty …

Search For B0 →Τ±∓ (ℓ=E, Μ) With A Hadronic Tagging Method At Belle Search For B0 →Τ±∓ (ℓ=E, Μ) With A ... H. Atmacan Et Al., H. Atmacan, A. J. Schwartz, K. Kinoshita, I. Adachi, K. Adamczyk, H. Aihara, S. Al Said, D. M. Asner, V. Aulchenko, T. Aushev, R. Ayad, V. Babu, S. Bahinipati, M. Bauer, P. Behera, K. Belous, J. Bennett, F. Bernlochner, M. Bessner

Faculty and Student Publications

We present a search for the lepton-flavor-violating decays B0→τ±∓, where ℓ=(e,μ), using the full data sample of 772×106 BB¯ pairs recorded by the Belle detector at the KEKB asymmetric-energy e+e- collider. We use events in which one B meson is fully reconstructed in a hadronic decay mode. The τ± lepton is reconstructed indirectly using the momentum of the reconstructed B and that of the ∓ from the signal decay. We find no evidence for B0→τ±∓ decays and set upper limits on their branching fractions at 90% confidence level of B(B0→τ±μ∓)<1.5×10-5 and B(B0→τ±e∓)<1.6×10-5.

Towards Balancing Vr Immersion And Bystander Awareness, Yoshiki Kudo, Anthony Tang, Kazuyuki Fujita, Isamu Endo, Kazuki Takashima, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Head-mounted displays (HMDs) increase immersion into virtual worlds. The problem is that this limits headset users' awareness of bystanders: headset users cannot attend to bystanders' presence and activities. We call this the HMD boundary. We explore how to make the HMD boundary permeable by comparing different ways of providing informal awareness cues to the headset user about bystanders. We adapted and implemented three visualization techniques (Avatar View, Radar and Presence++) that share bystanders' location and orientation with headset users. We conducted a hybrid user and simulation study with three different types of VR content (high, medium, low interactivity) with twenty …

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn

Research Collection School Of Computing and Information Systems

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian …

Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate …

Contrastive Pre-Training Of Gnns On Heterogeneous Graphs, Xunqiang Jiang, Yuanfu Lu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

While graph neural networks (GNNs) emerge as the state-of-the-art representation learning methods on graphs, they often require a large amount of labeled data to achieve satisfactory performance, which is often expensive or unavailable. To relieve the label scarcity issue, some pre-training strategies have been devised for GNNs, to learn transferable knowledge from the universal structural properties of the graph. However, existing pre-training strategies are only designed for homogeneous graphs, in which each node and edge belongs to the same type. In contrast, a heterogeneous graph embodies rich semantics, as multiple types of nodes interact with each other via different kinds …

Finding A Needle In A Haystack: Automatic Mining Of Silent Vulnerability Fixes, Jiayuan Zhou, Michael Pacheco, Zhiyuan Wan, Xin Xia, David Lo, Yuan Wang, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Following the coordinated vulnerability disclosure model, a vulnerability in open source software (OSS) is suggested to be fixed “silently”, without disclosing the fix until the vulnerability is disclosed. Yet, it is crucial for OSS users to be aware of vulnerability fixes as early as possible, as once a vulnerability fix is pushed to the source code repository, a malicious party could probe for the corresponding vulnerability to exploit it. In practice, OSS users often rely on the vulnerability disclosure information from security advisories (e.g., National Vulnerability Database) to sense vulnerability fixes. However, the time between the availability of a vulnerability …

Cs-Light: Camera Sensing Based Occupancy-Aware Robust Smart Building Lighting Control, Anuradha Ravi, Kasun Pramuditha Gamlath, Siyan Hu, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …

Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional pseudo-labeling operation heavily relies on the initial model trained by using a handful of labeled data and may produce many noisy labeled samples. We propose to solve the problem with three steps: firstly, cherry-picking searches valuable samples from pseudo-labeled data by using a soft weighting network; and then, cross-teaching allows the classifiers to teach mutually for rejecting more noisy …

Global 3d Radiation Hydrodynamic Simulations Of Proto-Jupiter’S Convective Envelope, Zhaohuan Zhu, Yan-Fei Jiang, Hans Baehr, Andrew N. Youdin, Philip J. Armitage, Rebecca G. Martin

Physics & Astronomy Faculty Research

The core accretion model of giant planet formation has been challenged by the discovery of recycling flows between the planetary envelope and the disc that can slow or stall envelope accretion. We carry out 3D radiation hydrodynamic simulations with an updated opacity compilation to model the proto-Jupiter’s envelope. To isolate the 3D effects of convection and recycling, we simulate both isolated spherical envelopes and envelopes embedded in discs. The envelopes are heated at given rates to achieve steady states, enabling comparisons with 1D models. We vary envelope properties to obtain both radiative and convective solutions. Using a passive scalar, we …

Toponym-Assisted Map Georeferencing: Evaluating The Use Of Toponyms For The Digitization Of Map Collections, Karim Bahgat, Daniel Runfola

Arts & Sciences Articles

A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map …

Exploratory Data Mining Techniques (Decision Tree Models) For Examining The Impact Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Machine Learning Approach, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Background: There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment …

Galaxy And Mass Assembly (Gama): The Merging Potential Of Brightest Group Galaxies, K. Banks, S. Brough, Benne Holwerda, A. M. Hopkins, Á. R. López-Sánchez, S. Phillipps, K. A. Pimbblet, A. S. G. Robotham

Faculty and Staff Scholarship

Using a volume-limited sample of 550 groups from the Galaxy And Mass Assembly Galaxy Group Catalogue spanning the halo mass range , we investigate the merging potential of central Brightest Group Galaxies (BGGs). We use spectroscopically confirmed close-companion galaxies as an indication of the potential stellar mass buildup of low-redshift BGGs, z ≤ 0.2. We identify 17 close-companion galaxies with projected separations rp < 30 kpc, relative velocities Δv ≤ 300 km s−1, and stellar mass ratios MBGG/MCC ≤ 4 relative to the BGG. These close-companion galaxies yield a total pair fraction of 0.03 ± 0.01. Overall, we find that BGGs …

Analytical Models For Traffic Congestion And Accident Analysis, Hongrui Liu, Rahul Ramachandra Shetty

Mineta Transportation Institute

In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records …

Medication Calculations Dimensional Analysis Tutorial, Jennifer Emilie Mannino Ph.D., R.N., Elizabeth Cotter Ph.D, R.N.

Faculty Works: NUR (2010-2023)

Tutorial on medication calculations from The Barbara H Hagan School of Nursing and Allied Sciences.

Silica Sinter And The Evolution Of Hot Springs In The Alvord/Pueblo Valleys, Southeast Oregon, Usa, Leslie Allen Mowbray, Michael L. Cummings

Geology Faculty Publications and Presentations

Hot springs in the Alvord/Pueblo valleys in southeastern Oregon are analogous to Basinand- Range hydrothermal systems where heat source and permeable pathways are met through crustal thinning. Silica sinter deposition at Mickey Springs, Alvord Valley, predates the late Pleistocene high stand of pluvial Lake Alvord. At Borax Lake, Pueblo Valley, sinter deposition occurred during the Holocene. This study examines the evolution of springs at Mickey Springs, where three morphologies of sinter are present: (1) basalt clasts surrounded by sinter in interbedded conglomerate and sandstone, (2) pool-edge and aprons of sinter surrounding depressions (12–32 m diameter), and (3) quaquaversal sinter mounds …

Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori

Masters Theses & Doctoral Dissertations

Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …

Estimated Amounts And Rates Of Carbon Mobilized By Landsliding In Old-Growth Temperate Forests Of Se Alaska, Bryce A. Vascik, Adam M. Booth, Brian Buma, Matteo Berti

Geology Faculty Publications and Presentations

Landslides, a forest disturbance, mobilize carbon (C) sequestered in vegetation and soils. Mobilized C is deposited either onto hillslopes or into the water, sequestering C from and releasing C to the atmosphere at different time scales. The C-dense old-growth temperate forests of SE Alaska are a unique location to quantify C mobilization rate by frequent landslides that often evolve into saturated moving masses known as debris flows. In this study, the amount of C mobilized by debris flows over historic time scales was estimated by combining a landslide inventory with maps of modeled biomass and soil carbon. We analyzed SE …

Development Of Sensor, Sensory System And Signal Processing Algorithm For Intelligent Sensing Applications, Xingzhe Zhang

Dissertations

Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.

In the first project, a multi-channel stethograph …

On Aggregating Salaries Of Occupations From Job Post And Review Data, Chih-Chieh Hung, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The popularity of job websites has significantly changed the way people learn about different occupations. Among the insights offered by these websites are the statistics of occupation salaries which are useful information for job seekers, career coaches, graduating students, and labor related government agencies. Such statistics include the distribution of job salaries of each occupation, such as average or quantiles. However, significant variability in salary (and review salary) can be found among jobs of the same occupation as we gather job post and review data from job websites. Such variability shows the existence of biases, including salary competitiveness in job …

Adaptive Posterior Knowledge Selection For Improving Knowledge-Grounded Dialogue Generation, Weichao Wang, Wei Gao, Shi Feng, Ling Chen, Daling Wang

Research Collection School Of Computing and Information Systems

In open-domain dialogue systems, knowledge information such as unstructured persona profiles, text descriptions and structured knowledge graph can help incorporate abundant background facts for delivering more engaging and informative responses. Existing studies attempted to model a general posterior distribution over candidate knowledge by considering the entire response utterance as a whole at the beginning of decoding process for knowledge selection. However, a single smooth distribution could fail to model the variability of knowledge selection patterns over different decoding steps, and make the knowledge expression less consistent. To remedy this issue, we propose an adaptive posterior knowledge selection framework, which sequentially …

Fleet Sizing And Allocation For On-Demand Last-Mile Transportation Systems, Karmel Shehadeh, Hai Wang, Peter Zhang

Research Collection School Of Computing and Information Systems

The last-mile problem refers to the provision of travel service from the nearest public transportation node to home or other destination. Last-Mile Transportation Systems (LMTS), which have recently emerged, provide on-demand shared transportation. In this paper, we investigate the fleet sizing and allocation problem for the on-demand LMTS. Specifically, we consider the perspective of a last-mile service provider who wants to determine the number of servicing vehicles to allocate to multiple last-mile service regions in a particular city. In each service region, passengers demanding last-mile services arrive in batches, and allocated vehicles deliver passengers to their final destinations. The passenger …