Physical Sciences and Mathematics Commons^™

The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees, Albert Y. Kim, David N. Allen, Simon P. Couch

Statistical and Data Sciences: Faculty Publications

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research …

Wav-Bert: Cooperative Acoustic And Linguistic Representation Learning For Low-Resource Speech Recognition, Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin

Research Collection School Of Computing and Information Systems

Unifying acoustic and linguistic representation learning has become increasingly crucial to transfer the knowledge learned on the abundance of high-resource language data for low-resource speech recognition. Existing approaches simply cascade pre-trained acoustic and language models to learn the transfer from speech to text. However, how to solve the representation discrepancy of speech and text is unexplored, which hinders the utilization of acoustic and linguistic information. Moreover, previous works simply replace the embedding layer of the pre-trained language model with the acoustic features, which may cause the catastrophic forgetting problem. In this work, we introduce Wav-BERT, a cooperative acoustic and linguistic …

Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question …

Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a …

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 …

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 …

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

Profiling Student Learning From Q&A Interactions In Online Discussion Forums, De Lin Ong, Kyong Jin Shim, Gottipati Swapna

Research Collection School Of Computing and Information Systems

The last two decades have witnessed an explosive growth in technology adoption in education. Proliferation of digital learning resources through Massive Open Online Courses (MOOCs) and social media platforms coupled with significantly lowered cost of learning has brought and is continuing to take education to every doorstep globally. In recent years, the use of asynchronous online discussion forums has become pervasive in tertiary education institutions. Online discussion forums are widely used for facilitating interactions both during the lesson time and beyond. Numerous prior studies have reported benefits of using online discussion forums including enhanced quality of learning, improved level of …

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 …

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 …

All-Sky Search For Short Gravitational-Wave Bursts In The Third Advanced Ligo And Advanced Virgo Run, R. Abbott, T. D. Abbott, F. Acernese, K. Ackley, Teviet Creighton, Mario C. Diaz, Francisco Llamas, Soma Mukherjee, Volker Quetschke, Wen Hui Wang

Physics and Astronomy Faculty Publications and Presentations

This paper presents the results of a search for generic short-duration gravitational-wave transients in data from the third observing run of Advanced LIGO and Advanced Virgo. Transients with durations of milliseconds to a few seconds in the 24–4096 Hz frequency band are targeted by the search, with no assumptions made regarding the incoming signal direction, polarization, or morphology. Gravitational waves from compact binary coalescences that have been identified by other targeted analyses are detected, but no statistically significant evidence for other gravitational wave bursts is found. Sensitivities to a variety of signals are presented. These include updated upper limits on …

Technique To Solve Linear Fractional Differential Equations Using B-Polynomials Bases, Muhammad I. Bhatti, Md. Habibur Rahman

Physics and Astronomy Faculty Publications and Presentations

A multidimensional, modified, fractional-order B-polys technique was implemented for finding solutions of linear fractional-order partial differential equations. To calculate the results of the linear Fractional Partial Differential Equations (FPDE), the sum of the product of fractional B-polys and the coefficients was employed. Moreover, minimization of error in the coefficients was found by employing the Galerkin method. Before the Galerkin method was applied, the linear FPDE was transformed into an operational matrix equation that was inverted to provide the values of the unknown coefficients in the approximate solution. A valid multidimensional solution was determined when an appropriate number of basis sets …

Effects Of Sesame Consumption On Inflammatory Biomarkers In Humans: A Systematic Review And Meta-Analysis Of Randomized Controlled Trials, Shabnam Rafiee, Roghaye Faryabi, Mohammad Ali Zareian, Jessie Hawkins, Nitin Shivappa Mbbs, Mph, Ph.D., Laila Shirbeigi

Faculty Publications

Objectives. Existing evidence produces conflicting findings regarding the effect of sesame intake on inflammatory biomarkers; thisknowledge gap has yet to be met through systematic review and meta-analysis. )is meta-analysis of randomized, controlledclinical trials (RCTs) was conducted to evaluate the effects of sesame consumption on markers of inflammation in humans. Methods. PubMed, Scopus, and the Cochrane Database of Systematic Reviews were searched through August 2020 to identify relevant papers for inclusion. Using the random-effects model, data were evaluated as weighted mean differences (WMD) with 95% confidence intervals (CI). Cochrane’s Q and I-squared (I2) tests were used to …

Searches For Continuous Gravitational Waves From Young Supernova Remnants In The Early Third Observing Run Of Advanced Ligo And Virgo, R. Abbott, T. D. Abbott, S. Abraham, Teviet Creighton, Mario C. Diaz, Soma Mukherjee, Volker Quetschke, Karla E. Ramirez, Wenhui Wang

Physics and Astronomy Faculty Publications and Presentations

We present results of three wide-band directed searches for continuous gravitational waves from 15 young supernova remnants in the first half of the third Advanced LIGO and Virgo observing run. We use three search pipelines with distinct signal models and methods of identifying noise artifacts. Without ephemerides of these sources, the searches are conducted over a fRequency band spanning from 10 to 2 kHz. We find no evidence of continuous gravitational radiation from these sources. We set upper limits on the intrinsic signal strain at 95% confidence level in sample subbands, estimate the sensitivity in the full band, and derive …

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 …

Self-Supervised Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) that requires only normal (healthy) training images is an important tool for enabling the development of medical image analysis (MIA) applications, such as disease screening, since it is often difficult to collect and annotate abnormal (or disease) images in MIA. However, heavily relying on the normal images may cause the model training to overfit the normal class. Self-supervised pre-training is an effective solution to this problem. Unfortunately, current self-supervision methods adapted from computer vision are sub-optimal for MIA applications because they do not explore MIA domain knowledge for designing the pretext tasks or the training process. …

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 …

Intercept Graph: An Interactive Radial Visualization For Comparison Of State Changes, Shaolun Ruan, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

State change comparison of multiple data items is often necessary in multiple application domains, such as medical science, financial engineering, sociology, biological science, etc. Slope graphs and grouped bar charts have been widely used to show a “before-and-after” story of different data states and indicate their changes. However, they visualize state changes as either slope or difference of bars, which has been proved less effective for quantitative comparison. Also, both visual designs suffer from visual clutter issues with an increasing number of data items. In this paper, we propose Intercept Graph, a novel visual design to facilitate effective interactive comparison …

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 …

Patchnet: Hierarchical Deep Learning-Based Stable Patch Identification For The Linux Kernel, Thong Hoang, Julia Lawall, Yuan Tian, Richard J. Oentaryo, David Lo

Research Collection School Of Computing and Information Systems

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical …