UMBC Student Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/33

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An Investigation of the Relationship Between Crime Rate and Police Compensation
(2024-11-21) Amarsingh, Jhancy; Appakondreddigari, Likhith Kumar Reddy; Nunna, Ashish; Tummala, Charishma Choudary; Winship, John; Zhou, Alex; Ashqar, Huthaifa
The goal of this paper is to assess whether there is any correlation between police salaries and crime rates. Using public data sources that contain Baltimore Crime Rates and Baltimore Police Department (BPD) salary information from 2011 to 2021, our research uses a variety of techniques to capture and measure any correlation between the two. Based on that correlation, the paper then uses established social theories to make recommendations on how this data can potentially be used by State Leadership. Our initial results show a negative correlation between salary/compensation levels and crime rates.
Listening for Expert Identified Linguistic Features: Assessment of Audio Deepfake Discernment among Undergraduate Students
(2024-11-21) Bhalli, Noshaba Nasir; Naqvi, Nehal; Evered, Chloe; Mallinson, Christine; Janeja, Vandana
This paper evaluates the impact of training undergraduate students to improve their audio deepfake discernment ability by listening for expert-defined linguistic features. Such features have been shown to improve performance of AI algorithms; here, we ascertain whether this improvement in AI algorithms also translates to improvement of the perceptual awareness and discernment ability of listeners. With humans as the weakest link in any cybersecurity solution, we propose that listener discernment is a key factor for improving trustworthiness of audio content. In this study we determine whether training that familiarizes listeners with English language variation can improve their abilities to discern audio deepfakes. We focus on undergraduate students, as this demographic group is constantly exposed to social media and the potential for deception and misinformation online. To the best of our knowledge, our work is the first study to uniquely address English audio deepfake discernment through such techniques. Our research goes beyond informational training by introducing targeted linguistic cues to listeners as a deepfake discernment mechanism, via a training module. In a pre-/post- experimental design, we evaluated the impact of the training across 264 students as a representative cross section of all students at the University of Maryland, Baltimore County, and across experimental and control sections. Findings show that the experimental group showed a statistically significant decrease in their unsurety when evaluating audio clips and an improvement in their ability to correctly identify clips they were initially unsure about. While results are promising, future research will explore more robust and comprehensive trainings for greater impact.
Interactive Assessment of Variances of High-Resolution Model Features in Digital Twin Simulations
(ACM, 2024-11-22) Kulkarni, Chhaya; Privé, Nikki; Janeja, Vandana
Prior to the deployment of expensive instruments into orbit, spatio-temporal digital twin systems modeling the whole earth are used to study the efficacy of these instruments. However, we need to make sure that the simulated instruments have realistic characteristics (to reflect the physics of the atmosphere and limits of the instrument itself) in order for the results of the digital twin to be robust and usable. If these simulations are done accurately, the instrument can be deployed, leading to more accurate weather forecasts and climate research. This demonstration system validates the simulations, specifically the realism of remotely sensed observations. The digital twin system is a low-cost way to improve instrument design used in meteorological and climatological research. The primary goal is to show how atmospheric data can improve the development and validation of new observational systems for meteorology and climate science. We have developed an interactive variability study system that uses a dynamic platform to visualize, assess, and grasp complex atmospheric dynamics. The dashboard is built using Python for backend operations and integrates tools such as the Streamlit framework for quick web application development and the Folium library for advanced geospatial visualizations. This dashboard acts as a bridge between advanced atmospheric modeling and spatio-temporal digital twin applications, showcasing the substantial benefits of integrating comprehensive model outputs into the simulation of observational systems.
THE ROLE OF DISASTER SUBCULTURES IN LOCAL BUSINESS COMMUNITY PREPAREDNESS: A CASE STUDY OF STAKEHOLDERS IN COASTAL MONMOUTH COUNTY, NEW JERSEY
(MSAAG, 2024) Howell, Nyla; Leichenko, R.; Clemens, M.; Cann, K.; Madajewicz, M.; Solecki, W.; Kaplan, M.; Herb, J.
Extreme weather events are increasingly affecting coastal communities, often leading to economic and social disruption within these areas. The businesses located within coastal communities are especially vulnerable to climate-related shocks, yet relatively little is known about how the experience of prior disaster events affects business preparedness and planning for future extreme events. This study applies the concept of a disaster subculture to investigate whether and how prior extreme events affect climate resilience practices among small and mediumsized businesses in coastal New Jersey. The methods for the study entailed qualitative analysis of interviews conducted with businesses and related stakeholders during the Spring of 2022. The results of the study indicate that elements of four possible disaster subcultures are present in the region and that these subcultures are influencing business mitigation and preparedness practices and community recovery. A future research direction could consider disaster subculture influence on an individual level and how subcultures may influence household preparedness.
Advancing Cell-Free Manufacturing: Challenges in Scale-up and Automation Workshop Report
(NIST, 2024-11-20) Romantseva, Eugenia; Oliveira, Fernanda Piorino Macruz de; Sundberg, Chad Alan; Sittampalam, G. Sitta; Strychalski, Elizabeth
The National Institute of Standards and Technology (NIST) and the National Center for Advancing Translational Sciences (NCATS) convened the workshop Advancing Cell-free Manufacturing: Challenges in Scale-up and Automation in Rockville, Maryland in February 2024. This workshop brought together over fifty participants, representing the interests and needs of stakeholders in academic, industrial, and government settings. Together, through various plenary discussions, case studies, and working groups, participants broadly surveyed the field and focused on identifying both near-term and long-term needs to support cell-free expression systems. This report synthesizes the workshop discussion and presents actionable recommendations aimed at removing the remaining barriers to realizing the full impact of CFE systems on biomanufacturing and applications of biotechnology.
Enhanced zero-phonon line emission from an ensemble of W centers in circular and bowtie Bragg grating cavities
(De Gruyter, 2024-11-19) Veetil, Vijin Kizhake; Song, Junyeob; Namboodiri, Pradeep N.; Ebadollahi, Nikki; Chanana, Ashish; Katzenmeyer, Aaron M.; Pederson, Christian; Pomeroy, Joshua M.; Chiles, Jeffrey; Shainline, Jeffrey; Srinivasan, Kartik; Davanco, Marcelo; Pelton, Matthew
Color centers in silicon have recently gained considerable attention as single-photon sources and as spin qubit-photon interfaces. However, one of the major bottlenecks to the application of silicon color centers is their low overall brightness due to a relatively slow emission rate and poor light extraction from silicon. Here, we increase the photon collection efficiency from an ensemble of a particular kind of color center, known as W centers, by embedding them in circular Bragg grating cavities resonant with their zero-phonon-line emission. We observe a ≈5-fold enhancement in the photon collection efficiency (the fraction of photons extracted from the sample and coupled into a single-mode fiber), corresponding to an estimated ≈11-fold enhancement in the photon extraction efficiency (the fraction of photons collected by the first lens above the sample). For these cavities, we observe lifetime reduction by a factor of ≈1.3 . For W centers in resonant bowtie-shaped cavities, we observed a ≈3-fold enhancement in the photon collection efficiency, corresponding to a ≈6-fold enhancement in the photon extraction efficiency, and observed a lifetime reduction factor of ≈1.1 . The bowtie cavities thus preserve photon collection efficiency and Purcell enhancement comparable to circular cavities while providing the potential for utilizing in-plane excitation methods to develop a compact on-chip light source.
Immigrant Status and Social Ties: An Intersectional Analysis of Older Adults in the United States
(Springer Nature, 2024-11-21) Nayak, Sameera Shukanta; Mair, Christine A.; Adewuyi, Suliyat O.
Diverse social ties are critical facilitators of well-being among older adults. Social ties might be especially important for aging immigrants who face multiple social and economic vulnerabilities over the life course. We investigated social ties (e.g., partners, children, other family, and friends) by immigrant status among older adults in the United States (U.S.). Data come from the 2018 Health and Retirement Study (N?=?4,006), a national sample of older adults in the U.S. We used multivariable logistic regression to compare social ties (e.g., partners, children, other family, and friends) by immigrant status. We further explored interactions with sex and race/ethnicity. Older immigrants are more likely to report that they can rely a lot on their partners (aOR?=?1.84, 95% CI 1.27, 2.68) but less likely to rely on friends (aOR?=?0.72, 95% CI, 0.55, 0.94) compared to non-immigrants. Older immigrants are also less likely to meet frequently with friends (aOR?=?0.66, 95% CI, 0.51, 0.86) and with other family (aOR?=?0.71, 95%, CI, 0.55, 0.91) compared to non-immigrants. Lastly, older immigrant men are significantly less likely to meet with friends compared to non-immigrant men (aOR?=?0.48, 95% CI, 0.32, 0.73). As the older population in the U.S. continues to diversify and immigrant older adults navigate their support options, older immigrants–especially men–may be at risk for less variation in their social support options, particularly from extended family members and friends.
In-flight characterization of the Hyper-Angular Rainbow Polarimeter (HARP2) on the NASA PACE mission
(SPIE, 2024-11-20) McBride, Brent; Sienkiewicz, Noah; Xu, Xiaoguang; Puthukkudy, Anin; Fernandez-Borda, Roberto; Martins, J. Vanderlei
The Hyper-Angular Rainbow Polarimeter (HARP2) is a novel wide-field of view imaging polarimeter instrument on the recently-launched NASA Plankton Aerosol Cloud ocean Ecosystem (PACE) mission. Since launch on February 8 2024, HARP2 has taken over 6 months of global Earth data. In order for this data to meet scientific quality standards, we must ensure that it is as accurate as possible and over long periods of time. We use well-characterized Earth targets, such as Saharan deserts, as well as regular views of the Sun and dark frames to trend our on-orbit calibration. In this work, we discuss the preliminary performance trends derived from these activities and how well they compare with the HARP2 prelaunch calibration.
First results and on-orbit performance of the Hyper-Angular Rainbow Polarimeter (HARP2) on the PACE satellite
(SPIE, 2024-11-20) Martins, J. Vanderlei; Fernandez-Borda, Roberto; Puthukkudy, Anin; Xu, Xiaoguang; Sienkiewicz, Noah; Smith, Rachel; McBride, Brent; Dubovik, Oleg; Remer, Lorraine
The Hyper-Angular Rainbow Polarimeter-2 (HARP2) was launched on board the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, in February 2024, for the global measurement of aerosol and cloud properties as well as to provide atmospheric correction over the footprint of the Ocean Color Instrument (OCI). HARP2 is designed to collect data over a wide field of view in the cross-track direction (+/-47deg) allowing for global coverage in about two days, as well as an even wider field of view in the along-track direction (+/-54deg) providing measurements over a wide range of scattering angles. HARP2 samples 10 angles at 440, 550, and 870nm focusing on aerosol and surface retrievals, and up to 60 angles at 670nm for the hyper-angular retrieval of cloud microphysical properties. The HARP2 instrument collects three nearly identical images with linear polarizers aligned at 0°, 45°, and 90° that can be converted to push-broom images of the I, Q, and U Stokes parameters for each angle, and each wavelength. The HARP2 technology was first demonstrated with the HARP CubeSat satellite which collected a limited dataset for 2 years from 2020 to 2022. HARP2 extends these measurements to a full global coverage in two days, seven days a week.
Optimizing Daily Fantasy Baseball Lineups: A Linear Programming Approach for Enhanced Accuracy
(2024-11-17) Grody, Max; Bansal, Sandeep; Ashqar, Huthaifa
Daily fantasy baseball has shortened the life cycle of an entire fantasy season into a single day. As of today, it has become familiar with more than 10 million people around the world who participate in online fantasy. As daily fantasy continues to grow, the importance of selecting a winning lineup becomes more valuable. The purpose of this paper is to determine how accurate FanDuel current daily fantasy strategy of optimizing daily lineups are and utilize python and linear programming to build a lineup optimizer for daily fantasy sports with the goal of proposing a more accurate model to assist daily fantasy participants select a winning lineup.
Flood Risk Assessment of the National Harbor at Maryland, United States
(2024-11-17) Negussie, Neftalem; Yesserie, Addis; Harris, Chinchu; Keita, Abou; Ashqar, Huthaifa
Over the past few decades, floods have become one of the costliest natural hazards and losses have sharply escalated. Floods are an increasing problem in urban areas due to increased residential settlement along the coastline and climate change is a contributing factor to this increased frequency. In order to analyze flood risk, a model is proposed to identify the factors associated with increased flooding at a local scale. The study area includes National Harbor, MD, and the surrounding area of Fort Washington. The objective is to assess flood risk due to an increase in sea level rise for the study area of interest. The study demonstrated that coastal flood risk increased with sea level rise even though the predicted level of impact is fairly insignificant for the study area. The level of impact from increased flooding is highly dependent on the location of the properties and other topographic information.
The Complex Structure of the Abell 548–Abell 3367 Region
(MDPI, 2024-12) Henriksen, Mark; Ahmed, Layla
Archival XMM and ROSAT X-Ray data are used to investigate the structure of the Abell 548–Abell 3367 region. Based on previous optical studies, this is a region likely to be rich in structure, although studies are in disagreement regarding the connection between Abell 3367 and Abell 548. We use the available archival X-Ray data together with kinematic data of counterpart galaxies to address this question and determine the structure in this region. The region is particularly rich in X-Ray structure elongated along a SW-NE axis and consisting of numerous extended X-Ray sources. In general, the structure consists of many galaxy groups and clusters which appear segregated in X-Ray luminosity, with the least luminous ~30% toward the outer region of the clusters, possibly tracing a filament. We find evidence to suggest a supercluster of three clusters at redshifts ~0.04, 0.045, and 0.06. Several of the X-Ray sources coincident with Abell 3367 have counterpart galaxy redshifts consistent with Abell 548, while others are significantly higher. This supports the formation of a supercluster consisting of Abell 548 and Abell 3667, with the higher-redshift X-Ray source being a background object. In addition, they are part of a larger structure consisting of a previously identified cluster at redshift 0.04 and two groups at redshift ~0.06. There is also filamentary structure at z ~0.103. The ubiquity of groups in the large-scale structure suggests that they provide an environment where galaxies are in close proximity and evolution via interaction can proceed well before the galaxies make their way into the dense central region of a cluster.
Impact of Covid-19 on Taxi Industry and Travel Behavior: A Case Study on Chicago, IL
(2024-11-12) Chinthala, Naga Sireesha; Lewis, Jenell; Vuppalapati, Sravan; Sivaraman, Khiran Kumar Chidambaram; Toley, Chinmay Vivek; Ashqar, Huthaifa
As the debate over the future of transportation continues in the midst of the COVID-19 pandemic as a deepening global crisis, taxi industry seems to be not spared by the quick and disrupting changes that may arise from the pandemic. The impact is relatively higher in major cities because of the high-density population and transportation congestion. In this study, we used spatial analysis and visualization to investigate the impact of the pandemic on the economics of the taxi industry and travel behavior using trip-by-trip data from the year of 2014 to 2020 in Chicago, IL. Results show that there is a drastic decline in the trips in the central city and airport areas. During the pandemic, people tended to travel longer distances, but travel times were considerably less because of the significant reduction in traffic volumes. Results also showed that the top twenty most popular pick-up and drop-off locations only included Chicago Downtown and OHare International Airport before the pandemic. However, during the pandemic, the top twenty most popular pick-up and drop-off locations is distributed between the Airport, the Downtown, as well as many other areas along Chicago Eastside.
Enhanced Learning Outcomes with Audio in E-learning: An Analysis
(IJAC, 2024-11-05) Rautela, Vijayshree
Audio in any form whether as narration, sound effects, background music, or commentary can make e-learning more interesting for a learner. It completes the sensory engagement in a learner where the visual meets the auditory to comprehend a learning component. As an instructional designer and trainer, over the years, I have created several e-learning courses for adult learners with and without audio. The learning outcomes of the two when compared using several surveys and other evaluation mechanisms showed that learners not only prefer learning instruction that has audio but were also able to retain the learning and apply it to their jobs. This paper analyses how the level of learning outcomes is better in e-learning courses with audio than without audio. It shares some of the science and research that supports this. In addition, it describes the learner surveys and evaluation mechanisms that I have used to prove this and discusses the national and international compliances that make audio a mandate in e-learning to support of my analysis.
What is the Point? Evaluating the Structure, Color, and Semantic Traits of Computer Vision Point Clouds of Vegetation
(MDPI, 2017-04-09) Dandois, Jonathan P.; Baker, Matthew; Olano, Marc; Parker, Geoffrey G.; Ellis, Erle C.
Remote sensing of the structural and spectral traits of vegetation is being transformed by structure from motion (SFM) algorithms that combine overlapping images to produce three-dimensional (3D) red-green-blue (RGB) point clouds. However, much remains unknown about how these point clouds are used to observe vegetation, limiting the understanding of the results and future applications. Here, we examine the content and quality of SFM point cloud 3D-RGB fusion observations. An SFM algorithm using the Scale Invariant Feature Transform (SIFT) feature detector was applied to create the 3D-RGB point clouds of a single tree and forest patches. The fusion quality was evaluated using targets placed within the tree and was compared to fusion measurements from terrestrial LIDAR (TLS). K-means clustering and manual classification were used to evaluate the semantic content of SIFT features. When targets were fully visible in the images, SFM assigned color in the correct place with a high accuracy (93%). The accuracy was lower when targets were shadowed or obscured (29%). Clustering and classification revealed that the SIFT features highlighted areas that were brighter or darker than their surroundings, showing little correspondence with canopy objects like leaves or branches, though the features showed some relationship to landscape context (e.g., canopy, pavement). Therefore, the results suggest that feature detectors play a critical role in determining how vegetation is sampled by SFM. Future research should consider developing feature detectors that are optimized for vegetation mapping, including extracting elements like leaves and flowers. Features should be considered the fundamental unit of SFM mapping, like the pixel in optical imaging and the laser pulse of LIDAR. Under optimal conditions, SFM fusion accuracy exceeded that of TLS, and the two systems produced similar representations of the overall tree shape. SFM is the lower-cost solution for obtaining accurate 3D-RGB fusion measurements of the outer surfaces of vegetation, the critical zone of interaction between vegetation, light, and the atmosphere from leaf to canopy scales.
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation
(2015-07-23) Ganesan, Ashwinkumar; Brantley, Kiante; Pan, Shimei; Chen, Jian
We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.
Cross-Domain Error Correction in Personality Prediction
(IOS Press, 2016) Kılıç, Işıl Doğa Yakut; Pan, Shimei
In this paper, we analyze domain bias in automated textbased personality prediction, and proposes a novel method to correct domain bias. The proposed approach is very general since it requires neither retraining a personality prediction system using examples from a new domain, nor any knowledge of the original training data used to develop the system. We conduct several experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy.
An Empirical Study of the Effectiveness of using Sentiment Analysis Tools for Opinion Mining
(SciTePress, 2016) Ding, Tao; Pan, Shimei
Sentiment analysis is increasingly used as a tool to gauge people’s opinions on the internet. For example, sentiment analysis has been widely used in assessing people’s opinions on hotels, products (e.g., books and consumer electronics), public policies, and political candidates. However, due to the complexity in automated text analysis, today’s sentiment analysis tools are far from perfect. For example, many of them are good at detecting useful mood signals but inadequate in tracking and inferencing the relationships between different moods and different targets. As a result, if not used carefully, the results from sentiment analysis can be meaningless or even misleading. In this paper, we present an empirical analysis of the effectiveness of using existing sentiment analysis tools in assessing people’s opinions in five different domains. We also proposed several effectiveness indicators that can be computed automatically to help avoid the potential pitfalls in misusing a sentiment analysis tool.
Personalized Emphasis Framing for Persuasive Message Generation
(ACL, 2016-11) Ding, Tao; Pan, Shimei
In this paper, we present a study on personalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individuals. With this framework, we directly model content selection decisions based on a set of psychologically-motivated domainindependent personal traits including personality (e.g., extraversion) and basic human values (e.g., self-transcendence). We also demonstrate how the analysis results can be used in automated personalized content selection for persuasive message generation.
$1 Today or $2 Tomorrow? The Answer is in Your Facebook Likes
(2017-03-24) Ding, Tao; Bickel, Warren K.; Pan, Shimei
Delay discounting, a behavioral measure of impulsivity, is often used to quantify the human tendency to choose a smaller, sooner reward (e.g., $1 today) over a larger, later reward ($2 tomorrow). Delay discounting and its relation to human decision making is a hot topic in economics and behavior science since pitting the demands of long-term goals against short term desires is among the most difficult tasks in human decision making [Hirsh et al., 2008]. Previously, small-scale studies based on questionnaires were used to analyze an individual’s delay discounting rate (DDR) and his/her realworld behavior (e.g., substance abuse) [Kirby et al., 1999]. In this research, we employ large-scale social media analytics to study DDR and its relation to people’s social media behavior (e.g., Facebook Likes). We also build computational models to automatically infer DDR from Social Media Likes. Our investigation has revealed interesting results.

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