Physical Sciences and Mathematics Commons^™

Secure And Privacy-Preserving Crowdsensing Using Smart Contracts: Issues And Solutions, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

The advent of Blockchain and smart contracts is empowering many technologies and systems to automate commerce and facilitate the exchange, tracking and the provision of goods, data and services in a reliable and auditable way. Crowdsensing systems is one type of systems that have been receiving a lot of attention in the past few years. In crowdsensing systems consumer devices such as mobile phones and Internet of Things devices are used to deploy wide-scale sensor networks. We identify some of the major security and privacy issues associated with the development of crowdsensing systems based on smart contracts and Blockchain. We …

An Assessment Of Grass Regeneration Nurseries At The Western Regional Plant Introduction Station, 1994-1997, V. L. Bradley, R. C. Johnson

IGC Proceedings (1977-2023)

The Western Regional Plant Introduction Station (WRPIS), Pullman, WA, USA, maintains over 17,000 accessions of forage and turf grasses that are mostly wind cross-pollinated and highly heterogenic. Regeneration procedures have been refined over the past nine years to include improved isolation distance and increased plant populations for regeneration. The grass regeneration nurseries planted from 1994 through 1997 were evaluated using data recorded in the Germplasm Resources Information Network (GRIN) and it was found that approximately 78% of the regenerations were successful. Reasons for failures were contributed to inadequate plant number, presence of disease, seed shattering, and unsuitable growing environment. Several …

Assessment Of Ensilability Of Six Tropical Grasses Using Three Different Approaches, B. Zierenberg, K. Friedel, A. Glatzle, A. Chudy

IGC Proceedings (1977-2023)

The preparation of well-preserved silages is considered increasingly important in the tropics and subtropics. In these regions silage production played a minor role in the past, as ensilability of tropical grasses is generally considered poor. In this study ensilability of six tropical grasses grown in the Paraguayan Chaco Boreal (Panicum maximum cv. Gatton, Cynodon plectostachyus, Cynodon sp. cv. Tifton 85, Digitaria eriantha var. pentzii, Panicum maximum cv. Tanzania and Digitaria milanjiana) was assessed in three different ways: (1) The chemical determination of the ratio between water soluble carbohydrates (WSC) and buffer capacity (BC), (2) the biological …

Improving The Feeding Value Of Cotton Stalk, Wheat Straw And Rice Straw With Ozonation, Meiji Okamoto, Masaaki Hanada, R. Aoyama, R. Kim, N. Kaiga

IGC Proceedings (1977-2023)

The experiment was conducted to study the effect of ozone treatment on the feed value of cotton stalk, wheat straw and rice straw. These feeds were cut into 2 cm and 4 cm lengths, and rolled before ozonizing. The ozonizing periods were 30 min. 60min and. 120 min. The Acid Detergent Lignin (ADL) concentration of the feeds decreased with ozone treatment. Except for rice straw, the short cutting treatment (2cm) decreased the concentration of ADL and Cellulose of the ozonized cotton stalk and wheat straw. Rolling and ozone treatment were effective in decreasing the ADL concentration of cotton stalk. IVDMD …

Fermentation Quality Of Phasey Bean And Guineagrass Silages, Y. Imura, T. Namihira, Yasuhiro Kawamoto

IGC Proceedings (1977-2023)

Silages were made from guineagrass (Panicum maximum Jacq. var. maximum) and phasey bean (Macroptilium lathyroides (L.) Urb.) at three-growth stages. The silages were investigated in relation fermentation quality. Phasey bean silage showed a better fermentation quality than guineagrass silage. The latic acid to total acid ratio of phasey bean silage was higher than 500g/kg DM, and the volatile basic nitrogen to total nitrogen ratio was lower than 100g/kg. It is concluded that phasey bean is an unique legume suitable for good silage fermentation.

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Reading Level Identification Using Natural Language Processing Techniques, William Arnost, Ellen Lull, Joseph Schueder, Joseph Engler

SMU Data Science Review

This paper investigates using the Bidirectional Encoder Representations from Transformers (BERT) algorithm and lexical-syntactic features to measure readability. Readability is important in many disciplines, for functions such as selecting passages for school children, assessing the complexity of publications, and writing documentation. Text at an appropriate reading level will help make communication clear and effective. Readability is primarily measured using well-established statistical methods. Recent advances in Natural Language Processing (NLP) have had mixed success incorporating higher-level text features in a way that consistently beats established metrics. This paper contributes a readability method using a modern transformer technique and compares the results …

Predicting Power Using Time Series Analysis Of Power Generation And Consumption In Texas, Joshua Eysenbach, Bodie Franklin, Andrew J. Larsen, Joel Lindsey

SMU Data Science Review

Due to the recent power events in Texas, power forecasting has been brought national attention. Accurate demand forecasting is necessary to be sure that there is adequate power supply to meet consumer's needs. While Texas has a forecasting model created by the Electricity Reliability Council of Texas (ERCOT), constant efforts are required to ensure that the model stays at the state-of-the-art and is producing the most reliable forecasts possible. This research seeks to provide improved short- and medium-term forecasting models, bringing in state-of-the-art deep learning models to compare to ERCOT’s forecasts. A model that is more accurate than ERCOT’s own …

Emotion Integrated Music Recommendation System Using Generative Adversarial Networks, Mrinmoy Bhaumik, Patrica U. Attah, Faizan Javed

SMU Data Science Review

Music can stimulate emotions within us; hence is often called the “language of emotion.” This study explores emotion as an additional feature in generating a playlist with a deep learning model to improve the current music recommendation system. This study will sample emotions from certain subjects for each song in a sample of the data. Since the effect of music on emotion is subjective and is different person to person, this study would need a considerable number of subjects to reduce subjectivity. Due to the limited resources, a portion of the data will be labeled with emotion from subjects and …

Alternative Methods For Deriving Emotion Metrics In The Spotify® Recommendation Algorithm, Ronald M. Sherga Jr., David Wei, Neil Benson, Faizan Javed

SMU Data Science Review

Spotify's® recommendation algorithm tailors music offerings to create a unique listening experience for each user. Though what this recommender does is highly impressive, there is always room for improvement given that these techniques are not fully prescient. This study posits that in addition to creating certain features based on audio analysis, incorporating new features derived from album art color as well as lyrical sentiment analysis may provide additional value to the end user. This team did not find that a significant difference existed between color valence and Spotify® valence; however, all other comparisons resulted in statistically significant difference of means …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Covid-19 - A Graph Network Approach, Nibhrat Lohia, Rajesh Satluri, Suchismita Moharana, Venkat Kasarla

SMU Data Science Review

The effects of COVID-19 and its spreads are attributed to various factors. This study uses CDC open-source data on COVID-19 effected population with features ranging from location to ethnicity, to create a Knowledge Graph to measure the similarity between COVID-19 cases and estimate the risk for people likely affected by COVID-19. This data could be used to find correlations between distinct factors, like ethnicity and pre-existing health conditions, to find the vulnerability of a given COVID-19 patient. Using the Jaccard similarity coefficient, in the knowledge graph, we are able to identify and explore relationships between COVID-19 cases as well as …

Rocket Learn, Daanesh Ibrahim, Jules Stacy, David Stroud, Yusi Zhang

SMU Data Science Review

Abstract. This paper covers the development, testing, and implementation of Reinforcement Learning methods designed to autonomously learn and optimize Rocket League play. This study aims to analyze and benchmark model frameworks commonly used in Reinforcement Learning applications. These models can be applied to tasks ranging in difficulty from simple to superhumanly complex, and this study will begin with and build upon simple models performing simple tasks. It will result in complex models performing difficult tasks. Models will be allowed to train autonomously on the game using mass parallelization to expedite training times with the goal of maximizing reward function scores. …

Real-Time Voice Biometric Speaker Verification, Inderbir Dhillon, Jason Rupp, Aniketh Vankina, Robert Slater

SMU Data Science Review

Abstract. Automated speaker verification has been an area of increased research in the last few years, with a special interest in metric learning approaches that compute distances between speaker voiceprints. In this paper, three metric learning systems are built and compared in a one-shot speaker verification task using contrastive max-margin loss, triplet loss, and quadruplet loss. For all the models, spectrograms are created from speaker audio. Convolutional Neural Network embedding layers are trained to produce compact voiceprints that allow users to be distinguished using distance calculations. Performances of the three models were similar, but the model with the best EER …

Pokégan: P2p (Pet To Pokémon) Stylizer, Michael B. Hedge, Morgan Nelson, Thomas Pengilly, Michael Weatherford

SMU Data Science Review

This paper covers the development, testing, and implementation of an automatic framework for converting common images of pets into a Pokémon cartoon with the style of a Pokémon trading card. The technique will first implement object detection for common animals to facilitate image segmentation and apply the appropriate style transfer model to ensure the most aesthetic stylization. It explores various methods to address artifacts in the results of common neural style transfer techniques using Generative Adversarial Networks (GANs). This research sets up a framework to create an app that converts user-submitted pet pictures to Pokémon styled images using the most …

Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo

SMU Data Science Review

Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …

Urban Traffic Simulation: Network And Demand Representation Impacts On Congestion Metrics, Aaron Faltesek, Balasubramaniam Dakshinamoorthi, Sreeni Prabhala, Akbar Thobani, Anu Kuncheria, Jane Macfarlane

SMU Data Science Review

Traffic simulations are often used by city planners as a basis for predicting the impact of policies, plans, and operations. The complexities underpinning traffic simulations are often not described in detail yet can significantly impact the simulation outcome. Conflating underlying data for simulations is complex and hinders the interest in this type of exploration. This paper aims to elucidate critical features of traffic simulations that drive the generated metrics of the modeled urban environment. Specifically, this paper examines differences in two road graph networks for the metropolitan region of Houston, TX: a reduced network composed of 45,675 road links and …

Intelligent Investment Portfolio Management Using Time-Series Analytics And Deep Reinforcement Learning, Sachin Chavan, Pradeep Kumar, Tom Gianelle

SMU Data Science Review

Abstract. – With globalization, the capital markets have exploded in size and value, making them exceedingly difficult to predict. These days the public has access to real-time data of the market-leading to more participation. As a positive step, this might lead to better wealth distribution in the society, and it also adds to the random nature of the market, making it more unpredictable. The portfolio accounts consisting of stocks and bonds are considered serious investment assets. They can make or break a person’s future. It is also a way of shielding one against market risk or rising inflation. These accounts, …

Identifying Vacant Lots To Reduce Violent Crime In Dallas, Texas, Laura Lazarescou, Andrew Mejia, Tina Pai, Sabrina Purvis, Robert Slater, Owen Wilson-Chavez

SMU Data Science Review

Vacant lots have been associated with community violence for many years. Researchers have confirmed a positive correlation between vacant lots and vacant buildings with increased violence in urban and rural geographies. However, identifying vacant lots has been a challenge, and modeling methods were largely manual and time-intensive. This prevented cities and non-profit organizations from acting on the information since it was expensive and high-risk to develop remediation programs without clearly understanding where or how many vacant lots existed.

The primary objective of this study was to provide a predictive model that accelerates and improves the accuracy of prior land classification …

Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods, Joshua Balson, Matt Chinchilla, Cam Lu, Jeff Washburn, Nibhrat Lohia

SMU Data Science Review

Across the United States, record numbers of wildfires are observed costing billions of dollars in property damage, polluting the environment, and putting lives at risk. The ability of emergency management professionals, city planners, and private entities such as insurance companies to determine if an area is at higher risk of a fire breaking out has never been greater. This paper proposes a novel methodology for identifying and characterizing zones with increased risks of forest fires. Methods involving machine learning techniques use the widely available and recorded data, thus making it possible to implement the tool quickly.

Qualitative Leveraging Natural Language Processing To Establish Judge Incrimination Statistics To Educate Voters In Re-Elections, Aurian Ghaemmaghami, Paul Huggins, Grace Lang, Julia Layne, Robert Slater

SMU Data Science Review

The prevalence of data has given consumers the power to make informed choices based off reviews, ratings, and descriptive statistics. However, when a local judge is coming up for re-election there is not any available data that aids voters in making data-driven decision on their vote. Currently court docket data is stored in text or PDFs with very little uniformity. Scaling the collection of this information could prove to be complicated and tiresome. There is a demand for an automated, intelligent system that can extract and organize useful information from the datasets. This paper covers the process of web scraping …

The Fierce Green Fire: Vol. 12 Issue 9, Wofford College. Department Of Environmental Studies

The Fierce Green Fire

No abstract provided.

The Emission Spectrum Of The Hot Jupiter Wasp-79b From Hst/Wfc3, Trevor O. Foote, Nikole K. Lewis, Brian M. Kilpatrick, Jayesh M. Goyal, Giovanni Bruno, Hannah R. Wakeford, Nina Robbins-Blanch, Tiffany Kataria, Ryan J. Macdonald, Mercedes López-Morales, David K. Sing, Thomas Mikal-Evans, Vincent Bourrier, Gregory Henry, Lars A. Buchhave

Information Systems and Engineering Management Research Publications

Here we present a thermal emission spectrum of WASP-79b, obtained via Hubble Space Telescope Wide Field Camera 3 G141 observations as part of the PanCET program. As we did not observe the ingress or egress of WASP-79b's secondary eclipse, we consider two scenarios: a fixed mid-eclipse time based on the expected occurrence time, and a mid-eclipse time as a free parameter. In both scenarios, we can measure thermal emission from WASP-79b from 1.1 to 1.7 μm at 2.4σ confidence consistent with a 1900 K brightness temperature for the planet. We combine our observations with Spitzer dayside photometry (3.6 and 4.5 …

The Effects Of Hydrologic Heterogeneity On Harmful Algal Blooms In Freshwater Reservoir, Lake Sinclair, Georgia, Margaret Blackledge

Biology Theses

Aquatic habitats are frequently studied after a major water quality problem like the occurrence of an algal bloom. In this study, A proactive rather than a reactive response was considered, where the complexity of conditions conducive to uncontrolled cell growth were studied before a bloom took place by sampling regularly. This study aimed to monitor water quality by monthly sampling of algal communities for approximately one year. As the base of the aquatic food web, algae are a highly diverse group of organisms with varying sensitivity to physical and chemical changes in the environment. Four shallow sites were monitored at …

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz

Computer Science Technical Reports

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or …

Epa Region 8 Qa Document Review Crosswalk, Trec Inc., A Woodard And Curran Company, Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Residential Metals Abatement Program Quality Assurance Project Plan (Non-Residential Parcels – Indoor Dust), Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Developing Techniques For The Identification Of Non-Canonical Rna Pairing And Analysis Of Lc-Ms Datasets, Christopher Jurich

Department of Chemistry: Dissertations, Theses, and Student Research

Non-canonical pairing dynamics in ribonucleic acid (RNA) structureand statistical analysis of metabolomics liquid chromatography mass spectrometry (LC-MS) datasets are two difficult problems that stand as open challenges.

RNA folding algorithms are used across a variety of disciplines to predict structures when experimental elucidation techniques are inconvenient or impractical. Though successful and widely adopted, folding algorithms make simplifying assumptions for loop regions due to their complex interactions and associated difficulty with generating energy parameters for relevant non-canonical pairing interactions. Modeling assumptions and a lack of energy parameters for loops limit accuracy in these functional critical regions of RNA. This work describes …

Numerical Study Of The Seiqr Model For Covid-19, Caitlin Holt

Departmental Honors & Graduate Capstone Projects

In this research project, we used numerical methods to investigate trends in the susceptible, exposed, infectious, quarantined, recovered, closed cases and insusceptible populations for the COVID-19 pandemic in 2021. We used the SEIQR model containing seven ordinary differential equations, based on the SIR model for epidemics. An analytical solution was derived from a simplified version of the model, created by making various assumptions about the original model. Numerical solutions were generated for the first 100 days of 2021 using algorithms based on Euler's Method, Runge-Kutta Method, and Multistep Methods. Our goal is to show that numerical methods can help us …

Uniting Non-Empirical And Semi-Empirical Density Functional Approximation Strategies Using Constraint-Based Regularization, Trine K. Quady

University Honors Theses

In this work, we present a general framework that unites the two primary strategies for constructing density functional approximations (DFAs): non-empirical (NE) constraint satisfaction and semi-empirical (SE) data-driven optimization. The proposed method employs B-splines--bell-shaped spline functions with compact support--to construct each inhomogeneity correction factor (ICF). This choice offers several distinct advantages over a polynomial basis by enabling explicit enforcement of linear and non-linear constraints as well as ICF smoothness using Tikhonov regularization and penalized B-splines (P-splines). As proof of concept, we use this approach to construct CASE21--a Constrained And Smoothed semi-Empirical global hybrid generalized gradient approximation that completely satisfies all …