ikatkov/journal-of-student-research-hs-articles huggingface dataset:
Automatically Labeling Offensive Formations in American Football Film Using Deep Learning
Authors: Kyle Zhou; Jason Galbraith
Date: 02-28-2023

Web services for storing, annotating, and sharing sports videos by high-school athletic teams have become more prevalent in recent years. However, most services lack the ability for coaches to automatically tag film, leading to many hours of manual annotation. For American football videos, coaches need to label formations, plays, and field positions in order to extract insights and create strategic game plans. This paper presents an end-to-end machine learning pipeline for automatically labeling American football offensive formations in videos. The pipeline includes pre-processing of videos, image classification, and a novel inference approach. The study compares a custom CNN model with pre-trained image classifier models using transfer learning. Specifically, CNN-based architectures (MobileNet, Inception, EfficientNet, etc.) and a transformer-based Vision Transformer (ViT) are compared. All models are trained on ~1400 images with the three most popular formation labels extracted from video clips of high-school football team games. The results show that several models, including the custom CNN model, achieved greater than 90% classification accuracy on the test dataset. The inference is performed by sampling multiple frames from a video clip, passing them through a trained image classifier, and taking a majority vote on the classification results to determine the final outcome. Our study found that using a sampling rate of 0.5 seconds, starting at 1 second, and taking five frames yields the highest inference accuracy of 95.4% using a trained customized CNN model. This system can assist all levels of football coaches in the analysis of game footage and formation identification.
The Role of Morphology and Heart Rate Variability Features in Detecting Arrhythmias from Short ECGs
Authors: Alice Hu; Shadi Ghiasi
Date: 02-28-2023

Detecting heart arrhythmias from short Electrocardiogram (ECG) recordings remains challenging since recordings are short and contaminated by noise. ECG morphology features and Heart Rate Variability (HRV) time and frequency domain features are widely used for classifying short ECG recordings. Here we investigate the relative roles of ECG morphology features and HRV time and frequency domain features in classifying short ECG recordings provided by the 2017 PhysioNet/Computing in Cardiology Challenge. The classification is performed separately by four machine learning models: Logistic Regression, Decision Tree, K Nearest Neighbors, and Convolutional Neural Network (CNN). Our best classification score is obtained using the deep learning 1-dimensional CNN model trained on HRV time domain features combined with ECG morphology features. It gives an overall F1 score of 0.70 and 0.73 for the cross validation and hidden test respectively when considering the average classification performance over all 4 categories: Atrial Fibrillation (AF), normal, other arrhythmias, and noisy signal. We found that HRV time domain features play an important role in detecting AF, normal, and other classes, whereas ECG morphology features play a key role in detecting the noisy class. When HRV frequency domain features are combined with HRV time domain features, they do not improve and often degrade classifications of short ECG recordings compared to classifications using only HRV time domain features. Combining ECG morphology features with HRV time domain features leads to a better classification performance for short ECG recordings. Feature-based deep learning could serve as a viable and less expensive approach for ECG classifications.
A Sentiment Analysis on Roe v. Wade Twitter Data
Authors: Hyewon Park; Monika Richardson
Date: 02-28-2023

In May of 2022, the US Supreme Court case Dobbs v. Jackson Women’s Health Organization threatened to and did overturn Roe v. Wade, the historic 1973 Supreme Court case that created prenatal healthcare guidelines, including specifications for regulation of abortion. This work aims to investigate tweets on Roe v. Wade during a period of interest through posts on Twitter to provide an understanding of general public opinion. This was done primarily by running sentiment analysis on a dataset of 35,999 tweets from May 1st, 2022 to July 8th, 2022 from the #RoeVWade, separating them into negative, positive, and neutral sentiments, then finding collocations and often used hashtags in each category. Although the sentiment analysis model used classified sentiment rather than “pro-choice” versus “pro-life,” negative and neutral tweets were more likely to use phrases associated with “pro-choice” beliefs. In contrast, positive tweets often used hashtags aligned with Republican or conservative views.
Building and Interpreting Clausal Possession in Isbukun Bunun
Authors: Hung-Chi Chen; Kuo-Chiao Lin
Date: 02-28-2023

This paper shows that clausal possession in Isbukun Bunun, a Formosan language, involves two distinct structures with one single thematic interpretation. Although thematically identical, the two distinct structures are not derived via agreement/movement from a single underlying structure. To best capture the (morpho)syntactic patterns within and beyond the phenomenon, the too-many-structures problem is best analyzed as involving distinct underlying structures with syntactic heads that are semantically null, and delayed saturation of thematic relations at the (morpho)syntax-semantics interface.
A Machine Learning Approach to Finding Factors that Lead to Environmental Friendliness
Authors: Sucheer Maddury; Anjana Manian
Date: 02-28-2023

                To maintain a sustainable society, environmental friendliness is necessary, an effort that all countries must take part in. The effort must be pioneered by developed nations with the resources to enact sustainable policies, reduce emissions and conserve energy, from which developing nations will follow the eroded path. Recognizing the factors that promote environmental friendliness is necessary for researchers, policymakers, and activists alike.                 Several past studies have examined the relationship between environmental performance and various nationwide factors such as economic strength, education, and corruption. In this paper, however, we introduce the machine learning approach Multiple-Linear Regression, allowing several variables to be used in tandem. We constructed a dataset using a variety of variables from a variety of sources, either examined in past literature or justified logically. We measured environmental friendliness through the Environmental Performance Index (EPI), and chose feature variables of Women in Parliament (%), Internet users (%), Freedom Index, Ethnic fractionalization, Technological development, Press Freedom Index, Corruption Perceptions Index, GDP per capita ($), and Education Index, and Population.                 We found that Multiple-Linear Regression is an effective way of measuring EPI, where several metrics indicate that EPI is almost completely determined by the feature variables. We end the study by presenting the correlations of each of the variables with EPI, and find that almost all exhibit strong linear relationships. These correlations should bring light to the characteristics of environmentally friendly countries, mainly Nordic nations.
Analyzing Properties of Monolayer MoS2 Using RHEED and Ultrafast Electron Diffraction
Authors: Calvin Chiu, Thomas Liang
Date: 02-28-2023

Reflection high-energy electron diffraction (RHEED) and ultrafast electron diffraction (UED) are techniques used to characterize crystal structures both statically and dynamically. These experimental methods are of academic interest due to their ability to visualize crystal structures on the atomic level and analyze dynamic changes on the picosecond scale. In this experiment, RHEED and UED are implemented to analyze monolayer molybdenum disulfide (MoS2), a compound that may contribute to the future of microelectronics. Images of various diffraction patterns are presented, and analysis is conducted on diffraction peaks, lattice spacing, and photoinduced intensity changes.
NanoMedicine; An Analysis of Hydrogels Scaffolds for Tissue Engineering Applications
Authors: Ananya Saridena, Abhaya Saridena; Jothsna Kethar
Date: 02-28-2023

Tissue damage and disease impose a significant burden on individuals, society, and the economy. Current tissue engineering techniques have limitations, and improvements are necessary to more effectively repair or regenerate damaged or diseased tissues. Hydrogels, a type of synthetic scaffold, have gained increasing attention in tissue engineering due to their mechanical support for cells, customizable characteristics, and potential to overcome challenges in specific applications. However, the full extent of their effectiveness and capabilities in tissue engineering is not yet determined. This research paper aims to analyze the uses and capabilities of hydrogels in tissue engineering through a meta-analysis of various studies, contributing to the advancement of regenerative medicine. Hydrogels present a versatile and promising scaffold for tissue engineering, with the potential to significantly improve the field of regenerative medicine.
Disentanglement of Latent Factors of Real and Fake Appearance for Deepfake Face Manipulation Detection
Authors: Suh-Yoon Hong, Dayul Park; GeunJung Yi
Date: 02-28-2023

A deepfake video is a video in which generative models are used to alter the facial features to make the subject appear to be a different person. There are various ways to utilize such content, including those that are positive such as entertainment. However, it is also very easy to exploit deepfake videos for harmful use, including for spreading fake news or creating unwanted content. Thus there have been numerous attempts to detect whether a video has been manipulated using deepfake technology so as to prevent further harm. Previous approaches for achieving this purpose have attempted to detect discrepancies in the video frames through the use of techniques such as exploiting the temporal consistency between each frame with convolutional neural networks. Though this has produced adequate results, its accuracy is insufficient for real-world use. In this paper, we propose a novel method of using a convolutional neural network based autoencoder to detect whether a video is pristine or deepfake. Our method successfully disentangles latent factors of real and fake appearance to increase the classification accuracy while maintaining a relatively low time complexity, enhancing real-world applicability. Results from extensive experimentation show significant improvement from state-of-the-art-methods by upwards of 18.51%.
Extended Kalman Filter State Estimation for Autonomous Competition Robots
Authors: Ethan Kou; Acshi Haggenmiller
Date: 02-28-2023

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.
Comparing Machine Learning Models to Determine Which is Most Effective at Detecting Brain Tumors
Authors: Samya Chauhan; Shreya Parchure, Jenna Scott
Date: 02-28-2023

In this project, by using machine learning techniques to analyze various brain tumor scans, the goal was to determine which techniques are the most efficient and accurate in determining the presence of brain tumors. Specifically, the goal was to adequately process a dataset to determine if brain tumors can be detected with more than 75% accuracy using machine learning. The chosen dataset includes just over 250 axial MRI brain scans, thus providing a sufficient dataset to properly analyze. This research is most applicable to the healthcare field, specifically relating to the work done by neurologists and radiologists. If the most efficient and accurate way to detect brain tumors is not determined soon, individuals may have to continue waiting longer than necessary for their results. In this project, K-Nearest Neighbors, Decision Trees, and Muti-Layer Perceptron Models were compared to determine which machine learning algorithm is most effective at determining the presence of a brain tumor in an axial brain scan.
How to Be a Perfect Woman: The Evolution of Italian Renaissance Female Portraiture and Decorum
Authors: Yiyuan Xue
Date: 02-28-2023

Renaissance portraiture represents more than the sitters. These images portray complex conceptualizations of identities, layered amalgams of the personal and political; dynastic and economic; amorous and pious; and present, future, and eternal. In the late fifteenth century, side profile portraits were the norm, characterized by the superiority of aloofness and nonchalance. Typically commissioned by husbands to celebrate marriages, these portraits memorialize the brides’ domesticity, piety, and above all, chastity. With Leonardo da Vinci’s groundbreaking Mona Lisa of ca. 1503 came the revolutionary change in female portrait composition, widely imitated by masters of the time. Females portrayed after this transition engaged the viewers directly, deviating from the previous detachment. These portraits are focused on harmony, with the prevalence of triangular formations along with scenic backdrops. The early sixteenth century saw another shift in female portrait ideals: artists were no longer constrained by traditional female decorum, but rather focused on exploring feminine sexuality. Belle Donne paintings and various counterparts grant viewers intimate insights into the lives of Renaissance women, whose sexuality was celebrated in the private sphere but remained taboo in public. Eventually, as more female art patrons rose to power in the mid-sixteenth century, the practice of female portraiture ceased to be dominated by male patriarchy, becoming rather a part of the female sphere. In this paper, I explored the evolution of female portraiture in chronological order, tracing the shift in feminine decorum along the way, ending with a survey of the connection between the art world and the literary realm.
Permanent Time as an Alternative to Counter the Detrimental Effects of DST on Canadian Society
Authors: Chengtai Li
Date: 02-28-2023

Daylight saving time (DST) is a practice that shifts the perceived time one hour forward in the spring and one hour behind in the fall, effectively granting more sunlight in the evenings of springs and summers. In recent years, an increasing number of people have been dissatisfied with this method of keeping the time for a variety of reasons. To begin with, DST no longer saves energy because artificial illumination no longer contributes to the majority of energy expenditure in the current Canadian society. Secondly, DST affects many different forms of economy, varying from farming and manufacturing to family owned businesses. DST also damages the well-being of Canadians by increasing the risks of health problems from every time change. From an online survey study to investigate the public opinion on the timekeeping ways, most participants have shown that they do not want the current DST arrangement; Around three-quarters indicated that they prefer one of the permanent time schemes, one that they do not need to change clocks twice a year. The method to keep time is one that affects everyone, and may have major impacts on the socioeconomic well-being of a society. Therefore, it is imperative that the significance of the current DST scheme on society be clarified and the people’s opinions be heard in subsequent survey studies that include more participants.
Combining CAR T-cell and immune checkpoint inhibitor therapy, a promising, yet unproven immuno-oncology approach
Authors: Kshetra Polavarapu; Thomas Shroka
Date: 02-28-2023

Chimeric antigen receptor (CAR) T-cell therapy is a method that extracts T cells from the patient's blood and virally introduces a genetically engineered T cell receptor targeting a specific cancer antigen and subsequently readministering these genetically engineered CAR T cells to the patient. These T cells are then better at identifying the tumors and attaching to these tumor cells resulting in a stronger cytotoxic immune response. The development of CAR T cells has been a huge success as an immunotherapy, especially for the targeting of non-solid tumors. Since their original inception in 1987, there are now six independent FDA approved CAR T cell therapies targeting a variety of blood cancers, with the first being approved in 2017. As a relatively new treatment, there is a continuous effort in improving the safety and efficacy of CAR T cell therapies. As mentioned previously, CAR T cells have undoubtedly been successful in the treatment of non-solid tumors, however their efficacy towards treatment of solid tumors has been limited. Additionally, the safety and long-term effects of CAR T cell treatments is still a concern. Combination therapy utilizing CAR-T cells and immune checkpoint inhibitors is being explored to potentially mitigate some of the limitations associated with CAR-T cells.
Corporate Sustainability in the Fashion Industry
Authors: Ethan Qiu; Tyler Vitone
Date: 02-28-2023

3.6 billion metric tons of carbon dioxide emissions. 342 million barrels of petroleum to produce plastic-based fibers (Wired, 2022). And 92 million tons of textile waste (Igini, 2022). This all stems from the fashion industry on a yearly basis. Climate change is the most pressing issue in our society today and as it progresses, comes a heavier emphasis on sustainability and environmental consciousness. Effective ESG (Environmental, Social, and Corporate Governance) has been shown to affect overall profit by 60% (Henisz, 2019). With this, companies are taking advantage of and capitalizing on the new sustainability “trend”, marketing themselves as corporately responsible and committed to environmental causes.&nbsp;&nbsp;  As we are dependent on the fashion industry for clothing and contributing to the immense impact of global warming, consumers need to be educated on the true nature of the fashion industry and the extent of its impacts. As such, this research paper will provide comprehensive information for consumers to navigate through the fashion industry and ultimately be more mindful in their purchasing decisions. This project explores the role of the government, corporations, and consumers in modern sustainability practices and dives into the fashion industry, building upon the abstract concepts discussed. This includes evaluating the business models of the fashion industry in response to market trends and discussing how it impacts overall environmental sustainability. The research paper concludes by thoroughly proposing best practices firms can adopt to improve upon their corporate responsibility, simultaneously bettering the environment and business performance.&nbsp;
Efficacy of virtual reality treatment with NIBS for Schizophrenia
Authors: Sashvathkumar Krishnakumar; Jothsna Kethar
Date: 02-28-2023

Technology plays a more significant role in healthcare today than it has ever done before. Technologies such as Artificial Intelligence and neurotechnology have advanced healthcare by leaps and bounds that wouldn’t have even been dreamt about 50 years ago. In recent years, virtual reality technology has advanced along with these technologies and medical professionals have taken an interest in its applications, especially in the treatment of treatment-resistant mental health conditions. Research on whether virtual reality is a viable option for psychotherapy is an ongoing process that is highly influential in the medical world. VR enables therapies that may be more accessible, need less delivery time, and have more ecological validity. While NIBS has the ability to directly modify activity in social brain regions in order to promote neuroplasticity, build neural connections, and improve brain function linked to social cognitive behaviors. This paper will aim to examine the benefits virtual reality along with NIBS (Non-invasive Brain Stimulation) has over today’s conventional treatment method for Schizophrenia through analyzing past case studies and discussing it in the context of a new virtual technology, Metaverse. As a result, it was concluded that virtual reality treatment is a better alternative to standard treatment today.&nbsp;
The Dymaxion Dwelling Machine: R. Buckminster Fuller’s Vision to House All of Humanity
Authors: Blake Lee
Date: 02-28-2023

Traditional housing is, and has for many centuries, been uniquely made for their individual clients, making houses difficult to mass-produce and, sometimes, too expensive to provide for those who need it most. R. Buckminster Fuller, an architect who believed that we had an obligation to provide adequate housing, attempted to address this challenge almost a century ago through his creation, the Dymaxion Dwelling Machine. The Dymaxion (a portmanteau of Fuller’s favorite words: dynamic, maximum, and tension) Dwelling Machine was a dome-shaped home optimized for mass-production, and it maintained a higher rate of efficiency and sustainability than the standard cube-shaped home. This paper will analyze several methods and facets that Fuller implemented within this atypical house, such as the Dymaxion Bathroom, Fog Gun, tensegrity, cooling/heating, autonomy, affordability, materials, maintenance, and construction, that inspired sustainable, versatile, and affordable solutions that have contributed to addressing the climate and inadequate housing crises of today.&nbsp;  &nbsp;  “You never change things by fighting the existing reality.&nbsp;  To change something, build a new model that makes the existing model obsolete.”
A Hybrid CNN-LSTM Model for Predicting Solar Cycle 25
Authors: Alice Hu; Antonio Rodriguez
Date: 02-28-2023

The solar cycle is linked to the number of sunspots and follows the fluctuations of the Sun’s magnetic field. It can have powerful global impacts on the Earth. Thus, predicting the timing and amplitude of the peak of the incoming solar cycle 25 is of great importance. This study uses a hybrid deep learning convolutional neural network (CNN) - long short-term memory (LSTM) model and the observed 13-month smoothed sunspot numbers to predict Solar Cycle 25. Here it is shown for the first time that the MinMax normalization method substantially reduces the error of the CNN-LSTM model’s solar cycle predictions compared to the Standard Deviation normalization method. The results also suggest that it is best to use four historical solar cycles to predict the future solar cycle. The predicted Solar Cycle 25 has a 13-month smoothed peak amplitude similar to that of Solar Cycle 24. The predicted Solar Cycle 25 peak spans a relatively long period of time between approximately August 2023 and July 2024.