ikatkov/journal-of-student-research-hs-articles huggingface dataset:
Predicting the 2022 Australian Federal Election with Actuarial Methodologies
Authors: Sumedh Kundu; Stuart Madgwick
Date: 02-28-2022

Election forecasting has, for a long time, been considered a major tool used by voters and politicians alike to understand the electoral mood. In Australia, tools such as opinion polling even drive many policy decisions made by governments. Yet, these same tools have been misleading and/or wrong in recent elections. The consequences of this have meant large fiscal loss for bookmakers, rash policy decisions, inappropriate leadership changes, and, recently, erosion of trust in the democratic process. Hence, we must look at new ways to analyse elections, and use a more appropriate system to mimic the actual voting system in the country where the election is being held. For example, in representative democracies with strong minor party representation like Australia, any forecasting method used must not use two-party preferred on a national basis, and, instead, analyse preference flows, and first preferences, for each seat. This study uses actuarial techniques to analyse probabilities of parties winning each of the 151 seats in the 2022 Election, and then calculates final party totals based on these probabilities.  &nbsp;  This study aims to accurately predict the 2022 Election using a methodology which will enable a final forecast which is not misleading and is, in fact, appropriate for a representative democracy. Data used includes first preferences and Preference Flow data from the 2019 Election, Demographic data from the 2016 Census, Economic Data across 2019-22, and Opinion Polls from The Australian and Roy Morgan. Further, political insight will be used to discuss these results and the potential variances.
A Comparative analysis of War Propaganda  Words Stronger than Guns
Authors: Alex Han; Gregory Slosek
Date: 02-28-2022

This paper analyzes propaganda leaflets in different wars. The importance of propaganda and its use and creation in different wars indicates its significance in terms of success. Every war has used certain different tactics and strategies in wars to win and influence the people through their war strategies and different types of leaflets. Following wars are discussed with their powerful propaganda during the war. For instance, the Korean war propaganda leaflets, German propaganda leaflets, Franco-Prussian War 1870 propaganda leaflets, Vietnam war 1954-75 propaganda, World War I Propaganda, and Spanish American War Propaganda. Propaganda has been used in these wars through different method either through leaflets, posters, music, through women, or yellow journalism etc. diverse impact of different propagandas were witnessed on the civilians and soldiers in these wars. That means that propaganda has remain an important element of war and has its strong crucial role in the winning war.
Machine Learning as a Tool to the Diagnosis of Diabetes
Authors: Mr. Yosef Granillo; Guillermo H. Goldsztein
Date: 02-28-2022

Machine learning is the field of computer science that uses data to make predictions and decisions. The problem we consider in this article belongs to the class known as supervised learning and the technique we use is logistic regression. After explaining supervised learning and logistic regression, we use a data set to develop a computational model able to give a diabetes diagnosis to patients. We discuss the accuracy of the model developed.
AOS: Anti-Obesity System With Deep Learning-based Classification Model Using a Novel Data Augmentation Technique
Authors: Sungmin Kim; Kyoung-Hyoun Kim
Date: 02-28-2022

Obesity has been a worldwide leading cause of associated health risks for decades, yet not much is being done to solve this issue. Previously, minimal efforts have been made to address this issue in regards to mandating nutrition labels on the back of packaged foods. However, the mundane and growingly elusive packing labels have diminished the proper effects of this constitution as people rarely ever take the time nor effort to thoroughly read through the labels. Moreover, most dishes served from restaurants do not serve with any nutritional information, which places many of the people dining out in complete darkness of what they are truly consuming. To solve this problem, we propose the deep learning-based AOS (Anti-Obesity System), which analyzes images of common junk foods and unhealthy meals to classify the category of food. The proposed system consists of a deep learning-based classifier and a post-processing module that outputs relevant nutritional information regarding each category of food. In addition, we propose a novel data augmentation technique in order to make the trained model produce better results. We also conduct the ablation study to experimentally prove that the proposed method enforces the model to work better in real-world situations. We achieve an AP of 59.30% on the proposed food dataset. Code will be available at https://github.com/smkim0508/ObesityNutritionClassifier.git.
The Persistence of Human Capital over Time: Evidence from a Positive Immigration Shock
Authors: Anoushka Khatri
Date: 02-28-2022

In this paper, I study the long-run effects of a positive shock to the labor force in Sao Paulo. Using a dataset based on Brazil and an estimation strategy based on regression analysis, I show both a positive direct and indirect effect of settlement on human capital. Being a recipient of highly skilled immigrants in 1872 is found to be positively associated with a rise in literacy rates, as well as higher per capita income and access to running water.
The Effects of Closed-door Baseball Games
Authors: Nathan Hong; Danilo Tadeo
Date: 02-28-2022

With the ongoing trend of closed-door baseball games, we researched to discover the actual effect of the crowd during this isolated season. Taking advantage of the current data provided by three reliable sports-related websites, we obtained significant results comparing previous years’ home team winning percentage data to this year’s. Initially, we found that the overall home team winning percentage of 2020 (the crowd-less season) was lower than that of the previous year (with crowds). From this finding, additional analyses were conducted, each controlling for another different factor of home winning percentage. Analysis 1 found that holding for skill difference amongst teams, the matchup of teams with a similar skill level showed a positive effect of crowds, aiding the home team wins. Analysis 2 found that home-field advantage of home teams in 2020 had decreased compared to 2019, proving once again that crowd does have a positive effect on home winning percentage. Thus, it was ultimately determined through the multiple qualifications and subsequent tests that there was evidence of a positive correlation between crowds and increased home winning.
Correlating Anxiety and Risk Perceptions of Future Academic Prospects Among High-Achieving Students
Authors: Joanne Ji; Mark Porto, Stacy Gil
Date: 02-28-2022

The twenty-first century is redefining the boundaries of adolescent mental health with its “epidemic of anxiety.” As a psychologically vulnerable demographic, academically intensive high school students are as unrivaled in their anxiety levels as they are in their ambition, which inflates their fear factors and undermines their personal values as well. In this paper, I expand on this subject by specifically exploring high-achieving students’ risk perceptions of their future academic prospects and how they correlate with symptoms of generalized anxiety disorder (GAD). This study dives into the inquiry by utilizing quantitative survey research followed by a mixed cross-examination analysis. A total of 47 Advanced Placement high schoolers responded to the survey, and, based on the final numerical values assigned to their anxiety and risk perception levels, I calculated several correlation coefficients to interpret the results. My findings demonstrated that there was a strong positive correlation between anxiety and risk perceptions of academic prospects among high-achieving students. Despite their superlative academic statistics, results showed that many of the participants not only had high anxiety levels but also perceived a substantial amount of risk for their future success. This target population comprises only a fraction of teenagers who experience such symptoms, but this study presents how they uphold negative trends in anxiety-related disorders that require more attention and research in the field of psychology as a whole.
To Transition to a Green Economy: Understanding World CO2 Greenhouse Gas Solutions to Climate Change
Authors: Neil Tamhankar; Smile Dube
Date: 02-28-2022

The importance of shifting to a green economy is clearly established within the UN Social Development Goal (SDG) 13.&nbsp;SDG 13 focuses on combating climate change and its impacts; the UN reports that over 197 parties have signed the UN document. However, the green economy definition is so broad that it encompasses all 17 SDGs. A feasible approach in this paper is to limit our discussion to CO2, which accounts for almost 75% of greenhouse gases. Methane, nitrous oxide (NOx), and fluorinated gases account for 14%, 8%, and 1% respectively. Thus, we use world data from the World Development Indicators to examine the main sources of CO2 from 1960 to 2014. We find the persistence of CO2 over the years, which indicates that most countries have failed to limit the output of CO2 and other greenhouse gases. The conclusions drawn are stronger within an econometric model relating CO2 to GDP by employing an Environmental Kuznets (EKC) model for each region of the world. NOx emissions are also discussed later in the paper as another application of the EKC model.&nbsp;
Nanophotonics: An Emerging and Promising Approach for COVID-19 Diagnostic Technology
Authors: Annmaria Antony, Eileen Chen, Shreya Kakhandiki; Ahsan Habib
Date: 02-28-2022

The public health crisis initiated by the emergence of the COVID-19 pandemic emphasizes the need for rapid and accurate diagnostic tests to monitor large populations through community mass testing. Many testing techniques have been implemented to prevent disease spread, critical to pandemic control. Polymerase chain reaction (PCR) tests for detecting viral RNA and immunoassay tests for detecting SARS-CoV-2 antibodies are currently used to diagnose COVID-19. PCR tests are time-consuming, with a 24–48 hours turnaround time. Samples undergoing PCR detection must also be sent to a laboratory to be processed by highly specialized workers, preventing a point-of-care diagnosis from being provided. Popular immunoassay tests have drawbacks as well. Enzyme-linked immunosorbent assays (ELISAs) are extremely labor-intensive and expensive, whereas lateral flow assays (LFAs) are primarily used for antigen detection. In this work, we propose a photonic SARS-CoV-2 detection method based on a ring resonator. We calculate the sensor performance using the finite-difference eigenmode (FDE) method. The sensor sensitivity in ring resonator resonance frequency is 29 nm/RIU, with an intrinsic detection level (iLOD) of 6.89 × 10-5 RIU. We envision ring resonator-based lab-on-chip devices being widely used for applications such as early diagnosis, with the added benefit of being ultra-compact and easily handled by non-specialists.
Cross-Cultural and Gender Comparison of Emotional Connotation of Words
Authors: Yaxuan Liang; Xiaohong Xu
Date: 01-04-2022

So far, scientists have researched to explore the emotional connotation of words in different native languages and genders. This study investigates how connotations of words differ by cultures and genders by inspecting valence values, which indicates how happy people feel about specific words in datasets obtained from USA, Spain, and Portuguese. To fulfill the objective, we categorized people’s feelings evoked by the words based on a criterion created by adding or subtracting standard deviation to or from valence means for the upper and lower bound. Then, words with valence values outside of the upper and lower bounds were categorized as emotional words (positive or negative) and were analyzed. The results show that people of different cultures are more connected regarding the perception of negative connotation of words than positive connotation. Moreover, Portuguese and Spanish are more connected than either of them with the US as they have more emotion words in common. On the gender aspect, we notice that females tend to give more extreme ratings for words than males. Furthermore, the analysis shows that males have a more positive feeling towards sex-related words than females. Overall, this study outlines a way for people interested in anthropology to understand the differences in the emotional connotation of words between males and females and across cultures.
The Likely Impact of COVID-19 on US Employment in the Medium Term
Authors: Lily Fu; Dr. Schonger
Date: 12-27-2021

COVID-19 caused an abrupt increase in unemployment rates for people across various fields of work. This paper compares the COVID-19 crisis with the Great Recession and the Spanish Influenza. After careful data collection and analysis, it is clear that COVID-19 caused a steep, immediate decline in the economy although it did not last long. By comparison, the Global Financial Crisis was a persistent recession though less severe. Comparison of the health effects for COVID-19 and the Spanish flu reveals that the influenza was much more contagious as it infected 29.3% of the US population in three years. In contrast, this percent is 2.4% for COVID-19 as of August, 2021. The flu outbreak also coincided with WWI which caused further spread of the virus, and an effective vaccine was never developed. Therefore, the current economic decline should not be worse than that of the influenza. Moreover, the government is more actively participating in the economy now than it was during the flu pandemic. The Spanish flu had a V-shaped economic trend despite having minimal government involvement. Because the flu and coronavirus are both pandemic-induced crises, we would expect COVID-19 to be V-shaped as well, which is seen through the graphs below. U.S. data from the Federal Reserve is analyzed graphically in this paper, and it shows that the COVID-19 crisis exhibits a distinct V-shape pattern. This suggests that economic recovery will be faster than the less severe Global Financial Crisis, and that countercyclical government policies may not be as necessary.
Mothers’ Behaviors and Children’s Social Confidence and Ability: Correlations from the NLSY
Authors: Kitty To; Ryan Lange
Date: 12-26-2021

The understanding of shyness and social withdrawal has been built over the past half century with research looking into factors that cause this in children. However, not many mention the natural phenomenon of if children mimic social confidence from their adult figure. This study used correlation analyses to investigate the relations between parents’ behaviors and attitudes and their children’s social confidence and ability. Data were from the National Longitudinal Survey of Youth, 1986 Child and Young Adult Survey Mother Supplement. Results showed weak correlation between social confidence and outings frequency, either with family and friends or not. However, children who were spanked more frequently positively correlated with their mothers rating them to be more high strung. Furthermore, all hypotheses showed results that follow the direction of the prediction, so further investigation into the topic may show more significant correlation or a causation relationship of the variables.
Daily Cryptocurrency Returns Forecasting and Trading via Machine Learning
Authors: Andrew Falcon; Tianshu Lyu
Date: 12-23-2021

We execute a comparative analysis of machine learning models for the time-series forecasting of the sign of next-day cryptocurrency returns. We begin by compiling a proprietary dataset that encompasses a wide array of potential cryptocurrency valuation factors (price trends, liquidity, volatility, network, production, investor attention), subsequently identifying and evaluating the most significant factors. We apply eight machine learning models to the dataset, utilizing them as classifiers to predict the sign of next day price returns for the three largest cryptocurrencies by market capitalization: bitcoin, ethereum, and ripple. We show that the most significant valuation factors for cryptocurrency returns are price trend variables, seven and thirty-day reversal, to be specific. We conclude that support vector machines result in the most accurate classifications for all three cryptocurrencies. Additionally, we find that boosted models like AdaBoost and XGBoost have the poorest classification accuracy. At length, we construct a probability-based trading strategy that secures either a daily long or short position on one of the three examined cryptocurrencies. Ultimately, the strategy yields a Sharpe of 2.8 and a cumulative log return of 3.72. On average, the strategy’s log returns outperformed standalone investments in all three cryptocurrencies by a factor of 5.64, and Sharpe ratios more than threefold.
Analysis of Pre-existing Investment Behavior and Influence of Trading Apps
Authors: Anish Guddati; Dhruva Bhat
Date: 12-22-2021

The last few years have seen a rise in trading apps, and Robinhood is one trading app that has attracted millennials. This paper explores trading apps such as Robinhood and their role in providing financial inclusion and safe trading opportunities. This paper discusses investment behavior in the status quo, explaining overconfidence, sociability, and the disposition effect. Investment behavior can include the behavioral biases and common notions investors utilize for trading. Furthermore, this paper assesses the design and business model of Robinhood. Five expert investors were interviewed (such as a professor and other MBA graduates from Wharton School of Business, financial experts from private equity firms in the US and Mexico, and a JP Morgan investment banking professional), and five casual investors were interviewed to understand their opinions on investment behavior, certain trading apps, common criticisms of stock trading, and solutions to these concerns. The findings led to the conclusion that investment behavior is harmful in the status quo. Results did indicate that Robinhood does promote at least some dangerous behavior through excessive active trading and is one example of a problematic trading app through the 4th Industrial Revolution, but trading apps can only amplify behavioral biases most retail investors already display.
Do Blockchain Stocks Exhibit More Herding Behaviour than Traditional Stocks?
Authors: Prakhar Goel; Abhishek Dev
Date: 12-21-2021

While the volatile behaviour of cryptocurrency is extensively studied, the stock market’s blockchain sector, which has not been given much attention in the academic world, operates very differently from traditional stock industries. The paper hypothesizes that blockchain stocks exhibit more herding behaviour than traditional stocks and uses quantitative data analysis techniques to study it. The automotive industry is taken as a representative of traditional stocks. Cross-Sectional Absolute Deviation, the academic standard for herding behaviour, is used as the primary comparative measure between blockchain and automotive stocks. It reveals that blockchain industry has significant herding, while rational pricing mechanisms prevail in the automotive industry. Supporting this conclusion, a correlation matrix of stock prices of small market capitalisation firms in each industry is constructed, analysing how closely stock price movements in an industry are related. The correlation coefficient for blockchain stocks is 20% higher than the coefficient for automotive stocks. This indicates that blockchain stocks likely exhibit higher levels of herding. The impact of social media on stock price movements in the two industries is analysed by conducting a correlation study between Google Trends data for industry-related keywords and individual stock returns. The blockchain industry saw a significantly higher correlation, likely suggesting that social media has a stronger influence on blockchain stock price movements. Finally, the paper provides possible explanations for why herding behaviour is more prominent in the blockchain stocks compared to traditional stocks. These include absence of traditional stock valuation metrics, lack of financial knowledge and role of social media.
Dienogest Derivatives Show Increased Selectivity to the Progesterone Receptor
Authors: Jiya Bhatia; Jessica Jimenez
Date: 11-30-2021

Progestin, the synthetic progesterone in the combination birth control pill, functions to prevent pregnancy. Unfortunately, it also results in a wide range of side effects associated with the pill, including nausea, headaches, abdominal cramping, breakthrough bleeding, and mood changes. These side effects are due to the interactions that take place when the progestin binds not only to its desired target, the progesterone receptor, but also to other steroid receptors, namely the androgen receptor, estrogen receptor, glucocorticoid receptor, and mineralocorticoid receptor. In hopes of diminishing these side effects, in this study, one progestin, dienogest, was computationally altered to improve binding to the progesterone receptor while minimizing the binding to other receptors. The most successful derivative created involved the addition of a methyl and a methylene group, which significantly decreased binding to the five other receptors while maintaining a similar affinity for the progesterone receptor. Importantly, in an assessment of the social aspect of the birth control pill via an online survey released in the San Francisco Bay Area (n=73), the side effects of the birth control pill, which could be improved by minimizing off target interactions, are a major concern among women who consider this form of contraception.
A Picture is Worth a Thousand Words: Using Cross-Modal Transformers and Variational AutoEncoders to Generate Images from Text
Authors: Satyajit Kumar; Ehsan Adeli
Date: 11-30-2021

Text-to-image generation is one of the most complex problems in deep learning, where the application of Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) has seen significant success. However, GANs prioritize the sharpness of the image rather than covering all the nuances of the text. Given that Transformers have recently outperformed RNNs and other neural network models in both the text and image spaces, we explored whether Transformer models can perform better in multi-modal tasks such as text-to-image synthesis. Our conclusion based on evaluating five Transformer based models on the MS-COCO dataset showed that Transformers perform better but would need a significant amount of memory and compute resources.
The Effects of Social Media Attention and Sentiment on Initial Public Offering Returns
Authors: Aiwen Li; Cierra Beck
Date: 11-30-2021

Going public is a monumental step for many companies. Not only does it increase a company's legitimacy in the business community, but it also gives opportunities for them to harness significant amounts of investment capital. However, entrepreneurs and investors often face uncertainty due to the unpredictable nature of initial public offerings (IPOs). This study evaluated the impact of the amount and sentiment of Twitter activity on stock returns using data from domestic companies who went public from June 1, 2020 - May 31, 2021. Overall market behavior, company size, and community population demographics and social economic status of the companies' headquarters locations were controlled. The analyses showed that Twitter activity is associated with higher returns during relatively long-term time frames. The results are relevant for companies and potential IPO investors to predict and maximize their profits in the market. They also open opportunities for future research to investigate more in-depth regarding social factors in relation to IPOs.