Interdisciplinary – Page 45 – UROP Spring Symposium 2021

Interdisciplinary

Financing and Counter-Financing of Violent Non-State Actors Project

There are numerous ways to deter terrorist groups from being active, one of them being obstruction of financing of the terrorist groups. It is a very important method governments and institutions take to stop violence, yet there is only a few organized dataset on the economic counterinsurgent actions taken against terrorist groups. This project collects and organizes data of counterinsurgent actions that hinder financing of the terrorist groups. Through a database Nexis Uni, this project records relevant events through articles from 1990 to 2018. The dataset classifies the counterinsurgent incidents into specific actions, showing clearly the frequency and impact of each type of actions. The dataset aims to cover most terrorist groups in the world and the project is still in the process of collecting data. The project provides analysis on the impact and effectiveness of each action, which will be useful in fighting against terrorist groups. It also gives further insight in how the actions and attitudes towards these violent non-state actors have changed over the last 30 years.

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Coronary artery segmentation for automatic stenosis detection

One type of coronary artery disease (CAD), stenosis, is characterized by the narrowing of the coronary arteries, which is the leading cause of death in the United States. One of the most popular ways to diagnose stenosis is the coronary angiogram, from which a doctor can diagnose stenosis by finding places of narrowing in the arteries with the video or the pictures acquired. A lot of studies are currently focusing on automatic coronary artery segmentation, a critical step of a computer-aided system that assists doctors in detecting coronary stenosis. Here we propose a deep learning pipeline using DenseNet-backbone U-Net for coronary artery segmentation in angiogram images, which could be combined with pre-processing and post-processing steps for stenosis detection.

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Consentful Messaging

In the offline world, people’s communication with others revolves around networks. People tend to communicate more with people they have a strong tie with, which is easy to accomplish in fluid and nuanced ways. However, current social media systems lack such mechanisms for controlling interaction and communication based on network strength, which often leads to massive online harassment and abuse. A major example is Twitter, a social platform well-known for its openness. In this work, we present a system called NetRule, a Chrome extension that augments Twitter and gives users the ability to author network rules to control incoming messages and notifications. Through NetRule, users can easily combine and apply network-based rules on Twitter accounts that initiate interactions, such as whether the number of mutuals is high enough, whether the account has been blocked by one’s following, etc. Users also have the freedom to decide what happens to the accounts flagged by the rules, such as muting, blocking, or visibly coloring the accounts on the interface as a warning. Our evaluation of the system, a field deployment study on Twitter, showed that XXX.

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COVID-19 bioinformatics research

Bioinformatics has been a powerful method to study COVID-19 and our project look at ontology-based application in rational drug design. In our previous study (https://www.nature.com/articles/s41597-021-00799-w), the Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent anti-coronaviral drugs, drug targets, host-coronavirus interaction (HCI), and their relations. A “HCI checkpoint cocktail” strategy was further proposed to interrupt the important checkpoints in the dynamic HCI network and ontologies support this design process. However, the users such as drug researchers might not have the required ontology knowledge to use the information represented by the CIDO. Therefore, our project aims to design and build a user-friendly tool/website to allow basic queries and facilitate rational drug design using “checkpoint cocktail” strategy. We have developed a MySQL relational database that systematically represent the drugs, bioentities, interactions, pathways, and their relations. A set of real life data were added to the database. MySQL queries were performed to demonstrate our capabiliities to query different contents from the database to support the query of anti-coronaviral drug information. A web interface is being developed in order to further support online query and analysis of anti-coronaviral drugs, leading to rational anticoronaviral drug cocktail design.

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Sentiment Analysis of International Trade Agreements

The aim was to examine the changes in sentiment that occurred within trade agreements over time using rule-based sentiment analysis. In this case, the sentiment of a text was measured by the degree to which it expresses or implies an opinion. The focus was on a collection of English hundred trade agreements written within the last few decades. Before analysis, a dictionary of trade terms was created using seminal texts. Terms were categorized based on if they expressed a cooperative, punitive, or bureaucratic sentiment. The analysis entailed assigning sentiment scores to trade agreements based on the number of categorized terms within. There is no clear expected result, but there will likely be some observable change in overall sentiment over time, whether it is more or less sentiment.

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What is on the table? Peer effects within A Large Canadian Restaurant Chain

Jianglai Zhang Pronouns: she/her/hers Research Mentor(s): Luchi He, Research Assistant of Dr. Yue Maggie Zhou Research Mentor School/College/Department: Strategy department, Ross School of Business Presentation Date: Thursday, April 22, 2021 Session: Session 1 (10am-10:50am) Breakout Room: Room 20 Presenter: 4 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors:

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Effective ways of communicating research on language learning by children and adults

Media use has been a beneficial tool for education in schools, especially over the past year, and can expand the knowledge that can be known by any individual person. While it advances education in the school systems, it may also have some negative effects regarding how children adapt to language acquisition, especially those who are not native to English in the U.S. In this study, we observed 23 children from Spanish-speaking homes. We asked about both the children’s and their parents Spanish and English media use through a questionnaire asking them to report their time spent on certain categories of media. In addition, two animated videos aiming to teach English possessive nouns and verbs were shown to the students. They were tested on English vocabulary, through the Peabody Picture Vocabulary Test, Spanish vocabulary through the Spanish version, TVIP, and on English and Spanish grammar through morphosyntax tests. The results of this project reinforce that educational content is beneficial for children’s language development. However, the results also show that parent’s media use negatively impacted childrens English morphosyntax scores, as well as their English and Spanish vocabulary knowledge. For future research, it would be beneficial to understand which aspects of educational media benefit bilingual children’s ability to develop their non-native language. Additionally, it would be interesting to see the impacts of interactive educational media compared to passive animated media.

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Effective ways of communicating research on language learning by children and adults

Language can be taught in a variety of ways, but is there a specific way that helps young children benefit the most? The present study of English language learners was conducted in order to see if children’s acquisition of possessive and past tense in English could be facilitated by watching “passive” but targeted child-centric animations, and if a child’s first language plays a role in their second language acquisition. For this study, native Spanish- and Mandarin-speaking children were tested on their English vocabulary and grammar before and after being shown immersive language-learning animations where both implicit and explicit teaching methods were used. Although this study is still in progress we found that both the Spanish and Mandarin-speaking children were able to learn English vocabulary and grammar from the animations. One question that we now have is whether children who received the same exposure to the target learning material but in an interactive game would be better able to learn and whether their learning would be affected by the amount of media exposure children get in their day to day lives.

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Ontology-based machine learning towards COVID-19 drug understanding

The pandemic caused by COVID 19 marked its one-year anniversary on March 12, 2021. Since last spring, millions have been victims of this terrible disease and millions have been infected across the globe. In the United States alone, there have been almost 30 million cases, and over 500 thousand people have passed away. Vaccines have been manufactured and distributed around the globe, however, officials predict that COVID 19 will never be fully eradicated, similar to the flu. That is why the objective of the COVID-19 Bioinformatics research project is to determine a drug or a cocktail of drugs using COVID-19 virology data and machine learning that can potentially provide treatment. The process of implementing the algorithms began with feeding data into an algorithm titled OpA2Vec that transformed ontology-based axioms into high dimensional vector representations using cosine similarities. These high-dimensional vectors will be compressed into two dimensions by running them through a t-distributed stochastic neighbor embedding (t-SNE) analysis in order to graph them on two dimensions. The vectors represent how effectively different drugs will react with the different target proteins of COVID19. The graph will help determine clusters or patterns to develop a proof of concept and a potential hypothesis for future experimental verification. A linear neural network modeling is also being implemented. The results will be able to demonstrate a potential drug design for the COVID19 virus that has completely transformed the world as we know it today. Our results will provide a proof of concept to potentially support the experimental verification of our theoretical findings.

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