Interdisciplinary – Page 46 – UROP Spring Symposium 2021

Interdisciplinary

Text Analysis of International Trade Agreements

Our project aims to analyze the sentiment and its impact reflected by the wording of trade agreements. We use machine learning to identify topics in the text of trade agreements and then using python to estimate the importance of these topics. Our contribution will be both identifying these topics that have the potential to affect trade flows through text analysis and estimating the sign and size of their impact.

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Development and Assessment of an In-Vehicle Cardiac Monitoring and Severe Event Prediction System

Cardiac issues are serious medical issues that affect a large part of the population. Many studies have looked into using programs to automate the identification process and help discover symptoms early without the need for a doctor’s visit. The project focuses on developing machine-learning algorithms that can rapidly detect cardiac issues, specifically arrhythmias, to be implemented directly into motor vehicles. This project involves conducting training and testing of non-parametric machine learning algorithms on the publicly available MIT-BIH Arrhythmia Database. Models are created on MATLAB and tested based on statistics such as accuracy, specificity, and sensitivity through a confusion matrix. So far, decision tree based models have shown accuracies around 90% on smaller datasets after tuning various hyperparameters. Testing is to be continued with different models such as Support Vector Machines (SVM) and Convoluted Neural Networks (CNN) on larger datasets that will ideally result in significant improvements to the final model. These models will be better suited to accurately predict cardiac issues even with the extra signal noise when built into embedded systems within motor vehicles. These systems will add an extra layer of security to vehicles and help identify symptoms earlier than traditionally possible.

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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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Reimagining Current Messaging Systems of Social Networks

While social networks have enabled improved communication globally and the widespread Human-Computer Interaction (HCI), it has also exasperated unsolicited communication and harassing messages which consistently target vulnerable, marginalized groups. Consentful Messaging offers Twitter users the ability to filter potential message senders before the risk of receiving unsolicited messages through a system built on Twitter’s API, Python, and JavaScript. The field deployment of Consentful Messaging will reveal how social media users choose to receive messages and to what extent they wish to customize potential message senders. Consentful Messaging is made of four possible customizable functions: the user can set a threshold of number of followers required for a potential message sender, the Consentful Messaging user can verify whether they follow the potential message sender, the Consentful Messaging user can determine whether the potential message sender is followed by at least one account that the Consentful Messaging user follows, and verify whether the Consentful Messaging user has ever replied to a message from the potential message sender. The result is that Consentful Messaging Twitter extension will be deployed in March of 2021. This field study will show how and to what extent Twitter users choose to protect themselves from unsolicited messages with the help of technology. Consentful Messaging will offer not only a customizable layer of protection to marginalized social media users by providing a board for healthy interactions among users, but also information as to how Twitter users interact with and modify personalized computer settings.

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Reimagining current messaging systems on the social internet

While social networks have enabled improved communication globally, it has also exacerbated unsolicited communication and harassing messages which consistently target vulnerable, marginalized groups. Consentful Messaging offers Twitter users the ability to filter potential message senders before the risk of receiving unsolicited messages through a system built on Twitter’s API, Django, MySQL, Python, and JavaScript. The field deployment of Consentful Messaging will reveal how social media users choose to exercise agency over receiving messages and to what extent they wish to customize potential message senders. Consentful Messaging provides the following customizable functions that a Twitter user can use to carve out the network that can initiate interactions: the user can set a threshold of number of followers required for a potential message sender, the system only allows notifications or messages from accounts that the user follows, the user can determine whether the potential message sender is followed by at least one account that the user follows, and verify whether the user has ever replied to a message/tweet from the potential message sender. In short, Consentful Messaging aims to offer a customizable layer of protection to social media users, especially marginalized groups, by providing a board for healthy interactions among users. We plan to deploy the system and conduct a field study sometime in the spring. This field study will help us understand the effectiveness of the approach, and show how and to what extent Twitter users choose to protect themselves from unsolicited messages with the help of technology.

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Consentful Messaging: Giving People Agency over Online Interactions using Network Data

Our project works on developing a Chrome extension that allows Twitter users to have more control over their inbox messages and analyzes other accounts to generate potential warnings. This extension tackles the growing issue of online harassment and unsolicited messages on social media by letting Twitter users apply network-based rules on the messages and notifications. We use coding languages such as HTML, Javascript, CSS, Python, and web framework Django to create the frontend of our extension/website as well as the backend where the server runs. Team members can choose to work on the frontend, backend, or both. We will evaluate the extension by conducting a field deployment study on Twitter. We will recruit active Twitter users and ask them to use the extension for at least two weeks. Once the participants are finished using the system, they will be invited to complete a post-study survey and participate in interviews. There aren’t exactly any results or conclusions at this stage of developing the extension. We are currently still working on different parts of the project and individually working on appropriate functions/code. We plan to provide a fully functional and helpful extension that is able to reduce the issue of online harassment (on the user’s side).

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Data Gathering Data Analysis Toward Better Air Quality Outcomes in SW Detroit

In support of the ongoing initiatives by community advocates to cut diesel emissions in Southwest Detroit, this research aims to gauge the effects on air quality that commercial vehicles have in residential areas when using their streets as routes. SW Detroit has some of the highest levels of PM and toxic pollutants measured in Detroit, with Detroit having the worst air quality conditions in Michigan. This study accesses the ambient air quality in residential areas that have a high frequency of commercial trucks traveling their streets. We want to know how each passing truck contributes to the air pollution on the street it is using.

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Data Gathering Data Analysis Toward Better Air Quality Outcomes in SW Detroit

In support of the ongoing initiatives by community advocates to cut diesel emissions in Southwest Detroit, this research aims to gauge the effects on air quality that commercial vehicles have in residential areas when using their streets as routes. SW Detroit has some of the highest levels of PM and toxic pollutants measured in Detroit, with Detroit having the worst air quality conditions in Michigan. This study accesses the ambient air quality in residential areas that have a high frequency of commercial trucks traveling their streets. We want to know how each passing truck contributes to the air pollution on the street it is using.

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A Review of People’s Perceptions of COVID-19 and Adherence to Public Health Policy

From early 2020 to 2021, the unprecedented outbreak of novel coronavirus (COVID-19) in Wuhan has evolved into a pandemic. As coronavirus impacted people’s daily life, public transportation and recreational facilities worldwide, countries implemented various public health guidelines to help their cities recover from the virus attack. To understand international differences and similarities in people’s thoughts about the novel coronavirus and associated public health guidelines, I conducted a literature review to examine: 1) people’s perceptions of the coronavirus; 2) people’s adherence to public health policies (e.g., face masks, social distancing, and hand hygiene); and 3) potential reasons for the differences (e.g., cultural beliefs). I reviewed research articles related to the effectiveness of general public health guidelines in major countries in North America, Europe and Asia (e.g., the U.S, U.K., and China respectively) and the opinions of residents regarding the current rules they need to follow. My findings indicate that even though residents in eastern and western nations share basic knowledge about the coronavirus, people from western countries still have some misconceptions about the coronavirus. If people are more optimistic, they are more willing to adhere to public health policies. Based on these findings, future research can focus on the development of new tailored strategies to combat transmission of the coronavirus, such as raising people’s awareness.

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Pancreas Segmentation via Image Processing and Machine Learning

Pancreas segmentation is consistently one of the least accurate out of all organ segmentation due to its low contrast in its boundary and high anatomical variability in its geometric properties. Many hospitals and biomedical laboratories therefore do not have accurate methodologies for cancer detection, 3D modeling, etcetera for pancreas scans. This paper serves to improve current pancreas segmentation methods by making use of a type of Convolutional Neural Network (CNN) called a U-Net in addition to image processing techniques such as an Integrated Hausdorff-Sine Loss Function for preprocessing and 3D Gaussian smoothing for post processing. The CNN was trained on Computed Tomography (CT) images in n-fold cross-validation from a public NIH dataset and a private dataset from Michigan Medicine. Data augmentation was performed on the CT scans using Keras, an open-source neural network library. Accuracy and precision of the CNN was tested using a Dice-Sørensen Similarity Coefficient (DSC) and Jaccard Index (JI). The mean DSC and JI achieved for the proposed methods are expected to be at least 70.00%.

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