Engineering – Page 14 – UROP Spring Symposium 2021

Engineering

C++ Programming for Brain-Computer Interface Calibration Innovations

The UM Direct Brain Interface Laboratory utilizes the classifier program included in the C++ distribution of the BCI2000 v3 to calibrate a P300 BCI to the brain activity of an individual. “BCI” refers to an electroencephalogram (EEG)-based brain-computer interface which allows participants with physical impairments to directly interact with a computer interface using their brains with minimal motor demands. In order to interact with this interface, participants use the P300 component of the event-related brain potential (ERP) (Farwell and Donchin 1988). Though the technology is promising, there are barriers to clinical implementation that the UM-DBI Laboratory aims to address. It is to aid the efficiency and effectiveness of UM-DBI studies that relevant C++ tool and usability additions are proposed. The P300 BCI Classifier calibrates via machine learning and this classifier program has been the point of focus for this project. Through the addition of various practical additions and refinements, the source code for the P300 BCI classifier may be better modified to provide more meaningful output, and allow for a more accessible and functional user interface when assessing output and input. These additions are made through careful coding and testing practice. The general methodology implemented in this research project includes assessing desired changes/additions to be made, understanding the context in which this modification should be implemented, and carefully testing input and output in order to assess adequate functionality (without any unintended consequences). This work is ongoing, and it is intended that all additions will provide added usability and functionality to UM-DBI laboratory researchers.

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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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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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Developing fast and unbiased computer vision algorithms

Images and videos are a common way to easily store data for research. However, turning these into usable and analyze data is much more time consuming and costly. In order to help research teams save time and money, DEVIATE is developing a computer vision algorithm to label image and video data for them. An essential part of the development process is to consider and mitigate any possible bias. In order to reduce bias, DEVIATE is using human coders to label the training and testing data. In addition, the pool of data represents a diversity of skin tones as well as different light levels. It is especially important to reduce bias since this product will be used by other research groups. If the algorithm was biased, it would affect countless other research studies as well, so it is essential that DEVIATE is able to minimize any harm coming from its product. As this is still an ongoing project, it is expected that the final product will be an unbiased comprehensive computer vision algorithm that can help research teams label data stored in images and video.

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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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Bioinformatics study on protein structures

The majority of proteins are composed of foldable, stable subunits called domains. The structures of these proteins can be made up of a single domain or multiple domains. Determining structures of multidomain proteins is a crucial step in elucidating their functions and designing new drugs to regulate these functions. However, it has been largely ignored by the mainstream of computational biology due to the difficulty in modeling inter-domain interactions. Therefore, almost all of the advanced protein structure prediction methods are optimized for modeling single domain proteins. In this study, we presented a method to construct a multidomain protein structure library with known full-length structures to assist the multidomain protein structure prediction. We collect all multidomain proteins from the Protein Data Bank based on the DomainParser, and multidomain proteins defined in CATH and SCOPe databases are also included in the library. This resulted in a total of 15,293 multidomain proteins in the library. The completeness of the library is examined by structurally matching a set of non-redundant multidomain proteins through the library using TM-align. The results show that most of the cases can obtain at least 1 template with correct global fold (TM-score >0.5) from the library, which indicates that the constructed multidomain protein library can likely be used to guide the multidomain protein structure modeling.

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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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Khichri

Although there is abundant evidence that humans are altering the climate in drastic ways, this information is not always readily available to the general public, especially in developing countries around the world. To combat this issue, this research project focuses on studying the general perception of climate change and its impact on food scarcity in Pakistan, and it utilizes foundational design elements to create an interactive web app that would help inform Pakistani youth about the harms of climate change and its impact on food security and costs.

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Khichri

Known to our scientific community are the inevitable impacts of Climate Change – catastrophic effects on agriculture and food availability, an increase in extreme weather, and a grander spread of deadly diseases and viruses as a result of more humid and hot climates. However, there is a problem with this information: It’s not being communicated to the general public, especially educated, Urban Pakistanis. There is a large gap between research and research communication; thus, the general public – especially young students and adults – are unaware of the upcoming effects of this global change. Khichri aims to bridge that gap, to bring this information to the jury of the common citizen, and to finally create this urge among the younger generation to take control of their own future. This project focuses on Pakistan (specifically the city of Karachi), the fifth-most vulnerable country to long-term anthropogenic effects of climate change and a country unable to address their own climate concerns. We look into the students’ current understandings of climate change by means of interviews and surveys.

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Khichri

Climate change is causing destruction on our environment and in turn, affecting our daily lives. This is most widely known in the form of natural disasters that cause destruction onto our homes, extreme heat waves in summer, etc. In fact, climate change is also causing a shortage in our food and water supply. Although climate change is an issue that should be taken seriously, many people are still oblivious to the scale of its effects. This project is a non traditional look on the effects of climate change on underdeveloped countries, especially Pakistan. We experiment with different factors to create an interactive website that is fun and creative to inform users of the possible effects of climate change on our food systems. The users will take a look into the future on how the supply of ingredients will increase or decrease based on climate change. In practice, this site can be adjusted for many scenarios in other countries. This will be beneficial for the planet as a whole when users communicate and spread their knowledge.

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