Engineering – Page 15 – UROP Spring Symposium 2021

Engineering

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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Does air-breathing constrain skull function and diversity in fishes?

The anabantoids are a diverse clade of tropical and subtropical freshwater fishes distributed throughout Africa, Asia, and the Indian subcontinent notable for possessing air-breathing organs (ABOs). The ABO allows the anabantoids to breathe outside of water, making it a key innovation: a trait critical to diversification within a particular lineage. While it is unknown if these organs developed independently or from a single ancestral phenotype, fishes with ABOs are dependent on atmospheric oxygen, even drowning without it. The size and arrangement of the ABO influences the skeleton around it (phenotypic integration), and we predict that the ABO will covary in size and shape with skull structure. We visualized skull anatomy with micro-computed tomography (CT) scanning and then used linear morphometrics to capture skull shape variation. By plotting ABO shape changes against skull shape, this study investigated whether fishes with ABOs have more strongly integrated skulls. Using these data, this study will reveal the evolvability of the ABO itself, and the skulls of fishes with or without these organs. We expect: (1) that skull shape will co-vary accordingly with ABO shape and (2) that fishes with ABOs will have less diverse head shapes. Our dataset can also demonstrate how many times ABOs have evolved in anabantoids and whether these complex organs are capable of being lost, perhaps if no longer needed in certain habitats. We found that the ABOs have evolved two to three times independently across anabantarians.

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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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Developing fast and unbiased computer vision algorithms

In an effort to improve driver safety and autonomous vehicle testing, video recordings of drivers allow for data to be analyzed. These videos are first examined by human coders, but a more efficient, automated algorithm would prevent the need for human coders entirely. However, in order to build the algorithm, human coders need to analyze videos of drivers and label various actions, such as if the driver is turning or tilting their head, or hand movements, such as texting, and if their hand is obscured. Once these labels are implemented, they are tested against each other for accuracy, so that the final algorithm is unbiased enough to be implemented into vehicle safety.

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Head CT image analysis for detecting edema

Cerebral edema, which is swelling in the brain, is commonly found in patients suffering from head trauma, injuries, or other diseases and can be fatal. The purpose of this research project was to develop a method for automatically detecting and segmenting edema in head CT scans in order to make it faster and easier for clinicians to diagnose and treat traumatic brain injury (TBI) patients. However, edema is difficult to segment due to its unclear boundaries and its similarity in pixel value to other brain tissue. In previous research, most methods for segmenting edema have either been semi-automated or for MRI scans. More accurate methods require MRI scans, but even though an MRI scan is more detailed and can make it easier to segment edema, CT scan is the gold standard for evaluating brain injuries and is faster and more widely available. Therefore, automatic segmentation of CT scans will be very beneficial. In this project, the active contours without edges method, developed by Chan and Vese, is used with manually segmented hematoma as the initial contour. The method was developed in MATLAB. The segmented edema was then compared with the manually segmented images, and the DICE score was used to measure the accuracy. Currently, this method successfully segments select CT scans. However, it needs improvement in order to be more generalizable. In the future, I will look further into other techniques such as deep learning that can help improve accuracy and generalizability in automatic edema segmentation.

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Developing Fast and Unbiased Computer Vision Algorithms

The research project I am participating in is “Developing Fast and Unbiased Computer Vision Algorithms” through the Multidisciplinary Design Program and the Transportation Research Institute. We’re trying to make a computer vision algorithm that could detect if drivers are paying attention to the road or distracted such as being on their phones. The algorithm itself should be as efficient and reliable as possible. To get our results, we look at frames of videos of people driving and create data sets and coding logs based on what the driver is doing. We have multiple people log the videos to create a benchmark of what the driver is doing. We also change our operational definitions of what we are looking for in the videos. The coding logs give us a benchmark for the algorithm so it can accurately judge what actions are distracted driving. By changing the variables we’re analyzing and improving the benchmark, we can make the algorithm more efficient, especially when we have a lot of different types of videos with varying lighting, subjects, and difficulty. Our research is vital because while transportation safety is important and an accurate algorithm detection distracted driving could help reduce the number of car accidents and car deaths, on a larger scale, our improvements of this computer vision algorithm would help improve how computer vision algorithms are created and applied in general. Overall, computer vision algorithms have shown to be biased especially with variables like skin color, sex/gender expression, and lighting. Those variables negatively affect the accuracy of the algorithm. Through our research, we could use our same methods and data to help other computer vision algorithms become more accurate and efficient.

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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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