The repository for this software can be found at https://gitlab.cluster.earlham.edu/senior-capstones-2020/laurence-ruberl-capstone
I have been working on cleaning up and refining my code as well as the frontend. I also worked on the final poster as well as the final version of the paper.
This week I mostly worked on the final video, as well as making some small final touches to the paper based on Charlie’s suggestions.
I finished out some final kinks and have a fully functional version now.
I mostly worked on my poster during this week. There were a number of small issues with my original poster created using LaTeX, which I could not fix without delving into a lot of code, so I recreated it using power point and improved it based on Charlie’s feedback. Igor, my advisor, also pointed out a number of improvements that could be made to my paper, so I worked on an intermediate draft for additional feedback before I complete the final version.
In the past week, I worked on modifying my diagram and finished generating results. I also tried to draw meaningful insights from the validation results. After chatting with Xunfei, I finalized my poster and thought about elements to add to my paper.
This week I prepared my poster for the final submission, while editing also reworking my paper for the final submission next week. Last week I received feedback regarding user interaction and did research this week into what is the best way to build a GUI for my software.
I completed the second draft of my paper which required changes in the diagram, motivation for the project. The final draft shall have all the required and suggested materials. I am working on the final version of the poster which requires similar changes as the paper. Currently the application is in testing process. The QR code generation, scanning and the user login/logout process is all set. The aim is to improve the return books feature by adding an additional security layer to prevent book thefts. The librarian gets a list of books away from the library on the homepage and the student is charged a fine if its returned late.
I am working on distance calculation. I had to start from scratch since there are no tutorials online to follow so it is taking up time. The image model is still not working so I am going to make the user to input fruit name instead. I am going to wrap up the application development. Next week will be for accuracy testing.
I worked on the project poster. I also continued working on software demo v2 and finally submitted it. I discussed with Becky on scaling the accuracy testing, and the method I should use. I have found the food density data that I was planning to use is very limited so I am going to put some more data by manual calculation. I am focusing on fruits instead of foods.
I worked on integrating the camera into my application. I found the bug for image processing, but I am working on finding a way to install the CUDA toolkit on Google Cloud VM. I also worked on software demo version 2. I made some changes in my project to scale it down discuss those with Becky.
I was mainly in the process of transitioning.
I have been working on wrapping up my project and worked on finishing up the final paper and the poster. The obstacle remains the same where the model gives out very erratic outputs for sentences that are not in its vocabulary but works well with sentences that it has seen before. I will keep continuing to keep working on this and try to figure out a way to make it work, but this goal might be outside the scope of the project given the current workload and time.
During the past week, I have submitted the second version of my paper. After submission, I have continued working on the final parts of the paper. These parts include finishing the social engineering results and making the recommended changes to certain images to enhance my paper. With the given feedback, I have also started making changes to my senior poster which is due Sunday.
This week I submitted my second draft of the paper, which required lots of results production as well as time to write. I graphed prediction trends between different materials of foreign data with my model which also was demonstrating how my model was performing. I received great feedback from my advisor regarding the poster and paper and will look to improve these both this week for final submission.
This last week I tried to connect my C++ code with my Python code. I failed because I couldn’t convert from cv::Mat to Numpy Array. There were several problems with that. I will instead save the output of my C++ code as a jpg, and read the jpg in python. This is still considerably faster than using Python for the image processing part.
In the past week, I finished the coding part of my project and did some validation work. I revised my diagram and worked on my paper and poster. Next week, I will be focusing more on my paper and poster.
The second video was primarily focussed on the student application. As of now, a user can search for books based on categories like sports, academics, fiction, etc. They can issue books by scanning the QR code as well. The homepage of the student app needs some work, it will show which book is currently issued and give a brief history of the user and their activities in the library. In the coming week, I shall work on the 2nd draft of my poster, hope to complete the project and work on testing.
I worked on finishing the frontend and wrapping up my project and figuring out what metrics to use measure and compare my models with. I also worked on the poster for my project. This upcoming week I will work on wrapping up my final paper and add the description of my second model to the final paper.
I finished the first draft of the poster. While doing that, I needed to get the accuracies for my AI model and my image processing algorithm overall.
My AI accuracy was around 90% for the testing set. However, I realized my initial idea for measuring image processing accuracy was flawed. I have been working on some new, improved ideas.
This week we had the first draft of the poster due, which meant producing and visualizing a lot of results from my project. From this motivation, I compared my predictions across very different data (news articles, fictional novels, ect) and also was able to produce a convolution matrix that showed just how accurate my model was. This coming week I want to transfer these results into the next paper draft and continue with the user flow of the software.
I’ve mostly been working on making the poster and validating results. I found out that my original plan for validation would not work as nicely, so I will discuss with Xunfei to figure out what I can do. My diagram also needs to be tweaked.
I worked on the feedback I received from Igor concerning some of the difficult notation, and overall organization of my paper. I also removed some obvious lemmas and simplified the basic results for the second draft.
I downloaded some past posters to understand how to present my project idea through a poster. Working on the second draft of paper. Finishing up the admin app, in progress to complete all the features of the student app by the end of this week.
I couldn’t do much due to comps, but collected all information from participants to conduct the validation process. I also ran their results on my software and recorded the results. This week, I will work on extracting relevant reviews from Sephora to evaluate the efficiency of my method.
This week I further improved the pre-processing of sentences so that they are cleaner and easier to read on output. I then downloaded some previous year project posters to help with designing my own and have already completed half of it. It showed me that now I need to work further on results to present, on the accuracy of my model outside of its dataset.
I have finished the front end of the project, and am trying to wrap it up. One obstacle that I am facing is that my project proposed to have human testing, which will not be possible due to the current situation. I will be working on the poster in the next few days.
This past week I mainly worked on the first draft of my poster. It was much easier to complete since I have the majority of my project finished. In the coming week, I plan to continue my testing with my virtual servers, Kali and Metasploitable. Luckily, I have not encountered any obstacles when trying to use these two for testing. I also plan to continue work on the second draft of my paper.
I am having trouble with binding the C++ code to Python. Besides that pretty much everything is finished.
This week was (is going to) spent on working on the poster.
I worked on presenting the proof in the most succinct way possible for the poster. I worked on several iterations and finally narrowed down on what is the current first draft. I also received feedback on the first draft of my paper from Charlie, and discussed the paper as well as Charlie’s feedback with Igor.
I submitted the second software video. Finishing up the first version of the poster. I also received feedback on the first draft of my paper from Charlie, and discussed the paper and feedback with Charlie. Got valuable suggestion to work and improve in the second draft.
Having implemented my first two models, I started working on the front end of my project for the user to interact with. This upcoming week I will keep working on the frontend as well as the poster and the paper.
I am struggling with linking my C++ code and Python code. The conversion from numpy array to cv2::Mat and back is the problem. There are libraries to help with that, but I fail at setting them up.
In the past week, I mainly worked on implementing a simple interface for my program. I decided to take text inputs for skin type and beauty effects and use a button that returns the recommended products when clicked. To test my program, I collected more input data from participants. I will be using them in the upcoming week to validate my methods.
During the past couple of weeks, I made some good progress on my project. I now have a functioning driver file that the user will run to train or validate a model, or to predict from their input data. This has allowed me to tie up different parts of my software into one functioning project. I also have predictions working (big step) and am currently working on the best way for the user to view their results.
I’ve done some work on the configuration parser to enable to core of the project to work: allowing a user to generate a ui with functionality from a text file. All i’ve been able to do so far runtime wise is to have the user select whether they are using VR mode or AR mode. Backend wise, i’ve done a lot of setup of methods and variables and i’ve done the first tests with ssh and telnet (long story) integration which didn’t turn out like i wanted. Next on my list is to create the actual objects that the user defines and start hooking up the ability for said objects to do stuff.
I spent this week on finishing up a very detailed proof that Parks is NP complete. I also worked on improving the exposition of ideas leading up to the main result in my paper, which really helped me with the software video.
Did not get much to do due to the college shut down caused by corona virus. Hoping to get in touch with my advisor and figuring out the remaining schedule.
This past week I worked on my other model that uses both engaging/readable and non-engaging/readable advertisements, I found that this model does not perform well because of the lack of parallel data and sentence structures between the two different sentences. For the next week, I will work on refining my initial model as well as writing the frontend for the project.
I parallelized some of the python code.
I also rewrote the color processing code into C++. The C++ code for individually changing each pixel is 600 times faster.
I read up on how to make images of food look better and added more algorithms. I want to improve the algorithms/add more algorithms. They are currently not hooped up to the rest of the code.
Next I need to link the C++ code to my Python code, and find a way to to generate meaning full varaibles to pass the processing algorithms. I might rewrite and benchmark the cropping-and-retargeting code in c++ as well.
I did not get to do much due to the various disruptions caused by Corona virus. I am currently working on getting things in order and figuring out meeting times with my adviser and whatnot.
Working on the logout procedure and to be able to pull up books from the database correctly when the QR code is scanned. The profile page of the librarian and student needs some design elements since the users can view the profile page to see the history of books issued. Planning on working on the second video in the coming week.
I read more papers and started thinking about the proposal idea.
My advisor: Xunfei Jiang
Set up a VM and I am installing Solo5 on VM
The feature extraction module is finished (ready to use) now, but I am still stuck on the modeling module… The model I am using is called VGGVOX which is available on Keras. I am stuck on input pre-processing. The bug is on a function on BatchNormalization(). This function normalizes the activations of the previous layer at each batch. But the issue is not on this function, instead, it is on some deep layers of tensorflow innate functions… which i cannot modify. I am kind of lost which step is wrong exactly.
Gender bias on Facebook might be measured by analyzing the difference in reactions on posts by women or men. My project is studying bias on Facebook pages of United States politicians using Facebook Reactions and post comments. Specifically, I am focusing on politicians running for US Senate in 2020. Data is being collected from Facebook pages of the politicians using a crawler and will be into a database.
The data will be analyzed by performing sentiment analysis on the comments and using an entropy function on the reactions for each post. The comment analysis is both focused on whether a comment contains more negative or positive words, and if it contains more personal or professional related words. My hypothesis is that female politicians may have comments directed at them that are both more negative, and more focused on personal issues. I am using an entropy function on the reactions to each post to measure how divided the reactions are. Related work used an entropy function on reactions to measure the controversy of a post. My hypothesis is that, in general, posts by female politicians will be more controversial than posts by male politicians.
My project aims to develop models where we can predict the risk of having cancer based on both numerical data and image data. After training all the models, they will be analyzed to see which has the best accuracy and possible ideas to improve the accuracy. After that we can decide the best model to use if we want to predict the risk of having cancer.
Since I uploaded the architecture design last week, this will I will go back to posting the normal updates here – I have been slowly working on my second model that I will compare my initial mode to. I have not faced any obstacles yet except the learning curve that comes with learning Keras, but since Keras is well documented it does not take much time for me to figure out something that I am stuck in. In the upcoming week, I will keep working on the second model and plan to have it finished by the end of spring break.
I fixed the problem where all returned images look the same. I started looking into how to parallelize the code and learning photography techniques to make images better.
I have also made a more presentable diagram, which I will also use in my paper and poster.
The overall design of the project:
The design of the User Interface/Data Finding process:
The design for the Data Formatting process:
The design for the Displaying Results process:
The Partial Machine Learning process has yet to be defined as this is a new development in my project and there needs to be more research in the best way to create something that would allow me to easily add future scripts to the work.
Information informs our entire lives. Information shapes public opinion which shapes things like public policy, elections, the health and safety of the public, and more. No one is above the harm that can come from misinformation, which is why we need to fight against its spread.
Fake News as an area of research is relatively new and so some of the aspects are not very well researched, and new aspects to research pop up. Some existing problems in this research are that all of the solutions to these aspects are made in isolation, therefore no one solution can be used to find all instances of fake news, and that most solutions do not have an accessible, comprehensive user platform to disseminate their solution to the people.
This solution that I will provide will be a functional model of a user platform that demonstrates how an engaging and accessible one-stop-shop for fake news detection can work. It allows the user to interact in many different ways that require different levels of effort and is able to scale to include many different automatic detection methods.
In the past week, I worked to finish implementing non content-based filtering which recommends products based on the user’s skin type and desired beauty effects. I was able to apply the concept of TF-IDF to judge which ingredients are heavily related to each beauty effect. Now that all my methods are working, I will implement widgets to the python notebook to create some sort of interface so that I don’t need to change the input each time. I will also start revising the paper and validating my method.
My project is about extracting features from images. Using low-cost collection techniques such as satellite imagery or drone surveys, a database of positive and negative cases can be created. Additional information will be extrapolated from each image in the database using a combination of modern algorithms and combined back into a single imager as different colored layers of a JPEG image. These processed images, the goal of which is to provide as much information as is possible, are used to train a machine learning model. Hypothetically, the additional information provided by the edge detection algorithms will enhance the accuracy and reliability of the machine learning model, reducing the need for expensive surveying equipment.
I am currently working on a proof that non-contiguous kPARKS is NP-Complete. I am also mostly done with my paper.
Please click on the title above to view my flow diagram.
This week I focused more on refining my idea and how it would flow for a user, which then helped me to create a flow diagram for this week. During this process, I realized some flows in my code were inefficient, so I changed the flow of information through certain functions to match up with my flow diagram.
I created a validation function to test a loaded model and also an argument parser to make it easier to pass values for different and important variables into the code.
I have been working on writing the literature review for ten papers on the topic I chose, which is “Detect Chinese Text in Images”. I also sketched out the workflow for my project and discussed with my advisor about this.
I read another six papers and wrote the second annotated bibliography for my three ideas. After reading the papers for my three ideas and doing some research on the sources of datasets, I chose my first idea, which is “Detect Chinese Text in Images”, to be my research topic.
I developed the feature extraction module for my project and it is working. It now converts a voice input file (.wav) to a sequence of acoustic feature vectors. I tested with my own voice. The two files of my voice recording produce two very different sequences of vectors. But I think we cannot tell my looking at these numbers. They are just a list of numbers of the .wav file. I am still having bugs on my modeling module. I followed Charlie’s suggestion to learn TensorFlow from the basic. I build and trained a model with TF’s dataset and it worked. But this is just a basic try. I will keep looking at it.
Last week CS was down so I couldn’t post my week6 updates. I finally finished the environment setting for my modeling module code. I am using a model called VGGVox Models which are created by the same authors of the dataset I am using. I almost gave up this resource because it is written in Matlab which I have never used before. But then I found a python resource guiding me how to import this model. However, I am still having bugs running this model. It says the true_fn and false_fn have different data types. I tracked the error and found that the error is in TF innate files which i cannot modify. But I don’t know which step that I pass data incorrectly.
My senior project is to develop a technology that provides higher performance and security for target applications. It is called unikernel which is an optimized library operating system. Unikernel consists of the minimum set of components that a target application requires from a complete operating system. Unikernel is light weight and has higher isolation than containers. It will be the trend of running environment for applications in many fields such as cloud computing and high performance computing in the near future.
In the past week, I worked on creating a survey to take inputs for content-based filtering, modified the skin type test questions, and obtained some responses. I also worked on implementing non-content-based filtering using TF-IDF which I am struggling with. I will be meeting with Xunfei on Thursday and try to finish this part as soon as possible.
My project aims to create a skincare product recommender system based on the user’s skin type and ingredient composition of a product. The main component of the project is content-based filtering and the secondary component is non content-based filtering. For content-based filtering, a user provides his or her skin type and selects a skincare product from sephora.com. The system then identifies the chemical components of products and uses cosine similarity to recommend products that have similar ingredient compositions. 5 recommendations for each product category are then made and returned to the user. Non content-based filtering allows users not to input the product if they lack knowledge or have not found a product they like. A user provides his or her skin type and desired beauty effect to obtain top 5 product recommendations across all 6 categories.