Week 39: STA Presentation

Documentation:

Currently, I am testing how to apply the design into the virtual website. My main challenge is to incorporate the text on top of the image so that it can scale appropriately.

 

 

 

Project: UT Instapoll

Started on: Mar 26, 2024

Staff Guidance: Chris Pittman

Description: 

• Task: Change ‘Normal’ grading explanation for non-MC polls #110: The explanation for ‘Normal’ grading reads “Students must select a correct answer”. This makes sense for MC polls, but not for text entry or attendance polls. Change the wording here when the poll type changes.
• Visual Description:

Breakdown of the task:

Update:

Phase 1: Operation Finding Where To Change

• Identify in the codebase the explanation of normal (resources/js/components/PageTeacherPollSetDefaults.vue) -> Actually, this vue file is for the poll defaults. Might need to consider: Should we also change the explanation in the poll defaults? ✅
• The actual location of the file is PageTeacherPollEdit instead of PageTeacherPollSetDefaults ✅
• After changing the file, need to do npm run build to update the vue file -> The PageTeacherPollEdit works ✅
• Change the explanation to some other text and test it out ✅

In this screenshot, the normal becomes crazy normal (works) ✅

Phase 2: Experiment with if/else statement

• Include if/else statement to change the explanation when the question type changes (include v-if for that label)
  Might do something like this so that whenever it detects the question type to “text entry”, the explanation changes to something else. ✅

• The third steps include the knowledge of how to retrieve the question type and when to trigger the change in the question type (only use if/else statement is sufficient) ✅
  Might look something like this:

– Test it fully with UT Instapoll

Some considerations:

• The text: What should the explanation for multiple choice be?
• Is this the optimal solutions
• Are there any cases where it breaks? (Think about edge cases?)

Something to take in mind when changing:

• Whenever the vue.js file gets updated, always npm run build
• Always use === instead of only == for stronger comparisons.

 

Project: STA Presentation

Started on: Mar 27, 2024

Staff Guidance: Mike, De’sha

Description: 

• Plan out the STA presentation outline

https://docs.google.com/document/d/1j4EJSDJxxyTbDPehnLjNEdy0JwYLmTp3yKytZXwnaOo/edit?usp=sharing

 

 

WEEK 36 + 37

Project: Shiny App

Started on: Feb 26, 2023

Ended on: Mar 4, 2023

Staff Guidance: Stacy

Description: 

• Simply put: the slider works perfectly after I put the whole map with slider into a temporary HTML file.
• The server is created and hosted with deployment and production site. https://cliomaps.development.la.utexas.edu/
• If the server does not work, use this instead: https://clioviz.shinyapps.io/Cliomaps_2024_02_27/.
• The server site only works if you are on campus. The client loves it.
• Use this CSV file for testing out the app: https://raw.githubusercontent.com/BrianTruong23/amazon_predicting_project/master/Voyages_2024_01_24.csv

 

 

Project: Shiny App (Part 2)

Started on: Mar 4, 2023

Ended on: Mar 7, 2023

Staff Guidance: Stacy

Description: 

• The client wants a new map to be hosted and developed in shiny app.
• The process is very similar to the previous project when I need to display a fully functional map with sliders and zoom working properly.
• Therefore, applying what I know from before, I can generate a temporary HTML file and display that on the website
• Also, I just finished the functionality of downloading the map.

 

Project: Whisper AI

Started on:  Feb 19, 2023

Ended on:  Mar 4, 2023

Staff Guidance: Chris

Description: Finish the evaluation for Whisper AI 

Whisper AI 

• The Whisper AI converts the audio file into a spectrogram to analyze and predict the corresponding text captions.
• It takes a 30 second chunk of input, converts it into a log-Model spectrogram, analyzes the image and applies encoding-decoding architecture to predict the text caption based on those images. 

Some key insights: 

• Lower HST produces much cleaner version of text (not including filter words as much as higher HST (from 0.2 to 0.5)) 
• There are some variation of inference ability of each time: eg. truth work done instead of tooth work done. Therefore, a lot of words are predicted differently 
• Even with the same threshold, there is variation in running the models from time to time. 
• A lot of models do not capture the first intro sentence from the lady. 
• 1.3 is the fastest and 0.1 is the slowest -> Might suggest that as the parameter increases, the time it takes is faster -> It means it does not need to re-process so many times. With 0.1, it means that it re-process every 0.1 sec after silence is detected -> suggesting more time to process

Link