The most significant risks that need to be mitigated is, again, our ML model accuracy, but now also our user testing. Our ML models are still currently not finished, and are not working at the level of accuracy we expected. However, we can mitigate this if absolutely necessary by allowing professors to provide written graph descriptions for each graph in their slide deck, then parsing these descriptions ourselves in order to output accurate descriptions. As for user testing, we want to try to have users test our product the following week, but this might not be viable because our integration is still not done. So, our mitigation plan is to collect data during the demo for our final report.
We changed the design of the system to require the professors to add bolded slide numbers with a red bounding box around them in order to increase the accuracy of our slide matching model, since without it, the slide matching model performed very poorly. We also limited our scope to just be line graphs and scatterplots because we did not have enough data or time to label new data for the other kinds of graphs. This would also increase the time we could spend on these two graphs, and thus produce a more accurate output.
No changes to the schedule.
See Nithya and Jaspreet’s status reports for images of the working slide matching ML model and 3D printed component case.