Month: October 2020

There was not much work for me to do this week, since the machine learning data is not yet available. I spent most of my time on getting familiar with AWS and reflecting on the ethics readings.

For AWS, I read up on the S3 product. S3 stands for Simple Storage Service, and we may use it to securely maintain and access our data on the cloud. It has multiple tiers like Standard, Infrequent Access and Glacier for varying levels of access amounts and latency requirements. After reading up on all of them, I will use the Standard S3 bucket. It has a durability of 99.999999999% and availability of 99.99%. The high durability ensures that the data will not be lost and the high availability will ensure good enough latency for the purpose of training a machine learning model. Other than S3, I also plan to use EC2 for this project, but I had researched EC2 before this week and my research into it this week did not reveal any new information that changed my plans.

I also spent a good amount of time this week on the ethics readings. I found Langdon Winner’s paper particularly interesting. I didn’t know the extent to which simple design choices made by engineers affected society and culture. It has made me more mindful of my project and gotten me to think about unintended consequences that may arise. While working with data containing images of real people, I must be very careful that the data is secure and used only for the purposes of this project. Otherwise, it could be misused and violate people’s privacy.

This week, I tested the pipeline for the machine learning model using some pictures taken from my phone. Until the real data is available, the model cannot be trained. However, in the meanwhile we can ensure that when the data is available, the code is able to run smoothly.

When I had last created the pipeline, I did not have any data to run the code on. However, this week when I used the images I took from my phone, it revealed some bugs. None of the bugs were individually worth mentioning, but I spent a large amount of time debugging. Once the dataset from the cameras is available, we should be able to run them through the model and start with transfer learning.

I also read up on web sockets. Last week, I spoke to Arjun about how the server would communicate with the central node. We had two options, post requests and web sockets. Post requests come with problems regarding the CSRF tokens. On the other hand, I personally did not have a lot of knowledge about sockets. This week, after reading up on sockets and Django Channels, I can implement web sockets for communication with the central node. This will be faster than a post request, since the socket is constantly connected, and so data can be sent over faster. We still have the option of implementing Post requests, but as of now I will experiment with making a web socket connection.

According to the schedule, there is not much to be done for the machine learning aspect of the project until we are able to collect data. In the meanwhile, I worked on the website interface of the project. I set up the Django server which communicates both with the central node and the users. As of now, there is no information to transfer, so I used dummy information.

I spent a majority of my time this week researching the CSRF token. CSRF stands for cross site request forgery. It occurs when a malicious website sends an HTTP Post request from an unknowing user’s computer to another website. Since it is a post request, this can change the state in the other website’s server. This could have consequences ranging from posting information on social media on the user’s behalf without the user’s knowledge to something potentially more dangerous. As a result, it is common practice to send the user a cross site request forgery token. Only the user’s browser has access to this token and it is required to make post requests. As a result, any malicious website cannot post on behalf of the user.

In our project, the only entity to make a post request to the server is the central node, which will not interact with other websites. Thus, it may not require a CSRF token. Additionally, if we require a CSRF token, we would need a two-way communication channel between the central node and the server, since the server will have to send the token. Without the token, we could simplify our design so that the only communications are from the central node to the server. At the time of writing this, I am still in the process of researching. In the next few days, we should have made a decision about the CSRF token.

Next week, I will continue to work on the website. Additionally, if we are able to generate data, I can also start working on the machine learning model.