Week 13: 11/22/2016 - 11/29/2016

This week I listened to Jenna's presentation of 'Multi-Label Classification: An Overview'. It was an evaluation of different classification methods to compute the best accuracy of each multi-label by using problem transformation methods. Three problem transformation methods were implemented in conjunction with the algorithms kNN, Naive Bayes and an addition of Support Vector Machine (SMO). The dataset focused on Genbase and Scene, and the best results were given when each set of labels was considered a single label and used the SMO algorithm. This performance achieved the highest mean accuracy for all of the learning algorithms used within each data set. 

On Saturday, Jenna will be giving a talk on Spark and data analysis with Tweet data. There, I will gain further insight on Apache Spark and learning Scala. I've been experimenting with test data on my machine, and I hope to gain more experience with machine learning algorithms.

Week 13: 11/22/2016 - 11/29/2016

This week we switched eye-tracking labs. I now have access to a different lab and a different machine. I was able to import my project there. This is where I will conduct my study in the upcoming week (I hope to get at least 5 participants to present data for my capstone). I configured the project to feature AOIs, which will be helpful when analyzing the data.  I also worked on some documents that go along with my study. I finalized the pre and post survey, this contains questions about personal background and details a participants experience with C/C++ and Stack Overflow. I think having this data will be interesting to compare to how they read and assign tags. I also updated the one page study to add new information and update any changes that have been made since I first wrote the summary (i.e. a sample screen to get users familiar with the layout they will be reading from).

Week 12: 11/15/2016 - 11/22/2016

For Week 12 I continued my work in the lab with our project. I noticed errors in the slide screen captures that had to be addressed. Since the images were changed, the current test data I had had to be deleted (since it would not be complete or correspond to the corrected screen).

For our ACM-W chapter, Alyssa and I did class visits to courses that contain first year students. We hoped to get some new recruits in as our chapter will lose some in the coming months with people graduating. We also talked about Penguin Hackers and ACM.

Week 12: 11/15/2016 - 11/22/2016

This past week I traveled to San Francisco, CA for an onsite interview with Pure Storage, visited Carnegie Mellon University (CMU) for a graduate school visit, and reviewed Alyssa's write-up of the paper, Predicting Tags for Stack Overflow Questions.

My onsite interview with Pure Storage lasted three hours. I received a tour of their office, had two technical interviews, and one behavioral interview. The Pure Storage office was very inviting. It had open desks and discussion rooms, white boards, two kitchens, and a game room. The first technical interview was difficult, because I wasn't used to the type of question I was asked. The second technical interview went really well. I was able to come up with two different solutions to the problem and discuss the pros and cons of using both. The behavioral interview also went well. I asked about the different types of projects available for me to work on as an intern and how my skills would fit those projects. Unfortunately, Pure Storage doesn't want to more forward with me as an intern for the summer, but the onsite interview was a great learning experience as this was my first one.

The highlight of my week was my graduate school visit to CMU. I spoke with four different faculty members and three different graduate students about their research in software engineering, programming languages, and machine learning. I also learned about the graduate student culture and about helpful tips for my application. The research I found the most interesting after all of my discussions was research to turn the written English language into code automatically. It encompasses all of my interests in machine learning and software engineering. After my visit, I can confirm that CMU is my top choice for graduate school.

Week 12: 11/15/2016 - 11/22/2016

This week I read and summarized 'Predicting Stack Overflow Tags', part of the Kaggle competition, and installed Apache Spark on my machine. Ali and I reached out to the Programming and Problem Solving 2610 class to promote the women's ACM group on our campus. We feel it's important to get involved in organizations designed to engage women in information technology and computing fields. 

Week 11: 11/8/2016 - 11/15/2016

This past week was pretty busy! I prepared for and had a Facebook phone interview on Friday, 11/11/16. I think it went pretty well. I was able to write almost a full working solution to the problem that was posed and to analyze the run-time. I spoke with an engineer who works on the back-end of the search feature of the Facebook website. His work involves combining different search result features into one system that searches for multiple different types of information in one search. It also tries to figure out which information would be most important to the searcher. Unfortunately, I was not chosen to move on to the Host Matching for Google, but I enjoyed the interview process.

I also studied for and took the GRE for a second time on Tuesday, 11/15/16. I did a little bit better in quantitative analysis than I did before, but I went down by 1 point in verbal reasoning. Overall my scores from the past two times I took it (including this time) puts me in the 70th percentile in verbal and the 76th percentile in quantitative. Ideally I would like to do better, but overall I am happy with my scores.

Finally, I made minor edits to and submitted our poster presentation proposal to OCWiC 2017 and reviewed the paper write-up that Alyssa did for the paper, Tag Recommendation in Software Information Sites.

Week 11: 11/8/2016 - 11/15/2016

This week I got to do some hands on work with the project. I was able to get into the eye-tracking lab and do some testing with the Tobii studio project on the machine we will be running experiment on. Since I created and configured the project on a smaller screen, viewing in the lab uncovered some room for improvement. So I did some work on creating visuals that were tailored to the screen size we would be using. After making some changes I was able to make our task samples larger and clearer, we hope this will help in the preciseness of the data we gather in the near future. Alyssa and I ran some tests and were able to see how some real data comes out from this software. Since we are about a month out from the end of the semester I plan on coming up with my capstone presentation soon. I hope to get a larger data set from participants in the next week or so to have some solid examples to present for this.

Week 11: 11/8/2016 - 11/15/2016

This week I read 'Tag Recommendations in Software Information Systems' and presented the topic to our group. A tag prediction system, Tag Combine, collects tags from CQA sites like Stack Overflow or Freecode and removes 'stop words' from each block of text. A multi-label learning algorithm is then used to build a multi-label classifier and give ranking scores of each tag based on similarity, tag terms and multi-labels. The results of Tag Combine outperformed earlier methods like Tag Rec by 22.65%, and the authors hope to achieve a higher percentage in the future. 

Next week, I'll continue to read about the successes of tag prediction systems and download Apache to my machine to start analyzing machine learning algorithms in preparation for the analysis of our gaze data. 

Week 10: 11/1/2016 - 11/8/2016

This week we continued putting the finishing touches on our OCWIC submission. I joined Jenna & Dr. Sharif in working on the poster and it was interesting to see more in depth how LaTeX worked, as it was always something I wanted to know how to use. Additionally, Alyssa presented an abstract, along with a one-page write-up on our project. We discussed as a group and made some changes to formatting and content. I plan on this week continuing through with our experiment. As my Tobi project is done I will be working in the eye-tracking lab running some tests and gathering some data.

Week 10: 11/1/2016 - 11/8/2016

This past week I finished our poster presentation's abstract and proposal for the 2017 Ohio Celebration of Women in Computing Conference. I will be presenting the expertise prediction work that we performed during the CREU 2014-2015 year. I learned how to modify .cls files in LaTeX to improve the formatting of the LaTeX proposal. I also learned how to find the most important details of our research in order to cut the proposal to one page. I look forward to submitting the proposal and abstract this week.

I also had two technical phone interviews with Google last Wednesday, 11/2/2016. I think they went well. I was able to come up with pseudo code or an algorithm for each problem I was posed. I also was able to analyze the runtimes of the algorithms and suggest improvements to the algorithms to optimize them. I should find out if I am moving forward in their interview process this week. Either way, it was a good learning experience and test of my technical skills.

Week 10: 11/1/2016 - 11/8/2016

This week I finished the one page write-up of our project to submit to OCWIC, Ohio Celebration of Women in Computing. I also added to the list of features I've been collecting from each study our group presents in order to gain a better understanding of training data sets and using machine learning algorithms once we are ready to conduct our study. 

I also volunteered for the East Central NA Regional Programming Contest last Friday with Ali and other CSIS students at YSU. It was an interesting experience and I was amazed at how fast each team was able to solve each problem given in the short time span. 

Week 9: 10/25/2016 - 11/1/2016

This week I presented the paper Using Eye Tracking to Investigate Reading Patterns and Learning Styles of Software Requirement Inspectors to Enhance Inspection Team Outcome. This was a research study done on inspectors during software life cycle; particularly inspectors of the requirements stage of a life cycle. This research focused in on how someone's background (particularly their learning styles) would affect their efficiency and effectiveness when searching for faults that will have negative effects on software later. We considered how learning styles might affect someone's ability to review code snippets and how we could apply their findings to our study. Another interesting concept from this paper was how they took data from the inspector's eye tracking and created the what they called virtual teams. They used average values and patterns to determine what groups of learning styles would work best together find faults. The outcome of this study concluded that the inspectors who had reading patterns that was in order, beginning to end, weren't very efficient. The best learning style was "sequential", as defined by "Felder Silverman Learning Style Model".

Also this week, I finished up and submitted the application to get funding for our ACM-W chapter. This would significantly help jump start our chapter and allow us to do some activities we have planned this school year. Our chapter is also working on recruitment in order to offset the loss of some of our group graduating this year. Finally, I took some time out this weekend to help moderate the hackathon our university sponsored. This was a fun event where I got a first-hand look at what takes place during these contests.

Week 9: 10/25/2016 - 11/1/2016

This week, I prepared the abstract for our tag prediction study that we will submit to OCWIC on November 22nd. I also read 'Using Eye Tracking to Investigate Reading Patterns and Learning Styles of Software Requirement Inspectors to Enhance Inspection Team Outcome' to continue learning about how we can hone our analysis techniques. After reviewing the feature information of every study we have analyzed so far, it's safe to say that the user information along with history has more of an impact on the accuracy of results than one would think. 

Week 9: 10/25/2016 - 11/1/2016

This past week I worked on preparing our submission material for OCWiC 2017. We intend to present the poster that I presented at Tapia two months ago from the CREU 2015-2016 year. We also intend to present a talk about the research we are working on this year in CREU (Alyssa is preparing that submission material).

I also participated in the ACM ICPC Regional programming competition this past Saturday. My team and I completed 1 problem and with a little more time we think we would have completed 2 problems. It was a fun experience that was helpful preparation for my Google and Twitter interviews.

I have a Google technical phone interview tomorrow evening that I spent the last 5 days preparing for. I reviewed data structures and algorithms including run-times and space complexities. I also practiced coding problems from the Crack the Coding Interview book and from HackerRank.

I am also working on my online technical coding challenge for Twitter that is going well. I will be scheduling a technical phone interview with Facebook soon. It seems that my trip to GHC was very fruitful!