For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a
significant percentage of defects observed in real-world codebases belong to this
category. Existing work focuses on semantic static analysis, while this thesis attempts to
tackle the challenges that can be solved using syntactic static analysis. This thesis
proposes a tool for quickly identifying defects at the time of injection due to
dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in
string identifiers. The proposed solution reduces the delta (time) between defect injection
and defect discovery with the use of a dedicated just-in-time syntactic string identifier
resolution tool. The solution focuses on modeling the nature of syntactic dependencies
across the stack, and providing a tool that helps developers discover such dependencies.
This thesis reports on an empirical study of the tool usage by developers in a realistic
scenario, with the focus on defect injection and defect discovery times of defects of this
nature (syntactic errors in string identifiers) with and without the use of the proposed
tool. Further, the tool was validated against a set of real-world codebases to analyze the
significance of these defects.
Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.
Anthemy is a web app that I created so that Spotify users could connect with other uses and see their listening statistics. The app has a chat feature that matches concurrent users based on a variety of search criteria, as well as a statistics page that contains a breakdown of a user's top artists, songs, albums, and genres as well as a detailed breakdown of each of their liked playlists.