An Analysis of the Impact of Advances in Generative Artificial Intelligence on Programming Assignments and Competitions

Recent advances in large language models have made large-scale generative artificial intelligence systems more widely available. One such system is ChatGPT, created by OpenAI, which has the ability to handle a variety of tasks, including answering complex questions, analyzing data, and engaging in contextual conversation. This paper assesses the impact of ChatGPT on programming assignments by examining its strengths and limitations in solving challenging problems presented in the IEEEXtreme programming contest problem set, which includes problems that were not included in ChatGPT's training data. ChatGPT's performance varies depending on the type of problem, performing well in identifying underlying concepts and generating basic solutions for easier to medium-level problems, but facing difficulties with edge cases and lengthy code for more complex problems. Additionally, ChatGPT's obfuscation mechanisms can make plagiarism detection more challenging, even when it cannot solve a problem completely.

Method and System for Tabular Information Extraction

A method and a system for extracting information from a table in a document is provided. The method includes: receiving a document that includes information that is arranged in a table; determining three sets of coordinates that respectively relate to lines, words, and characters included in the document; extracting a list of lines based on the first set of coordinates; reconstructing the rows of the table based on list of lines and the second set of coordinates; reconstructing the columns of the table based on the reconstructed rows and the third set of coordinates; and outputting a reconstruction of the table. The three sets of coordinates are expressible in an hOCR format that is based on an open standard for representation of scanned information that is obtainable by using an optical character recognition (OCR) technique.