In 2022, the revenue generated from accounting services hit an all-time high of 119.48 billion USD (“Accounting Services in the US - Market Size”, 2022). On top of this, research has shown that 45% of all accounting professionals would like to automate something about their workflow (Thomas, 2020). Indeed, a lot of bookkeeping accountancy has been phased out by simple automation. However, larger accounting tasks like business mergers still require a team of accountants despite being a largely iterative process. This project chronicles one such attempt at automating accounting events or transactions that are performed by businesses both large and small. With the help of accounting students Madeline Stolper and Heddie Liu we were able to build a fully-functioning website to automate accounting transactions. For this project, we used industry-standard software frameworks React and Express to build the site with dynamic accounting applications. These applications were built with reusable components, making the development of future applications very simple. We also leveraged cutting-edge technological solutions from Amazon Web Services to make the website available on the Internet with rapid response times. Lastly, we incorporated an agile approach to project management and communication, in order to create functionality in the most efficient and organized manner possible. On a large scale, something like this has never been attempted and TurboIFRS/GAAP represents a revolutionary leap in accounting automation.

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.

The process of learning a new skill can be time consuming and difficult for both the teacher and the student, especially when it comes to computer modeling. With so many terms and functionalities to familiarize oneself with, this task can be overwhelming to even the most knowledgeable student. The purpose of this paper is to describe the methodology used in the creation of a new set of curricula for those attempting to learn how to use the Dynamic Traffic Simulation Package with Multi-Resolution Modeling. The current DLSim curriculum currently relates information via high-concept terms and complicated graphics. The information in this paper aims to provide a streamlined set of curricula for new users of DLSim, including lesson plans and improved infographics.

The HLA, Human Leukocyte Antigens, are encoded by a polymorphic set of genes where even a single base change can impact the function of the body’s immune response to foreign antigens [1]. Although many methods exist to type these alleles using whole-genome sequencing (WGS), few can use RNA sequencing (RNA-seq) to show the functional expression of the alleles with its inconsistency in coverage, and none of these allow for novel allele discovery. We present an approach using partially ordered graphs to project sequenced data onto the known alleles allowing for accurate and efficient typing of the HLA genes with flexibility for discovering new alleles and tolerance for poor sequence quality. This graph-guided approach to assembling and typing the HLA genes from RNA-seq has applications throughout precision medicine, facilitating the prevention and treatment of autoimmune diseases where allele expression can change. It is also a necessary step for determining donors for organ transplants with the least likelihood of rejection. This novel approach of combining database matching with partially ordered graphs for assembling genetic sequences of RNA-seq data could be applied towards typing other alleles.