PolyFlowBuilder: An Intuitive Tool for Academic Planning at Cal Poly San Luis Obispo

PolyFlowBuilder is a web application that lets users create visually intuitive flowcharts to aid in academic planning at Cal Poly. These flowcharts can be customized in a variety of ways to accurately represent complex academic plans, such as double majors, minors, taking courses out- of-order, etc. The original version of PolyFlowBuilder, released Summer 2020, was not written for continued expansion and growth. Therefore, a complete rewrite was determined to be necessary to enable the project to grow in the future. This report details the process to completely rewrite the existing version of PolyFlowBuilder over the course of six months, using NodeJS, SvelteKit, TypeScript, MySQL, Prisma, and TailwindCSS + DaisyUI for the primary tech stack. The project was determined to be largely successful by a variety of holistic evaluation criteria, with the main limiting factor to complete success being time constraints. The rewritten version of PolyFlowBuilder will ensure the project’s continued success

UML Profile for Mining Process: Supporting Modeling and Simulation Based on Metamodels of Activity Diagram

An UML profile describes lightweight extension mechanism to the UML by defining custom stereotypes, tagged values, and constraints. They are used to adapt UML metamodel to different platforms and domains. In this paper we present an UML profile for models supporting event driving simulation. In particular, we use the Arena simulation tool and we focus on the mining process domain. Profiles provide an easy way to obtain well-defined specifications, regulated by the Object Management Group (OMG). They can be used as a presimulation technique to obtain solid models for the mining industry. In this work we present a new profile to extend the UML metamodel; in particular we focus on the activity diagram. This extended model is applied to an industry problem involving loading and transportation of minerals in the field of mining process.Fil: Giubergia, Andrea. Universidad Nacional de San Luis. Facultad de Ciencias Fisico- Matematicas y Naturales; ArgentinaFil: Riesco, Daniel. Universidad Nacional de San Luis. Facultad de Ciencias Fisico Matematicas y Naturales. Departamento de Informatica; ArgentinaFil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Printista, Alicia Marcela. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; Argentin

Bounding the Computational Complexity of Flowchart Programs with Multi-dimensional Rankings

Proving the termination of a flowchart program can be done by exhibiting a ranking function, i.e., a function from the program states to a well-founded set, which strictly decreases at each program step. A standard method to automatically generate such a function is to compute invariants for each program point and to search for a ranking in a restricted class of functions that can be handled with linear programming techniques. Our first contribution is to propose an efficient algorithm to compute ranking functions: It can handle flowcharts of arbitrary structure, the class of candidate rankings it explores is larger, and our method, although greedy, is provably complete. Our second contribution is to show how to use the ranking functions we generate to get upper bounds for the computational complexity (number of transitions) of the source program, again for flowcharts of arbitrary structure. This estimate is a polynomial, which means that we can handle programs with more than linear complexity. We applied the method on a collection of test cases from the literature. We also point out important extensions, mainly to do with the scalability of the algorithm and, in particular, the integration of techniques based on cutpoints