26,846 research outputs found
VMEXT: A Visualization Tool for Mathematical Expression Trees
Mathematical expressions can be represented as a tree consisting of terminal
symbols, such as identifiers or numbers (leaf nodes), and functions or
operators (non-leaf nodes). Expression trees are an important mechanism for
storing and processing mathematical expressions as well as the most frequently
used visualization of the structure of mathematical expressions. Typically,
researchers and practitioners manually visualize expression trees using
general-purpose tools. This approach is laborious, redundant, and error-prone.
Manual visualizations represent a user's notion of what the markup of an
expression should be, but not necessarily what the actual markup is. This paper
presents VMEXT - a free and open source tool to directly visualize expression
trees from parallel MathML. VMEXT simultaneously visualizes the presentation
elements and the semantic structure of mathematical expressions to enable users
to quickly spot deficiencies in the Content MathML markup that does not affect
the presentation of the expression. Identifying such discrepancies previously
required reading the verbose and complex MathML markup. VMEXT also allows one
to visualize similar and identical elements of two expressions. Visualizing
expression similarity can support support developers in designing retrieval
approaches and enable improved interaction concepts for users of mathematical
information retrieval systems. We demonstrate VMEXT's visualizations in two
web-based applications. The first application presents the visualizations
alone. The second application shows a possible integration of the
visualizations in systems for mathematical knowledge management and
mathematical information retrieval. The application converts LaTeX input to
parallel MathML, computes basic similarity measures for mathematical
expressions, and visualizes the results using VMEXT.Comment: 15 pages, 4 figures, Intelligent Computer Mathematics - 10th
International Conference CICM 2017, Edinburgh, UK, July 17-21, 2017,
Proceeding
Deriving Models for Software Project Effort Estimation By Means of Genetic Programming
Software engineering, effort estimation, genetic programming, symbolic regression. This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships between the project’s features and the required work. We selected to investigate the effectiveness of this methodology into two software engineering domains. The system was proved able to generate models in the form of handy mathematical expressions that are more accurate than those found in literature.
Performance Evaluation and Optimization of Math-Similarity Search
Similarity search in math is to find mathematical expressions that are
similar to a user's query. We conceptualized the similarity factors between
mathematical expressions, and proposed an approach to math similarity search
(MSS) by defining metrics based on those similarity factors [11]. Our
preliminary implementation indicated the advantage of MSS compared to
non-similarity based search. In order to more effectively and efficiently
search similar math expressions, MSS is further optimized. This paper focuses
on performance evaluation and optimization of MSS. Our results show that the
proposed optimization process significantly improved the performance of MSS
with respect to both relevance ranking and recall.Comment: 15 pages, 8 figure
A study of the power spectral density of an FM signal
Mathematical expressions describe an FM signal modulated by a composite video wave. Three general spectrum equations for FM signals are obtained by using three types of video information
Divergence of high order Gaussian modes
A simple expression for the beam divergence from a laser oscillating in a high order Gaussian mode can be derived from the properties of the mathematical expressions for these modes
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