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2 results for “Category Theory”
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
<p>The file <strong>Corpora.txt </strong>keeps the corpus used to train the model and the different instances of the classifier. It is basically a text file with one sentence per line from the original corpus called <strong>test.tsv</strong> available at <a href="https://github.com/google-research-datasets/wiki-split.git">https://github.com/google-research-datasets/wiki-split.git</a>. We eliminated punctuation marks and special characters from the original file putting each sentence per line.</p> <p><strong>Enju_Output.txt </strong>holds the outputs generated by Enju in -so mode (Output in stand-off format) using Corpora.txt as input. This file has basically a natural language English per-sentence parse with a wide-coverage probabilistic for HPSG grammar.</p> <p>The file <strong>Supervision.txt </strong>keeps the grammatical tags of the corpus. This file holds a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file<strong> Word_Category.txt</strong> carries the coarse-grained word category information needed by the model and introduced in it by apical dendrites. Each word in the corpus has a word-category tag which provides additional constraints to those provided by lateral dendrites. This file contains a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file <strong>SynSemTests.xlsx</strong> keeps all the grammar classification results as well as the statistical analysis in the classification tests.</p>
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