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26 results for “UML”
A dataset used to determine a semantic similarity metric based on UMLS for PMC-OA
<p>We have performed a series of in-silico experiments in order to determine a semantic similarity metric based on UMLS annotations for PubMed Central Open Access. Here we have stored the data used for and obtained from such experiments. We have worked with relevant and partially relevant articles from the TREC-2005 Genomics Track Collection, from now referred as the initial collection, including a total of 4240 unique PubMed articles. From those 4240 articles, only 62 had publicly available; those 62 articles correspond to the full-text collection.</p> <p>Our data comprises flat files using tabs as separators and one Excel sheet. Tab separated values always include a first row with headings:</p> <ul> <li>Stems extracted from title and abstract for articles in the initial collection. Each row contains a stem with its inverse-document-frequency (IDF) within the initial collection. Stems were calculated following the Porter algorithm (available at http://tartarus.org/martin/PorterStemmer/java.txt) <ul> <li>stems.TA.tsv</li> </ul> </li> <li>Article profiles, i.e., terms (either word stems or UMLS concepts) found in the articles with term frequency (TF) and IDF. The first two columns correspond to PubMed Identifier (PMID) and PubMed Central identifier (PMC). PMC identifier was set to 0 whenever full-text was not available. <ul> <li>profiles.TA.tsv: Profiles according word stems in title and abstract for the initial collection</li> <li>profiles.PMID.tsv: Profiles according to UMLS concpets in title and abstract for the initial collection</li> <li>profiles.PMC_TA.tsv: Profiles according to UMLS concepts in title and abstract for the full-text collection</li> <li>profiles.PMC.tsv: Profiles according to UMLS concepts in the full-text for the full-text collection</li> </ul> </li> <li>Similarity matrixes calculated on the article profiles with PubMed Related Article metric (PMRA), BM25, and Cosine. There are matrixes for terms found in title-and-abstract as well as full-text. In a similarity matrix, a reference article (an interest has been already expressed for it) correspond to a row, while the columns correspond to all the other articles for which the similarity was calculated. <ul> <li>Matrixes for our initial collection <ul> <li>similarity.PMRA.TA.profiles.TA.tsv: Similarity matrix for profiles.TA.tsv following the algorithm PMRA. This matrix is considered the baseline for further analyses</li> <li>similarity.PMRA.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm PMRA</li> <li>similarity.BM25_1.2_0.75.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.COSINE.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm Cosine</li> </ul> </li> <li>Matrixes for our full-text collection <ul> <li>similarity.PMRA.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm PMRA</li> <li>similarity.PMRA.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm PMRA</li> <li>similarity.BM25.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.BM25.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm BM25 with k= 1.2 and b= 0.75</li> <li>similarity.COSINE.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm Cosine</li> <li>similarity.COSINE.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm Cosine</li> </ul> </li> </ul> </li> <li>Correlation matrixes for similarities calculated for title-and-abstract taking as reference the similarity values obtained with PMRA for word stems on title-and-abstract. <ul> <li>pearsonCorrelation.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv discriminated by TREC topics</li> </ul> </li> <li>Precision and recall summaries for the similarities calculated based on title-and-abstract. <ul> <li>StatsAllSummary.xlsx: Precision and recall at a global level, i.e., without considering TREC topics. This file includes information for BM25 with multiples values for constants k and b</li> </ul> </li> </ul> <p>Visualization for correlation matrixes as well as scattered plots for full-text based similarity is available at http://ljgarcia.github.io/semsim.benchmark</p>
ChatGPT's Aptitude in Utilizing UML Diagrams for Software Engineering Exercise Generation
<p>The integration of Artificial Intelligence (AI) technologies into educational settings has paved the way for innovative teaching and learning approaches. In Software Engineering (SE) education, using Unified Modeling Language (UML) diagrams is a fundamental teaching element for understanding complex software systems. This research addresses the ability of ChatGPT to utilize UML class and sequence diagrams for creating SE modeling exercises. We use ChatGPT to generate exercises based on the information from uploaded UML diagrams by analyzing textual UML representations such as Mermaid and graphical diagrams. The research explores ChatGPT's ability to synthesize UML-specific information from class and sequence diagrams, enabling the generation of various exercises tailored to strengthen conceptual understanding and practical application. Furthermore, we investigate generating graphical UML class and sequence diagrams based on natural language as input. By bridging the gap between AI-driven natural language understanding and the comprehension of UML diagrams, this study highlights the potential of ChatGPT to improve SE education. Our concise findings address educators, practitioners, and other researchers engaged in the field of SE education with a special focus on UML.</p>
Supplementary Material for "To Do or Not to Do: Semantics and Patterns for Do Activities in UML PSSM State Machines"
<p>This dataset provides artifacts about the semantics of doActivity in the Precise Semantics of UML State Machines (PSSM) specification. It collects:</p> <ul> <li>execution traces and screenshots from two simulators (Cameo, Papyrus Moka),</li> <li>analysis about doActivity features present in PSSM test suite, focusing on concurrency,</li> <li>collection of state machine models and doActivity patterns used in the Thirty Meter Telescope (TMT) SysML model.</li> </ul> <p>Find the related paper at <a href="https://arxiv.org/abs/2309.14884" target="_blank" rel="noopener">arXiv:2309.14884</a>.</p>
UMLS-Wikidata
<p><em>UMLS_Wikidata</em> is a German biomedical entity linking knowledge base that provides good coverage for German entity linking datasets such as <a href="../records/8188966">WikiMed-DE-BEL</a>. The knowledge base is created by filtering out the Wikidata items that contain the Concept Unique Itentifier (CUI) of UMLS. Each entry in the knowledge base consists of Wikidata QID, label, description, UMLS CUI and aliases. The resulting KB has 731,414 Wikidata QIDs, 599,330 unique CUIs and 671,797 unique (mention, CUI) pairs where mention includes <em>label</em> and <em>aliases</em>. </p>
Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"
<p>Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."</p>
The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 2. The UML representation of the artefact as it exists as a metaphor
<p>The accumulation of energy that exists in each ingenious metaphor is progressively released, thus contributing to the transformation of a theoretical promise into effective reality. The artefact successively goes through several maturation stages, as the creator is preoccupied with obtaining an as precise and as close as possible description of the artefact as it exists as a metaphor. The completion of these successive stages is achieved through a methodic abstraction process, while leaving open the possibility to innovate and targeting three main objectives: — broadening the abstraction scope; — adding new details; — detecting and eliminating abstraction errors.</p>
UML Diagram Dataset from the paper Creating and Validating a Ground Truth Dataset of UML Diagrams Using Deep Learning Techniques
<p>Dataset of six UML diagram classes, comprising a total of 2,626 images (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams). Importantly, unlike other existing datasets, ours contains no duplicate elements and all diagrams are correctly classified.</p>
Urheberrechtsgesetz (UrhG): software and data related definitions depicted using UML class modeling — Release 10
<p>Diagram depicting definitions and conceptual relationships within the German Copyright and Related Rights Act or Urheberrechtsgesetz (UrhG) up to and including the amendments of 28 November 2018. The diagram focuses on software and data related definitions and presents these using Unified Modeling Language (UML) class modeling. Please contact the author is you require the underlying Inkscape SVG vector art.</p>
The Chilean Waiting List sub-Corpus with medical entities normalized to UMLS terminology
<p>A collection of 2000 medical referrals from the Chilean Waiting List Corpus, manually annotated with six entity types (Finding, Procedure, Disease, Family Member, Body Part, and Medication) and manually normalized to the Unified Medical Language System (UMLS).</p>
Dataset of empirical study on product configuration and traceability in UML-based product-lines
<p>Dataset of empirical study on product configuration and traceability in UML-based product-lines</p>
Evaluating usefulness, ease of use and usability of an UML-based Software Product Line Tool
<p>Vídeo de apresentação do artigo "Evaluating usefulness, ease of use and usability of an UML-based Software Product Line Tool" para o 34° Simpósio Brasileiro de Engenharia de Software (SBES'20), 21-23 de Outubro de 2020, Natal, RN, Brasil. Autores: Leandro F. da Silva e Edson OliveiraJr.</p>
Visual Variables in UML: a First Empirical Assessment
<p>We join the 8 anonymised interviews transcriptions, a word document containing the important information for our analysis.</p> <p>We also joint the results of the classification of the diagrams related to open source projects from GitHub: excel document and the images themselves. </p>
Code generation for classical-quantum software systems modelled in UML - Dataset and EGL Transformation
<p>This dataset contains all the elements necessary for carry out the EGL transformation from UML models to Hybrid and Quantum code, as well as to carry its validation. </p> <blockquote> <p><em>Quantum computing is gaining an increasing interest since it can solve certain problems exponentially faster than classical computing. Thus, many organizations are researching and launching investments for integrating quantum software into their existing systems. Software modernization (as based on Model-Driven Engineering) has been proposed to migrate from/to the so-called hybrid software systems, which integrate classical and quantum software. In that process, both, reverse engineering and restructuring phases, have already been investigated. However, forward engineering phase for generating hybrid source code from high-level design models has not yet been addressed. Thus, this research proposes a quantum code generation technique from extended UML design models. It consists of a set of Model-to-Text transformations (defined through Epsilon Generation Language) to generate both Python and Qiskit code, which respectively integrate classical and quantum code. The transformation has been validated through a multi-case study with 7 hybrid software systems modelled in UML, which demonstrated that the transformation is effective and efficient. The implication of this work is that the software modernization process for hybrid software systems can be completed by tackling forward engineering phase, and that Model-Driven Engineering can therefore globally facilitate industry adoption of quantum software.</em></p> </blockquote>
An integrated framework for analysing, simulating and testing UML models
<h1>Project Overview</h1> <p>This is the artefact associated with the paper "An integrated framework for analysing, simulating and testing UML models", submitted to the 27th Brazilian Symposium on Formal Methods (SBMF 2024).</p> <h2>Directory Structure</h2> <ul> <li><strong>CSP_Validation</strong>: Contains the validation of our mapping rules from UML diagrams to CNL requirements.</li> <li><strong>NAT2TEST_Projects</strong>: Contains the NAT2TEST projects for two case studies: the classical Dijkstra's dining philosophers problem, and a distributed ring-buffer model.</li> <li><strong>UML2CNL</strong>: Contains the implementation of our mapping rules from UML diagrams to CNL requirements.</li> </ul>
Detecting Patterns In UML Diagrammatic Readings - An Experimental Study
<p>This experiment was developed within the scope of the thesis "Constructing An Auditory Notation In Software Engineering - Understanding UML Models with voice and sound", with the main purpose of understanding if there any emerging patterns in how different people carry out diagrammatic readings of UML diagrams (Class and State Machine Diagrams, specifically). This is achieved by observing the most common actions amongst the participants.<br> <br> The transcriptions of this study are included here.</p>
A Catalogue Of Sounds For UML Class Diagrams - Experimental Study
<p>This experiment was developed within the scope of the thesis "Constructing An Auditory Notation In Software Engineering - Understanding UML Models with voice and sound", and aims to determine if the construction of a catalogue of sounds that follows a more methodical approach is more effective for comprehension, when compared to a catalogue where the choice for sounds is purely arbitrary.</p> <p> </p> <p>Here are contained the results, along with the sounds used and the form that was presented to the participants.</p>
Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
<p><strong>Objective</strong>: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥ 95%.</p> <p><strong>Materials and Methods</strong>: We used train-test datasets from successive 2020AA-2022AB UMLS Metathesaurus releases. Our heuristic "waterfall" approach employed a sequence of seven different SG prediction methods. Atoms not qualifying for a method were passed on to the next method. The DL approach generated BioWordVec and SapBERT embeddings for atom names, BioWordVec embeddings for source vocabulary names, and BioWordVec embeddings for atom names of the second-to-top nodes of an atom's source hierarchy. We fed a concatenation of the four embeddings into a fully connected multi-layer neural network with an output layer of 15 nodes (one for each SG). Both methods were capable of estimating the probability that their predicted SG for an atom would be correct. We developed two hybrid SG prediction methods combining the strengths of heuristic and DL methods.</p> <p><strong>Results</strong>: The heuristic waterfall approach accurately predicted 94.3% of SGs for 1,563,692 new unseen atoms. The DL accuracy on the same dataset was also 94.3%. The hybrid approaches achieved an average accuracy of 96.5%.</p> <p><strong>Conclusion</strong>: Our study demonstrated that AI methods can predict SG assignments for new UMLS atoms with sufficient accuracy to be potentially useful as an intermediate step in the time-consuming task of assigning new atoms to UMLS concepts (CUIs). We showed that for SG prediction, combining heuristic methods and DL methods can produce better results than either alone.</p>
Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
Open the record for dataset details and reuse information.
Appendix for "Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
<p>This is an online appendix for the paper called "", submitted to LLM4CODE 2024.</p><p>The package contains:</p><ul><li>The diagrams used in the study.</li><li>Some example prompts we used.</li><li>The results of source-code generation. </li></ul><p> </p>
UML vs OntoUML analysis results
<p>The results of an exercise in which subjects answered a set of biological questions by using two versions of the same conceptual schema: one designed with UML and another one designed with OntoUML</p>
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OpenNeuro
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