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Automatically multi-paradigm requirements modeling and analyzing: An ontology-based approach.

, , and . Sci. China Ser. F Inf. Sci., 46 (4): 279-297 (2003)

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Measuring Inconsistency in Requirements Specifications., , , and . ECSQARU, volume 3571 of Lecture Notes in Computer Science, page 440-451. Springer, (2005)Automatically multi-paradigm requirements modeling and analyzing: An ontology-based approach., , and . Sci. China Ser. F Inf. Sci., 46 (4): 279-297 (2003)An Approach to Generating Proposals for Handling Inconsistent Software Requirements., , and . KSEM, volume 7091 of Lecture Notes in Computer Science, page 32-43. Springer, (2011)Learning Embeddings of API Tokens to Facilitate Deep Learning Based Program Processing., , , and . KSEM, volume 9983 of Lecture Notes in Computer Science, page 527-539. (2016)Verification Based on Hyponymy Hierarchical Characteristics for Web-Based Hyponymy Discovery., , , and . KSEM, volume 8793 of Lecture Notes in Computer Science, page 81-92. Springer, (2014)TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing., , , , and . CoRR, (2014)Measuring the blame of each formula for inconsistent prioritized knowledge bases., , and . J. Log. Comput., 22 (3): 481-516 (2012)Toward Better Summarizing Bug Reports With Crowdsourcing Elicited Attributes., , , , and . IEEE Trans. Reliab., 68 (1): 2-22 (2019)TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer., , , , , , , and . IEEE ACM Trans. Comput. Biol. Bioinform., 20 (6): 3623-3634 (November 2023)CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism., , , , , , , and . Bioinform., (February 2023)