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pro vyhledávání: '"Martínez, Gil"'
Autor:
Martinez-Gil, Jorge
Assessing the degree of similarity of code fragments is crucial for ensuring software quality, but it remains challenging due to the need to capture the deeper semantic aspects of code. Traditional syntactic methods often fail to identify these conne
Externí odkaz:
http://arxiv.org/abs/2410.05275
Autor:
Martinez-Gil, Jorge
This paper presents a novel approach for source code similarity detection that integrates an additional output feature into the classification process with the goal of improving model performance. Our approach is based on the GraphCodeBERT model, ext
Externí odkaz:
http://arxiv.org/abs/2408.08903
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tai
Externí odkaz:
http://arxiv.org/abs/2407.02106
Autor:
Martinez-Gil, Jorge
The capability of accurately determining code similarity is crucial in many tasks related to software development. For example, it might be essential to identify code duplicates for performing software maintenance. This research introduces a novel en
Externí odkaz:
http://arxiv.org/abs/2405.02095
Autor:
Martinez-Gil, Jorge
Assessing similarity in source code has gained significant attention in recent years due to its importance in software engineering tasks such as clone detection and code search and recommendation. This work presents a comparative analysis of unsuperv
Externí odkaz:
http://arxiv.org/abs/2401.09885
Autor:
Martinez-Gil, Jorge
Data catalogs play a crucial role in modern data-driven organizations by facilitating the discovery, understanding, and utilization of diverse data assets. However, ensuring their quality and reliability is complex, especially in open and large-scale
Externí odkaz:
http://arxiv.org/abs/2307.15464
Autor:
Martinez-Gil, Jorge
Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategie
Externí odkaz:
http://arxiv.org/abs/2307.00925
Autor:
Martinez-Gil, Jorge
The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have
Externí odkaz:
http://arxiv.org/abs/2305.03520
Autor:
Martinez-Gil, Jorge
This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of wo
Externí odkaz:
http://arxiv.org/abs/2301.00374