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In an effort to improve fairness or reduce backlogs, machine-learning models are sometimes designed to mimic human decision making, such as deciding whether social media posts violate toxic content po...
Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform bett...
Designers often seek modular architectures to better accommodate expected changes and to enable parallel development. However, we lack a formal theory and model of modularity and software evolution, w...
In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The i...
Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar).Though this practice improves translation by weakening independence a...
Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based systems that need parse trees from either or both sides of the bi-text. The curr...
In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization.The training ...
Component-based software development is considered to be a promising technology to increase software development productivity. However, developing componentbased applications faces different challenge...

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