SQL Injection attacks target the database of applications to extract private information or inject malicious code. In this paper, we attempt to present a well-researched and practiced methodology to detect SQL Injecti...
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In response to the practical application of new COVID-19 transmission dynamics models such as new coronary pneumonia, the number of contacts associated with real-time data of confirmed cases is proposed, and the new C...
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We fed short overt Latin stress patterns to 100 virtual language learners whose grammars consist of a universal set of 12 Optimality-Theoretic constraints. For 50 learners the learning algorithm was Error-Driven Const...
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In this paper we develop a new approach for learning decision trees and multivariate polynomials via interpolation of multivariate polynomials. This new approach yields simple learning algorithms for multivariate poly...
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In this paper we develop a new approach for learning decision trees and multivariate polynomials via interpolation of multivariate polynomials. This new approach yields simple learning algorithms for multivariate polynomials and decision trees over finite fields under any constant bounded product distribution. The output hypothesis is a (single) multivariate polynomial that is an Ε-approximation of the target under any constant bounded product distribution. The new approach demonstrates the learnability of many classes under any constant bounded product distribution and using membership queries, such as j-disjoint DNF and multivariate polynomial with bounded degree over any field. The technique shows how to interpolate multivariate polynomials with bounded term size from membership queries only. This in particular gives a learning algorithm for O(log n)-depth decision tree from membership queries only and a new learning algorithm of any multivariate polynomial over sufficiently large fields from membership queries only. We show that our results for learning from membership queries only are the best possible.
According to the principles of security management, intuitive scientificity, and scalability, an information security system architecture based on representation and metric deep learning algorithms was designed. Two k...
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Purpose: We describe registration accuracy studies of a custom hardware-software system called eeDAP that registers fields of view (FOVs) of a glass slide on a microscope to the digital presentations of regions of int...
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Machine learning (ML) algonthms have recently become one ol the most important fields of industrial development efforts. Many companies in the automotive sector see ML methods as an enabler of autonomous driving, due ...
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Adaptive mobile learning algorithms offer a promising approach to addressing the challenges of mobile learning, such as learner engagement and personalized learning. Recent advances and innovations in these algorithms...
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In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of ...
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In recent years, with the rapid development of machine learning algorithm technology, research algorithms based on machine learning have also received increasing attention. Based on the characteristics of an automated...
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