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Songshen Han, Kaiyong Xu, Songhui Guo, Miao Yu and Bo Yang
Automatic Speech Recognition (ASR) provides a new way of human-computer interaction. However, it is vulnerable to adversarial examples, which are obtained by deliberately adding perturbations to the original audios. Thorough studies on the universal feat...
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Carlos Alberto Cortés-Rodríguez, Gladys Martínez-Gómez, José Luis Romo-Lozano and Ezequiel Arvizu-Barrón
Entrepreneurial skills are fundamental to the success of agricultural production units. Since small-scale farmers have developed production skills but not market-oriented skills, it is necessary to assess their entrepreneurial skills to provide a referen...
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Raz Lapid, Zvika Haramaty and Moshe Sipper
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating advers...
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Dejian Guan, Wentao Zhao and Xiao Liu
Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two...
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Temirlan Bidzhiev,Dmitry Namiot
Pág. 21 - 31
In recent years, neural networks have shown their potential as a new paradigm for solving problems in the field of information technology. They have shown their effectiveness in many areas, but training neural networks is expensive in terms of computing ...
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Alena Kajanová, Tomá? Urbánek, Tomá? Mrhálek, Stanislav Ondrá?ek, Olga Shivairová and Jan Hynek
The objective of the article is to present an item analysis of selected subtests of the Czech version of the WJ IV COG battery from a group of Romani children, ages 7?11. The research sample consisted of 400 school-aged Romani children from the Czech Rep...
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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user?s dissatisfactio...
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