Titlebook: Artificial Intelligence and Soft Computing; 19th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2020

開玩笑

https://doi.org/10.1007/978-81-322-2301-6gnized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions.

南極

Conference proceedings 2020ft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020..The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts:??neural networks and their applications; fuzzy sys

buoyant

Conference proceedings 2020ns; artificial intelligence in modeling and simulation..The papers included in the second volume?are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control..*The conference was held virtually due to the COVID-19 pandemic..

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Systems Science in Retrospect and Prospect In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.

misanthrope

Certainty

amorphous

Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.

難解

3D Convolutional Neural Networks for Ultrasound-Based Silent Speech Interfacestial and temporal convolutions in a decomposed form, which proved very successful recently in video action recognition. We find experimentally that our 3D network outperforms the CNN+LSTM model, indicating that 3D CNNs may be a feasible alternative to CNN+LSTM networks in SSI systems.