The fast spread of hate speech on social media impacts the Internet environment and our
society by increasing prejudice and hurting people. Detecting hate speech has aroused …

Adversarial examples are inputs with imperceptible perturbations that easily mislead deep
neural networks (DNNs). Recently, adversarial patch, with noise confined to a small and …

Due to its open-source nature, Android operating system has been the main target of
attackers to exploit. Malware creators always perform different code obfuscations on their …

With the development of the open source community, the code is often copied, spread, and
evolved in multiple software systems, which brings uncertainty and risk to the software …

With the development of modern sensors, numerous images are collected in edge
application scenarios; however, their utilization is quite expensive because a massive effort …

Abstract We compare Convolutional Neural Networks (CNN) and Deep Belief Networks
(DBN) ability to withstand common image classification attacks. CNNs makes a strong …

Speech emotion recognition is a challenge and an important step towards more natural
human-computer interaction (HCI). The popular approach is multimodal emotion recognition …

Unsupervised extraction of objects from low-level visual data is an important goal for further
progress in machine learning. Existing approaches for representing objects without labels …

Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks
is recently shown with puzzling low performances on the SPARE3D dataset. Based on the …

Voice control systems enable people to control their computers by speaking to them. After a
review of the state-of-the-art in sequence modeling, speech recognition, and language …