Deep Neural Networks are Really Undefeatable for Human Conflicting and Non-conflicting Event Detection

We propose using derivative features for sound event detection based on deep neural networks. As input to the networks, we used log-mel-filterbank and its first and second derivative features for each frame of the audio signal. Two deep neural networks were used to evaluate the effectiveness of these derivative features. Specifically, a convolutional recurrent neural network (CRNN) was constructed by combining a convolutional neural network and a recurrent neural networks (RNN) followed by a feed-forward neural network (FNN) acting as a classification layer. In addition, a mean-teacher model based on an attention CRNN was used. Both models had an average pooling layer at the output so that weakly labeled and unlabeled audio data may be used during model training. Under the various training conditions, depending on the neural network architecture and training set, the use of derivative features resulted in a consistent performance improvement by using the derivative features. Experiments on audio data from the Detection and Classification of Acoustic Scenes and Events 2018 and 2019 challenges indicated that a maximum relative improvement of 16.9% was obtained in terms of the F-score.

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CASIE: Extracting Cybersecurity Event Information from Text

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6401 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8749-8757 ◽

Cited By ~ 2

Author(s):

Taneeya Satyapanich ◽

Francis Ferraro ◽

Tim Finin

Keyword(s):

Neural Networks ◽

Event Detection ◽

Deep Neural Networks ◽

Knowledge Graph ◽

Semantic Model ◽

Word Embeddings ◽

Linguistic Features ◽

Semantic Roles ◽

Event Information ◽

Event Based

We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

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Eye Movement Event Detection with Deep Neural Networks

Computational Vision and Bio-Inspired Computing - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-37218-7_98 ◽

2020 ◽

pp. 921-930

Author(s):

K. Anusree ◽

J. Amudha

Keyword(s):

Neural Networks ◽

Eye Movement ◽

Event Detection ◽

Deep Neural Networks

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