In this paper, we extensively present our solutions for the MuSe-Stress sub-challenge and the MuSe-Physio sub-challenge of Multimodal Sentiment Challenge (MuSe) 2021. The goal of MuSe-Stress sub-challenge is to predict the level of emotional arousal and valence in a time-continuous manner from audio-visual recordings and the goal of MuSe-Physio sub-challenge is to predict the level of psycho-physiological arousal from a) human annotations fused with b) galvanic skin response (also known as Electrodermal Activity (EDA)) signals from the stressed people. The Ulm-TSST dataset which is a novel subset of the audio-visual textual Ulm-Trier Social Stress dataset that features German speakers in a Trier Social Stress Test (TSST) induced stress situation is used in both subchallenges. For the MuSe-Stress sub-challenge, we highlight our solutions in three aspects: 1) the audio-visual features and the biosignal features are used for emotional state recognition. 2) the Long Short-Term Memory (LSTM) with the self-attention mechanism is utilized to capture complex temporal dependencies within the feature sequences. 3) the late fusion strategy is adopted to further boost the model's recognition performance by exploiting complementary information scattered across multimodal sequences. Our proposed model achieves CCC of 0.6159 and 0.4609 for valence and arousal respectively on the test set, which both rank in the top 3. For the MuSe-Physio sub-challenge, we first extract the audiovisual features and the bio-signal features from multiple modalities. Then, the LSTM module with the self-attention mechanism, and the Gated Convolutional Neural Networks (GCNN) as well as the * Both authors contributed equally to this research.
As the first step of cybersecurity situational awareness, the accuracy of cybersecurity element recognition will directly affect the results of situational understanding and situational prediction. In this paper, we propose a network element recognition method based on the convolutional attention mechanism combined with a long- and short-term memory network. The input network traffic data is successively passed through the convolutional neural network, attention mechanism, and long- and short-term memory network, which not only takes into account the influence degree of different network attributes on different network behaviors but also realizes that the feature information extracted in the early stage can be circulated in the network, thus providing a discriminant basis for the final network behaviors To verify the effectiveness of our proposed method, we perform experimental validation on the KDD-Cup 1999 (kdd-99) dataset. The results show that our proposed method achieves an accuracy of 98.48% in the identification of network security elements. In addition to this, we also compare and analyze our proposed algorithm with other mainstream algorithms, and the results also validate the effectiveness of our proposed method.
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