Electroencephalography (EEG)-based affective computing has a scarcity problem. As result, it is difficult to build effective, highly accurate and stable models using machine learning algorithms, especially deep models. Data augmentation recently shown performance improvements in with increased accuracy, stability reduced overfitting. In this paper, we propose novel data framework, named the gen...

With the lack of failure data, class imbalance has become a common challenge in fault diagnosis industrial systems. The oversampling methods can tackle class-imbalanced problem by generating minority samples to balance training set. However, one main challenges existing is how generate high-quality samples. Traditional regard all synthetic as ones be added set without filtering. low-quality wou...

In this paper, we argue that many Automatic Knowledge Base Construction (AKBC) tasks which have previously been addressed separately can be viewed as instances of single abstract problem: multiview semi-supervised learning with an incomplete class hierarchy. We also present a general EM framework for solving this abstract task, and summarize past work on various special cases of multiview semi-...

In this paper, we propose a self-supervised contrastive learning method to learn video feature representations. traditional methods, constraints from anchor, positive, and negative data pairs are used train the model. such case, different samplings of same treated as positives, clips videos negatives. Because spatio-temporal information is important for representation, set temporal more strictl...

In the systems of industrial robotics and autonomous vehicles, instance segmentation is widely employed. However, manually labelling an object outline time-consuming. order to reduce annotation costs, we present a weakly supervised method in this article. A deeply convolutional network first used construct multi-scale feature maps for each input image. After that, encoder-decoder framework with...

Automatic analysis of facial actions (AFA) can reveal a person’s emotion, intention, and physical state, and make possible a wide range of applications. To enable reliable, valid, and efficient AFA, this thesis investigates automatic analysis of facial actions through transductive, supervised and unsupervised learning. Supervised learning for AFA is challenging, in part, because of individual d...

The Italian lexical sample task at SENSEVAL-3 provided a framework to evaluate supervised and semi-supervised WSD systems. This paper reports on the task preparation – which offered the opportunity to review and refine the Italian MultiWordNet – and on the results of the six participants, focussing on both the manual and automatic tagging procedures.