Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective representations from time series data with limited labels. The prevalent approach in existing contrastive learning methods consists of two separate stages: pre-training the encoder on unlabeled datasets and fine-tuning the well-trained model on a small-scale labeled dataset. However, such two-stage approaches suffer from several shortcomings, such as the inability of unsupervised pre-training contrastive loss to directly affect downstream fine-tuning classifiers, and the lack of exploiting the classification loss which is guided by valuable ground truth. In this paper, we propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification). SLOTS receives semi-labeled datasets, comprising a large number of unlabeled samples and a small proportion of labeled samples, and maps them to an embedding space through an encoder. We calculate not only the unsupervised contrastive loss but also measure the supervised contrastive loss on the samples with ground truth. The learned embeddings are fed into a classifier, and the classification loss is calculated using the available true labels. The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier. We evaluate SLOTS by comparing it with ten state-of-the-art methods across five datasets. The results demonstrate that SLOTS is a simple yet effective framework. When compared to the two-stage framework, our end-to-end SLOTS utilizes the same input data, consumes a similar computational cost, but delivers significantly improved performance. We release code and datasets at https://anonymous.4open.science/r/SLOTS-242E.Comment: Submitted to NeurIPS 202

Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm

Research on the Impact of Game Users’ Perceived Value on Satisfaction and Loyalty - Based on the Perspectives of Hedonic Value and Utilitarian Value

As Chinese game market growing mature, cultivating loyal game users has become the new goals for game companies. Based on the theory of game users experience, this paper constructs the structural model of customer with the variables of perceived value, customer satisfaction and customer loyalty and studies the relationship between the game users’ hedonic/utilitarian value and customer satisfaction/customer loyalty from the perspective of the game user utilitarian value and hedonic value. The study finds that the game users’ perceived value has a positive effect on customer satisfaction and customer loyalty; while hedonic value has a more significant effect on customer satisfaction than utilitarian value, the latter one has a greater significant effect on customer loyalty than the former one; customer satisfaction has a positive effect on customer loyalty; hedonic value and utilitarian value interact and influence with each other. Implication and recommendation of this research is that enhancing the hedonic and utilitarian value of game users by game companies which is one of the effective ways to improve game users’ satisfaction and loyalty