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As social media data grows to a tremendous size, understanding posts in social media becomes important for many applications such as commercial or political analysis. It is helpful because it gives us insight into how significant social issues affect a person or a group of people. Therefore, this paper proposes a method to find a news article that relates to a user's posts on Facebook. A classification model based only on keywords does not work well because there are different news articles with similar keywords. We propose adding an emotion feature to the classification model to handle this problem, as we observed that many news articles have a distinguishing emotional distribution. We show that classification models with an emotion feature yield better performance than models without an emotion feature. Furthermore, a classification model with an emotion feature works well when there is apparent emotion, and it does not perform well if there is language play or puns in the text.

The goal of semi-supervised learning is to improve the performance of supervised learning tasks using unlabeled data. Deep learning has a high demand for making use of large-scale unlabeled data. We propose a simple and novel method of utilizing unlabeled data for semi-supervised learning to improve the performance of the deep learning model. First, we train a Gaussian process classifier (GPC), and use its output on unlabeled data as a pseudo-label. Then, we pre-train the deep learning model with the pseudo-labeled data to initialize the parameters of the model. Finally, we fine-tune it with the labeled data. We apply this method to five classes of video from the UCF101 data set which contains a small amount of labeled data. The experiment results show significant performance improvements compared to those of the deep learning model without pre-training. We further study the pre-training effect using pseudo-labeled data with a different probability, and give some advice for practical application. Moreover, we propose to train GPC with stratified sampled data (SGPC) to reduce the computation time of GPC when there are large amounts of data. Finally, we generalize proposed pseudo-labeling method to any classifier having good performance and give some advice for pseudo-labeling method selection.

There are early studies to attempt users for psychiatric counseling with chatbot. They lead to changes in drinking habit based on intervention approach via chat bot. The application does not consider the user's psychiatric status through the conversations, continuous user monitoring, and ethical judgment in the intervention. We contend that more accurate and continuous emotion recognition gives better satisfaction to users who need mental health care. In addition, appropriate clinical psychological response based on ethical responses is as well. We suggest a conversational service for psychiatric counseling that is adapted methodologies to understand counseling contents based on of high-level natural language understanding (NLU), and emotion recognition based on multi-modal approach. The methodologies enable continuous observation of emotional changes sensitively. In addition, the case-based counseling response model that combines ethical judgment model provides a suitable response to clinical psychiatric counseling.

Deep neural networks (DNNs), which show outstanding performance in various areas, consume considerable amounts of memory and time during training. Our research led us to propose a controlled dropout technique with the potential of reducing the memory space and training time of DNNs. Dropout is a popular algorithm that solves the overfitting problem of DNNs by randomly dropping units in the training process. The proposed controlled dropout intentionally chooses which units to drop compared to conventional dropout, thereby possibly facilitating a reduction in training time and memory usage. In this paper, we focus on validating whether controlled dropout can replace the traditional dropout technique to enable us to further our research aimed at improving the training speed and memory efficiency. A performance comparison between controlled dropout and traditional dropout is carried out by implementing an image classification experiment on data comprising handwritten digits from the MNIST dataset (Mixed National Institute of Standards and Technology dataset). The experimental results show that the proposed controlled dropout is as effective as traditional dropout. Furthermore, the experimental result implies that controlled dropout is more efficient when an appropriate dropout rate and number of hidden layers are used.

By the number of people aged 60 or over and people with disabilities growing, homecare mobile robot draws increasing attention. However, there are challenges of autonomous navigation for homecare robot such as frequent changes of environment, obstacles and goal position. In this paper, we focus on verifying potential of neural network-based autonomous navigation for homecare mobile. And we compare recurrent neural network with multilayer perceptron in the navigation of an autonomous mobile robot. The result suggested that the recurrent neural network can do better robot navigation because of its capability to handle the temporal dependency of a data sequence. Also, it shows that neural network-based navigation can be a good alternative since it has decent generalization ability for new environment, obstacles and goals.