Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal datasets, there are not enough Korean multi-modal datasets. In this paper, we introduce a KTS (Korean tourist spot) dataset for Korean multi-modal deep-learning research. The KTS dataset has four modalities (image, text, hashtags, and likes) and consists of 10 classes related to Korean tourist spots. All data were extracted from Instagram and preprocessed. We performed two experiments, image classification and image captioning with the dataset, and they showed appropriate results. We hope that many researchers will use this dataset for multi-modal deep-learning research.

Currently, large data streams are constantly being generated in diverse environments, and continuous storage of the data and periodic batch-type principal component analysis (PCA) are becoming increasingly difficult. Various online PCA algorithms have been proposed to solve this problem. In this study, we propose an online PCA methodology based on online eigenvector transformation with the moving average of the data stream that can reflect concept drift. We compared the network intrusion detection performance based on online transformation of eigenvectors with that of offline methods by applying three machine learning algorithms. Both online and offline methods demonstrated excellent performance in terms of precision. However, in terms of the recall ratio, the performance of the proposed methodology with integrated online eigenvector transformation was better; thus, the F1-measure also indicated better performance. The visualization of the principal component score shows the effectiveness of our method.

No abstract

No abstract

No abstract