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Accurately‐labeled data is crucial for the training of machine learning models. For singing‐related tasks in the music information retrieval field, accurately‐labeled data is limited because annotating singing is time‐consuming. Several studies create vocal datasets using a two‐step annotation method which creates coarse labels first and then executes a manual calibration procedure. However, manually calibrating coarsely‐labeled singing data is expensive and time‐consuming. To address this problem, in this study we propose a singing‐label calibration framework, which aims to automatically calibrate the coarsely‐labeled singing data with higher accuracy. This framework contains a data augmentation method to generate training and testing data, a reasonable data preprocessing method to handle music audio and symbolic labels, a fully‐convolutional neural network to estimate the difference between coarse labels and accurate labels, and a novel calibration function to correct the coarse labels. Various experiments are conducted to examine the effect of our research. The results show that our model can highly reduce the cost time and slightly increase the labeling accuracy of the manual calibration process. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The Man 'yōshū is known as an anthology of poems but is also consulted as a source of political and particularly cultural history. The present examination, which is intended as an introduction to historical studies on the Man 'yōshū, does not address the content of the poems but focuses on the so-called hyōmoku, "spatial-historical section headings," and the sachū and kachū, i.e., annotations that were later added to the end of the poems, headings, titles, etc. Therefore, this article is less about history than about formal aspects of historiography. The character of the Man 'yōshū anthology of literature as historiography is most evident in the first two books, to which this investigation is limited. On the one hand, the selective nature of this unique kind of historiography is to be shown, and on the other hand, its deviations from the Nihon shoki, which represents the official historiography. It is possible that different sources of material were available at the time the anthology was compiled. A case study will be used to show that the deviations from the official historiography may have been intentional. [ABSTRACT FROM AUTHOR]

Since the large‐scale protein sequence data is available, applying deep neural networks to mine better features from the sequences becomes possible. Eukaryotic protein subcellular localization prediction which makes a contribution in many biology process, has used protein sequences in many automatic predicting methods. Moreover, gene ontology (GO) annotation has been shown to be helpful in improving the prediction accuracy of subcellular localization. However, experimentally annotated proteins are not always available. On the other hand, experimentally annotated proteins are available for certain species such as human, mouse, Arabidopsis thaliana, etc. It is highly motivated to perform deep learning of GO annotations on the available experimentally annotated proteins and to transfer it to subcellular localization prediction on other species. In this paper, we propose a deep protein subcellular localization predictor, consisting of a linear classifier and a deep feature extractor of convolution neural network (CNN). The deep CNN feature extractor is first shared and pre‐trained in a deep GO annotation predictor, and then is transferred to the subcellular localization predictor with fine‐tuning using protein localization samples. In this way, we have a deep protein subcellular localization predictor enhanced with transfer learning of GO annotation. The proposed method has good performances on the Swiss‐Prot datasets, when transfer learning using the protein samples both within and out species. Moreover, it outperforms the state‐of‐the‐art traditional methods on benchmark datasets. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Gene ontology annotation is known to be a very complicated multilabel classification task, and the hierarchical multilabel classification (HMC) approaches with local classifiers have been shown to be effective for the task. In a traditional HMC method, a set of hierarchically organized simple local classifiers are usually used, each of which for one hierarchical level separately. In this paper, we propose a novel hierarchical multilabel classifier implementing the whole set of hierarchically organized local classifiers in one deep convolution neural network (CNN) model with multiple heads and multiple ends (MHME). The proposed MHME CNN model consists of three parts: the body part of a deep CNN model shared by different local classifiers for feature extraction and feature mapping; the multiend part of a set of autoencoders performing feature fusion transforming the input vectors of different local classifiers to feature vectors with the same length so as to share the feature mapping part; and the multihead part of a set of linear multilabel classifiers. Furthermore, a sophisticated recursive algorithm is designed to train the MHME CNN model to realize the functions of a set of hierarchically organized local classifiers. In this way, by sharing a deep CNN with multiple local classifiers, we are able to construct more powerful local classifiers for each level with limited training samples, and to achieve better classification performance. Experiment results on various benchmark datasets show that the proposed deep CNN‐based model has better performance than the state‐of‐the‐art traditional models. Moreover, it gives rather good performance even under a transfer learning. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Miaofalianhuajing Shiwen, the annotations of Miaofalianhuajing, is a very significant "writing composed by a Japanese scholar. It was finished in A. D 976. This book involves various problems, such as characters, phonology and exegetics, so there are great values for the language research. The research value of phonology in Miaofalianhuajing Shiwen will be revealed in this paper. We will give some examples such as stress, weak stress, and changing the pronunciation because of taboo to discuss these problems initially. [ABSTRACT FROM AUTHOR]