Long-Term Preservation of PDF Files in Institutional Repositories in Japan

Poste

Long-term Preservation of pdf files in institutional repositories in Japan - iPRES 2019 Amsterdam

In the open access environment, many textual resources have become available in the PDF format on the Web. This research aims to survey PDF files in Japanese institutional repositories (IRs) to address the problems encountered during their longterm preservation. With that aim, 1.5 million PDF files collected from Japanese IRs were analyzed with regard to file format, encryption, and metadata. Most PDF files did not conform to PDF/A. A total of 30.5% of PDFs were encrypted and many PDFs did not have embedded metadata. These results imply that PDF files in Japanese IRs have several serious problems for their long-term preservation

The development of a search engine for academic papers in Web

Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Frechet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution.Comment: NIPS 201