Today, PDF is one of the most popular document formats in the web. Many PDF documents are not images, but remain untagged. They have no tags for identifying the logical reading order, paragraphs, figures, and tables. One of the challenges with these documents is how to extract tables from them. The paper discusses a new system for table structure recognition in untagged PDF documents. It is formulated as a set of configurable parameters and ad-hoc heuristics for recovering table cells. We consider two different configurations for the system and demonstrate experimental results based on the existing competition dataset for both of them.

Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples.

The language FlexT (Flexible Types) is intended for specification of binary data formats. The language is declarative and designed to be well understood for human readers. Its main elements are the data type declarations, which look very much like the usual type declarations of the imperative programming languages, but are more flexible. While the primary purpose of the language FlexT development was to make the binary data understandable by displaying them according to the format specifications, recently we have implemented the code generator, which can produce data reading code in some imperative languages from the specifications. By now we have implemented the code generation for the most widely used FlexT data types, but some complex types like that used in specifications of machine instructions' encoding are not supported yet. The current capabilities of the code generator are well characterized by that it have successfully produced a full-featured data reader code for the well-known for the GIS community Shape file format. In the article we first give a review of the features of the modern projects oriented to specification of binary file formats and consider their code generation capabilities. Then we briefly review the main features of the FlexT language. Finally we describe the main approaches that we use for the code generation.