Enabling FAIR Discovery of Rare Disease Digital Resources

Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solution to improve FAIR for machines for the EJP RD API specification. Methods: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. Results: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. Conclusion: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

Applying Code Transform Model to Newly Generated Program for Improving Execution Performance

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance.

Procedural interior generation for artificial intelligence training and computer graphics

Since the creation of computers, there has been a lingering problem of data storing and creation for various tasks. In terms of computer graphics and video games, there has been a constant need in assets. Although nowadays the issue of space is not one of the developers' prime concerns, the need in being able to automate asset creation is still relevant. The graphical fidelity, that the modern audiences and applications demand requires a lot of work on the artists' and designers' front, which costs a lot. The automatic generation of 3D scenes is of critical importance in the tasks of Artificial Intelligent (AI) robotics training, where the amount of generated data during training cannot even be viewed by a single person due to the large amount of data needed for machine learning algorithms. A completely separate, but nevertheless necessary task for an integrated solution, is furniture generation and placement, material and lighting randomisation. In this paper we propose interior generator for computer graphics and robotics learning applications. The suggested framework is able to generate and render interiors with furniture at photo-realistic quality. We combined the existing algorithms for generating plans and arranging interiors and then finally add material and lighting randomization. Our solution contains semantic database of 3D models and materials, which allows generator to get realistic scenes with randomization and per-pixel mask for training detection and segmentation algorithms.

The establishment of square elements of garden landscapes using GIS-technologies

Садово-парковые ландшафты являются частью городских лесов Санкт- Петербурга. Они выполняют средообразующие функции, являются основой градостроительства и нуждаются в кадастровом учете. Для учета зеленых насаждений общего пользования нужна картографическая и семантическая база данных об элементах садово-парковых ландшафтов. При разработке кадастра растительности необходим учет всех объектов города с учетом каждого элемента озеленения: общей площади; площади, занятой деревьями, кустарниками, газонами, цветниками и прочей недвижимостью на объекте исследований. Целостное представление о садово-парковых ландшафтах может дать их картографическое изображение и использование современных географических информационных систем (ГИС). Результаты обследований зеленых насаждений нужны для целей грамотного управления растительными ресурсами и разработки Генерального плана Санкт-Петербурга. Landscape gardens and parks are part of the urban forests of St. Petersburg. They perform environmental functions, are the basis of urban planning and need cadastral registration. To account for public green spaces need a cartographic and semantic database of elements of landscape gardening. When developing the cadastre of vegetation, it is necessary to take into account all the objects of the city, taking into account each element of landscaping: the total area, the area of trees, shrubs, lawns, flower beds and other real estate at the research object. A holistic view of the landscape of gardens and parks can give a cartographic image and the use of modern geographic information systems (GIS). The results of surveys of green spaces are necessary for the purposes of competent management of plant resources of St. Petersburg.

Kreteńskie hydronimy pochodne od nazw terenowych

Cretan Hydronyms Derived from Field NamesIn the article, the author discusses as many as 515 Cretan river names derived from proper names of various geographical objects. This group of hydronyms represents 11% of all of modern hydronyms of the island (4666 names). Most of them (501) were attested only in the 20th century. From the formal point of view, we can distinguish in the analysed semantic database: simple and composed hydronyms (respectively, 370; 71.8%, and 121; 23.5%), and elliptic river names (24; 4.7%). Numerous hydronyms have been copied from the names of Cretan gorges (110; 21.3%), slopes (51; 10%), hills and mountains (40; 7.8%), valleys and mountain valleys (32; 6.2%), caves (9), plains (8), and headlands (6).

FinnFN 1.0: The Finnish frame semantic database

The article describes the process of creating a Finnish language FrameNet or FinnFN, based on the original English language FrameNet hosted at the International Computer Science Institute in Berkeley, California. We outline the goals and results relating to the FinnFN project and especially to the creation of the FinnFrame corpus. The main aim of the project was to test the universal applicability of frame semantics by annotating real Finnish using the same frames and annotation conventions as in the original Berkeley FrameNet project. From Finnish newspaper corpora, 40,721 sentences were automatically retrieved and manually annotated as example sentences evoking certain frames. This became the FinnFrame corpus. Applying the Berkeley FrameNet annotation conventions to the Finnish language required some modifications due to Finnish morphology, and a convention for annotating individual morphemes within words was introduced for phenomena such as compounding, comparatives and case endings. Various questions about cultural salience across the two languages arose during the project, but problematic situations occurred only in a few examples, which we also discuss in the article. The article shows that, barring a few minor instances, the universality hypothesis of frames is largely confirmed for languages as different as Finnish and English.