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The construction of CO2 geological storage GIS (Geographic Information System) basic data platform is an important basis for deploying the CCUS (Carbon Capture, Utilization and Storage) full-chain demonstration project. By studying typical carbon storage data platforms at home and abroad, the characteristics of carbon storage data are analyzed: multi-source, multi-scale, and multi-type. There are several areas that need to be further investigated: the platform function is basically a data storage management display, and a further enhancement of model embedding and dynamic analysis is needed. Research on the synergy between CO2 storage and energy resources needs to be further strengthened. On this basis, the basic data management and processing platform architecture of CO2 geological storage GIS is preliminarily designed based on the data of Map GIS spatial database and SQL Server attribute database, and the system is constructed by using the Microsoft. Net Frame development framework and the Web GIS technology. The core logical structure of the system is divided into four layers, namely, resource data layer, technical support layer, business application layer, and user permission layer. There are three peripheral support systems including the security system, management support system and standard specification system. The key technologies of data platform construction include the multi-heterogeneous data fusion of carbon sequestration geological GIS data and three models, i.e., secondary development and import of sequestration capacity evaluation model, source and sink matching dynamic model, and energy and resource collaboration model. Four interfaces are preliminarily designed including the data platform home page interface, thematic maps and results output interface, basin-level results map interface, storage potential - source and sink matching - energy and resources collaborative data analysis interface. The construction of the CO2 geological storage GIS basic data management and processing platform will provide a scientific basis for the selection of carbon reduction and decarburization solutions at the source, the selection of geological storage sites for CCUS regional large-scale deployment and the implementation of CCUS large-scale demonstration projects.

Problems. An applied problem of automating the search for relevant legal positions on a set of court decisions in the judiciary of Ukraine. The possibility of developing a high-quality NLP system for searching for a relevant, contextually justified legal position on a set of court decisions in the judiciary of Ukraine, which, based on the user’s request, produces expert answers to the user’s request by applying a reasoning-search algorithm based on large language models. Objective. To develop and formalize the concept and conceptual model of the NLP system for finding legal positions on the basis of interdisciplinary knowledge of a technical and philosophical direction, which covers the existence of a person, community, enterprises, institutions and organizations, state authorities, and also substantiates the formation of a legal position on the basis of a plurality. legislative acts and court decisions. Methodology of implementation. The method of implementation of the NLP system for searching for a context-based legal position lies in the interaction of the essences of tasks related to the judiciary of Ukraine, tools for analysis, processing, text generation and available available court decisions of the judiciary of Ukraine based on the Erikson-Penker business profile and large language models. Results. The proposed interdisciplinary model of the concept of the NLP system of finding a solution to the problem, tasks and tasks of searching court documents for more rendering of court decisions and/or formation of a legal position in the form of a Venn diagram as a set of entities and their cooperation, which integrates philosophical, legal and technical knowledge. The Erikson–Penker business profile of the conceptual model of the NLP system for searching for a context-based legal position is formalized. A formalized model of the work process of the NLP system of searching for a context-based legal position based on the RAG method. A conceptual model of the NLP system for searching for a context-based legal position has been developed in the form of a structural mapping based on a component diagram and a dynamic mapping based on an activity diagram with a description of the internal structure of the components. Conclusions. A number of important philosophical, legal and technical problems have been identified in the process of searching for legal positions on many court decisions. The interdisciplinary nature of the problem of developing the concept and conceptual model of the NLP system for finding a context-based legal position on a set of court decisions and the need to automate the process of finding a legal position for judicial practice has been established. At the meta levels, the concept of the NLP system for finding context-based legal positions on Venn diagrams and the conceptual model of the NLP system based on the modified Erikson-Penker business profile as a system of entities and cooperation are formalized. A conceptual model of the NLP system for finding a context-based legal position has been developed in the form of a structural mapping based on a diagram of components and a dynamic mapping based on an activity diagram with a description of the internal structure of the components. The functionality of the NLP system was compared with other main existing systems for searching court decisions in the judiciary and it was established that the developed system, at least, is not implemented in terms of functionality of existing systems and has additional opportunities for users. [ABSTRACT FROM AUTHOR]

Federated learning can synergize the local model training with private data samples from geo-distributed users. Nevertheless, the unification process of a comprehensive global model through periodical parameter sharing can be time-consuming at a high cost. On the one hand, the data samples are collected from users with diverse preferences, and the data distribution can be non-independent and identically distributed (non-IID). On the other hand, the consequent model trained locally needs to communicate with a remote parameter server periodically for parameter synchronization, which leads to overwhelming communication and synchronization overhead given heterogeneous device capacities and network conditions of end-users. Generally, a hierarchical system design with a clustered group is ideal for accommodating diversity. Actually, it is still challenging to maintain the relationship of the local model training without knowing the data samples in advance for privacy concerns. Therefore, we present hierarchical federated model embedding to formulate the relationship between local data distributions. Initially, the local models are embedded through the global shared dataset to obtain feature latent representation vectors. The cloud server groups the clients according to the vectors, making clients with similar data distribution train collaboratively in a same group. Then, these vectors are used to train the predictor on the cloud server, which is utilized for efficient group assignment when new clients join the system. Compared with the baseline, the accuracy of the group model can be improved by 1. 22 % ∼ 5. 63 % and that of the global model can be improved by 3. 97 % ∼ 14. 25 % on different datasets. [ABSTRACT FROM AUTHOR]

In this paper, we present a vision-based approach for transmitting virtual models for Augmented Reality, which we name In-Place Augmented Reality (IPAR). A two-dimensional representation of the virtual models is embedded in a printed image. We apply computer vision techniques to interpret the printed image and extract the virtual models, which are then overlaid on the printed image. The main advantages of our approach are: (1) the image of the embedded virtual models and their behaviors are understandable to a human without using an AR system and (2) no database or network communication is required to retrieve the models. To demonstrate the technology and test its usability, we implemented several applications and performed a user evaluation. We discuss how the proposed technique can be used for the development of applications in different domains such as education, advertisement, and gaming. [ABSTRACT FROM AUTHOR]