Procedural modeling methods are used to automatically generate virtual scenes. There is a large number of available top‐down methods for generating partial content for specific purposes. However, little research was done on enabling the generation of content in the presence of manually modeled elements, from the bottom‐up direction, or without significant assistance from the user. No existing approach provides a platform that can combine the results of different methods, which leaves them isolated. This paper presents an integration approach that generates complete virtual space organizations by combining the usage of top‐down and bottom‐up procedural generation of content, with support for the placement of manually modeled content. The integration is made possible by using shape conversion to match the input and output shape types of different methods. The evaluation of the proposed approach was performed on a 2D polygon dataset by using four different scenarios, validating that it works as intended. Additional testing was performed by using a case study of organizing 3D virtual space around the manually modeled element of virtual heritage Tašlihan to demonstrate all capabilities of the integration approach and the different outputs depending on the level of user interaction and the desired results.

The impressive results achieved by language recognition using a generative pre-trained transformer have led to divided opinions on whether or not the Turing test has finally been passed. After understanding the working principles of the GPT programs, it was remarked that the tokenization concept, used by GPT, resulted in the loss of the word-to-letter relationship. Through about 36 specially prepared anagrams with a description of a term in a verse in the languages of the South Slavs, it was shown that ChatGPT and similar programs are far more capable of understanding the semantic connection between words and allusions than in performing the relatively simple task of searching for an adequate word from the offered letters.

Digital credentials represent a cornerstone of digital identity on the Internet. To achieve privacy, certain functionalities in credentials should be implemented. One is selective disclosure, which allows users to disclose only the claims or attributes they want. This paper presents a novel approach to selective disclosure that combines Merkle hash trees and Boneh-Lynn-Shacham (BLS) signatures. Combining these approaches, we achieve selective disclosure of claims in a single credential and creation of a verifiable presentation containing selectively disclosed claims from multiple credentials signed by different parties. Besides selective disclosure, we enable issuing credentials signed by multiple issuers using this approach.

Digital credentials represent crucial elements of digital identity on the Internet. Credentials should have specific properties that allow them to achieve privacy-preserving capabilities. One of these properties is selective disclosure, which allows users to disclose only the claims or attributes they must. This paper presents a novel approach to selective disclosure BLS-MT-ZKP that combines existing cryptographic primitives: Boneh-Lynn-Shacham (BLS) signatures, Merkle hash trees (MT) and zero-knowledge proof (ZKP) method called Bulletproofs. Combining these methods, we achieve selective disclosure of claims while conforming to selective disclosure requirements. New requirements are defined based on the definition of selective disclosure and privacy spectrum. Besides selective disclosure, specific use cases for equating digital credentials with paper credentials are achieved. The proposed approach was compared to the existing solutions, and its security, threat, performance and limitation analysis was done. For validation, a proof-of-concept was implemented, and the execution time was measured to demonstrate the practicality and efficiency of the approach.

Digital credentials represent crucial elements of digital identity on the Internet. Credentials should have specific properties that allow them to achieve privacy-preserving capabilities. One of these properties is selective disclosure, which allows users to disclose only the claims or attributes they must. This paper presents a novel approach to selective disclosure BLS-MT-ZKP that combines existing cryptographic primitives: Boneh-Lynn-Shacham (BLS) signatures, Merkle hash trees (MT) and zero-knowledge proof (ZKP) method called Bulletproofs. Combining these methods, we achieve selective disclosure of claims while conforming to selective disclosure requirements. New requirements are defined based on the definition of selective disclosure and privacy spectrum. Besides selective disclosure, specific use cases for equating digital credentials with paper credentials are achieved. The proposed approach was compared to the existing solutions, and its security, threat, performance and limitation analysis was done. For validation, a proof-of-concept was implemented, and the execution time was measured to demonstrate the practicality and efficiency of the approach.

As virtual worlds continue to rise in popularity, so do the expectations of users for the content of virtual scenes. Virtual worlds must be large in scope and offer enough freedom of movement to keep the audience occupied at all times. For content creators, it is difficult to keep up by manually producing the surrounding content. Therefore, the application of procedural modelling techniques is required. Virtual worlds often mimic the real world, which is composed of organized and connected outdoor and indoor layouts. It is expected that all content is present on the virtual scene and that a user can navigate streets, enter buildings, and interact with furniture within a single virtual world. While there are many procedural methods for generating different layout types, they mostly focus only on one layout type, whereas complete scene generation is greatly underrepresented. This paper aims to identify the coverage of layout types by different methods because similar issues exist for the generation of content of different layout types. When creating a new method for layout generation, it is important to know if the results of existing methods can be appended to other methods. This paper presents a survey of existing procedural modelling methods, which were organized into five categories based on the core approach: pure subdivision, grammar‐based, data‐driven, optimization, and simulation. Information about the covered layout types, the possibility of user interaction during the generation process, and the input and output shape types of the generated content is provided for each surveyed method. The input and output shape types of the generated content can be useful to identify which methods can continue the generation by using the output of other methods as their input. It was concluded that all surveyed methods work for only a few different layout types simultaneously. Moreover, only 35% of the surveyed methods offer interaction with the user after completing the initial process of space generation. Most existing approaches do not perform transformations of shape types. A significant number of methods use the irregular shape type as input and generate the same shape type as the output, which is sufficient for coverage of all layout types when generating a complete virtual world.