"12084","Data model-driven smart contract development for enterprise blockchains","Aktív (aktuális, lehet rá jelentkezni)","Attila Klenik","","The topic explores the possibilities of model-based development for modern blockchain platforms. The key question is how to bring together the modeling experience gained in traditional program design and the new programming paradigm offered by blockchains.","2023","2023-05-03 15:13","2023-05-03 15:31","Yes","Recently, the use of blockchain has become widespread, even in the corporate world. One of the best-known platforms is Hyperledger Fabric, a permissioned blockchain solution for the corporate sector. In Hyperledger Fabric, however, there can be a significant difference between a general ledger approach capable of storing simple key-value pairs and the data representation style used to develop a smart contract. For example, Java has classes, objects, and references rather than keys and values. Currently, there are no really good tools to facilitate ""object-key-value mapping,"" at least nothing approaching the classic Object Relational Mapping (ORM). This not only makes smart contract development difficult, but naive mapping can also lead to performance problems (e.g., logically unnecessary data access conflicts between transactions). In addition, an explicit object-oriented ledger data model would allow the introduction of data-driven constraints on the ledger's contents, either for runtime verification or development-time verification and validation. During this topic, students can get to know model-based development methods that are widely used today, an increasingly popular blockchain technology, and the potential integration of the two. " "11813","Developing a Natural Language Processing System","Aktív (aktuális, lehet rá jelentkezni)","Tamás Mészáros","","Developing various kinds natural language processing software including automated text annotation, information extraction, document classification, stylometry analysis etc.","2016","2021-08-24 14:42","2023-08-10 21:22","Yes","

Project proposal

During the project work students develop natural language processing (NLP) systems for various real-world tasks like text classification (e.g. sentiment analysis, authorship attribution, topic identification etc.) and automated text annotation (e.g. named entity recognition, part-of-speech tagging etc.). Application areas include business and medical information systems and digital humanities. Cooperating with our industrial and academic partners is a possibility for skillful and motivated students.

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