Parse tree based machine translation for less-used languages

The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two partsthe first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraintsthe training part was implemented and incorporated into a functional translation systemthe inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method

Automated Implementation Process of Machine Translation System for Related Languages

The paper presents an attempt to automate all data creation processes of a rule-based shallow-transfer machine translation system. The presented methods were tested on four fully functional translation systems covering language pairs: Slovenian paired with Serbian, Czech, English and Estonian language. An extensive range of evaluation tests was performed to assess the applicability of the methods

A Decentralized Authoritative Multiplayer Architecture for Games on the Edge

With the ever growing number of edge devices, the idea of resource sharing systems is becoming more appealing. Multiplayer games are a growing area of interest due to the scalability issues of current client-server architectures. A paradigm shift from centralized to decentralized architectures that would allow greater scalability has gained a lot of interest within the industry and academic community. Research on peer to peer network protocols for multiplayer games was mainly focused on cheat detection. Previously proposed solutions address the cheat detection issues on a protocol level but do not provide a holistic solution for the architecture. Additionally, existing solutions introduce some level of centralization, which inherently introduces single point of failures. We propose a blockchain-based, completely decentralized architecture for edge devices with no single point of failure. Our solution relies on an innovative consensus mechanism based on verifiable delay functions that additionally allows the network to derive verifiable randomness. We present simulation results that show the assignment of players and referees to instances is pseudo-random, which inherently prevents collusion-based cheats and vulnerabilities