C88 - Data Collection and Data Estimation Methodology; Computer Programs: Other Computer SoftwareReturn

Smart IoT devices, such as lights, locks, washing machines, security cameras, etc., are becoming omnipresent in households and companies across all industries. However, most of these devices communicate over non-secure local protocols or via cloud services where security policies are not transparent. Vulnerabilities may lead to unauthorized access to such IoT devices. Blockchain is a technology that brings security by design and can be exploited also in the area of controlling access to IoT devices. The goal of the paper is to test the use of blockchain with IoT devices to increase the security of device usage while ensuring that the user experience remains efficient and user-friendly. Three approaches to use blockchain are proposed and tested: a) application without the blockchain using standard HTTPS protocol; b) an application using blockchain, where users sign the transactions themselves; c) an application using blockchain where the server signs the transactions. The paper successfully shows that blockchain can be used to enhance IoT device security, with an focus on user-friendliness testing to ensure the solutions are practical for everyday use.

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand-crafted features and rely on feature-inferring neural network systems based on word embeddings. The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far. Various aspects of word vectors preparation were experimentally examined to draw useful conclusions. The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters. The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.

This paper introduces a system for detection of plagiarism in source codes written in the PHP computer language, part of the plagiarism detection tool Anton. We used the greedy string tiling algorithm together with tokenization and hash calculation. The efficiency of the system was tested on both an artificial dataset and on real data coming from a course taught at our university. Our results are compared with other similar systems and solutions, concluding that Anton can detect all examined types of plagiarism with higher accuracy than other systems.