Data visualisation assists domain experts in understanding their data and helps them make critical decisions. Enhancing their cognitive insight essentially relies on the capability of combining domain-specific semantic information with concepts extracted out of the data and visualizing the resulting networks. Data scientists have the challenge of providing tools able to handle the overall network lifecycle. In this paper, we present how the combination of two powerful technologies namely the REST architecture style and JSON Schema/JSON Meta Schema enable data scientists to use a RESTful web service that permits the construction of knowledge graphs, one of the preferred representations of large and semantically rich networks.
The Extreme Energy Events (EEE) Project is a Centro Fermi - CERN - INFN - MIUR Collaboration Project for the study of extremely high energy cosmic rays, which exploits the Multigap Resistive Plate Chamber (MRPC) technology. The excellent time resolution and good tracking capability of this kind of detector allows us to study Extensive Air Showers (EAS) with an array of MRPC telescopes distributed across the Italian territory. Each telescope is installed in a high school, with the further goal to introduce students to particle and astroparticle Physics. The status of the experiment and the results obtained are reported.
In our research we have created a text summarization software tool for Hungarian using multilingual and Hungarian BERT-based models. Two types of text summarization method exist: abstractive and extractive. The abstractive summarization is more similar to human generated summarization. Target summaries may include phrases that the original text does not necessarily contain. This method generates the summarized text by applying keywords that were extracted from the original text. The extractive method summarizes the text by using the most important extracted phrases or sentences from the original text. In our research we have built both abstractive and extractive models for Hungarian. For abstractive models, we have used a multilingual BERT model and Hungarian monolingual BERT models. For extractive summarization, in addition to the BERT models, we have also made experiments with ELECTRA models. We find that the Hungarian monolingual models outperformed the multilingual BERT model in all cases. Furthermore, the ELECTRA small models achieved higher results than some of the BERT models. This result is important because the ELECTRA small models have much fewer parameters and were trained on only 1 GPU within a couple of days. Another important consideration is that the ELECTRA
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