{"id":1013,"date":"2022-10-23T05:52:58","date_gmt":"2022-10-23T05:52:58","guid":{"rendered":"https:\/\/prg.inf.unibe.ch\/?page_id=1013"},"modified":"2022-10-23T05:53:28","modified_gmt":"2022-10-23T05:53:28","slug":"research-2","status":"publish","type":"page","link":"https:\/\/prg.inf.unibe.ch\/index.php\/research-2\/","title":{"rendered":"Research _new"},"content":{"rendered":"\n
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Current projects<\/h1>\n<\/div>\n<\/div><\/section>\n\n\n\n
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TITLE: A PROSE – Advanced PROofreading SErvices (2022 — 2026)<\/strong><\/p>\n\n\n\n

Abstract: Many proof-reading services are currently focused on orthography, grammar, punctuation, and typography. In these areas, unresolved challenges for automated proofreading exist, comprising detection of so-called real-word errors, compliance with specific typesetting rules and company-specific spelling guidelines, and ensuring the use of gender-neutral language. Further tasks that currently require better solutions are the automatic detection of subjective language and incorrect information (e.g. dates, addresses) in customer documents. <\/em>…<\/a><\/p>\n<\/div>\n<\/div><\/section>\n\n\n\n

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Spatio-temporal graph convolutional networks – a novel deep learning approach to forecasting river temperatures (2022 — 2026)<\/strong><\/p>\n\n\n\n

Abstract:<\/em> The Federal Office for the Environment (FOEN) analyses several environmental aspects of Switzerland. The monitoring of water temperatures over long time periods belongs to one of the most important tasks of the Hydrology Division of FOEN. At the moment, the Hydrology Division maintains approximately 80 metering stations. Stations measure various parameters such as water temperature, discharge, water level etc. …<\/a><\/strong><\/em><\/p>\n<\/div>\n<\/div><\/section>\n\n\n\n

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Novel State-of-the-Art Graph Matching Algorithms (2020 – 2024)<\/strong><\/p>\n\n\n\n

Abstract:Due to fast developments in both storage media and data acquisition, we observe rapidly increasing amounts of data available in diverse areas in both science and industry. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs the most useful and adequate data structure available to date. These two developments evoke the need for ongoing research of robust and efficient methods that assist humans in understanding and handling their pools of big sets of graph data.<\/em> …<\/a><\/p>\n<\/div>\n<\/div><\/section>\n\n\n\n

Past Projects<\/h1>\n\n\n\n