Verification of Systems with Degradation

We focus on systems that naturally incorporate a degrading quality, such as electronic devices with degrading electric charge or broadcasting networks with decreasing power or quality of a transmitted signal. For such systems, we introduce an extension of linear temporal logic (Linear Temporal Logic with Degradation Constraints, or DLTL for short) that provides a user-friendly formalism for specifying properties involving quantitative requirements on the level of degradation. We investigate the possibility of translating DLTL verification problem for systems with degradation into previously solved MITL verification problem for timed automata, and we show that through the translation, DLTL model checking problem can be solved with limited, yet arbitrary, precision. For a specific subclass of DLTL formulas, we present a full precision verification technique based on translation of DLTL formulas into a specification formalism called Buchi Automata with Degradation Constraints (BADCs) developed earlier

Események detektálása, osztályozása és szemantikus szerepeik címkézése

Natural Language Processing (NLP) is the processing of human languages by means of computers ranging from speech processing to semantics. Information extraction (IE) is an important part of NLP. It collects information from unstructured or semi-structured documents and stores them in a structured form. Event Extraction (EE) is an important subtask of IE. Its goal is to extract event information from unstructured documents. The task of event detection is the identification of event-occurrences in texts. Most events belong to verbs in texts and verbs usually denote events. But other parts of speech (e.g. noun, participle) can also denote events. Because of the ambiguity, words analysis is insufficient; also, the context must be analyzed. Besides event detection another important task is to determine the roles of the events discovered. It is known as Semantic Role Labelling (SRL). It is the task of natural language processing to detect the semantic arguments of a sentence predicate and to classify them according to specific roles. This dissertation is concerned with computer processing of events expressed in natural languages. Its main tasks are event detection, event classification and the labelling of their semantic roles

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions

Disambiguation of Taxonomy Markers in Context: Russian Nouns

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 111-117. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

A Dataset and Strong Baselines for Classification of Czech News Texts

Pre-trained models for Czech Natural Language Processing are often evaluated on purely linguistic tasks (POS tagging, parsing, NER) and relatively simple classification tasks such as sentiment classification or article classification from a single news source. As an alternative, we present CZEch~NEws~Classification~dataset (CZE-NEC), one of the largest Czech classification datasets, composed of news articles from various sources spanning over twenty years, which allows a more rigorous evaluation of such models. We define four classification tasks: news source, news category, inferred author's gender, and day of the week. To verify the task difficulty, we conducted a human evaluation, which revealed that human performance lags behind strong machine-learning baselines built upon pre-trained transformer models. Furthermore, we show that language-specific pre-trained encoder analysis outperforms selected commercially available large-scale generative language models.Comment: 12 pages, Accepted to Text, Speech and Dialogue (TSD) 202