Bayesian Networks for Named Entity Prediction in Programming Community Question Answering

Within this study, we propose a new approach for natural language processing using Bayesian networks to predict and analyze the context and how this approach can be applied to the Community Question Answering domain. We discuss how Bayesian networks can detect semantic relationships and dependencies between entities, and this is connected to different score-based approaches of structure-learning. We compared the Bayesian networks with different score metrics, such as the BIC, BDeu, K2 and Chow-Liu trees. Our proposed approach out-performs the baseline model at the precision metric. We also discuss the influence of penalty terms on the structure of Bayesian networks and how they can be used to analyze the relationships between entities. In addition, we examine the visualization of directed acyclic graphs to analyze semantic relationships. The article further identifies issues with detecting certain semantic classes that are separated in the structure of directed acyclic graphs. Finally, we evaluate potential improvements for the Bayesian network approach.Comment: 14 page

Dynamic Selection of Ensemble Members in Multi-model Hydrometeorological Ensemble Forecasting

AbstractMulti-model prediction ensembles show significant ability to improve forecasts. Nevertheless, the set of models in an ensemble is not always optimal. This work proposes a procedure that allows to select dynamically ensemble members for each forecast. Proposed procedure was evaluated for the task of the water level forecasting in the Baltic See. The regression-based estimation of ensemble forecasts errors was used to implement the selection procedure. Improvement of the forecast quality in terms of mean forecast RMS error and mean forecast skill score are demonstrated

Workflow-based Collaborative Decision Support for Flood Management Systems

AbstractSimulation-based decision making is the one of prospective applications of computational sciences which is central to advances in many scientific fields. The complexity and interdisciplinarity of scientific problems lead to the new technologies of simulation software implementation based on cloud computing, workflow tools and close interaction between experts and decision-makers. The important challenge in this field is to combine simulation scenarios, expert decisions and distributed environment to solve the complex interdisciplinary problems. In this paper, we describe a way to organize the collaborative decision support on the basis of e-Science platform CLAVIRE with the emphasis on urgency. A case study on decision making is the gates maneuvering for the flood prevention in Saint-Petersburg as a part of flood management system

ВИДАЛЕННЯ НЕВИЗНАЧЕНОСТІ В РАМКАХ НАБЛИЖЕННЯ ІМОВІРНІСНОГО РОЗПОДІЛУ ПО АБРАЗИВНО-ДИФУЗІЙНІЙ МОДЕЛІ ОЦІНКИ ЗНОСУ ПО НАЙБІЛЬШ ОБМЕЖЕНОМУ ХАРАКТЕРУ РОЗПОДІЛУ

There are considered single-parameter output models of tool wear evaluation, grounded on abrasion, adhesion, and diffusion phenomena. A mathematical framework of removing such three-model uncertainty, using the multi-lap-measurement-approximated probabilistic distribution off most-precautious distribution pattern, is stated.There are considered single-parameter output models of tool wear evaluation, grounded on abrasion, adhesion, and diffusion phenomena. A mathematical framework of removing such three-model uncertainty, using the multi-lap-measurement-approximated probabilistic distribution off most-precautious distribution pattern, is stated

Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogram

In this study we applyed machine-learning algorithms to determine the post-COVID state of a person. During the study, a marker of the post-COVID state of a person was found in the electrocardiogram data. We have shown that this marker in the patient's ECG signal can be used to diagnose a post-COVID state

Assessment of cognitive characteristics in intelligent systems and predictive ability

The article proposes a universal dual-axis intelligent systems assessment scale. The scale considers the properties of intelligent systems within the environmental context, which develops over time. In contrast to the frequent consideration of the 'mind' of artificial intelligent systems on a scale from 'weak' to 'strong', we highlight the modulating influences of anticipatory ability on their 'brute force'. In addition, the complexity, the 'weight' of the cognitive task and the ability to critically assess it beforehand determine the actual set of cognitive tools, the use of which provides the best result in these conditions. In fact, the presence of 'common sense' options is what connects the ability to solve a problem with the correct use of such an ability itself. The degree of 'correctness' and 'adequacy' is determined by the combination of a suitable solution with the temporal characteristics of the event, phenomenon, object or subject under study