Mining Linguistic Associations for Emergent Flood Prediction Adjustment

Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed

Three-way Decisions with Evaluative Linguistic Expressions

We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc. Our results highlight new connections between two different research areas: three-way decisions and the theory of evaluative linguistic expressions

Fuzzy Natural Logic in IFSA-EUSFLAT 2021

The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

From ‘motivational climate’ to ‘motivational atmosphere’: a review of research examining the social and environmental influences on athlete motivation in sport

This chapter is intended to provide a comprehensive review of the various theories of social and environmental factors that influence athletes’ motivation in sport. In order to achieve this, a short historical review is conducted of the various ways in which motivation has been studied over the past 100 years, culminating in the ‘social-cognitive’ approach that undergirds several of the current theories of motivation in sport. As an outcome of this brief review, the conceptualisation and measurement of motivation are discussed, with a focus on the manner in which motivation may be influenced by key social agents in sport, such as coaches, parents and peers. This discussion leads to a review of Deci & Ryan’s (2000) self-determination theory (SDT), which specifies that environments and contexts which support basic psychological needs (competence, relatedness and autonomy) will produce higher quality motivation than environments which frustrate of exacerbate these needs. The research establishing the ways in which key social agents can support these basic needs is then reviewed, and the review depicts a situation wherein SDT has precipitated a way of studying the socio-environmental influences on motivation that has become quite piecemeal and fragmented. Following this, the motivational climate approach (Ames, 1992) specified in achievement-goals theory (AGT – Nicholls, 1989) is also reviewed. This section reveals a body of research which is highly consistent in its methodology and findings. The following two sections reflect recent debates regarding the nature of achievement goals and the way they are conceptualised (e.g., approach-avoidance goals and social goals), and the implications of this for motivational climate research are discussed. This leads to a section reviewing the current issues and concerns in the study of social and environmental influences on athlete motivation. Finally, future research directions and ideas are proposed to facilitate, precipitate and guide further research into the social and environmental influences on athlete motivation in sport. Recent studies that have attempted to address these issues are reviewed and their contribution is assessed

Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations

Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR

How to Obtain Valid Generalized Modal Syllogisms from Valid Generalized Syllogisms

Making full use of the truth value definitions of sentences with quantification, possible world semantics and/or fuzzy logic, one can prove the validity of generalized modal syllogisms. This paper shows that the proof of the validity of a generalized modal syllogism can be transformed into that of its corresponding generalized syllogism, and that the generalized syllogism obtained by removing all modalities in any valid generalized modal syllogism is still valid. Therefore, the simplest way to screen out valid generalized modal syllogisms is to add modalities to valid generalized syllogisms, and then to delete all invalid syllogisms by means of the basic rules with which valid generalized modal syllogisms should meet. And then the remainders are valid. This paper illustrates how to obtain 12 valid generalized modal syllogisms by adding necessary modalities and/or possible modalities to any valid generalized syllogism. The two kinds of syllogisms discussed in this paper are composed of sentences with quantification which is the largest number of sentences in natural language. Hence, this innovative research can provide theoretical support for linguistics, logic, artificial intelligence, and among other fields

Homonymy and the Comparability of Goods in Aristotle

My dissertation will draw attention to an underexplored problem in Aristotle\u27s theory of the good and advance two alternative proposals about how it can be solved. Aristotle endorses an inconsistent triad of premises concerning homonymy, comparability, and goodness. First, he argues that the good is homonymous: there is no single characteristic, goodness, which is shared by all good things. Rather, he argues that different kinds of good things require different accounts specifying what it is for them to be good. Second, he holds that homonyms are incomparable. If two things are homonymously F, then we are not entitled to claim that one is more F than the other, or that they are F to an equal degree. The incomparability of homonyms entails, for example, that if two goods are homonymous, we cannot claim that that one is better than the other or that they are equally valuable. Finally, however, Aristotle holds that goods typically are comparable. Indeed, several passages throughout corpus suggest that he thinks of the cosmos as an axiological hierarchy in which every being can be ranked on a single scale of better and worse.This inconsistent triad constitutes a seldom recognized problem for Aristotle\u27s theory of the good which I call the incomparability problem. In the dissertation, I clarify the shape of the incomparability problem, explore the conceptual resources Aristotle has available to resolve it, and critically engage with the relatively small body of secondary literature that discusses it. Finally, I develop two possible solutions to it, both of which, I argue, are more promising than any alternatives in the literature thus far

A logical approach to fuzzy truth hedges

The starting point of this paper are the works of Hájek and Vychodil on the axiomatization of truth-stressing and-depressing hedges as expansions of Hájek's BL logic by new unary connectives. They showed that their logics are chain-complete, but standard completeness was only proved for the expansions over Gödel logic. We propose weaker axiomatizations over an arbitrary core fuzzy logic which have two main advantages: (i) they preserve the standard completeness properties of the original logic and (ii) any subdiagonal (resp. superdiagonal) non-decreasing function on [0, 1] preserving 0 and 1 is a sound interpretation of the truth-stresser (resp. depresser) connectives. Hence, these logics accommodate most of the truth hedge functions used in the literature about of fuzzy logic in a broader sense. © 2013 Elsevier Inc. All rights reserved.The authors acknowledge partial support of the MICINN projects TASSAT (TIN2010-20967-C04-01) and ARINF (TIN2009-14704-C03-03), and the FP7-PEOPLE-2009-IRSES project MaToMUVI (PIRSES-GA-2009-247584). Carles Noguera also acknowledges support of the research contract “Juan de la Cierva” JCI-2009-05453.Peer Reviewe