This book constitutes the major results of the EU COST (European Cooperation in the field of Scientific and Technical Research) Action 274: TARSKI - Theory and Applications of Relational Structures as Knowledge Instruments - running from July 2002 to June 2005. The papers are devoted to further understanding of interdisciplinary issues involving relational reasoning by addressing relational structures and the use of relational methods in applicable object domains.
Relational structures abound in our daily environment: relational databases, data mining, scaling procedures, preference relations, etc. As the documentation of scientific results achieved within the European COST Action 274, TARSKI, this book advances the understanding of relational structures and the use of relational methods in various application fields. The 12 revised full papers were carefully reviewed and selected for presentations. The papers are devoted to mechanization of relational reasoning, relational scaling and preferences, and algebraic and logical foundations of real world relations.
This book presents logical foundations of dual tableaux together with a number of their applications both to logics traditionally dealt with in mathematics and philosophy (such as modal, intuitionistic, relevant, and many-valued logics) and to various applied theories of computational logic (such as temporal reasoning, spatial reasoning, fuzzy-set-based reasoning, rough-set-based reasoning, order-of magnitude reasoning, reasoning about programs, threshold logics, logics of conditional decisions). The distinguishing feature of most of these applications is that the corresponding dual tableaux are built in a relational language which provides useful means of presentation of the theories. In this way modularity of dual tableaux is ensured. We do not need to develop and implement each dual tableau from scratch, we should only extend the relational core common to many theories with the rules specific for a particular theory.
This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; knowledge acquisition, representation and reasoning; logical structures of opposition and logical syllogisms; mathematical fuzzy logics; theoretical and applied aspects of imprecise probabilities; data science and machine learning; decision making modeling and applications; e-health; fuzzy methods in data mining and knowledge discovery; soft computing and artificia intelligence techniques in image processing; soft methods in statistics and data analysis; uncertainty, heterogeneity, reliability and explainability in AI; weak and cautious supervised learning.
Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.
This volume collects the extended abstracts of 45 contributions of participants to the Seventh International Summer School on Aggregation Operators (AGOP 2013), held at Pamplona in July, 16-20, 2013. These contributions cover a very broad range, from the purely theoretical ones to those with a more applied focus. Moreover, the summaries of the plenary talks and tutorials given at the same workshop are included. Together they provide a good overview of recent trends in research in aggregation functions which can be of interest to both researchers in Physics or Mathematics working on the theoretical basis of aggregation functions, and to engineers who require them for applications.
Harrie de Swart is a Dutch logician and mathematician with a great and open int- est in applications of logic. After being confronted with Arrow’s Theorem, Harrie became very interested in social choice theory. In 1986 he took the initiative to start up a group of Dutch scientists for the study of social choice theory. This initiative grew out to a research group and a series of colloquia, which were held approximately every month at the University of Tilburg in The Netherlands. The organization of the colloquia was in the hands of Harrie and under his guidance they became more and more internationally known. Many international scholars liked visiting the social choice colloquia in Tilburg and enjoyed giving one or more presentations about their work. They liked Harrie’s kindness and hospitality, and the openness of the group for anything and everything in the eld of social choice. The Social Choice Theory Group started up by Harrie consisted, and still c- sists, of scholars from several disciplines; mostly economics, mathematics, and (mathematical) psychology. It was set up for the study of and discussion about anything that had to do with social choice theory including, and not in the least, the supervision of PhD students in the theory. Members of the group were, among o- ers, Thom Bezembinder (psychologist), Hans Peters (mathematician), Pieter Ruys (economist), Stef Tijs (mathematician and game theorist) and, of course, Harrie de Swart (logician and mathematician).
In this first book of the series Survey Methods in Educational Research, we have brought together leading authors and scholars in the field to discuss key introductory concepts in the creation, implementation, evaluation and dissemination of survey instruments and their resultant findings. While there are other textbooks that might introduce these concepts adequately well, the authors here have focused on the pragmatic issues that inevitably arise in the development and administration process of survey instruments. Drawing from their rich experiences, the authors present these potential speed bumps or road blocks a survey researcher in education or the social sciences might encounter. Referencing their own work and practice, the authors provide valuable suggestions for dealing with these issues “your advisor never told you about.” And all of the recommendations are aligned with standard protocols and current research on best practices in the field of research methodology. This book is broken into four broad units on creating survey items and instruments, administering surveys, analyzing the data from surveys, and stories of successful administrations modeling the entire research cycle. Each chapter focuses on a different concept in the survey research process, and the authors share their approaches to addressing the issues. These topics include survey item construction, scale development, cognitive interviewing, measuring change with self-report data, translation issues with surveys administered in multiple languages, working with school and program administrators when implementing surveys, a review of current software used in survey research, the use of weights, response styles, assessing validity of results, and effectively communicating your results and findings … and much more. The intended audience of the volume will be practitioners, administrators, teachers as researchers, graduate students, social science and education researchers not experienced in survey research, and students learning program evaluation. In brief, if you are considering doing survey research, this book is meant for you.
This volume is the post conference proceedings of the 8th International Seminar on Relational Methods in Computer Science (RelMiCS 8), held in conjunction with the 3rd International Workshop on Applications of Kleene Algebra and a COST Action 274 (TARSKI) Workshop. This combined meeting took place in St. Catharines, Ontario, Canada, from February 22 to February 26, 2005.
Mechanizing hypothesis formation is an approach to exploratory data analysis. Its development started in the 1960s inspired by the question “can computers formulate and verify scientific hypotheses?”. The development resulted in a general theory of logic of discovery. It comprises theoretical calculi dealing with theoretical statements as well as observational calculi dealing with observational statements concerning finite results of observation. Both calculi are related through statistical hypotheses tests. A GUHA method is a tool of the logic of discovery. It uses a one-to-one relation between theoretical and observational statements to get all interesting theoretical statements. A GUHA procedure generates all interesting observational statements and verifies them in a given observational data. Output of the procedure consists of all observational statements true in the given data. Several GUHA procedures dealing with association rules, couples of association rules, action rules, histograms, couples of histograms, and patterns based on general contingency tables are involved in the LISp-Miner system developed at the Prague University of Economics and Business. Various results about observational calculi were achieved and applied together with the LISp-Miner system. The book covers a brief overview of logic of discovery. Many examples of applications of the GUHA procedures to solve real problems relevant to data mining and business intelligence are presented. An overview of recent research results relevant to dealing with domain knowledge in data mining and its automation is provided. Firsthand experiences with implementation of the GUHA method in the Python language are presented.
