Special Sessions

The submission of contributions for IPMU2018 is open until 22 November. The submission rules are available in http://ipmu2018.uca.es/submission/
The papers will be uploaded in PDF format, via Easy Chair system
Herein, authors should choose among the topics in the new submission form, one of the proposed special sessions.

The special sessions accepted until 30 September 2017 are listed below:

  • Advances on Explainable Artificial Intelligence
    In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data.
    Our focus is on knowledge representation and how to enhance human-machine interaction. As remarked in the last challenge stated by the USA Defense Advanced Research Projects Agency (DARPA), «even though current AI systems offer many benefits in many applications, their effectiveness is limited by a lack of explanation ability when interacting with humans». Accordingly, non-expert users, i.e., users without a strong background on AI, require a new generation of explainable AI systems. They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made.
    The goal of this special session is to discuss and disseminate the most recent advancements focused on explainable artificial intelligence. The session goes a step ahead with respect to the previous events we organized (which were mainly focused on interpretable fuzzy systems) in some other conferences: joint IFSA-EUSFLAT 2009, ISDA 2009, WCCI 2010, WILF 2011, ESTYLF 2012, WCCI 2012, EUSFLAT 2013, IFSA-EUSFLAT2015, and FUZZ-IEEE2017.
    The topics of interest include (but are not limited to):
    • Explainable Computational Intelligence
    • Theoretical Aspects of Interpretability
    • Dimensions of Interpretability: Readability versus Understandability
    • Learning Methods for Interpretable Systems and Models
    • Interpretability Evaluation and Improvements
    • Relations between Interpretability and other Criteria (such as Accuracy, Stability, Relevance, etc.)
    • Design Issues
    • Successful Applications of Interpretable AI Systems
    • Interpretable Fuzzy Systems
    • Interpretable Machine Learning
    • Models for Explainable Recommendations
    • Explainable Agents
    • Self-explanatory Decision-Support Systems
    • Argumentation Theory for Explainable AI
    • Natural Language Generation for Explainable AI

    • Aggregation Operators, Fuzzy Metrics and Applications
      In many practical problems one needs to process information which is affected by some type of uncertainty. In order to take a working decision in such problems is very common to carry out two types of actions: aggregation of input data and similarity measurement between objects under study.
      In the aforesaid aggregation processes the pieces of information are symbolized via some numerical values and the fusion of the data is made by means of the so-called aggregation functions. Nowadays the study of such functions became a research field in the fuzzy framework. The interest and the work in this field is rapidly growing and have led to a deep study of not only classical aggregation functions such as weighted means, t-norms or t-conorms, but also of others such as those constructed by means of Choquet or Sugeno integrals, copulas, overlap and grouping functions or ignorance functions, among many others, as well as of generalizations of the notion of aggregation function, as it is the case of pre-aggregation functions.
      When the measurement of similarity between objects, that play a central role in the problem under consideration, must carried out in such a way that there is a limitation on the accuracy of the performed measurement (such as the intrinsic error of the apparatus used to measure) or a certain degree of similarity can be only determined between the objects being compared, fuzzy metrics and indistinguishability operators become of great importance and a natural tool. The numerical values provided by the both aforesaid types of similarities are interpreted as the degree of nearness between objects. In the particular case of fuzzy metrics such a degree is relative to a positive real parameter. In the last years, due to its versatility, the theory of fuzzy metrics and indistinguishability operators have been studied in depth providing a breakthrough in the theoretical context and a large number of papers which grows rapidly.
      All these developments in both frameworks have been closely linked to an increasing number of applications to many different topics, image processing, machine learning, decision making, pattern recognition, fuzzy control, social sciences and robotics, just to mention a few of them.
      Motivated by the interest aroused in the scientific community by the exposed topics, the purpose of this special session is to bring researchers in the field of Aggregation Functions, Fuzzy Metrics, Indistinguishability Operators and its Applications, to exchange their ideas and approaches, to discuss and to present latest results on this field, both from a theoretical and an applied point of view. In this way, it will follow the rich tradition of Special Sessions in Aggregation Functions from previous IPMU Conferences, some of them co-organized by Tomasa Calvo, Humberto Bustince and Pilar Fuster.
      Related topics:
      Theoretical aspects:
      • Generalizations of the idea of aggregation function
      • Properties of aggregation functions
      • Copulas and triangular norms
      • Fuzzy measures and integrals
      • Averaging aggregation functions
      • Aggregations with ordinal and nominal scales
      • Aggregation functions for extensions of fuzzy sets
      • Aggregation on posets and lattices
      • Aggregation of preferences
      • Fuzzy Metrics
      • Similarities
      • Indistinguishability operators
        Practical aspects:
        • Security intelligence, analysis and decision support
        • Evaluation problems
        • Hybrid intelligent systems and computational intelligence
        • Approximate reasoning
        • Image processing
        • Model identification and parametrization
        • Diagnostics and prognostics
        • Data mining
        • Robotics
        • Machine Learning

      • Belief Function Theory and its Applications
        During the past few years Belief function theory, also known as Dempster-Shafer theory or Evidence theory, has attracted considerable attention within the Artificial Intelligence community as a promising method of dealing with uncertainty in expert systems. As presented in the literature, the Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that is allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and modeling of conflict between them.
        The objective of the special session is to bring together researchers to report and discuss recent developments of belief function theory, the relationship between belief function theory ant he other theories such as probability theory, possibility theory, rough set theory, fuzzy set theory, … with their applications in artificial intelligence. We invite original submissions in this area. The topics of interest include, but not limited to:
        • Belief function theory
        • Conflict management
        • Data mining
        • Fuzzy sets
        • Rough sets
        • Temporal information fusion
        • Applications in artificial intelligence, pattern recognition, classification, data fusion.
        • Didier Coquin: LISTIC, Université de Savoie Mont-Blanc, France
        • Reda Boukezzoula, LISTIC, Université de Savoie Mont-Blanc, France

      • Current techniques to model, process and describe time series
        Today, there is a great interest on the research of time series since they are used in many situations in real life. Researchers are very interested in extracting the relevant information from data that can be modelled as time series. Initially, time series are represented as raw data, and usually, they are computed using mathematical methods to obtain information from them while other approaches develop models to represent series. When time series has been modelled, different techniques can be used to find patterns and/or to study trends in such series. Finally, another relevant line of research concerns the description of the series using natural language where researchers aims to extract information expressed in natural language. For all these reasons, the goal of this special session is to provide an international forum for the presentation of recent results in the research of this field.
        A non-exhaustive list of topics includes:
        • Methods for processing time series represented as raw data
        • Current models to represent time series
        • Efficient modelling of time series
        • Querying time series
        • Linguistic description of time series
        • Current techniques for extracting specific information from time series

      • Decision making modeling and applications
        Decision making is an inherent activity to mankind that can be seen as a process composed of different phases such as information gathering, analysis and selection based on different mental and reasoning processes that led to choose a suitable alternative among a set of possible alternatives in a given situation. Nowadays human beings daily face situations that rapidly change decision environments increasing its complexity.
        Moreover, Decision making is a core area in a wide range of disciplines such as Psychology, Economics, Political Sciences, Social Choice, Operations Research, Medicine, Artificial Intelligence, Engineering, etc. Because of this variety of disciplines, this special session aims at providing an opportunity for researchers working this research area to discuss in fundamental, approaches, methodologies, software systems, and applications, to share their novel ideas, original research results and practical experiences.

      • Discrete models and Computational Intelligence
        The topics include, but are not limited to:
        • Fuzzy graphs
        • Evolutionary search in graphs
        • fuzzy cognitive maps
        • Laszlo Koczy, Budapest University of Technology and Economics, Hungary

      • Formal concept analysis and uncertainty
        Formal Concept Analysis (FCA) is being recently adopted as a solid alternative to the process of information treatment to be used in real applications with some automated methods. More specifically, it has been appreciated with a significant growth of their use in a wide variety of areas: Biomedicine, Tourism, Education, Social Networks, etc.
        A great part of this recent interest is due to its unique and general framework which allows to develop from the beginning to the end all the stages involved in the way from information to knowledge and, moreover, to automatically reason about it.
        During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress: Fuzzy Formal Concept Analysis, FCA with granular computing, interval-valued, possibility theory, triadic and more intend to handle the uncertainty and vagueness in data.
        This session aims to gather a number of reseachers concerning FCA and imprecision in data management. The topics include but not are limited to:
        • Theoretical foundations in FCA
        • Logic and fuzzy logic in FCA
        • Attribute implications,association rules and data dependencies
        • Redundancy and dimensionality reduction
        • Knowledge discovery and data analysis
        • Conceptual Exploration
        • Ontologies
        • Algorithms and applications

      • Fuzzy implication functions
        In recent years, fuzzy implication functions have become one of the main research lines of the fuzzy logic community. These logical connectives are the generalization of the classical two-valued implication to the infinite-valued setting. In addition to modelling fuzzy conditionals, they are also used to perform backward and forward inferences in different fuzzy rule based systems. Moreover, they have proved to be useful not only in fuzzy control and approximate reasoning, but also in many other fields like Multi-Valued Logic, Image Processing, Data Mining, Computing with Words and Rough Sets, among others.
        Due to this great variety of applications, fuzzy implication functions have attracted the efforts of many researchers also from the theoretical perspective focusing on problems whose solutions provide important insights from the point of view of their applications. Therefore, this special session seeks to bring together researchers interested in recent advances in the theory of fuzzy implication functions, concerning, among others, characterizations, representations, generalizations and their relationships with fuzzy negations, triangular norms, uninorms and other fuzzy logic connectives.

      • Fuzzy Logic and Artificial Intelligence Problems
        This is a special event to honor Miguel Delgado. Fuzzy logic is a theory and a technique frequently used in the development of Artificial Intelligence systems. The aim of this special session is to serve as a meeting point and open discussion forum for researchers and practitioners on the latest developments on Fuzzy Logic theory and methodologies as a tool that can be applied in Artificial Intelligence Problems, focusing on problems challenges our societies have to face, and to offer an opportunity for researchers to identify new and promising research directions. We will welcome both theoretical and more application oriented contributions addressing: (a) Analysis about Artificial Intelligence problems where the use of Fuzzy Logic allows to obtain better problem models or solutions, (b) proposals of Fuzzy Logic based applications for solving real life problems, and (c) theoretical advances related to Fuzzy Logic models and concepts directed towards a better understanding or analysis of some AI problems.
        Topics of interest include, but are not limited to, the following topics:
        • Fuzzy Knowledge Representation
        • Fuzzy Reasoning
        • Intelligent and Fuzzy Systems
        • Fuzzy concepts
        • Fuzzy Logic models
        • Applications of Fuzzy Logic for real life problems
        • Challenges for Fuzzy Logic Applications.

      • Fuzzy Mathematical Analysis and Applications
        Fuzzy Mathematical Analysis is a research area in constant evolution. There are many articles on optimality conditions for fuzzy problems which are a proof of the great interest that this topic arouses among researchers.
        The classical analysis methods do not work in the fuzzy setting. It is well-known that in classical optimization convexity plays a central role in order to get sufficient conditions to characterize the solutions. Nevertheless in the fuzzy context, it has been demonstrated that such results do not remain valid.
        This session will focus on new theoretical steps on fuzzy mathematical analysis and its applications. We invite original submissions in this area. The topics of interest include, but are not limited to:
        • Fuzzy/interval valued functions
        • Fuzzy and interval optimization problems
        • Fuzzy and set differential equations
        • Possibilistic and interval optimization problems including optimal control
        • Spaces of fuzzy sets
        • Fuzzy arithmetic
        • Fuzzy metric spaces
        • General fuzzy-valued functions
        • Convexity and fuzzy-valued functions
        • Differentiability of fuzzy-valued functions
        • Fuzzy integrals
        • Fuzzy optima control
        • Decision Making
        • Applications in these areas.

      • Fuzzy methods in Data Mining and Knowledge Discovery
        The objective of the special session is to provide a forum for the discussion of recent advances in the application of Data Mining and Knowledge Discovery technologies to diverse problems, focusing on those involving fuzzy methods, and to offer an opportunity for researchers to identify new and promising research directions.
        Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision- making in the current ultra-competitive markets; (b) the large volume of data managed by organizations makes it impossible to carry out an analysis process manually.
        Nowadays, the volume of information digitally stored has considerably increased not only in database format but also in text format which is available in open source bases such as the Web, including log files registering the use of the information or social media content. This has contributed to increase the interest on Text and Web Mining techniques. In one hand, these techniques aim to automatize the analysis process by introducing a variety of intelligent techniques to learn, optimize and represent uncertain and imprecise knowledge. On the other hand, these tools offer the possibility to analyze massive data offering more efficient algorithms and a suitable selection of obtained results in terms of their novelty, usefulness and interpretability.
        Topics of interest include, but are not limited to, the following topics:
        • Data, text and web mining
        • Stream data mining
        • Temporal data series
        • Big data mining
        • Imprecision, uncertainty and vagueness in data mining
        • Data pre- and post- processing in data mining
        • Parallel and distributed data mining algorithms
        • Information summarization and visualization
        • Human-machine interaction for data access
        • Semantic models to represent input data and extracted knowledge in a Data Mining process
        • Applications of Data Mining techniques: health, tourism, biological process, customer profiles, anomaly detection, emergency management, situation recognition, etc.

      • Fuzzy transforms: theory and applications to data analysis and image processing
        Fuzzy (F)-transforms successfully link various transforms (Fourier, Laplace, integral, Wavelet, etc.) with fuzzy approximation models. The general idea is to bring an original model into a special space where succeeding computations are easier. In particular, the F-transform transforms an infinitary object (a real function) into a finitary one (a finite vector). Another specific feature of the F-transform consists in including a fuzzy partition in its formal representation.
        In the recent ten years, the theory of F-transform became an important constituent in the field of computational intelligence. It has a well justified theory and many sophisticated applications in image, signal and time series processing. Moreover, it can be successfully used in numerical methods for differential and integro-differential equations including the case when uncertainty is included in their formulation. The exceptional feature of the F-transform is that it successively and efficiently copes with classical problems as well as with problems that are affected by uncertainty or vagueness.
        In image and signal processing, the F-transform effectively solves problems connected with up- (down-) scaling, reconstruction, edge detection, fusion, registration, etc. In time series analysis, the F-transform is used for trend extraction. In big data area, it works as a successful method of pattern recognition.
        The F-transform propagates usefulness and effectiveness of fuzzy methods on all levels of data processing.
        The aim of this special session is to present recent developments and trends in the theory and applications of the F-transform, including all mentioned above. Beside theoretical aspects, the session will be focused on advanced applications in data analysis including handling big data.
        We invite contributions that extend traditional ways of data analysis and propose adequate methods for various kinds of data processing including, but not limited to the following topics:
        • Theoretical aspects of the F-transform and its higher degree versions
        • Inverse F-transform and how to improve its approximation quality
        • Numerical methods on the basis of F-transform
        • F-Transform and Aggregation Operators
        • Big data processing on the basis of the F-transform
        • Applications to Image Processing and Computer Vision
        • Time series analysis and forecasting

      • Imprecise probabilities: foundations and applications
        This session is devoted to Imprecise Probability Theory. This theory encompasses all the mathematical models that can be used as more flexible tools than usual Probability Theory when the available information is scarce, vague or incomplete. It includes lower previsions, n- monotone capacities, belief functions and possibility measures, among others.
        We would like to attract papers that discuss and solve foundational questions, or clearly demonstrate the usefulness of imprecise probabilistic models in an application. We would particularly welcome papers going from theoretical advances to the solution of an associated applied problem.
        Note also that Imprecise Probability Theory is connected to other topics within the scope of IPMU, such as Dempster-Shafer Evidence Theory, Fuzzy Measures and their connections to Game Theory. Papers in these topics emphasizing the role of Imprecise Probabilities are also welcome.

      • Logical methods in mining knowledge from big data
        Internet is an extremely huge source of data containing knowledge on many levels. The highest level is apparent: what the data just say directly (e.g., height, age, sizes, etc.). There are, however, deep hidden layers containing knowledge far from being apparent. It is a big challenge to develop methods enabling us to gain it. Since knowledge can be characterized as familiarity, awareness, or understanding to facts, information, descriptions, or skills, we argue that logic has potential to formalize and represent the knowledge and to suggest methods how it can be obtained from the data. The prototypical example is the first data-mining method GUHA (established in 1968 by P. Hájek) that is an effective and ingenious combination of logic and probability. But there are other methods based on logic, e.g., fuzzy quantifiers, fuzzy/linguistic IF-THEN rules, association rules, concept lattices, and others.
        The aim of this special session is to present recent developments and trends in the theory and applications of special logical methods focused on representation of knowledge and mining it from the data. We invite contributions that extend traditional ways of mining knowledge and propose adequate methods for processing various kinds of data including, but not limited to the following topics:
        • Theoretical foundations of special logical methods for formalization and representation of knowledge
        • The theory of generalized quantifiers and their special cases (e.g., fuzzy or intermediate ones).
        • Mining knowledge in the form of special rules (e.g., association or fuzzy/linguistic ones).
        • Mining knowledge from time series.
        • Case studies in mining knowledge from various kinds of data.
        • Vilém Novák, University of Ostrava, Inst. for Research and Applications of Fuzzy Modeling, Czech Republic
        • Petra Murinová, University of Ostrava, Inst. for Research and Applications of Fuzzy Modeling, Czech Republic

      • Management of Uncertainty in Brain Computer Interface (BCI)
        Uncertainty is omnipresent in Brain Computer Interface applications, from capture of the brain signals to their interpretation and to modeling of their underlying causes. This session on BCI seeks to bring together research in BCI highlighting the need to capture uncertainty – probabilistic and fuzzy – in applications of BCI to robotics, thought controlled communication and movement support, virtual reality,and medicine.

      • Management of Uncertainty in Renewable Energy
        Energy consumption is one of the principal indicators of progress and welfare in a society, and thus, there is great concern regarding its different stages of development. World energy is currently fundamentally sustained by crude oil and its derivatives. In the wake of many years of price increases and due to the importance of that it has in the manufacturing industry and transportation, nations and researchers haver been pushed to think about creating alternative energy sources.
        The aim of this proposal is to show recent and novel applications of Management of Uncertainty in the field of Energy, especially Renewable Energy. To be precise, to different technological environments within these fields and showing the technical superiority of the solutions provided.
        The related topics are management of uncertainty, applications in renewable energy:
        • Renewable energy resources: solar, wind, geothermal, biomass, biogas, biofuels, hydropower, hydrogen and ocean (wave, tidal…).
        • Applications in renewable energy.
        • Applications and services of renewable energy in rural areas, isolated areas, buildings, industry, electricity and transport.
        • Policy of environmental impact and sustainability: economic aspects, environmental impact
        • Others: Smart Cities, Life Cycle Assessment (LCA).

      • Mathematical Morphology
        Mathematical morphology, in its deterministic view, defines operators on lattices, which are, in turn, the cornerstone in various domains of knowledge representation and information or data processing. Mathematical morphology has been applied successfully for more than 50 years in image processing and image understanding, and the developed operations can also be poweful in all these other domains. Accordingly, mathematical morphology is becoming a suitable theory for data processing beyond image processing.
        This session aims at presenting advances on mathematical morphology on different types of data representations, including structures such as knowledge bases, graph or spatial databases, on imprecise data (e.g. represented as fuzzy sets, rough sets, fuzzy graphs, fuzzy logics, etc.). Links between mathematical morphology and other approaches in computer science are also considered in this session (e.g. concept lattices, rough sets, fuzzy transforms, etc).
        Another focus of this session will be to highlight potential applications, such as image processing, image understanding, preference modeling, logical reasoning…

      • Mathematical fuzzy logic
        This special session is devoted to the most recent developments in the realm of fuzzy logic from a mathematical point of views, with particular emphasis on theoretical advances related to many-valued logics, algebraic semantics, combinatorial aspects, topological and categorical methods, proof theory and game theory, many-valued computation.
        A partial list of topics is the following:
        • Algebraic semantics of many-valued logics
        • Applications of many-valued logics to Formal Concept Analysis and Relational Methods
        • Applications of many-valued logics to Fuzzy Sets and to Rough Sets
        • Combinatorial or topological dualities
        • Computational complexity of many-valued logics
        • Modal logic approaches to probability and uncertainty in many-valued logics
        • Natural and alternative semantics for many-valued logics
        • Proof theory for many-valued logics
        • Subjective probability approaches to many-valued logics and non-classical events

      • Measures of comparison and entropies for fuzzy sets and their extensions
        In the framework of fuzzy sets and their extensions, many different measures of comparison have been suggested in the literature. Some of them are based on the degree of equality as, for example, the similarities. Analogously, some of them are based on the degree of difference between two sets, like distances, dissimilarities and divergences. These measures is very important, mainly for applications. In particular, they can be used to measure the degree of imprecision of a set, that is, to measure the entropy of this set. The study of these measures of comparison and the measures of fuzziness or imprecision are the main topics of this special session.

      • Metaheuristics and machine learning
        In this special session, we look for innovative papers dealing with the use of computational intelligence techniques for the resolution of complex problems. Some techniques of interest are machine learning, evolutionary computation, and other metaheuristics to solve complex (discrete, continuous, or multi-objective) problems from different domains as telecommunications, engineering, bioinformatics, logistics, or scheduling, just to name a few.
        Related topics:
        • Metaheuristics for solving complex problems
Using machine learning for solving complex problems
        • Hybrid algorithms

        • Robust optimization

        • Dealing with uncertainty

        • Parallel algorithms

        • Multi-objective optimization
        • Dynamic optimization
Real world applications
        • Juan Carlos de la Torre, University of Cádiz, Spain
        • Patricia Ruiz, University of Cádiz, Spain
        • Bernabe Dorronsoro, University of Cádiz, Spain

      • New trends in data aggregation
        Data aggregation is a field currently experiencing a boost of attention. From new theoretical developments to an expansion of the application field and passing by the development of algorithms for effectively aggregating huge amounts of data, we aim at providing a forum for discussing the most updated research in the field.
        Topics of interest to this session include but are not limited to:
        • Theory: aggregation on new types of structures, penalty functions, optimization challenges in data aggregation…
        • Applications: decision making, social choice, machine learning, pattern recognition, bioinformatics…
        • Bernard De Baets, KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium
        • Raúl Pérez Fernández, KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium

      • Optimization models for modern analytics
        This special session offers a comprehensive, practice-oriented introduction to the field of fuzzy analytics. It includes an introduction to the basic concepts, together with extensive information about computational intelligence models and techniques that have been used to date. Here, a special emphasis is given to fuzzy optimization models. It covers different fields including transportation, logistics, supply chain management, data mininig and, more in general, decision-making problems. It represents a valuable resource for researchers, data scientists and practitioners in the fields of computational intelligence and analytics.
        Topics of interest include, but are not limited to theory and application of:
        • Fuzzy optimization
        • Fuzzy transportation problems
        • Data mininig
        • Supply Chain Management
        • Shortest path planning
        • Routing problem
        • Fuzzy constraints Optimization problems
        • Fuzzy Multiobjective optimization
        • Metaheuristic methods
        • Data envelopment analysis
        • Vehicle routing with swarm intelligence
        • Resource allocation with metaheurisitc methods
        • Telecommunication network routing with evolutionary algorithms
        • Discrete optimization problems
        • Permutations using optimization.

      • Pre-aggregation functions and generalized forms of monotonicity
        This session will focus on recent developments in the field of fusion functions when generalized forms of monotonicity are considered, both from a theoretical and from an applied point of view. In this sense, the session aims at providing researchers in the field with an opportunity to present their most recent developments and for discussing recent trends in this area, as well as to identify potential problems of interest for researchers. In particular, this session will consider generalizations of the notion of aggregation functions, as pre-aggregation functions, which have appeared in the literature in recent years and which lead to new classes of functions that encompass both classical aggregation functions and other functions which are relevant, specially from the point of view of the applications, but which do not fulfil all the conditions required to an aggregation. Finally, applications which make use of these extensions are also welcomed.
        The following is a non-exhaustive list of topics that this session intends to cover:
        • Directional monotonicity
        • Weak monotonicity
        • Ordered directional monotonicity
        • Pre-aggregation functions
        • Applications of pre-aggregation functions in classification
        • Applications of pre-aggregation functions in image processing.

      • Recent Advances of Transportation Problem and Game Theory
        Transportation problem (TP) is the most important and particular type network- structured linear programming problem which provides us to control the optimum shipping patterns between the origin and the destinations. Most of the real world problems cannot be modeled with real (crisp) data and single objective function. Researchers have developed many efficient algorithms for tackling the multi-objective transportation problem (MOTP) where the parameters are uncertain (fuzzy, rough, random and their hybridization etc.) environment in the recent years.
        Game theory considers the mathematical solutions of conflict situations. Again a single criterion is not sufficient to design the real-life practical problems in game theory. Researchers have designed the game problems with the aid of multi-criteria decision making (MCDM) and uncertain environment in recent years.
        This special session invites to put together a large number of researchers concerning on transportation problem and game problem in uncertain data in multi-objective decision making (MODM)/MCDM in recent developments.
        The following topics include but not are limited to:
        • TP (Fixed Charge TP and Solid TP) in uncertain environment and MODM
        • Game Theory (Matrix game and Bi- matrix game) in uncertain environment and MCDM
        • Transportation-game problem in uncertain environment
        • Algorithms and applications.
        • Sankar Kumar Roy, Vidyasagar University, Midnapore, West Bengal, India, sankroy2006@gmail.com

      • Rough and Fuzzy Similarity Modelling Tools
        For centuries the concept and properties of similarity has been the subject of study and a vehicle for establishing and describing the existing relations between objects. Beginning with Plato who considered it in the form of «analogy», the notion of similarity manifested itself throughout the develop- ment of philosophical investigations regarding reasoning. Nowadays it plays an ever-increasing role in describing and understanding reasoning and cognition. Similarity that binds entities in an approximate, imprecise way is in the core of soft and granular methods of computing.
        There are studies on numerous approaches to modelling, expressing and utilizing similarity relations within the widely understood area of soft computing. In rough sets, modelling similarity is essential for both standard approaches and extensions concerning the analysis of complex data. Analogously, fuzzy systems strongly relate to modeling of similarities as well. Numerous applications of rough and fuzzy sets in the fields of information processing, decision support, recommendation and others tes- tify to the importance and usefulness of similarity-based tools.
        In the proposed special session, we intend to bring together researchers whose work corresponds to similarity modelling and handling, in particular with a use of rough and fuzzy approaches. The overall goal is to collect and review various methods for modelling similarity and scenarios of their applications.
        Topics include but are not limited to:
        • Rough sets and their extensions
        • Tolerance and neighborhood models
        • Similarity in granular computing
        • Fuzzy rough sets
        • Fuzzy similarity
        • Similarity modelling tools
        • Tversky’s contrast model
        • Ontology-based similarities
        • Similarities in hierarchical systems
        • Similarity aggregations
        • Applications of similarity models
        • Similarity in recommendation systems
        • Similarity in data clustering
        • Similarity-based reasoning
        • Similarity in decision support systems
        • Similarity in recommendation systems
        • Similarity in three-way decision-making

        • Soft computing for decision making in uncertainty
          This special session aims to bring together researchers, engineers and practitioners to present the latest achievements and innovations in the field of Decision making in Uncertainty based on the theoretical foundation of soft computing, which can help (a) to decrease the level of input information uncertainty, (b) to improve the quality of decision-making processes by introduction new and modified fuzzy/neuro information processing algorithms and methods of structural- parametric optimization, (c) to discuss the main peculiarities of decision making systems’ applications taking into account specific uncertain data in different fields, and (d) to consider potential future directions in the area of Soft Computing for Decision Making in Uncertainty.
          Related topics
          • Soft computing methods and algorithms for Decision Making in Uncertainty
          • Methods for decreasing the level of input information uncertainty in decision-making
          • Fuzzy/neuro decision making and decision support systems
          • Optimization of intelligent systems using soft computing approach
          • New and modified soft-computing-methods of information processing for increasing
            efficiency of decision-making processes in uncertainty
          • The successful cases of the design and applications of the intelligent systems for decision-making in uncertainty

        • Soft computing in information retrieval and sentiment analysis
          In the current global Information Technology scenario, voluminous information from sources like webpages, blogs, social networks among many others, is available for processing. For this reason, new Information Retrieval Systems more and more powerful are necessary nowadays, and relatedto this issue, new fields are emerging to improve and complete the information provided by these
          Information Retrieval Systems. One of these trendiest topics may be Sentiment Analysis.
          Sentiment Analysis, also called Opinion Mining, studies the extraction of opinions or sentiments using mainly Information Retrieval, Natural Language Processing and Artificial Intelligence, especially due to the fact that the information treated is heterogeneous in nature and lack in precision and completeness. Traditional systems are incompetent to handle these data, for that reason, it is also necessary to use advance techniques like proposed by Soft Computing.
          Soft Computing refers to a family of several techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to cope with lots of complex real-world problems. Therefore, this special session on “Soft Computing in Information Retrieval and Sentiment Analysis” provides a forum to the scientists, researchers, students and private sector parties to show original research works and real applications mainly related to possible uses of Soft Computing techniques for extracting, inferring, modeling, representing and handling information from heterogeneous sources like Internet.
          Potential topics of interest include but are not limited to:
          • Sentiment analysis/Opinion minig
          • Emotion detection
          • Search/meta search engines
          • Semi or unstructured information representation/modeling
          • Ontologies
          • Multi-Lingual and cross-Lingual Issues
          • Recommender systems

        • Tri-partitions and uncertainty
          Tri-partition is here intended as an umbrella name to represent all those situations where we can differentiate among three different scenarios such as decisions or division in three parts of the universe under investigation. Under this name, we can find rough sets and three-way decision theory, two tools to deal with decision making problems in presence of unavailable information or incomplete data. Other examples of tri-partition based tools are interval sets, shadowed sets, orthopairs and the hexagon of oppositions whose emphasis is more on knowledge representation and abstraction.
          This special session collects papers discussing theories and applications in knowledge representation and decision- making under uncertainty with tri-partition related paradigms.
          Topic are including but not limited to:
          • Uncertainty representation and measurement
          • Rough sets and extensions: Fuzzy-Rough sets, DTRS, GTRS, PRS, Generalized RS
          • Three-way decision
          • Orthopairs
          • Square of opposition
          • Shadowed Sets
          • Granular Computing
          • Reducts and Bireducts.
          • Davide Ciucci, University of Milano-Bicocca, Italy
          • JingTao Yao, University of Regina, Canada
          • Yiyu Yao, University of Regina, Canada

        • Uncertainty in Medicine
          Uncertainty is a serious problem in everyday medical practice and it is observed and described in medical literature. There are many meanings and types of imprecision in medicine with each of them having a different effect on the diagnosis or treatment. The imprecision types may be divided into objective (caused by the complexity or nature of the phenomenon) or subjective (caused by a personal opinion or doctor’s interpretation) or caused by the low quality of the information, e.g. due to incompleteness of the data.  Through the use of soft computing methods, it is possible to construct models that, when uncertainty is taken into account properly, increase the effectiveness of medical decision making.
          The session aims at exchanging the experiences of scientists struggling with uncertainty in medicine and discussing possible future developments in this area.
          Potential topics of interest include but are not limited to:
          • Intelligent decision-making systems in medical environments
          • Machine learning based on medical data
          • Uncertain data aggregation and fusion
          • Applications of Fuzzy sets, Rough sets and their extensions
          • Modeling different types of uncertainty in medical data
          • Assessment of medical data and models quality
          • Krzysztof Dyczkowski, Adam Mickiewicz University in Poznań, Poland
          • Anna Stachowiak, Adam Mickiewicz University in Poznań, Poland
          • Patryk Żywica, Adam Mickiewicz University in Poznań, Poland
          • Andrzej Wójtowicz, Adam Mickiewicz University in Poznań, Poland

        • Uncertainty in video/image processing (UVIP)
          Fuzzy logic provides powerful tools in computer vision and video/image processing applications, where some data or results are uncertain due to inherent ambiguity and vagueness of video/image data. This uncertainty principle is one of the fundamental results in signal processing. Due to the recently increasing interest in using fuzzy set theory combined with computer vision techniques to create intelligent systems, this special session is designed to serve researchers and developers to publish their original, innovative and state-of-the-art works in these topics.
          This special session is aimed to cover a wide range of works and projects on computer vision, image processing, biometrics, neural networks, intelligent systems or related areas where the uncertainty is managed, providing a platform for academics and industry related researchers to discuss and share experiences. We hope that this session can provide a common forum to exchange ideas and the latest discoveries in the area.
          • Computer vision
          • Image processing
          • Image Segmentation
          • Biometric identification and recognition
          • Pattern recognition
          • Hardware implementations

        • Uncertainty Models in Intelligent Image Understanding
          This session aims to bring together researchers interested in exploring the sources and treatment of uncertainty in intelligent image processing and understanding. Contributions using probabilistic models, evidential reasoning, and fuzzy models in all areas of image understanding are welcome. Models may be related to the images or to the knowledge used to interpret them. It is expected that this session will focus on higher level image interpretation beyond low level image processing.