fca-list May 2024

fca-list@cs.uni-kassel.de

4 participants
5 discussions

XVI Madrid UPM Machine Learning and Advanced Statistics Summer School (June 17th - June 28th, 2024) - Early registration deadline

by mlas＠fi.upm.es

Dear colleagues, We would like to remind you that early registration for the Madrid UPM Machine Learning and Advanced Statistics summer school finishes on May, 27th (included). The summer school will be held in Boadilla del Monte, near Madrid, from June 17th to June 28th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses. Early registration is *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/MLAS There is a 25% discount for members of Spanish AEPIA and SEIO societies. Please, forward this information to your colleagues, students, and whomever you think may find it interesting. Best regards, Pedro Larrañaga, Concha Bielza, Bojan Mihaljević and Laura Gonzalez Veiga. -- School coordinators. *** List of courses and brief description *** # Week 1 (June 17th - June 21st, 2024) ## 1st session: 9:45-12:45 ### Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: R. ### Course 2: Time Series(15 h) Basic concepts in time series. Linear models for time series. Time series clustering. Practical demonstration: R. ## 2nd session: 13:45-16:45 ### Course 3: Supervised Classification (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: python. ### Course 4: Statistical Inference (15 h) Introduction. Some basic statistical tests. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. ## 3rd session: 17:00 - 20:00 ### Course 5: Deep Learning (15 h) Introduction. Learning algorithms. Learning in deep networks. Deep Learning for Computer Vision. Deep Learning for Language. Practical session: Python notebooks with Google Colab with keras, Pytorch and Hugging Face Transformers. ### Course 6: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. # Week 2 (June 24th - June 28th, 2024) ## 1st session: 9:45-12:45 ### Course 7: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Additional topics. Practical session: R and python. ### Course 8: Clustering (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advice. Practical session: R. ## 2nd session: 13:45-16:45 ### Course 9: Gaussian Processes and Bayesian Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian processes. Deep Gaussian processes. Introduction to Bayesian optimization. Bayesian optimization in complex scenarios. Practical demonstration: python using GPytorch and BOTorch. ### Course 10: Explainable Machine Learning (15 h) Introduction. Inherently interpretable models. Post-hoc interpretation of black box models. Basics of causal inference. Beyond tabular and i.i.d. data. Other topics. Practical demonstration: Python with Google Colab. ## 3rd session: 17:00-20:00 ### Course 11: SVMs, Kernel Methods and Regularized Learning (15 h) Regularized learning. Kernel methods. SVM models. SVM learning algorithms. Practical session: Python Anaconda with scikit-learn. ### Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK.

1 year, 1 month

CFP for KoDis/CAKR 2024, the Joint Workshop on Knowledge Diversity and Cognitive Aspects of KR (colocated with KR 2024) - Paper deadline: 17.06.2024

by Lucia Gomez Alvarez

CALL FOR PAPERS Joint Workshop on Knowledge Diversity and Cognitive Aspects of KR (KoDis/CAKR) ============================================================================== Co-located with the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), November 2 – 8, 2024 in Hanoi, Vietnam This workshop is the joint continuation of the previous Workshop on Cognitive Aspects of KR (CAKR) and of the Workshop on Knowledge Diversity (KoDis). In view of the partial overlap of topics and target audience, we organise the KoDis and CAKR workshops jointly this year. Website: https://kodis-cakr24.krportal.org/ Important Dates: ---------------- All dates are given Anywhere on Earth (AoE). - Papers due: July 17, 2024 - Notification to authors: August 21, 2024 - Camera-ready version due: September 18, 2024 - Workshop date: November 2, 3, or 4, 2024 Overview: --------- The KoDis workshop intends to create a space of confluence and a forum for discussion for researchers interested in knowledge diversity in a wide sense, including diversity in terms of diverging perspectives, different beliefs, semantic heterogeneity and others. The importance of understanding and handling the different forms of diversity that manifest between knowledge formalisations (ontologies, knowledge bases, or knowledge graphs) is widely recognised and has led to the proposal of a variety of systems of representation, tackling overlapping aspects of this phenomenon. Besides understanding the phenomenon and considering formal models for the representation of knowledge diversity, we are interested in the variety of reasoning problems that emerge in this context, including joint reasoning with possibly conflicting sources, interpreting knowledge from alternative viewpoints, consolidating the diversity as uncertainty, reasoning by means of argumentation between the sources and pursuing knowledge aggregations among others. A non-exhaustive list of topics of interest for the KoDis workshop is given below. - Philosophical and cognitive analysis of knowledge diversity. - Formal models for the representation of knowledge diversity. - Ontological approaches capturing multiple perspectives and viewpoints. - Context and concept formation in such systems. - Consistency (or not) in multi-perspective systems; assessment and mitigation of inconsistencies. - Communication between knowledge-diverse systems. - Argumentation-based approaches for dealing with inconsistency. - Aggregation of diverse or inconsistent knowledge; judgement aggregation. - Uncertainty in the context of knowledge diversity. - Applications of formal models of knowledge diversity. The CAKR workshop deals with cognitively adequate approaches to knowledge representation and reasoning. Knowledge representation is a lively and well-established field of AI, where knowledge and belief are represented declaratively and suitable for machine processing. It is often claimed that this declarative nature makes knowledge representation cognitively more adequate than e.g. sub-symbolic approaches, such as machine learning. This cognitive adequacy has important ramifications for the explainability of approaches in knowledge representation, which in turn is essential for the trustworthiness of these approaches. However, exactly how cognitive adequacy is ensured has often been left implicit, and connections with cognitive science and psychology are only recently being taken up. The goal of the CAKR workshop is to bring together experts from fields including artificial intelligence, psychology, cognitive science and philosophy to discuss important questions related to cognitive aspects of knowledge representation, such as: - How can we study the cognitive adequacy of approaches in AI? - Are declarative approaches cognitively more adequate than other approaches in AI? - What is the connection between cognitive adequacy and explanatory potential? - How to develop benchmarks for studying cognitive aspects of AI? - Which results from psychology are relevant for AI? - What is the role of the normative-descriptive distinction in current developments in AI? Call for Papers: --------------- We invite both long and short papers, as well as reports on recently published papers in reputed venues. Submissions will be peer-reviewed to ensure quality and relevance to the workshop. At least one author of each accepted paper will be required to attend the workshop to present the contribution. Submissions should be of one of the following types: - long papers reporting unpublished research (10–12 pages excluding references), - short papers reporting unpublished research (5–6 pages excluding references), or - extended abstracts (up to 3 pages including references) presenting work relevant to the workshop already published in other conferences or journals. Such an abstract should summarize the contributions of the article and its relevance for the workshop, as well as include bibliographic details of the article and a link to the article. Publication: ----------- We plan to publish informal proceedings in the CEUR Workshop Proceedings. Organizing Committee: --------------------- Lucía Gómez Alvarez, Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France Jonas Haldimann, FernUniversität in Hagen, Germany Jesse Heyninck, OpenUniversiteit, the Netherlands; University of Cape Town and CAIR, South Africa Srdjan Vesic, CRIL CNRS Univ. Artois, France

1 year, 1 month

XVI Madrid UPM Machine Learning and Advanced Statistics Summer School (June 17th - June 28th, 2024) - Early registration reminder

by mlas＠fi.upm.es

Dear colleagues, We would like to remind you that early registration for the Madrid UPM Machine Learning and Advanced Statistics summer school is open until June 2nd (included). The summer school will be held in Boadilla del Monte, near Madrid, from June 19th to June 30th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses. Early registration is *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/MLAS There is a 25% discount for members of Spanish AEPIA and SEIO societies. Please, forward this information to your colleagues, students, and whomever you think may find it interesting. Best regards, Pedro Larrañaga, Concha Bielza, Bojan Mihaljević and Laura Gonzalez Veiga. -- School coordinators. *** List of courses and brief description *** * Week 1 (June 19th - June 23rd, 2023) * 1st session: 9:45-12:45 Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: R. Course 2: Time Series(15 h) Basic concepts in time series. Linear models for time series. Time series clustering. Practical demonstration: R. 2nd session: 13:45-16:45 Course 3: Supervised Classification (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: python. Course 4: Statistical Inference (15 h) Introduction. Some basic statistical tests. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. 3rd session: 17:00 - 20:00 Course 5: Neural Networks and Deep Learning (15 h) Introduction. Learning algorithms. Learning in deep networks. Deep Learning for Images. Deep Learning for Text. Practical session: Jupyter notebooks in Python Anaconda with keras and tensorflow. Course 6: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. * Week 2 (June 26th - June 30th, 2023) * 1st session: 9:45-12:45 Course 7: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Additional topics. Practical session: R and python. Course 8: Clustering (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advice. Practical session: R. 2nd session: 13:45-16:45 Course 9: Gaussian Processes and Bayesian Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian processes. Deep Gaussian processes. Introduction to Bayesian optimization. Bayesian optimization in complex scenarios. Practical demonstration: python using GPytorch and BOTorch. Course 10: Explainable Machine Learning (15 h) Introduction. Inherently interpretable models. Post-hoc interpretation of black box models. Basics of causal inference. Model-specific explanations: Bayesian networks. Other topics. Practical demonstration: R. 3rd session: 17:00-20:00 Course 11: Support Vector Machines and Regularized Learning (15 h) Introduction. SVM models. SVM learning algorithms. Regularized learning. Convex optimization with proximal methods. Practical session: Python Anaconda with scikit-learn. Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK. If you wish to stop receiving emails regarding the Madrid UPM Machine Learning and Advanced Statistics summer school, please reply to this email with the title STOP. Dear colleagues, We would like to remind you that early registration for the Madrid UPM Machine Learning and Advanced Statistics summer school is open until May, 27th (included). The summer school will be held in Boadilla del Monte, near Madrid, from June 17th to June 28th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses. Early registration is *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/MLAS There is a 25% discount for members of Spanish AEPIA and SEIO societies. Please, forward this information to your colleagues, students, and whomever you think may find it interesting. Best regards, Pedro Larrañaga, Concha Bielza, Bojan Mihaljević and Laura Gonzalez Veiga. -- School coordinators. *** List of courses and brief description *** # Week 1 (June 17th - June 23rd, 2024) ## 1st session: 9:45-12:45 ### Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: R. ### Course 2: Time Series(15 h) Basic concepts in time series. Linear models for time series. Time series clustering. Practical demonstration: R. ## 2nd session: 13:45-16:45 ### Course 3: Supervised Classification (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: python. ### Course 4: Statistical Inference (15 h) Introduction. Some basic statistical tests. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. ## 3rd session: 17:00 - 20:00 ### Course 5: Deep Learning (15 h) Introduction. Learning algorithms. Learning in deep networks. Deep Learning for Computer Vision. Deep Learning for Language. Practical session: Python notebooks with Google Colab with keras, Pytorch and Hugging Face Transformers. ### Course 6: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. # Week 2 (June 26th - June 28th, 2024) ## 1st session: 9:45-12:45 ### Course 7: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Additional topics. Practical session: R and python. ### Course 8: Clustering (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advice. Practical session: R. ## 2nd session: 13:45-16:45 ### Course 9: Gaussian Processes and Bayesian Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian processes. Deep Gaussian processes. Introduction to Bayesian optimization. Bayesian optimization in complex scenarios. Practical demonstration: python using GPytorch and BOTorch. ### Course 10: Explainable Machine Learning (15 h) Introduction. Inherently interpretable models. Post-hoc interpretation of black box models. Basics of causal inference. Beyond tabular and i.i.d. data. Other topics. Practical demonstration: Python with Google Colab. ## 3rd session: 17:00-20:00 ### Course 11: SVMs, Kernel Methods and Regularized Learning (15 h) Regularized learning. Kernel methods. SVM models. SVM learning algorithms. Practical session: Python Anaconda with scikit-learn. ### Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK.

1 year, 2 months

EXTENDED Deadline: CfP - Demystifying machine learning for population researchers

by MPIDR - Career

EXTENDED DEADLINE: May 15, 2024 Advances in computational power and statistical algorithms, in conjunction with the increasing availability of large datasets, have led to a Cambrian explosion of machine learning (ML) methods. For population researchers, these methods are useful not only for predicting population dynamics but also as tools to improve causal inference tasks. However, the rapid evolution of this literature, coupled with terminological disparities from conventional approaches, renders these methods enigmatic and arduous for many population researchers to grasp. This workshop on November 5 to 6, 2024 at the Max Planck Intsitute for Demographic Research (MPIDR) in Rostock, Germany, clarifies the goals, techniques, and applications of machine learning methods for population research. The workshop covers * an introduction to ML methods for population researchers, * showcases of ML applications to answer causal questions, * discussions of the current developments of ML for population health, fertility and family dynamics, and * fosters critical discussions about the shortfalls of these techniques. The main focus of this workshop is on ML techniques using quantitative population data and research questions, not on ML language models. The workshop consists of keynotes, contributed sessions, and a tutorial. One keynote lecture will be delivered by Prof. Ian Lundberg (Cornell University, https://www.ianlundberg.org/). Prof. Jennie E. Brand (UCLA, https://www.profjenniebrand.com/) will deliver an online talk. This in-person workshop will take place in November 5-6 at the Max Planck Institute for Demographic Research in Rostock. We invite population researchers with interest in ML applications. We aim to receive contributions from different fields of population sciences, such as population health, formal and social demography, public health and economics, among others. We invite submission of original research abstract with relevance to ML and population sciences (max 500 words) and a CV (max. one page) to MLworkshop(a)demogr.mpg.de<mailto:MLworkshop@demogr.mpg.de>. Submission Deadline: May 15, 2024 Decisions on the selection will be communicated before May 31. Please direct any questions to MLworkshop(a)demogr.mpg.de<mailto:MLworkshop@demogr.mpg.de>. Organization committee: Angela Carollo, Aapo Hiilamo, Mikko Myrskyla. The workshop has no fees. Participants are expected to cover their travel and accommodation but limited financial support, offered on a competitive basis, is available for junior scientists or scientists from low-middle income countries. Please indicate the request for such funding at the time of abstract submission. The workshop is organized by the Max Planck Institute for Demographic Research and The Max Planck - University of Helsinki Center for Social Inequalities in Population Health. -- This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance.

1 year, 2 months

LAST Call for Extended Abstracts for CONCEPTS 2024

by IPCABRERA＠uma.es

Call for Extended Abstracts CONCEPTS 2024 1st International Joint Conference on Conceptual Knowledge Structures 28th Intl. Conf. on Conceptual Structures (ICCS) 18th Intl. Conf. on Formal Concept Analysis (ICFCA) 17th Intl. Conf. on Concept Lattices and their Applications (CLA) September 9–13 2024, Cádiz, Spain Website: https://concepts2024.uca.es <https://concepts2024.uca.es/> Email contact address: concepts24(a)lists.cs.uni-kassel.de <mailto:concepts24@lists.cs.uni-kassel.de> The 1st International Joint Conference on Conceptual Knowledge Structures (CONCEPTS) is a merger of the three conferences CLA, ICCS, and ICFCA, which have been essential venues for researchers and practitioners working on theoretical and applied aspects of formal concept analysis and representation of conceptual knowledge, as well as closely related areas, such as data mining, information retrieval, knowledge management and discovery. This new conference aims to continue the tradition and standards of previous conferences and become a key annual meeting to take along all members of the three communities of CLA, ICCS, and ICFCA and to keep abreast of the advances and new challenges in the field. Main topics include but are not limited to - Formal concept analysis: concept lattices, implications, algorithms and computational complexity - Conceptual graphs, graph-based models for human reasoning - Knowledge spaces and learning spaces - Ontologies, semantic web, knowledge graphs - Conceptual structures in natural language processing and linguistics - Conceptual knowledge acquisition and management - Conceptual knowledge discovery, data analysis, and visualization - Probabilistic approaches to conceptual knowledge representation and knowledge discovery - Approximation techniques in application to conceptual structures - Bridging conceptual structures to information sciences, artificial intelligence, data mining, machine learning, information retrieval, database theory, software engineering, and other areas of computer science - Understanding real-world data and modeling real-world phenomena with conceptual structures With this call, we invite the authors of recently published papers (in 2023 or 2024) relevant to the conference to submit extended abstracts (up to 2 pages) of these papers. If accepted, the authors will have an opportunity to present their work at the conference on par with regular submissions and have the abstract included in the CONCEPTS 2024 Book of Abstracts. We will consider abstracts of papers published in a Scopus-indexed journal or presented at a CORE-ranked conference in 2023 or 2024. Papers from previous editions of CONCEPTS 2024 ancestor conferences (CLA, ICCS, and ICFCA) are not eligible. Please upload your submissions of up to two pages at https://equinocs.springernature.com/service/CONCEPTS2024 <https://equinocs.springernature.com/service/CONCEPTS2024>. Please select the category for your submitted manuscript as “Extended abstract”. (the categories “regular paper” or “short paper” are no longer available). Please format your submissions according to Springer’s style: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu… <https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu…> Please include a reference to the paper on which the abstract is based. If possible, please indicate a link to the online version of the paper or submit the paper or its draft version together with the abstract. Submission deadline: May 20, 2024 (extended deadline) Notification of acceptance: May 31, 2024 Organization: General and Conference Chair: Jesús Medina, University of Cádiz, Spain Program Chairs: Inma P. Cabrera, University of Málaga, Spain Sébastien Ferré, University of Rennes, France Sergei Obiedkov, TU Dresden, Germany Local organizer Committee: María José Benítez Caballero, University of Cádiz, Spain Fernando Chacón-Gómez, University of Cádiz, Spain Samuel José Molina Ruiz, University of Cádiz, Spain Francisco José Ocaña Alcázar, University of Cádiz, Spain Executive Board: Jaume Baixeries, Polytechnic University of Catalonia, Spain Radim Belohlavek, Palacký University Olomouc, Czech Republic Tanya Braun, University of Münster, Germany Madalina Croitoru, University of Montpellier, France Sébastien Ferré, University of Rennes, France Sergei Kuznetsov, HSE University, Moscow, Russia Rokia Missaoui, Université du Québec en Outaouais, Canada Amedeo Napoli, LORIA, Nancy, France Sergei Obiedkov, TU Dresden, Germany Manuel Ojeda-Aciego, University of Malaga, Spain Uta Priss, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany Gerd Stumme, University of Kassel, Germany Program Committee: Cristina Alcalde, University of Basque Country, Spain Alexandre Bazin, LIRMM, Montpellier, France Sadok Ben Yahia, Tallinn University of Technology, Estonia Karell Bertet, La Rochelle University, France Peggy Cellier, IRISA/INSA Rennes, France Pablo Cordero, University of Málaga, Spain Maria Eugenia Cornejo Piñero, University of Cádiz, Spain Miguel Couceiro, University of Lorraine, France Christophe Demko, La Rochelle University, France Xavier Dolques, Université de Strasbourg, France Bernhard Ganter, Ernst-Schröder--Zentrum für Begriffliche Wissensverarbeitung e.V., Darmstadt, Germany Mohamed Hamza Ibrahim, University of Quebec in Outaouais, Canada Tom Hanika, University of Kassel, Germany Dmitry Ignatov, HSE University, Moscow, Russia Jan Konecny, Palace University Olomouc, Czech Republic Francesco Kriegel, TU Dresden, Germany Markus Krötzsch, TU Dresden, Germany Leonard Kwuida, Bern University of Applied Sciences, Switzerland Florence Le Ber, University of Strasbourg, France Domingo López-Rodríguez, University of Málaga, Spain Pierre Martin, University of Montpellier, France Jesús Medina, University of Cádiz, Spain Engelbert Mephu Nguifo, University Clermont Auvergne, France Jan Outrata, Palacký University Olomouc, Czech Republic Eloisa Ramírez-Poussa, University of Cádiz, Spain Sebastian Rudolph, TU Dresden, Germany Christian Sacarea, Babes-Bolyai University, Romania Baris Sertkaya, Frankfurt University of Applied Sciences, Germany Henry Soldano, Université Paris 13, France Martín Trnecka, Palacky University Olomouc, Czech Republic Petko Valtchev, Université du Québec à Montréal, Canada Francisco J. Valverde-Albacete, Rey Juan Carlos University, Spain We look forward to meeting you in Cádiz. Please, feel free to contact us for any further information. Sincerely yours, Inma P. Cabrera, Sébastien Ferré, Sergei Obiedkov. CONCEPTS 2024 Program chairs.

1 year, 2 months

2025

2024

2023

2022

2021

fca-list May 2024