ECML PKDD 2020 Workshops

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020.

ECML PKDD 2020 Workshops

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.

Advanced Analytics and Learning on Temporal Data

5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim ...

Advanced Analytics and Learning on Temporal Data

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.

Machine Learning and Data Mining for Sports Analytics

7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann ...

Machine Learning and Data Mining for Sports Analytics

This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.

Information for a Better World Shaping the Global Future

In: ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): INRA 2020, Ghent, Belgium, 14–18 September 2020, Proceedings, vol. 1323, pp.

Information for a Better World  Shaping the Global Future

This two-volume set LNCS 13192 – 13193 constitutes the refereed proceedings of the 17th International Conference on Information for a Better World: Shaping the Global Future, held in February 2022. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and the 29 short papers presented in this volume were carefully reviewed and selected from 167 submissions. They cover topics such as: Library and Information Science; Information Governance and Ethics; Data Science; Human-Computer Interaction and Technology ̧ Information Behaviour and Retrieval ̧ Communities and Media ̧ Health Informatics.

Data Methods and Theory in the Organizational Sciences

Statistical rethinking: A Bayesian course with examples in R and Stan. ... Bischl B. (2020) Interpretable machine learning – A brief history, stateof-the-art and challenges. In I. Koprinska et al. (Eds.) ECML PKDD 2020 Workshops.

Data  Methods and Theory in the Organizational Sciences

Data, Methods and Theory in the Organizational Sciences explores the long-term evolution and changing relationships between data, methods, and theory in the organizational sciences. In the last 50 years, theory has come to dominate research and scholarship in these fields, yet the emergence of big data, as well as the increasing use of archival data sets and meta-analytic methods to test empirical hypotheses, has upset this order. This volume examines the evolving relationship between data, methods, and theory and suggests new ways of thinking about the role of each in the development and presentation of research in organizations. This volume utilizes the latest thinking from experts in a wide range of fields on the topics of data, methods, and theory and uses this knowledge to explore the ways in which behavior in organizations has been studied. This volume also argues that the current focus on theory is both unhealthy for the field and unsustainable, and it provides more successful ways theory can be used to support and structure research, and demonstrates the most effective techniques for analyzing and making sense of data. This is an essential resource for researchers, professionals, and educators who are looking to rethink their current approaches to research, and who are interested in creating more useful and more interpretable research in the organizational sciences.

IoT Streams for Data Driven Predictive Maintenance and IoT Edge and Mobile for Embedded Machine Learning

Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers Joao Gama, Sepideh Pashami, Albert Bifet, ...

IoT Streams for Data Driven Predictive Maintenance and IoT  Edge  and Mobile for Embedded Machine Learning

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Mining Data for Financial Applications

5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini ...

Mining Data for Financial Applications

This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.* The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain. *The workshop was held virtually due to the COVID-19 pandemic. “Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets” and “Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Analysis of Images Social Networks and Texts

ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol. 1323, pp. 475–491. Springer, Cham (2020). https://doi.org/10.1007/978-3-03065965-332 3. Binns, R.: On the apparent conflict between ...

Analysis of Images  Social Networks and Texts

This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held during October 15-16, 2020. The conference was planned to take place in Moscow, Russia, but changed to an online format due to the COVID-19 pandemic. The 27 full papers and 4 short papers presented in this volume were carefully reviewed and selected from a total of 108 qualified submissions. The papers are organized in topical sections as follows: invited papers; natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; and process mining.

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II Michael Kamp, ... MLCS 2020) co-located with ECML PKDD 2019 and ECML PKDD 2020 - in both editions the workshop gained strong interest, ...

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops: Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021) Workshop on Parallel, Distributed and Federated Learning (PDFL 2021) Workshop on Graph Embedding and Mining (GEM 2021) Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021) Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021) Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021) Workshop on Bias and Fairness in AI (BIAS 2021) Workshop on Workshop on Active Inference (IWAI 2021) Workshop on Machine Learning for Cybersecurity (MLCS 2021) Workshop on Machine Learning in Software Engineering (MLiSE 2021) Workshop on MIning Data for financial applications (MIDAS 2021) Sixth Workshop on Data Science for Social Good (SoGood 2021) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021) Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021).

Misinformation and Disinformation

(Eds.), ECML PKDD 2020 workshops (pp. ... 3-030-65965-3_40 Dyakon, T. (2020, December 14). Poynter's MediaWise training significantly increases people's ability to detect disinformation, new Stanford study finds. Poynter.

Misinformation and Disinformation

This book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals. Examines how artificial intelligence (AI) and psychology can aid in detecting mis-/disinformation and the language of deceit in digital media content; Suggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news; Presents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans.

Active Inference

This book constitutes the refereed proceedings of the First International Workshop on Active Inference, IWAI 2020, co-located with ECML/PKDD 2020, held in Ghent, Belgium, in September 2020.

Active Inference

This book constitutes the refereed proceedings of the First International Workshop on Active Inference, IWAI 2020, co-located with ECML/PKDD 2020, held in Ghent, Belgium, in September 2020. The 13 full papers along with 6 short papers were thoroughly reviewed and selected from 25 submissions. They are organized in the topical sections on ​active inference and continuous control; active inference and machine learning; active inference: theory and biology.

Service Oriented Computing ICSOC 2021 Workshops

ECML PKDD 2020. LNCS (LNAI), vol. 12460, pp. 122–138. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67667-4_8 6. Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine-reading 7. Vaswani, A., et al.

Service Oriented Computing     ICSOC 2021 Workshops

This book constitutes the selected papers from the scientific satellite events held in conjunction with the19th International Conference on Service-Oriented Computing, ICSOC 2021. The conference was held Dubai, United Arab Emirates in November 2021. This year, these satellite events were organized around three main tracks, including a workshop track, a demonstration track, and a tutorials track. The ICSOC 2021 workshop track consisted of the following three workshops covering a wide range of topics that fall into the general area of service computing. • International Workshop on Artificial Intelligence for IT Operations (AIOps) • 3rd Workshop on Smart Data Integration and Processing (STRAPS 2021) • International Workshop on AI-enabled Process Automation (AI-PA 2021)

Interpretability for Industry 4 0 Statistical and Machine Learning Approaches

In: ECML PKDD 2020 workshops. Springer International Publishing, Cham, pp 417–431. https://doi.org/10.1007/978-3-030-65965-3_28 51. Morris MD (1991) Factorial sampling plans for preliminary computational experiments.

Interpretability for Industry 4 0   Statistical and Machine Learning Approaches

This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.

Scalable Uncertainty Management

In: ECML/PKDD 2020 Tutorial and Workshop on Uncertainty in Machine Learning (2020) 15. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28, 129–137 (1982) 16. Manski, C.: Partial Identification of Probability ...

Scalable Uncertainty Management

This book constitutes the refereed proceedings of the 15th International Conference on Scalable Uncertainty Management, SUM 2022, which was held in Paris, France, in October 2022. The 19 full and 4 short papers presented in this volume were carefully reviewed and selected from 25 submissions. Besides that, the book also contains 3 abstracts of invited talks and 2 tutorial papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. The chapter "Defining and Enforcing Descriptive Accuracy in Explanations: the Case of Probabilistic Classifiers" is licensed under the terms of the Creative Commons Attribution 4.0 International License.

MicroFinTech

(Eds.), ECML PKDD 2020 workshops. Communications in Computer and Information Science (Vol. 1323). Springer Verlag. Saxena, A., & Deb, A. T. (2014). Paradigm paranoia or mission drift? Lessons from microfinance crisis in India.

MicroFinTech

Microfinance is a renowned albeit controversial solution for giving financial access to the unbanked, even if micro-transactions increase costs, limiting outreach potential. The economic and financial sustainability of Microfinance Institutions (MFIs) is a prerequisite for widening a potentially unlimited client base. Automation decreases costs, expanding the outreach potential, and improving transparency and efficiency. Technological solutions range from branchless mobile banking to geo-localization of customers, digital/social networking for group lending, blockchain validation, big data, and artificial intelligence, up to “MicroFinTech” - FinTech applications adapted to microfinance. Of interest to both scholars, students, and professors of financial technology and microfinance, this book examines these trendy solutions comprehensively, going beyond the existing literature and showing potential applications to the traditional sustainability versus outreach trade-off.

Process Mining Workshops

ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers Jorge Munoz-Gama, Xixi Lu. 6. Di Francescomarino, C., Ghidini, C., ... ECML PKDD 2020. LNCS (LNAI), vol. 12461, pp.

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.

Graph Based Representation and Reasoning

In: Graph Embedding and Mining, ECML-PKDD 2020 Workshop Proceedings (2020) 12. Leborgne, A., Le Ber, F., Degiorgis, L., Harsan, L., Marc-Zwecker, S., Noblet, V.: Analysis of brain functional connectivity by frequent pattern mining in ...

Graph Based Representation and Reasoning

This book constitutes the proceedings of the 26th International Conference on Conceptual Structures, ICCS 2021, held virtually in September 2021. The 12 full papers and 4 short papers presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The papers are organized in the following topical sections: applications of conceptual structures; theory on conceptual structures, and mining conceptual structures.

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021.

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops: Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021) Workshop on Parallel, Distributed and Federated Learning (PDFL 2021) Workshop on Graph Embedding and Mining (GEM 2021) Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021) Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021) Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021) Workshop on Bias and Fairness in AI (BIAS 2021) Workshop on Workshop on Active Inference (IWAI 2021) Workshop on Machine Learning for Cybersecurity (MLCS 2021) Workshop on Machine Learning in Software Engineering (MLiSE 2021) Workshop on MIning Data for financial applications (MIDAS 2021) Sixth Workshop on Data Science for Social Good (SoGood 2021) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021) Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021).

Machine Learning and Knowledge Discovery in Databases Applied Data Science and Demo Track

European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V Yuxiao Dong, ... European machine learning and data mining conference and builds upon a very successful series of ECML PKDD conferences.

Machine Learning and Knowledge Discovery in Databases  Applied Data Science and Demo Track

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I Frank Hutter, ... European machine learning and data mining conference and builds upon a very successful series of ECML PKDD conferences.

Machine Learning and Knowledge Discovery in Databases

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.