Discovery science : 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022 : proceedings
This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022.The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions.
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| Published in | Lecture Notes in Computer Science Vol. 13601 |
|---|---|
| Main Authors | , , |
| Format | eBook Book Conference Proceeding |
| Language | English |
| Published |
Cham
Springer
2022
Springer Nature Switzerland |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022.The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. |
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| AbstractList | The Discovery Science conference presents a unique combination of latest advances in the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, with their application in various scientific domains. The 25th International Conference on Discovery Science (DS 2022) was held in Montpellier, France, during October 10–12, 2022. This was the second time the conference was organized as a stand-alone physical event.For its first 20 editions, DS was co-located with the International Conference on Algorithmic Learning Theory (ALT). In 2018 it was co-located with the 24th Interna- tional Symposium on Methodologies for Intelligent Systems (ISMIS 2018). DS 2019 was a stand-alone event, whereas DS 2020 and DS 2021 were online-only events.DS 2022 received 56 international submissions. Each submission was reviewed by at least two Program Committee (PC) members in a single-blind manner. The PC decided to accept 27 regular papers and 12 short papers. This resulted in an acceptance rate of 48% for regular papers.The conference included three keynote talks. Leman Akoglu (Carnegie Mellon Uni- versity) contributed a talk titled “Unsupervised Model Selection in Outlier Detection: The Elephant in the Room”; Luca Maria Aiello (IT University of Copenhagen) gave a presentation titled “Coloring Social Relationships”; and Stefan Kramer (University of Mainz) contributed a talk titled “35 Years of ‘Scientific Discovery: Computational Explorations of the Creative Processes’ – From the Early Days to the State of the Art”. Abstracts of the invited talks are included in the front matter of these proceedings. Besides the presentation of regular and short papers in the main program, the conference offered a session titled “Late Breaking Contributions” featuring poster and spotlight presentations of very recent research results on topics related to discovery science.We are grateful to Springer for their continued long-term support. Springer publishes the conference proceedings, as well as a regular special issue of the Machine Learning journal on discovery science. The latter offers authors a chance to publish significantly extended and reworked versions of their DS conference papers in this prestigious journal, while being open to all submissions on DS conference topics.This year, Springer (LNCS) supported the best student paper award. For DS 2022, the awardees are Annunziata D’Aversa, Stefano Polimena, Gianvito Pio, and Michelangelo Ceci (for the paper “Leveraging spatio-temporal autocorrelation phenomena to improve the forecasting of the energy consumption in smart grids”). We would like to thank Roberto Interdonato who joined the Program Chairs of the conference for the selection of the best student paper.On the program side, we would like to thank all the authors of submitted papers and the PC members for their efforts in evaluating the submitted papers, as well as the keynote speakers. On the organization side, we would like to thank all the members of the Organizing Committee, in particular Virginie Feche and Elena Demchenko, for the smooth preparation and organization of all conference associated activities. We are also grateful to the people behind EasyChair for developing the conference organization system that proved to be an essential tool in the paper submission and evaluation process, as well as in the preparation of the Springer proceedings.The DS 2022 conference was organized under the auspices of several universities and research institutes in Montpellier: the University of Montpellier, the University of Paul Valery, INRAE, Inria, and CIRAD. Significant support, especially through human resources, was also provided by the University of Montpellier and INRAE. Finally, we are indebted to all conference participants, who contributed to making this exciting event a worthwhile endeavor for all involved. This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022.The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. |
| Author | Ienco, Dino Pascal, Poncelet International Conference on Discovery Science |
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| Keywords | Computer vision Network protocols Image processing Artificial Intelligence Data Mining and Knowledge Discovery Pattern Recognition and Graphics Information retrieval Computers and Education Pattern recognition Data mining Computer systems Image analysis Correlation analysis Neural networks Vision Clustering algorithms Machine learning Linguistics Signal processing Computer Imaging Computer networks Information Systems Applications |
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| Notes | "LNCS sublibrary: SL7 - Artificial intelligence"--T.p. verso Includes bibliographical references and index |
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| RelatedPersons | Hartmanis, Juris Gao, Wen Steffen, Bernhard Bertino, Elisa Goos, Gerhard Yung, Moti |
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| Snippet | This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12,... The Discovery Science conference presents a unique combination of latest advances in the development and analysis of methods for discovering scientific... |
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| SubjectTerms | Artificial Intelligence Computer Appl. in Social and Behavioral Sciences Computer Imaging, Vision, Pattern Recognition and Graphics Computer Science Computers and Education Data Mining and Knowledge Discovery Discoveries in science Discoveries in science -- Congresses Information Systems Applications (incl. Internet) Research -- Data processing -- Congresses Science Science -- Philosophy -- Congresses |
| TableOfContents | Multi-attribute Transformers for Sequence Prediction in Business Process Management -- 1 Introduction -- 2 Definitions and Problem Statement -- 3 Related Work -- 4 Proposed Architectures -- 4.1 Encoder Architectures -- 4.2 Simplified Decoder Architectures -- 5 Experiments and Discussion -- 6 Conclusions and Final Remarks -- References -- Social Media Analysis -- Data-Driven Prediction of Athletes' Performance Based on Their Social Media Presence -- 1 Introduction -- 2 Related Work -- 2.1 Social Media as a Mood and Behaviour Detection Proxy -- 2.2 Social Media as a Distraction Factor -- 3 Methodology -- 3.1 Data Selection -- 3.2 Data Preparation -- 3.3 Predictive Significance Analysis -- 3.4 Implementation Details -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Link Prediction with Text in Online Social Networks: The Role of Textual Content on High-Resolution Temporal Data -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Graph Construction and Sequence-Based Framework -- 3.2 Learning Algorithms for Link Prediction in Temporal OSNs -- 3.3 Features for Link Prediction -- 4 Dataset -- 5 Results -- 5.1 Results for Traditional Models -- 5.2 Results for Graph Neural Networks -- 6 Discussion -- References -- Weakly Supervised Named Entity Recognition for Carbon Storage Using Deep Neural Networks -- 1 Introduction -- 2 Overview -- 2.1 Contributions -- 3 Background -- 4 Methodology -- 4.1 Noisy Data Set Creation -- 4.2 Overcoming Noisy Labels Effect -- 5 Evaluation -- 6 Related Work -- 7 Conclusion -- References -- Predicting User Dropout from Their Online Learning Behavior -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Data Set -- 3.2 Features -- 3.3 Pre-processing -- 3.4 Predictive Model -- 3.5 Evaluation -- 4 Results -- 4.1 Predictive Model -- 4.2 Evaluation -- 5 Discussion -- 6 Conclusions -- References 3 Training a Neural Network by Imitation Learning -- 3.1 Imitation Learning -- 3.2 Neural Network Input and Output Encoding -- 3.3 Pre-selection -- 4 Evaluation -- 4.1 Experiment Details -- 4.2 Comparison with Other Active Learning Strategies -- 5 Conclusion -- References -- Incremental/Continual Learning -- Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph -- 1 Introduction -- 2 Related Work -- 2.1 Online Live Streaming Service -- 2.2 Dynamic Graph Learning -- 3 Methodology -- 3.1 Dataset -- 3.2 Dynamic Graph Generation -- 3.3 Temporal Graph Neural Network -- 3.4 Strategies for Data Imbalance -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Experiment Setup -- 4.3 Baselines -- 4.4 Evaluation -- 4.5 Case Study -- 5 Conclusion -- References -- Semi-supervised Change Point Detection Using Active Learning -- 1 Introduction -- 2 AL-CPD -- 2.1 Algorithm Outline -- 2.2 Selecting Candidate Change Points -- 2.3 Finding New Candidate Change Points -- 3 Experiments -- 3.1 Datasets -- 3.2 Methodology -- 3.3 Q1: Comparison to Existing Change Point Detection Algorithms -- 3.4 Q2: Labelling Effort of AL-CPD -- 3.5 Q3: Contribution of Each Component of AL-CPD -- 3.6 Q4: Sensitivity Analysis -- 4 Conclusion -- References -- Adaptive Neural Networks for Online Domain Incremental Continual Learning -- 1 Introduction -- 2 Related Work -- 3 Online Domain Incremental Networks -- 4 Experiments -- 5 Conclusion -- References -- Incremental Update of Locally Optimal Classification Rules -- 1 Introduction -- 2 The Lord Algorithm -- 3 Incremental Lord -- 3.1 Incremental Updates -- 3.2 Overall Algorithm -- 4 Experiments -- 4.1 Comparison to HoeffdingTree and VFDR -- 4.2 Sensitivity to Parameter Settings -- 5 Conclusion -- References -- Policy Evaluation with Delayed, Aggregated Anonymous Feedback -- 1 Introduction 2 Related Work -- 3 Preliminaries -- 4 Policy Evaluation with DAAF -- 5 Methodology -- 6 Results -- 7 Discussion and Future Work -- 8 Summary and Conclusions -- References -- Spatial and Temporal Analysis -- Spatial Cross-Validation for Globally Distributed Data -- 1 Introduction -- 2 Related Work -- 3 Spatial k-Fold Cross-Validation -- 4 Evaluation of Performance -- 4.1 Data Sets -- 4.2 Experimental Design -- 4.3 Analysis of Performance -- 5 Conclusions -- References -- .26em plus .1em minus .1emLeveraging Spatio-Temporal Autocorrelation to Improve the Forecasting of the Energy Consumption in Smart Grids -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Modeling the Temporal Autocorrelation -- 3.2 Modeling the Spatial Autocorrelation -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Elastic Product Quantization for Time Series -- 1 Introduction -- 2 Background -- 2.1 Dynamic Time Warping -- 2.2 Product Quantization -- 3 Approximate Dynamic Time Warping with Product Quantization -- 3.1 Training Phase -- 3.2 Encoding Time Series -- 3.3 Computing Distances Between Time Series -- 3.4 Memory Cost -- 3.5 Pre-alignment of Subspaces -- 4 Data Mining Applications -- 4.1 NN Search with PQ Approximates -- 4.2 Clustering with PQ Approximates -- 5 Experimental Settings -- 6 Experimental Results -- 6.1 Empirical Time Complexity -- 6.2 1NN Classification -- 6.3 Hierarchical Clustering -- 7 Conclusions -- References -- Stress Detection from Wearable Sensor Data Using Gramian Angular Fields and CNN -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Sample Construction -- 2.4 Convolutional Neural Network -- 3 Results -- 3.1 Implementation -- 3.2 Experiments -- 4 Conclusions and Future Work -- References Efficient Multivariate Data Fusion for Misinformation Detection During High Impact Events -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 High-Level Feature Extraction -- 2.3 Multi-modal Data Fusion Framework Based on Independent Vector Analysis -- 2.4 Effective Density Model for Capturing Multi-modal Associations -- 2.5 Classification Procedure -- 3 Results and Discussion -- 3.1 Classification Performance -- 3.2 Explainability -- 4 Conclusion -- References -- Fairness and Outlier Detection -- MQ-OFL: Multi-sensitive Queue-based Online Fair Learning -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 2.2 Fairness Definitions -- 2.3 Gerrymandering -- 2.4 Imbalanced and Drifted Data Stream -- 3 MQ-OFL Framework -- 3.1 Balanced and Fairness-Aware Pre-processing -- 3.2 Classifier Pool -- 3.3 Decision Boundary Adjustment -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Experimental Results -- 5 Conclusion -- References -- Multi-fairness Under Class-Imbalance -- 1 Introduction -- 2 Related Work -- 3 Basics and Multi-Max Mistreatment (MMM) Fairness -- 3.1 Multi-Max Mistreatment(MMM) Measure -- 4 Multi-Fairness-Aware Learning -- 4.1 Multi-discrimination-Free Learning Under Class-Imbalance -- 4.2 The MMM-Fair Boosting Post Pareto (MFBPP) Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Evaluation Results -- 5.3 Internal Analysis -- 5.4 Flexibility of MFBPP -- 6 Conclusions and Outlook -- References -- When Correlation Clustering Meets Fairness Constraints -- 1 Introduction -- 2 Related Work -- 3 Fairness Constraints in Correlation Clustering -- 3.1 Background on Correlation Clustering -- 3.2 Problem Statement -- 4 Algorithm -- 5 Fairness Evaluation -- 6 Experimental Methodology -- 6.1 Competing Methods -- 6.2 Data -- 6.3 Evaluation Goals -- 6.4 Hyper-parameters and Configurations -- 7 Results 8 Conclusions Intro -- Preface -- Organization -- Keynote Talks -- Unsupervised Model Selection in Outlier Detection: The Elephant in the Room -- Coloring Social Relationships -- 35 Years of 'Scientific Discovery: Computational Explorations of the Creative Processes' - From the Early Days to the State of the Art -- Contents -- Regression and Limited Data -- Model Optimization in Imbalanced Regression -- 1 Introduction -- 2 Related Work -- 3 Imbalanced Regression -- 3.1 Relevance Function -- 3.2 Squared Error Relevance Area (SERA) -- 4 Optimization Loss Function for Imbalanced Regression -- 5 Experimental Study -- 5.1 Experimental Setup -- 5.2 Results on Model Optimization -- 5.3 Results in Out-of-Sample -- 6 Conclusions -- A SERA numerical approximation -- B Tables of Results -- References -- Discovery of Differential Equations Using Probabilistic Grammars -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Algebraic Equations and Numeric Differentiation -- 3.2 Differential Equations and Direct Simulation -- 3.3 Parallel Computation -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Hyperparameter Importance of Quantum Neural Networks Across Small Datasets -- 1 Introduction -- 2 Background -- 2.1 Functional ANOVA -- 2.2 Supervised Learning with Parameterized Quantum Circuits -- 3 Methods -- 3.1 Hyperparameters and Configuration Space -- 3.2 Assessing Hyperparameter Importance -- 3.3 Verifying Hyperparameter Importance -- 4 Dataset and Inclusion Criteria -- 5 Results -- 5.1 Performance Distributions per Dataset -- 5.2 Surrogate Verification -- 5.3 Marginal Contributions -- 5.4 Random Search Verification -- 6 Conclusion -- References -- ImitAL: Learned Active Learning Strategy on Synthetic Data -- 1 Introduction -- 2 Simulating AL on Synthetic Training Data |
| Title | Discovery science : 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022 : proceedings |
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