Discovery Science : 25th International Conference, DS 2022, Montpellier, France, October 10–12, 2022, Proceedings / edited by Poncelet Pascal, Dino Ienco.

Format:

Book

Contributor:

Ienco, Dino, editor.

Pascal, Poncelet, editor.

Series:

Lecture Notes in Artificial Intelligence, 2945-9141 ; 13601

Language:

English

Subjects (All):

Artificial intelligence.

Education--Data processing.

Education.

Data mining.

Application software.

Social sciences--Data processing.

Social sciences.

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial Intelligence.

Computers and Education.

Data Mining and Knowledge Discovery.

Computer and Information Systems Applications.

Computer Application in Social and Behavioral Sciences.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Artificial Intelligence.

Computers and Education.

Data Mining and Knowledge Discovery.

Computer and Information Systems Applications.

Computer Application in Social and Behavioral Sciences.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (576 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

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. .

Contents:

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

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

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets

2 Background

2.1 Functional ANOVA

2.2 Supervised Learning with Parameterized Quantum Circuits

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

ImitAL: Learned Active Learning Strategy on Synthetic Data

2 Simulating AL on Synthetic Training Data.

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

Incremental/Continual Learning

Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph

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

Semi-supervised Change Point Detection Using Active Learning

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

Adaptive Neural Networks for Online Domain Incremental Continual Learning

3 Online Domain Incremental Networks

Incremental Update of Locally Optimal Classification Rules

2 The Lord Algorithm

3 Incremental Lord

3.1 Incremental Updates

3.2 Overall Algorithm

4.1 Comparison to HoeffdingTree and VFDR

4.2 Sensitivity to Parameter Settings

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

Spatial and Temporal Analysis

Spatial Cross-Validation for Globally Distributed Data

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

.26em plus .1em minus .1emLeveraging Spatio-Temporal Autocorrelation to Improve the Forecasting of the Energy Consumption in Smart Grids

3 The Proposed Method

3.1 Modeling the Temporal Autocorrelation

3.2 Modeling the Spatial Autocorrelation

4.1 Experimental Setting

4.2 Results and Discussion

Elastic Product Quantization for Time Series

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

Stress Detection from Wearable Sensor Data Using Gramian Angular Fields and CNN

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.

Multi-attribute Transformers for Sequence Prediction in Business Process Management

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

Social Media Analysis

Data-Driven Prediction of Athletes' Performance Based on Their Social Media Presence

2.1 Social Media as a Mood and Behaviour Detection Proxy

2.2 Social Media as a Distraction Factor

3.1 Data Selection

3.2 Data Preparation

3.3 Predictive Significance Analysis

3.4 Implementation Details

4 Results

5 Discussion

Link Prediction with Text in Online Social Networks: The Role of Textual Content on High-Resolution Temporal Data

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

5.1 Results for Traditional Models

5.2 Results for Graph Neural Networks

6 Discussion

Weakly Supervised Named Entity Recognition for Carbon Storage Using Deep Neural Networks

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

Predicting User Dropout from Their Online Learning Behavior

3.1 Data Set

3.2 Features

3.3 Pre-processing

3.4 Predictive Model

3.5 Evaluation

4.1 Predictive Model

4.2 Evaluation

Efficient Multivariate Data Fusion for Misinformation Detection During High Impact Events

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

Fairness and Outlier Detection

MQ-OFL: Multi-sensitive Queue-based Online Fair Learning

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

4.1 Datasets

4.2 Evaluation Metrics

4.3 Experimental Results

Multi-fairness Under Class-Imbalance

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

When Correlation Clustering Meets Fairness Constraints

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Pascal, Poncelet Discovery Science

ISBN:

9783031188404

3031188403