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
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...
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| Main Authors | , , , , , , , , , |
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| Format | eBook |
| Language | English |
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Cham
Springer International Publishing AG
2022
Springer International Publishing |
| Edition | 1 |
| Series | Communications in computer and information science |
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| Online Access | Get full text |
| ISBN | 9783030937324 3030937321 |
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| Abstract | 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) |
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| AbstractList | 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) |
| Author | Bouadi, Tassadit Krishnamurthy, Yamuna énay, Benoît Kamp, Michael Adilova, Linara Kang, Bo Oramas, José Galárraga, Luis Bibal, Adrien Koprinska, Irena |
| Author_xml | – sequence: 1 fullname: Kamp, Michael – sequence: 2 fullname: Koprinska, Irena – sequence: 3 fullname: Bibal, Adrien – sequence: 4 fullname: Bouadi, Tassadit – sequence: 5 fullname: énay, Benoît – sequence: 6 fullname: Galárraga, Luis – sequence: 7 fullname: Oramas, José – sequence: 8 fullname: Adilova, Linara – sequence: 9 fullname: Krishnamurthy, Yamuna – sequence: 10 fullname: Kang, Bo |
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| Snippet | This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and... |
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| SubjectTerms | Artificial intelligence Machine learning |
| Subtitle | International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II |
| TableOfContents | On Neural Forecasting and News Emotions: The Case of the Spanish Stock Market -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Neural Machine Translation -- 3.2 Emotion Classification -- 3.3 DeepAR -- 4 Preliminary Findings -- 5 Conclusions -- References -- Adaptive Supervised Learning for Financial Markets Volatility Targeting Models -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 2 Models Presentation -- 2.1 Volatility Targeting Models -- 2.2 Supervised Learning Overlay -- 2.3 Features -- 2.4 Walk-Forward Methodology -- 3 Financial Data Experiments -- 3.1 Market Data -- 3.2 Hyperparameters Selection and Used Features -- 3.3 Comparison with Benchmark -- 3.4 Future Works -- 4 Conclusion -- References -- Efficient Analysis of Transactional Data Using Graph Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Methods and Graph Neural Networks (GNNs) -- 2.2 Fraud Analysis -- 3 Methodology -- 3.1 Constructing Transaction Graphs -- 3.2 Graph Convolutional Networks -- 3.3 Parameter Setup -- 4 Experiments -- 4.1 Datasets -- 4.2 Node Classification -- 4.3 Mitigating Imbalance with Downsampling -- 5 Results and Discussion -- 5.1 Down Sampling -- 5.2 Visualization -- 6 Conclusion -- References -- A Reasoning Approach to Financial Data Exchange with Statistical Confidentiality -- 1 Introduction and Problem Setting -- 2 The VADA-SA Framework -- 3 Experiments -- 4 Conclusion -- References -- Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods -- 1 Introduction -- 2 Materials and Methods -- 2.1 Generalised Age-Period-Cohort Stochastic Mortality Models -- 2.2 RNN with LSTM Architecture -- 2.3 RNN with GRU Architecture -- 2.4 Forecasting Accuracy Metrics -- 2.5 Data -- 3 Results -- 3.1 Hyperparameter Calibration -- 3.2 Forecasts of Life Annuity Prices and Mortality Rates -- 4 Conclusion References -- Sixth Workshop on Data Science for Social Good (SoGood 2021) -- Workshop on Data Science for Social Good (SoGood 2021) -- Organization -- Workshop Co-chairs -- Program Committee -- Ensuring the Inclusive Use of NLP in the Global Response to COVID-19 -- 1 Introduction and Context -- 2 Low-Resource Languages -- 3 Alternative Modalities -- 4 Out-of-the-Box Tools for Infodemic Management -- 5 Importance of Partnerships -- 6 Conclusion -- References -- A Framework for Building pro-bono Data for Good Projects -- 1 Introduction -- 2 Stakeholders -- 2.1 Beneficiary -- 2.2 Volunteers -- 2.3 DSSG PT Lead Team -- 2.4 Ethics Committee -- 3 Project -- 3.1 Project Roles and Responsibilities -- 3.2 Project Lifecycle -- 4 Sustainability of the Framework -- 4.1 Sponsors -- 4.2 Partners -- 5 Future Improvements -- 5.1 Information Management -- 5.2 Marketing and Communication -- 5.3 Volunteer Engagement -- 5.4 Post-project Maintenance -- 6 Conclusions -- References -- Improving Smart Waste Collectionpg Using AutoML -- 1 Introduction -- 2 Smart Waste Management -- 3 Case Study -- 3.1 Data Set -- 3.2 Data Preprocessing -- 3.3 Exploratory Data Analysis -- 4 Experimental Results and Discussion -- 4.1 Experimental Setup -- 4.2 Classification Task: Prediction of Presence of Waste in Containers -- 4.3 Regression Task: Prediction of Quantity of Containers with Waste -- 4.4 AutoML - Classification and Regression Tasks -- 5 Conclusions -- References -- Applying Machine Learning for Traffic Forecasting in Porto, Portugal -- 1 Introduction -- 2 Methodology -- 2.1 Data Set Description -- 2.2 Exploratory Data Analysis -- 2.3 Target Definition -- 2.4 Data Processing and Feature Extraction -- 2.5 Modeling and Evaluation -- 3 Results -- 4 Discussion -- 5 Conclusion and Future Work -- References IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Machine Learning for CyberSecurity -- Workshop on Machine Learning for Cybersecurity (MLCS 2021) -- Organization -- MLCS 2021 Chairs -- Program Committee -- Dealing with Imbalanced Data in Multi-class Network Intrusion Detection Systems Using XGBoost -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Oversampling -- 3.2 Feature Selection -- 3.3 XGBoost -- 3.4 OneVsOne and OneVsRest -- 3.5 Implementation Details -- 4 Evaluation Study -- 4.1 Dataset Description -- 4.2 Experimental Setting and Evaluation Metrics -- 4.3 Pipeline Analysis -- 4.4 Oversampling Versus Feature Relevance Analysis -- 4.5 Related Method Analysis -- 5 Conclusion -- References -- Adversarial Robustness of Probabilistic Network Embedding for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Link Prediction with Probabilistic Network Embedding -- 3.2 Virtual Adversarial Attack -- 4 Quantifying the Sensitivity to Small Perturbations -- 4.1 Problem Statement and Re-Embedding (RE) -- 4.2 Incremental Partial Re-Embedding (IPRE) -- 4.3 Theoretical Approximation of the KL-Divergence -- 5 Experiments -- 5.1 Case Studies -- 5.2 Quality and Runtime of Approximations -- 6 Conclusion -- References -- Practical Black Box Model Inversion Attacks Against Neural Nets -- 1 Introduction -- 2 Background -- 2.1 The Fredrikson et al. Attack -- 2.2 Jacobian Dataset Augmentation -- 3 Methodology -- 3.1 Threat Model -- 3.2 Attack Pipeline -- 4 Experimental Evaluation -- 4.1 Experimental Set up -- 4.2 Effects of Training Data Composition -- 4.3 Realistic Scenario: Where the Target and Substitute Models Are Different -- 4.4 Inverting a CNN -- 4.5 Numerical Approximation -- 5 Related Work -- 6 Discussion -- 7 Conclusion and Future Work -- References 3.1 Conditional Variational Autoencoders for Generating Fixes -- 3.2 Enabling Diverse Samples Using a Best of Many Objective -- 3.3 DS-SampleFix: Encouraging Diversity with a Diversity-Sensitive Regularizer -- 3.4 Beam Search Decoding for Generating Fixes -- 3.5 Selecting Diverse Candidate Fixes -- 3.6 Model Architecture and Implementation Details -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation -- 5 Conclusion -- References -- Linguistic Analysis of Stack Overflow Data: Native English vs Non-native English Speakers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis -- 1 Introduction -- 2 Related Work -- 3 Problem Overview -- 4 IReEn: Iterative Reverse-Engineering of Black-Box Functions -- 4.1 Finding Programs Given Input-Output Constraints -- 4.2 Sample Rejection Strategy -- 4.3 Iterative Refinement -- 4.4 Fine-Tuning -- 5 Experiments -- 5.1 The Karel Task and Dataset -- 5.2 Training and Inference -- 5.3 Functional Equivalence Metric -- 5.4 Evaluation -- 6 Conclusion -- References -- Machine Learning for Intelligent Industrial Design -- 1 Introduction -- 2 Industrial Design -- 3 Methodology -- 4 Machine Learning in Industrial Design -- 4.1 Data -- 4.2 Recent Studies -- 5 Research Opportunities -- 6 Conclusion -- References -- MIning DAta for financial applicationS -- 6th Workshop on MIning DAta for financial applicationS (MIDAS 2021) -- Organization -- Program Chairs -- Program Committee -- Financial Forecasting with Word Embeddings Extracted from News: A Preliminary Analysis -- 1 Introduction -- 2 Preliminary Notions -- 2.1 Word Representation -- 2.2 Contextual Word Embeddings -- 2.3 Neural Forecasting -- 3 Data -- 4 Experiments Setup -- 5 Preliminary Results -- 6 Conclusion and Overlook -- References NBcoded: Network Attack Classifiers Based on Encoder and Naive Bayes Model for Resource Limited Devices -- 1 Introduction -- 2 Background -- 2.1 The Naive Bayes Classifier -- 2.2 Autoencoders -- 2.3 Related Work -- 3 Proposal -- 3.1 NBcoded Architecture -- 3.2 NBcoded Learning -- 4 Experimental Framework -- 4.1 Dataset Overview -- 4.2 Dataset Preprocess and Feature Selection -- 4.3 Evaluation Metrics -- 4.4 Machine Learning Models Parameters and Computer Characteristics -- 5 Experimental Study -- 6 Results -- 6.1 Discussion -- 7 Conclusions and Future Works -- References -- Workshop on Machine Learning in Software Engineering -- 1st Workshop on Machine Learning in Software Engineering (MLiSE 2021) -- Organization -- Organizing Committee -- Program Committee -- A Stacked Bidirectional LSTM Model for Classifying Source Codes Built in MPLs -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Bi-LSTM Neural Network -- 3.2 Stacked Bi-LSTM -- 4 Dataset and Preprocessing -- 5 Experimental Results -- 5.1 Hyperparameters -- 5.2 Activation Functions -- 5.3 Evaluation Methods -- 5.4 Results -- 6 Conclusion -- References -- Metamorphic Malware Behavior Analysis Using Sequential Pattern Mining -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Analyzing API Calls with SPM -- 5 Results and Discussion -- 6 Conclusion -- References -- Applying Machine Learning to Risk Assessment in Software Projects -- 1 Introduction -- 2 Risk and Risk Management in Software Projects -- 3 Data and Methods -- 4 Results and Discussion -- 4.1 Risk Impact -- 4.2 Risk Likelihood -- 5 Conclusion -- References -- SampleFix: Learning to Generate Functionally Diverse Fixes -- 1 Introduction -- 2 Related Work -- 2.1 Neural Machine Translation -- 2.2 Variational Autoencoders -- 2.3 Learning-Based Program Repair -- 3 SampleFix: Generative Model for Diversified Code Fixes |
| Title | Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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