4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25¿28, 2020, Proceedings Herausgegeben:Holzinger, Andreas; Kieseberg, Peter; Tjoa, A Min; Weippl, Edgar
4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25¿28, 2020, Proceedings Herausgegeben:Holzinger, Andreas; Kieseberg, Peter; Tjoa, A Min; Weippl, Edgar
This book constitutes the refereed proceedings of the 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, held in Dublin, Ireland, in August 2020. The 30 revised full papers presented were carefully reviewed and selected from 140 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity. Due to the Corona pandemic CD-MAKE 2020 was held as a virtual event.…mehr
This book constitutes the refereed proceedings of the 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, held in Dublin, Ireland, in August 2020.
The 30 revised full papers presented were carefully reviewed and selected from 140 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.
Due to the Corona pandemic CD-MAKE 2020 was held as a virtual event.
Explainable Artificial Intelligence: concepts, applications, research challenges and visions.- The Explanation Game: Explaining Machine Learning Models Using Shapley Values.- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI.- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification.- Explainable Reinforcement Learning: A Survey.- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance.- Explaining predictive models with mixed features using Shapley values and conditional inference trees.- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case.- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters.- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert.- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images.- The European legal framework for medical AI.- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction.- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules.- Non-Local Second-Order Attention Network For Single Image Super Resolution.- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers.- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints.- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection.- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks.- Active Learning for Auditory Hierarchy.- Improving short text classification through global augmentation methods.- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM.- A Clustering Backed Deep Learning Approach for Document Layout Analysis.- Calibrating Human-AI Collaboration: Impactof Risk, Ambiguity and Transparency on Algorithmic Bias.- Applying AI in Practice: Key Challenges and Lessons Learned.- Function Space Pooling For Graph Convolutional Networks.- Analysis of optical brain signals using connectivity graph networks.- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models.- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge.- Inter-Space Machine Learning in Smart Environments.
Explainable Artificial Intelligence: concepts, applications, research challenges and visions.- The Explanation Game: Explaining Machine Learning Models Using Shapley Values.- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI.- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification.- Explainable Reinforcement Learning: A Survey.- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance.- Explaining predictive models with mixed features using Shapley values and conditional inference trees.- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case.- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters.- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert.- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images.- The European legal framework for medical AI.- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction.- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules.- Non-Local Second-Order Attention Network For Single Image Super Resolution.- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers.- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints.- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection.- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks.- Active Learning for Auditory Hierarchy.- Improving short text classification through global augmentation methods.- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM.- A Clustering Backed Deep Learning Approach for Document Layout Analysis.- Calibrating Human-AI Collaboration: Impactof Risk, Ambiguity and Transparency on Algorithmic Bias.- Applying AI in Practice: Key Challenges and Lessons Learned.- Function Space Pooling For Graph Convolutional Networks.- Analysis of optical brain signals using connectivity graph networks.- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models.- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge.- Inter-Space Machine Learning in Smart Environments.
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