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E-raamat: Machine Learning and Knowledge Extraction: 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 - September 1, 2023, Proceedings

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This volume LNCS-IFIP constitutes the refereed proceedings of the 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 in Benevento, Italy, during August 28 – September 1, 2023.  

The 18 full papers presented together were carefully reviewed and selected from 30 submissions. The conference focuses on integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security.


Controllable AI - An alternative to trustworthiness in complex AI
systems?.- Efficient approximation of Asymmetric Shapley Values using
Functional Decomposition.- Domain-Specific Evaluation of Visual Explanations
for Application-Grounded Facial Expression Recognition.- Human-in-the-Loop
Integration of Domain-Knowledge Graphs for Explainable and Federated Deep
Learning.- The Tower of Babel in explainable Artificial Intelligence
(XAI).- Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big
Data.- Transformers are Short-text Classifiers.- Reinforcement Learning with
Temporal-Logic-Based Causal Diagrams.- Using Machine Learning to Generate an
ESG Dictionary.- Let me think! Investigating the effect of explanations
feeding doubts about the AI advice.- Enhancing Trust in Machine Learning
Systems by Formal Methods.- Sustainability Effects of Robust and Resilient
Artificial Intelligence.- The Split Matters: Flat Minima Methodsfor Improving
the Performance of GNNs.- Probabilistic framework based on Deep Learning for
differentiating ultrasound movie view planes.- Standing Still is Not An
Option: Alternative Baselines for Attainable Utility
Preservation.- Memorization of Named Entities in Fine-tuned BERT
Models.- Event and Entity Extraction from Generated Video
Captions.- Fine-Tuning Language Models for Scientific Writing Support.