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[null,"1.","Record Nr.",null,"UNISA996464524103316"]
[null,null,"Titolo",null,"KI 2021: advances in artificial intelligence : 44th German Conference on AI, virtual event, September 27 - October 1, 2021 : proceedings / / edited by Stefan Edelkamp, Ralf Möller, Elmar Rueckert"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-87626-8"]
[null,null,"Descrizione fisica",null,"1 online resource (388 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Ser. ; ; v.12873"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Optical data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Special Events -- Tutorial -- Workshops -- Organization -- Abstracts of Invited Talks -- Monte Carlo Search -- Autonomy in AI: Reactive Synthesis, Planning and Reinforcement Learning in Linear Temporal Logic on Finite Traces -- Ontologies for Providing Map Knowledge to Autonomous Vehicles -- The Third Wave of AI -- Motion Intelligence for Human-Centred Robots -- Human-Compatible Artificial Intelligence -- Contents -- Technical Programme -- RP-DQN: An Application of Q-Learning to Vehicle Routing Problems -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 4.1 Original Attention-Model -- 4.2 RP-DQN -- 5 Experiments -- 5.1 Baselines -- 5.2 Data -- 5.3 CVRP Results -- 5.4 MDVRP Results -- 5.5 Learning Curves -- 5.6 Runtime Comparison -- 5.7 Generalization Study -- 6 Conclusion -- References -- -Circulant Maximum Variance Bases -- 1 Introduction -- 2 Preliminaries -- 2.1 Principal Component Analysis -- 2.2 Dynamic Principal Component Analysis -- 2.3 -Circulant Matrices -- 3 Maximum Variance Bases -- 3.1 Simple Matched Circulants -- 3.2 Matched -Circulant Matrices -- 3.3 Relation to PCA, DPCA and DFT -- 4 Numerical Results -- 4.1 MA Process -- 4.2 Circular Process -- 5 Conclusion -- References -- Quantified Boolean Solving for Achievement Games -- 1 Introduction -- 2 Quantified Boolean Formulas -- 3 Harary's Tic-Tac-Toe -- 4 Related Work -- 5 The Pairing Encoding -- 6 Experimental Results -- 7 Conclusion -- References -- Knowledge Graph Based Question Answering System for Financial Securities -- 1 Introduction -- 2 Framework -- 2.1 Knowledge Graph Construction -- 2.2 Semantic "]
[{"$nulls":4},"Question Answering System -- 3 Experiments -- 4 Conclusion -- References -- Semi-unsupervised Learning: An In-depth Parameter Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Learning Paradigms."]
[{"$nulls":4},"3.2 Semi-unsupervised Learning with Deep Generative Models -- 4 Datasets -- 5 Experiments -- 5.1 Semi-unsupervised Classification -- 5.2 Parameter Analysis -- 6 Summary and Outlook -- References -- Combining Transformer Generators with Convolutional Discriminators -- 1 Introduction -- 2 Related Work -- 2.1 Generative Models Using CNNs -- 2.2 Generative Models Using Attention -- 2.3 Hybrid Models -- 3 Model Architecture -- 3.1 Transformer Generator -- 3.2 Convolutional Discriminator -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 4.3 Frequency Analysis -- 5 Discussion -- 6 Conclusion -- References -- Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations -- 1 Introduction -- 2 A Relational Knowledge Domain -- 3 Learning an Interpretable Model with ILP -- 4 Multi-level and Multi-modal Explanations -- 4.1 Explanation Generation -- 4.2 Explanatory Dialogue -- 4.3 Proof-of-Concept Implementation -- 5 Conclusion and Outlook -- References -- Multi-Type-TD-TSR - Extracting Tables from Document Images Using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: From OCR to Structured Table Representations -- 1 Introduction -- 2 Related Work -- 3 End-to-End Multistage Pipeline -- 4 Methods -- 4.1 Table Alignment Pre-processing -- 4.2 Table Detection -- 4.3 Bordered TSR -- 4.4 Unbordered TSR -- 4.5 Partially Bordered TSR -- 4.6 Color Invariance Pre-Processing -- 5 Evaluation -- 6 Conclusion -- References -- A High-Speed Neural Architecture Search Considering the Number of Weights -- 1 Introduction and Related Works -- 2 Proposed Method -- 2.1 DARTS Algorithm -- 2.2 Loss Function with the Number of Weights -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusions -- References -- Semantic Segmentation of Aerial Images Using Binary Space Partitioning -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 3 Semantic Segmentation -- 3.1 BSP-based Segmentation Model -- 3.2 Differentiable BSP Tree Rendering -- 3.3 Comparison to State of the Art -- 4 Evaluation -- 4.1 Datasets -- 4.2 Training and Test Setup -- 4.3 Ground Truth as BSP Trees -- 4.4 Semantic Segmentation -- 4.5 Prediction Confidence -- 5 Conclusion -- A Dataset Class Distributions -- B Hyperparameters and Model Details -- C Metrics -- D Additional Sample Images -- E Confidence -- References -- EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 EVARS-GPR -- 5 Experimental Setup -- 5.1 Simulated Data -- 5.2 Real-World Datasets -- 5.3 Evaluation -- 6 Experimental Results -- 6.1 Behavior on Simulated Data -- 6.2 Results on Real-World Datasets -- 6.3 Discussion -- 7 Conclusion -- Appendix A: Gaussian Process Regression -- Appendix B: List of Symbols -- Appendix C: Online Change Point Detection -- Appendix D: Data Augmentation -- Appendix E: EVARS-GPR Parameters -- Appendix F: Real-World Datasets -- Appendix G: Further Simulated Scenarios -- References -- Selective Pseudo-Label Clustering -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Formal Description -- 3.2 Implementation Details -- 4 Proof of Correctness -- 4.1 Agreed Pseudo-Labels Are More Accurate -- 4.2 Increased Pseudo-Label Accuracy Improves Clustering -- 5 Experimental Results -- 5.1 Ablation Studies -- 5.2 Ensemble Size -- 6 Conclusion -- A  Appendix A: Full Proofs -- A.1  More Accurate Pseudo-Labels Supplement -- A.2  Lemma 1 Supplement -- A.3  "]
[{"$nulls":4},"Lemma 2 Supplement -- A.4  Lemma 3 Supplement -- A.5  Theorem 5 Supplement -- B  Appendix C: Extended Results -- References -- Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"3 Method Overview -- 4 Experiments -- 5 Visual Inspection -- 6 Discussion -- 7 Conclusions -- A  MedNode Results -- B  ISIC2016 Results -- References -- A Demonstrator for Interactive Image Clustering and Fine-Tuning Neural Networks in Virtual Reality -- 1 Introduction -- 2 Architecture -- 3 Visualization in VR -- 3.1 PCA/t-SNE Approach -- 3.2 VAE Approach -- 4 Interactive Fine-Tuning -- 5 Conclusion and Future Work -- References -- HUI-Audio-Corpus-German: A High Quality TTS Dataset -- 1 Introduction -- 2 Related Work -- 3 Data Processing Pipeline -- 3.1 Acquisition of Suitable Audio Data -- 3.2 Splitting of Audio Data -- 3.3 Audio Normalization -- 3.4 Transcription of Audio Data for Subsequent Alignment -- 3.5 Acquisition of Text for Audio Data -- 3.6 Text Normalization -- 3.7 Transcript Alignment -- 4 Dataset Summary -- 4.1 Full Dataset -- 4.2 Clean Dataset -- 4.3 Discussion -- 4.4 Evaluation with Tacotron 2 -- 5 Conclusion and Outlook -- References -- Negation in Cognitive Reasoning -- 1 Introduction -- 2 Background and Related Works -- 2.1 Negation in Logic and Natural Language -- 2.2 Commonsense Reasoning and Negation -- 3 Methods -- 3.1 A System for Cognitive Reasoning -- 3.2 Negation Scope and the Negatus - Why Size Matters -- 3.3 Approach to Negation Treatment for Cognitive Reasoning -- 4 Experiments -- 4.1 Data Preparation and Evaluation -- 5 Summary, Conclusions, and Future Work -- References -- Learning to Detect Adversarial Examples Based on Class Scores -- 1 Introduction -- 1.1 Related Work -- 1.2 Contributions -- 2 Detecting Adversarial Examples from Class Scores -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- An Agent Architecture for Knowledge Discovery and Evolution -- 1 Introduction -- 2 Background and Related Work -- 2.1 The BDI Architecture -- 2.2 Integrating AI into BDI Agents."]
[{"$nulls":4},"2.3 KDE Systems and Approaches -- 3 Design of the KDE Agent Architecture -- 4 The KDE Agent Architecture -- 4.1 The Exogenous Modules -- 4.2 The Endogenous Modules -- 5 Use Case - Domestic Electricity Consumption -- 5.1 Cluster Analysis Service -- 5.2 Perception -- 5.3 Deliberation to Generate Explanations -- 6 Discussion and Conclusion -- References -- Demystifying Artificial Intelligence for End-Users: Findings from a Participatory Machine Learning Show -- 1 Introduction -- 2 Related Work -- 2.1 Virtual Agents in Education and Edutainment -- 2.2 Explainable AI -- 2.3 Trust in Technical Systems -- 3 Field Study -- 3.1 Demonstrator Setup -- 3.2 Study Procedure -- 3.3 Evaluation Method -- 4 Results -- 4.1 Information About Participants -- 4.2 Results of the ML-show -- 4.3 Comparison Between Participating and Non-participating Museum Visitors -- 5 Discussion -- 5.1 Take Users' Attitudes and Experiences into Account -- 5.2 Think About Who You Want to Reach with XAI Edutainment -- 5.3 Trust and Distrust Are Important Components in XAI Interaction Design -- 6 Conclusion -- References -- Recent Advances in Counting and Sampling Markov Equivalent DAGs -- 1 Introduction -- 2 Main Results -- References -- An Approach to Reduce the Number of Conditional Independence Tests in the PC Algorithm -- 1 Introduction -- 2 Preliminaries -- 3 Detection of V-Structures in Advance -- 4 The ED-PC Algorithm -- 5 Proof of Correctness -- 6 Experimental Analysis -- 7 Conclusions and Outlook -- References -- Poster Papers -- Unsupervised Anomaly Detection for Financial Auditing with Model-Agnostic Explanations -- 1 Introduction -- 2 Related Work -- 3 Explainable Anomaly Detection in the Context "]
[{"$nulls":4},"of Auditing -- 3.1 Data -- 3.2 Feature Engineering -- 3.3 Ensemble-Based Architecture -- 3.4 Model-Agnostic and Receiver-Dependent Explanations -- 4 Conclusion and Future Work."]
[{"$nulls":4},"A  Anomaly Detection Ensemble."]