LEADER 01448nam 2200445 450 001 9910828866803321 005 20210618051841.0 010 $a1-119-28354-X 010 $a1-119-28345-0 010 $a1-119-28355-8 035 $a(CKB)4330000000009949 035 $a(MiAaPQ)EBC6125447 035 $a(EXLCZ)994330000000009949 100 $a20200609h20192019 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aReservoir simulation and well interference $eparent-child, multilateral well and fracture interactions /$fWilson C. Chin and Xiaoying Zhuang 210 1$aHoboken, NJ :$cWiley,$d[2020] 215 $a1 online resource (391 pages) $ccolor illustrations 225 0 $aAdvances in petroleum engineering series 225 1 $aHandbook of petroleum engineering 311 $a1-119-28344-2 320 $aIncludes bibliographical references and index. 410 0$aHandbook of petroleum engineering series. 606 $aOil reservoir engineering$vSoftware 606 $aEngineering$vSoftware 615 0$aOil reservoir engineering 615 0$aEngineering 700 $aChin$b Wilson C.$0860858 702 $aZhuang$b Xiaoying 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910828866803321 996 $aReservoir simulation and well interference$94015836 997 $aUNINA LEADER 05333nam 22006375 450 001 9910872194103321 005 20250806175942.0 010 $a3-031-63797-6 024 7 $a10.1007/978-3-031-63797-1 035 $a(CKB)32775557100041 035 $a(MiAaPQ)EBC31523154 035 $a(Au-PeEL)EBL31523154 035 $a(DE-He213)978-3-031-63797-1 035 $a(EXLCZ)9932775557100041 100 $a20240710d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aExplainable Artificial Intelligence $eSecond World Conference, xAI 2024, Valletta, Malta, July 17?19, 2024, Proceedings, Part II /$fedited by Luca Longo, Sebastian Lapuschkin, Christin Seifert 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (0 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v2154 311 08$a3-031-63796-8 327 $a -- XAI for graphs and Computer vision. -- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems. -- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study. -- Explainable AI for Mixed Data Clustering. -- Explaining graph classifiers by unsupervised node relevance attribution. -- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention. -- Graph Edits for Counterfactual Explanations: A comparative study. -- Model guidance via explanations turns image classifiers into segmentation models. -- Understanding the Dependence of Perception Model Competency on Regions in an Image. -- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation. -- Explainable Emotion Decoding for Human and Computer Vision. -- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. -- Logic, reasoning, and rule-based explainable AI. -- Template Decision Diagrams for Meta Control and Explainability. -- A Logic of Weighted Reasons for Explainable Inference in AI. -- On Explaining and Reasoning about Fiber Optical Link Problems. -- Construction of artificial most representative trees by minimizing tree-based distance measures. -- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. -- Model-agnostic and statistical methods for eXplainable AI. -- Observation-specific explanations through scattered data approximation. -- CNN-based explanation ensembling for dataset, representation and explanations evaluation. -- Local List-wise Explanations of LambdaMART. -- Sparseness-Optimized Feature Importance. -- Stabilizing Estimates of Shapley Values with Control Variates. -- A Guide to Feature Importance Methods for Scientific Inference. -- Interpretable Machine Learning for TabPFN. -- Statistics and explainability: a fruitful alliance. -- How Much Can Stratification Improve the Approximation of Shapley Values?. 330 $aThis four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v2154 606 $aArtificial intelligence 606 $aNatural language processing (Computer science) 606 $aApplication software 606 $aComputer networks 606 $aArtificial Intelligence 606 $aNatural Language Processing (NLP) 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aNatural language processing (Computer science) 615 0$aApplication software. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aNatural Language Processing (NLP). 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 676 $a006.3 700 $aLongo$b Luca$01337583 701 $aLapuschkin$b Sebastian$01744132 701 $aSeifert$b Christin$01744133 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910872194103321 996 $aExplainable Artificial Intelligence$94173978 997 $aUNINA