03859nam 2200697 450 991081826380332120200520144314.01-5128-0087-20-585-12648-810.9783/9781512800876(CKB)111004368592294(EBL)3442567(SSID)ssj0000257647(PQKBManifestationID)12112367(PQKBTitleCode)TC0000257647(PQKBWorkID)10253968(PQKB)11412550(OCoLC)44960259(MdBmJHUP)muse48941(DE-B1597)463573(OCoLC)940673718(DE-B1597)9781512800876(Au-PeEL)EBL3442567(CaPaEBR)ebr11085847(CaONFJC)MIL819774(MiAaPQ)EBC3442567(EXLCZ)9911100436859229419960606h19961996 ub| 0engur|n|---|||||txtccrTexts of the passion Latin devotional literature and medieval society /Thomas H. BestulPhiladelphia, Pennsylvania :University of Pennyslvania Press,[1996]©19961 online resource (273 p.)Middle Ages seriesDescription based upon print version of record.0-8122-3376-X Includes bibliographical references (pages [239]-257) and index.Front matter --Contents --Acknowledgments --Chapter 1. Introduction: Methodology and Theoretical Orientations --Chapter 2. Medieval Narratives of the Passion of Christ --Chapter 3. The Representation of the Jews in Medieval Passion Narratives --Chapter 4. Gender and the Representation of Women in Medieval Passion Narratives --Chapter 5. The Passion of Christ and the Institution of Torture --Appendix 1: Meditation by Bernard on the Lamentation of the Blessed Virgin --Appendix 2: Preliminary Catalogue of Medieval Latin Passion Narratives --Notes --Bibliography --IndexIn this book Thomas H. Bestul constructs the literary history of the Latin Passion narratives, placing them within their social, cultural, and historical contexts. He examines the ways in which the Passion is narrated and renarrated in devotional treatises, paying particular attention to the modifications and enlargements of the narrative of the Passion as it is presented in the canonical gospels. Of particular interest to Bestul are the representations of Jews, women, and the body of the crucified Christ. Bestul argues that the greatly enlarged role of the Jews in the Passion narratives of the twelfth and thirteenth centuries is connected to the rising anti-Judaism of the period. He explores how the representations of women, particularly the Virgin Mary, express cultural values about the place of women in late medieval society and reveal an increased interest in female subjectivity.Middle Ages series.Christian literature, Latin (Medieval and modern)History and criticismDevotional literature, Latin (Medieval and modern)History and criticismLiterature and societyEuropeHistoryCivilization, Medieval, in literatureChristian literature, Latin (Medieval and modern)History and criticism.Devotional literature, Latin (Medieval and modern)History and criticism.Literature and societyHistory.Civilization, Medieval, in literature.232.96/094/0902Bestul Thomas H(Thomas Howard),1942-1696060MiAaPQMiAaPQMiAaPQBOOK9910818263803321Texts of the passion4075731UNINA01245nas# 22003373i 450 VAN0026332820240806101511.9391718-324300065116820230915b19681974 |0itac50 bafreCA|||| |||||aA|||||||||zr i e bActa criminologicaMontréalPresses de l'Université de Montréal1968-1974.Sociology, Social Sciences, Criminology & Criminal Justice, Law.001VAN002633262001 Criminologie210 MontréalPresses de l'Université de Montréal1975-MontrealVANL000553Presses de l'Université de MontréalVANV217672650ITSOL20240906RICAhttps://www.jstor.org/journal/actacrimE-journal - Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIAIT-CE0119VAN161968-1974.JSTOR ;NVAN00263328BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA161968-1974.Acta criminologica1988110UNICAMPANIA12846nam 22008535 450 991085198290332120240826123235.0981-9722-42-X10.1007/978-981-97-2242-6(CKB)31801389200041(MiAaPQ)EBC31305375(Au-PeEL)EBL31305375(MiAaPQ)EBC31319822(Au-PeEL)EBL31319822(DE-He213)978-981-97-2242-6(EXLCZ)993180138920004120240424d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvances in Knowledge Discovery and Data Mining 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part I /edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (406 pages)Lecture Notes in Artificial Intelligence,2945-9141 ;14645981-9722-41-1 Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models.2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations.5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References.Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results.4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation.4 Benchmark Generation and Empirical Evaluation.The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7–10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.Lecture Notes in Artificial Intelligence,2945-9141 ;14645Artificial intelligenceAlgorithmsEducationData processingComputer scienceMathematicsSignal processingComputer networksArtificial IntelligenceDesign and Analysis of AlgorithmsComputers and EducationMathematics of ComputingSignal, Speech and Image ProcessingComputer Communication NetworksArtificial intelligence.Algorithms.EducationData processing.Computer scienceMathematics.Signal processing.Computer networks.Artificial Intelligence.Design and Analysis of Algorithms.Computers and Education.Mathematics of Computing.Signal, Speech and Image Processing.Computer Communication Networks.006.3Yang De-Nian1737375Xie Xing1734375Tseng Vincent S1737376Pei Jian868267Huang Jen-Wei1737377Lin Jerry Chun-Wei1453271MiAaPQMiAaPQMiAaPQBOOK9910851982903321Advances in Knowledge Discovery and Data Mining4159072UNINA