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[null,"1.","Record Nr.",null,"UNINA9910699558503321"]
[null,null,"Titolo",null,"Farm rescue and EMS : a state by state directory"]
[null,null,"Pubbl/distr/stampa",null,"[Washington, D.C.] : , : U.S. Department of Health and Human Services, Health Resources and Services Administration, Office of Rural Health Policy, , [2006?]"]
[null,null,"Descrizione fisica",null,"1 online resource (vii, 40 pages) : color map"]
[null,null,"Soggetti",null,"Rescue work - United States"]
[{"$nulls":4},"First responders"]
[{"$nulls":4},"Emergency medical personnel - Training of - United States"]
[{"$nulls":4},"Farmers - Diseases - United States"]
[{"$nulls":4},"Agriculture - Accidents - United States"]
[{"$nulls":4},"Medical emergencies - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from title screen (viewed May 29, 2013)."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (page 35)."]
[null,"2.","Record Nr.",null,"UNINA9910789667003321"]
[null,null,"Autore",null,"Sweetman Brendan"]
[null,null,"Titolo",null,"Religion [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"London, : Continuum International Publishing, 2007"]
[null,null,"ISBN",null,"1-283-20791-5"]
[{"$nulls":4},"9786613207913"]
[{"$nulls":4},"1-4411-9706-0"]
[null,null,"Descrizione fisica",null,"1 online resource (181 p.)"]
[null,null,"Collana",null,"Key Concepts in Philosophy"]
[null,null,"Disciplina",null,"210"]
[null,null,"Soggetti",null,"Faith and reason"]
[{"$nulls":4},"Religion -- Philosophy"]
[{"$nulls":4},"Religion -- Philosophy -- History"]
[{"$nulls":4},"Religion -- Philosophy -- Periodicals"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"Contents; 1. Introduction: The God question; 2. First Cause and Design; 3. Necessary Being, Morality and Miracles; 4. What is God Like?; 5. God and Evil; 6. Religious Experience and God; 7. Religion and Science; 8. Religious Diversity: Is there a true religion?; Notes; Guide to Further Reading; Index"]
[null,null,null,"Sommario/riassunto","The philosophy of religion is a core area in the study of philosophy, as well as being a subject of growing contemporary interest.  It encompasses some of the major and most complex philosophical questions. Does God exist? What is God's nature? Why does God allow evil? What is a religious experience? Are religion and science compatible? What relevance does evolution have for religious belief? It also covers questions concerning the challenge of religious pluralism, a topic of great contemporary relevance. Written with the specific needs of students new to philosophy in mind, Religion: Key Conc"]
[null,"3.","Record Nr.",null,"UNINA9910851987103321"]
[null,null,"Titolo",null,"Advances 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 V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9789819722624"]
[{"$nulls":4},"9819722624"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (431 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14649"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Signal processing"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Design and Analysis of Algorithms"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Mathematics of Computing"]
[{"$nulls":4},"Signal, Speech and Image Processing"]
[{"$nulls":4},"Computer Communication Networks"]
[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 -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part V -- Multimedia and Multimodal Data -- Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Human Activity Recognition (HAR) -- 2.2 Hierarchical Label Modeling -- 3 Problem Formulation -- 4 Our Proposals -- 4.1 Hierarchy-Aware Label Encoding -- 4.2 Activity Data Encoding -- 4.3 Label-Data Joint Embedding Learning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Study -- 6 Discussions and Conclusion -- References -- Geometrically-Aware Dual Transformer "]
[{"$nulls":4},"Encoding Visual and Textual Features for Image Captioning -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Features Extractor -- 3.2 Caption Generator -- 3.3 Attention Block -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Experiment Result -- 5 Conclusions -- References -- MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 MHDF Model -- 3.1 Model Overview -- 3.2 Multi-source Heterogeneous Data Amplification -- 3.3 News Textual Feature Fusion -- 3.4 News Visual Feature Fusion -- 3.5 Sentiment Feature Extractor -- 3.6 Feature Integration Classifier -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Experiments and Validity Verification -- 4.5 Conclusions -- References -- Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-Based Curriculum Learning -- 1 Introduction -- 2 Related Works -- 2.1 Automatic Speech Recognition Methods -- 2.2 Connectionist Temporal Classification (CTC) Loss -- 3 Proposed Method -- 3.1 Multiple Hypotheses for Unlabeled Instances."]
[{"$nulls":4},"3.2 Training ASR Model with Multiple Hypotheses -- 3.3 Curriculum Learning -- 3.4 Theoretical Analysis -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transcription Performance (Q1) -- 4.3 Speed of Convergence (Q2) -- 4.4 Ablation Study (Q3) -- 5 Conclusions -- References -- MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting -- 1 Introduction -- 2 Related Works -- 2.1 Available Datasets -- 2.2 Large Multimodal Models and Chain-of-Thought -- 3 Novel Dataset -- 3.1 Original Dataset Creation -- 3.2 Data Augmentation Procedure -- 3.3 Chain of Thought Variant -- 3.4 MM-PhyQA Dataset Topics -- 4 Methodology -- 4.1 Multi-image Chain-of-Thought (MI-CoT) -- 5 Experiments -- 5.1 Models -- 6 Results and Discussion -- 6.1 Model Performance -- 6.2 Zero Shot Prompting Vs Supervised Fine-Tuning -- 6.3 Effect of Chain of Thought Prompting -- 6.4 Error Analysis -- 7 Conclusion -- References -- Adversarial Text Purification: A Large Language Model Approach for Defense -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Large Language Models -- 3.2 Adversarial Text Purification -- 4 LLM-Guided Adversarial Text Purification -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Results and Discussion -- 6 Conclusion -- References -- lil'HDoC: An Algorithm for Good Arm Identification Under Small Threshold Gap -- 1 Introduction -- 2 Background -- 2.1 Good Arm Identification -- 3 Problem Setting -- 4 Preliminary -- 5 Algorithm -- 5.1 Correctness of lil'HDoC -- 5.2 First  Arms Sampling Complexity -- 5.3 Total Sample Complexity -- 6 Experiment -- 6.1 Dataset -- 6.2 Baseline -- 6.3 Results -- 7 Conclusion -- References -- Recommender Systems -- ScaleViz: Scaling Visualization Recommendation Models on Large Data -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Proposed Solution -- 4.1 Cost Profiling -- 4.2 RL Agent."]
[{"$nulls":4},"5 Evaluations -- 5.1 Experimental Setup -- 5.2 Speed-Up in Visualization Generation -- 5.3 Budget vs. Error Trade-Off -- 5.4 Need for Dataset-Specific Feature Selection -- 5.5 Scalability with Increasing Data Size -- 6 Conclusion -- References -- Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation -- 1 Introduction -- 2 Related Work and Problem Formulation -- 2.1 Problem Formulation -- 3 Methodology -- 3.1 Overview -- 3.2 Statistical Model in CFLS -- 3.3 Optimization -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparisons -- 4.4 Influence of Different Cold-Start Levels -- 4.5 Diversity, Interpretability and User "]
[{"$nulls":4},"Controllability -- 5 Conclusions -- References -- On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 4 Variants of a Session-Based Recommender System -- 4.1 Quality Metrics -- 5 Experiments and Evaluation -- 5.1 Selection of Real-World Datasets -- 5.2 Task Definition and Parameter Configuration -- 5.3 Evaluation of Experimental Results -- 6 Conclusion and Future Work -- References -- HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 HMAR -- 2.3 Multi-task Learning -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation Protocol -- 3.3 Model Performance (RQ1) -- 3.4 Effect of Auxiliary Behaviors and Individual Model Components (RQ2 &amp -- RQ3) -- 4 Related Work -- 5 Conclusion -- References -- Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Long-Term Dependence Module -- 3.3 Short-Term Dependence Module -- 3.4 Sample Balancer."]
[{"$nulls":4},"4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance -- 4.3 Ablation Study -- 5 Conclusions -- References -- Conditional Denoising Diffusion for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Stepwise Diffuser -- 3.2 Sequence Encoder -- 3.3 Cross-Attentive Conditional Denoising Decoder -- 3.4 Optimization -- 4 Experiments -- 4.1 Plateau of Ranking Prediction -- 4.2 Overall Experiments -- 4.3 Ablation Study -- 4.4 Hyperparameter Sensitivity -- 4.5 Case Study for Stepwise Generation -- 5 Conclusion -- References -- UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Explainable and Transparent Recommender Models -- 2.3 The Prototype-Based Collaborative Filtering -- 3 Methodology -- 3.1 User-Item Prototypes Connections Matrix Factorization (UIPC-MF) -- 3.2 Loss Function -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 Baseline Models -- 4.3 Training Details -- 4.4 Evaluation Results -- 4.5 Explaining UIPC-MF Recommendations -- 4.6 The Impact of L1-Norm in Reduction of Learning Bias -- 5 Conclusion -- References -- Towards Multi-subsession Conversational Recommendation -- 1 Introduction -- 2 Related Works -- 3 MSMCR Scenario -- 3.1 Definition -- 3.2 General Framework -- 4 Methodology -- 4.1 Context-Aware Recommendation -- 4.2 Policy Learning -- 4.3 Model Training -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Overall Performance -- 5.3 Further Experiments -- 6 Conclusion -- References -- False Negative Sample Aware Negative Sampling for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Methodology -- 4.1 False Negatives Identification -- 4.2 False Negatives Elimination -- 5 Experiment -- 5.1 Experiment Settings."]
[{"$nulls":4},"5.2 Performance Comparison -- 5.3 Study of EDNS -- 6 Conclusion -- References -- Multi-sourced Integrated Ranking with Exposure Fairness -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Model -- 3.1 Input Layer -- 3.2 Dual RNN Module -- 3.3 Multi-task Module -- 3.4 Model Training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Baselines -- 4.3 Model Selection -- 4.4 Performance Comparison -- 4.5 Ablation Study -- 4.6 Online A/B Testing -- 5 Conclusion -- References -- Soft Contrastive Learning for Implicit Feedback Recommendations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notations -- 3.2 The SCLRec Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance "]
[{"$nulls":4},"(RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Robustness to Interaction Noises (RQ3) -- 5 Conclusion -- References -- Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation -- 1 Introduction -- 2 Problem Formulation -- 3 Approach -- 3.1 Dual-Graph Construction -- 3.2 Dual-Graph Network for Member Preference -- 3.3 Dual-View Fusion for Group Preference -- 3.4 Group Recommendation and Model Training -- 4 Experiments -- 4.1 Experimental Dataset and Setup -- 4.2 Experimental Results and Analysis -- 4.3 Parameter Sensitivity -- 5 Related Works -- 6 Conclusion and Future Work -- References -- TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System -- 1 Introduction -- 2 Literature Review -- 2.1 Electricity Subsidy and Operating Reserve -- 2.2 Electricity Management System -- 2.3 Electricity Storage -- 3 Problem Definition and Simulation Environment -- 4 Proposed TripleS -- 5 Experimental Results -- 5.1 Performance Evaluation -- 5.2 Performance Evaluation Under MS Attack -- 5.3 Influence of Self-discharge -- 6 Conclusion -- References -- Spatio-temporal Data."]
[{"$nulls":4},"Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting."]
[null,null,null,"Sommario/riassunto","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."]