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[null,"1.","Record Nr.",null,"UNINA990001783500403321"]
[null,null,"Autore",null,"Castino, Mario"]
[null,null,"Titolo",null,"Vini bianchi : tecnologia di produzione / Mario Castino"]
[null,null,"Pubbl/distr/stampa",null,"Bologna : Edagricole, 1993"]
[null,null,"ISBN",null,"88-206-3494-5"]
[null,null,"Descrizione fisica",null,"XIV, 286 p. ; 24 cm"]
[null,null,"Disciplina",null,"663.2"]
[null,null,"Locazione",null,"FAGBC"]
[null,null,"Collocazione",null,"60 663.2 CASM 1993"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910460860803321"]
[null,null,"Autore",null,"Dennett D. C (Daniel Clement)"]
[null,null,"Titolo",null,"Elbow room : the varieties of free will worth wanting / / Daniel C. Dennett"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge, Massachusetts ; ; London, England : , : MIT Press, , [2015]"]
[null,null,"ISBN",null,"0-262-33202-7"]
[null,null,"Edizione",null,"[New edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (243 p.)"]
[null,null,"Disciplina",null,"123/.5"]
[null,null,"Soggetti",null,"Free will and determinism"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"A Bradford Book.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Contents; Preface to the New Edition; Preface to the First Edition; 1 Please Don ' t Feed the Bugbears; 1 The Perennial, Gripping Problem; 2 The Bogeymen; 3 Sphexishness and Other Worries; 4 Overview; 2 Making Reason Practical; 1 Where Do Reasons Come From?; 2 Semantic Engines, Perpetual Motion Machines, and a DefectiveIntuition Pump; 3 Reflection, Language, and Consciousness; 4 Community, Communication, and Transcendence; 3 Control and Self-Control; 1 \" Due to Circumstances Beyond Our Control \"; 2 Simple Control and Simple Self-Control; 3 Agentless Control and Our Concept of Causation"]
[{"$nulls":4},"4 Agents in Competition5 The Uses of Disorder; 6 \" Let Yourself Go \"; 4 Self-Made Selves; 1 The Problem of the Disappearing Self; 2 The Art of Self-Definition; 3 Trying Our Luck; 4 Overview; 5 Acting Under the Idea of Freedom; 1 How Can You Go On Deliberating at a Time Like This?; 2 Designing the Perfect Deliberator; 3 Real Opportunities; 4 \"\"Avoid,\"\" \"\"Avoidable,\"\" \"\"Inevitable\"\"; 6 \"\"Could Have Done Otherwise\"\"; 1 Do We Care Whether We Could Have Done Otherwise?; 2 What We Care About; 3 The Can of Worms; 7 Why Do We Want Free Will?; 1 Nihilism Neglected"]
[{"$nulls":4},"2 Diminished Responsibility and the Specter of Creeping Exculpation3 The Dread Secret Denied; Afterword; Bibliography; Index"]
[null,null,null,"Sommario/riassunto","\"In this landmark 1984 work on free will, Daniel Dennett makes a case for compatibilism. His aim, as he writes in the preface to this new edition, was a cleanup job, 'saving everything that mattered about the everyday concept of free will, while jettisoning the impediments.' In Elbow Room, Dennett argues that the varieties of free will worth wanting--those that underwrite moral and artistic responsibility--are "]
[{"$nulls":4},"not threatened by advances in science but distinguished, explained, and justified in detail. Dennett tackles the question of free will in a highly original and witty manner, drawing on the theories and concepts of fields that range from physics and evolutionary biology to engineering, automata theory, and artificial intelligence. He shows how the classical formulations of the problem in philosophy depend on misuses of imagination, and he disentangles the philosophical problems of real interest from the 'family of anxieties' in which they are often enmeshed--imaginary agents and bogeymen, including the Peremptory Puppeteer, the Nefarious Neurosurgeon, and the Cosmic Child Whose Dolls We Are. Putting sociobiology in its rightful place, he concludes that we can have free will and science too. He explores reason, control and self-control, the meaning of 'can' and 'could have done otherwise, ' responsibility and punishment, and why we would want free will in the first place. A fresh reading of Dennett's book shows how much it can still contribute to current discussions of free will. This edition includes as its afterword Dennett's 2012 Erasmus Prize essay\"--MIT CogNet."]
[null,"3.","Record Nr.",null,"UNISA996587868503316"]
[null,null,"Autore",null,"Liu Qingshan"]
[null,null,"Titolo",null,"Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IX / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9985-46-3"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (520 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14433"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Computer systems"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer System Implementation"]
[{"$nulls":4},"Machine Learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part IX -- Neural Network and Deep Learning II -- Decoupled Contrastive Learning for Long-Tailed Distribution -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Online Tail Samples Discovery -- 3.2 Hard Negatives Generation -- 3.3 Contrastive Loss Reweighting -- 4 Experiments -- 4.1 Linear Probing Evaluations -- 4.2 Analysis -- 5 Conclusion -- References -- MFNet: A Channel Segmentation-Based Hierarchical Network for Multi-food Recognition -- 1 Introduction -- 2 Related Work -- 3 Food Image Datasets Construction -- 3.1 Food Images Collection -- 3.2 Annotation and Statistics -- 4 Method -- 4.1 CWF: Channel-Level Whole Image Food Information Acquisition -- 4.2 SGC: "]
[{"$nulls":4},"Spatial-Level Global Information Constraints -- 4.3 SPF: Spatial-Level Part Image Food Information Acquisition -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 Performance Comparison with Other Method -- 5.4 Ablation Study -- 5.5 Visualization Result -- 6 Conclusion -- References -- Improving the Adversarial Robustness of Object Detection with Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Attacks -- 2.2 Adversarial Defenses -- 2.3 Contrastive Learning -- 3 Proposed Method -- 3.1 Contrastive Learning Module -- 3.2 Contrastive Adversarial SSD -- 3.3 Contrastive Adversarial YOLO -- 3.4 Adversarial Training with Contrastive Learning -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Defense Capability Evaluation -- 5 Conclusion -- References -- CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock -- 1 Introduction -- 2 Related Work -- 2.1 Attacked Watermarking Algorithm -- 2.2 Watermarking Attack Techniques -- 3 The Proposed Method -- 3.1 CAWNet -- 3.2 Attention Mechanism -- 3.3 CWABlock -- 4 Experiments and Results Analysis."]
[{"$nulls":4},"4.1 Evaluation Criteria -- 4.2 Ablation Experiment -- 4.3 Effects of Traditional Attack and Different Deep Learning Attack Methods -- 4.4 Stability and Suitability Testing -- 5 Conclusion -- References -- Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Middle-Level Feature Auxiliary -- 3.2 Intra-class Consistency Enhancement -- 3.3 Critical Region Suppression -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 CAM Performance -- 4.4 Segmentation Performance -- 4.5 Ablation Study -- 5 Conclusion -- References -- Enhancing Model Robustness Against Adversarial Attacks with an Anti-adversarial Module -- 1 Introduction -- 2 Related Works -- 2.1 Gradient Masking -- 2.2 Adversarial Examples Detection -- 2.3 Robust Optimization -- 3 Methods -- 3.1 Counter-Adversarial Module -- 3.2 Enhancing Defense Against Black-Box Attacks -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- FGPTQ-ViT: Fine-Grained Post-training Quantization for Vision Transformers -- 1 Introduction -- 2 Related Work -- 2.1 CNN Quantization -- 2.2 Vision Transformer Quantization -- 3 Method -- 3.1 FGPTQ-ViT Framework -- 3.2 Fine-Grained ViT Quantization -- 3.3 Adaptive Piecewise Point Search Algorithm -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional and Transformer Models -- 2.2 Fourier Transform and Decomposition Models -- 3 Proposed Approach -- 3.1 Time Series Hierarchical Decomposition -- 3.2 Trend Forecasting Module -- 3.3 Seasonal Forecasting Module -- 4 Experiments."]
[{"$nulls":4},"4.1 Dataset -- 4.2 Baselines and Setup -- 4.3 Implement Details and Evaluation Metrics -- 4.4 Main Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- DeCAB: Debiased Semi-supervised Learning for Imbalanced Open-Set Data -- 1 Introduction -- 2 Related Work -- 2.1 General SSL Methods -- 2.2 Imbalanced SSL Methods -- 2.3 Open-Set SSL Methods -- 3 Proposed Method -- 3.1 Problem Setting and Notations -- 3.2 Class-Aware Threshold -- 3.3 Selective Sample Reweighting -- 3.4 Positive-Pair Reweighting -- 3.5 Overall Training Objective -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Numerical Comparison -- 4.3 Analysis on Impact of OOD Data -- 4.4 Ablation Experiments -- 5 Conclusion -- A Analysis "]
[{"$nulls":4},"of the Effect of OOD Data to SSL Methods -- B Algorithm Flowchart -- C Visualized Comparison -- References -- An Effective Visible-Infrared Person Re-identification Network Based on Second-Order Attention and Mixed Intermediate Modality -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Second-Order Attention Module -- 3.2 Mixed Intermediate Modality Module -- 3.3 Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- Quadratic Polynomial Residual Network for No-Reference Image Quality Assessment -- 1 Introduction -- 2 Related Work -- 2.1 IQA and Information Entropy -- 2.2 IQA and Deep Learning -- 3 Design of Network -- 3.1 Two-Dimensional Information Entropy for Patch Sampling -- 3.2 Network Architecture -- 4 Experiment Result -- 5 Conclusion -- References -- Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Patch Selection Strategy -- 3.2 Semantic-Aware Self-Teaching -- 4 Experiments."]
[{"$nulls":4},"5 Conclusion -- References -- Adaptive and Compact Graph Convolutional Network for Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Structure Graph -- 3 Method -- 3.1 Cheek Included Facial Graph -- 3.2 Tightly Connected Strategy -- 3.3 Small Region Module -- 3.4 Adaptive and Compact Graph Convolutional Network -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Quantitative Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI -- 1 Introduction -- 2 Methods -- 2.1 Hypergraph and Hypergraph Construction with FCN -- 2.2 Proposed CG-MHGEL with Multiatlas-Based FCNs -- 3 Experiment -- 3.1 Experimental Settings -- 3.2 Experimental Results and Analysis -- 4 Conclusion -- References -- A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Framework -- 3.3 Diffusion Simulation User Behavior Embedding -- 3.4 User Behavior Fusion Transformer -- 3.5 Cascade Perception Attention Network -- 3.6 Diffusion Prediction -- 4 Experiment -- 4.1 Results -- 5 Conclusion -- References -- A Representation Learning Link Prediction Approach Using Line Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The NLG-GNN Framework -- 4 Experimental Setup -- 4.1 Experimental Results -- 5 Conclusion -- References -- Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data -- 1 Introduction -- 2 Related Work -- 2.1 Graph Representations -- 2.2 Dynamic Graph Neural Network -- 3 Methods -- 3.1 Local Self Attention -- 3.2 Global Temporal Attention -- 4 Experiments -- 4.1 Datasets."]
[{"$nulls":4},"4.2 Setup -- 4.3 Continuous Data Inductive Learning -- 4.4 Discrete Data Inductive Learning -- 4.5 Discrete Data Transductive Learning -- 5 Conclusion -- References -- Federated Learning Based on Diffusion Model to Cope with Non-IID Data -- 1 Introduction -- 2 Method -- 2.1 The First Stage -- 2.2 The Second Stage -- 2.3 The Third Stage -- 3 Experiments -- 3.1 Setup -- 3.2 Performance Comparison -- 3.3 Experimental Factors Analysis -- 4 Conclusion -- References -- SFRSwin: A Shallow Significant Feature Retention Swin Transformer for Fine-Grained Image Classification of Wildlife Species -- 1 Introduction -- 2 Related Works -- 2.1 Convolutional Neural Network -- 2.2 Vision Transformer -- 3 Methodology -- 3.1 Self-attentive Mechanism Based "]
[{"$nulls":4},"on Shifted Windows -- 3.2 Random Data Enhancement -- 4 Evaluation -- 4.1 Datasets and Implementation Details -- 4.2 Model Complexity Analysis -- 5 Conclusion -- References -- A Robust and High Accurate Method for Hand Kinematics Decoding from Neural Populations -- 1 Introduction -- 2 Related Works -- 2.1 iBMI Cortical Control Decoding Algorithm -- 2.2 Attention Module -- 3 Method -- 3.1 Neural Recording System and Behavioral Task -- 3.2 Experimental Procedure of the Cortical Control -- 3.3 Temporal-Attention QRNN -- 3.4 Evaluation Metrics -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Comparison of Decoding Results -- 4.3 Discussion -- 5 Conclusion -- References -- Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Neural Networks on Graph -- 2.2 Neural Networks on Hypergraph -- 3 Methodology -- 3.1 Definitions and Notations -- 3.2 Hypergraph Construction -- 3.3 Vertex Convolution -- 3.4 Hyperedge Convolution -- 3.5 The Proposed Algorithm -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results and Discussion."]
[{"$nulls":4},"4.4 Ablation Studies."]
[null,null,null,"Sommario/riassunto","The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. ."]