LEADER 00970nam a22002531i 4500 001 991003958759707536 005 20040706130919.0 008 040802s1974 it |||||||||||||||||ita 035 $ab13152683-39ule_inst 035 $aARCHE-110529$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a128 100 1 $aLicciardello, Pasquale$0489815 245 13$aIl famismo nella cultura contemporanea /$cPasquale Licciardello 260 $aRoma :$bCiranna,$c1974 300 $a175 p. ;$c21 cm 440 0$aHomo homini ;$v11 650 4$aUomo$xConcezione materialista$ySec. 20. 907 $a.b13152683$b02-04-14$c05-08-04 912 $a991003958759707536 945 $aLE002 Fondo Giudici N 1151$g1$iLE002G-13946$lle002$nC. 1$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i13790948$z05-08-04 996 $aFamismo nella cultura contemporanea$9310991 997 $aUNISALENTO 998 $ale002$b05-08-04$cm$da $e-$fita$git $h3$i1 LEADER 03692nam 2200589 a 450 001 9910963062103321 005 20240516162612.0 010 $a9781283956727 010 $a1283956721 010 $a9781780428673 010 $a1780428677 035 $a(CKB)2670000000180969 035 $a(EBL)915110 035 $a(OCoLC)793511419 035 $a(MiAaPQ)EBC915110 035 $a(Au-PeEL)EBL915110 035 $a(CaPaEBR)ebr10622199 035 $a(CaONFJC)MIL426922 035 $a(FR-PaCSA)88835611 035 $a(FRCYB88835611)88835611 035 $a(EXLCZ)992670000000180969 100 $a20121123d2012 uy 0 101 0 $afre 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$a[Temptis]$iAmerican graffiti /$f[Auteur, Margo Thompson] 205 $a1st ed. 210 $aNew York $cParkstone Press International$d[2012] 215 $a1 online resource (256 p.) 225 1 $aTemporis 300 $aDescription based upon print version of record. 311 08$a9781844845651 311 08$a1844845656 320 $aIncludes bibliographical references and index. 327 $aSommaire; Introduction; Authenticite?; Primitivisme; L'Avant-Garde; Remerciements; Les Artistes du me?tro; The?mes; Lettrage et style; BLADE; RAMMELLZEE; NOC 167; QUIK et SEEN; DONDI, FUTURA 2000, ZEPHYR et LEE; DONDI; FUTURA 2000; ZEPHYR; Graffiti 1980; LEE; LEE et FAB 5 FREDDY a? la galerie la Medusa; FAB FIVE FREDDY; La Fashion Moda; CRASH; DAZE; LADY PINK; « Graffiti Art : Success for America »; L'Art du graffiti et la sce?ne artistique d'East Village, 1980-1981; Le « Times Square Show »; « Events : Fashion Moda » au New Museum; « The Fire Down Below »; « New York/ New Wave » 327 $a« Beyond Words : Graffiti-Based, -Rooted, and - Inspired Work »Graphiti Productions and Graffiti : Aboveground; L'Ouverture de la Fun Gallery; « The Radiant Child »; Le Graffiti dans les galeries; Expositions en solo a? la Fun Gallery et a? 51X; L'Art du graffiti a? la Fashion Moda; L'Art du graffiti et le phe?nome?ne d'East Village; L'Art du graffiti dans Art in America et Art News; L'Exposition consacre?e a? Basquiat a? la Fun Gallery; Le Serment d'alle?geance; Harold et Dolores Neumann; « Post-Graffiti »; Le Graffiti apre?s « Post-Graffiti »; L'Art du graffiti, 1984-1988 327 $aE?valuation du travail des artistes du graffiti au milieu des anne?es 1980East Village : e?tat des lieux; La Hype et le commerce de l'art contemporain; La Fin d'East Village; Le Graffiti ame?ricain en Europe; Le Graffiti dans les galeries et les muse?es en Europe; Notes; Bibliographie; Index 330 $aDepuis le de?but des anne?es 1970, les graffeurs de?corent l'exte?rieur des rames de me?tro avec des tags toujours plus grands et toujours plus e?labore?s.Dans le sche?ma primitiviste, les graffeurs, en tant qu'artistes en marge, offraient de nouvelles perspectives a? la socie?te? ame?ricaine. Ils tendaient un miroir a? la culture he?ge?monique.Les re?fe?rences aux me?dias ou a? des elements culturels que les artistes inte?graient dans leurs cre?ations reve?tent aujourd'hui une importance toute particulie?re, parce qu'elles repre?sentent un point de contact entre les cultures et ont rendu cette « sousculture » plus a 410 0$aTemporis 606 $aGraffiti$zUnited States 615 0$aGraffiti 676 $a394 700 $aThompson$b Margo$01794808 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910963062103321 996 $aTemptis$94337897 997 $aUNINA LEADER 12674nam 22006975 450 001 9910878992303321 005 20251225201959.0 010 $a981-9755-97-2 024 7 $a10.1007/978-981-97-5597-4 035 $a(CKB)33734461000041 035 $a(MiAaPQ)EBC31583121 035 $a(Au-PeEL)EBL31583121 035 $a(DE-He213)978-981-97-5597-4 035 $a(EXLCZ)9933734461000041 100 $a20240801d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Intelligent Computing Technology and Applications $e20th International Conference, ICIC 2024, Tianjin, China, August 5?8, 2024, Proceedings, Part VI /$fedited by De-Shuang Huang, Zhanjun Si, Jiayang Guo 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (516 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14867 311 08$a981-9755-96-4 327 $aIntro -- Preface -- Organization -- Contents - Part VI -- Image Processing -- Dual-Stream Input Gabor Convolution Network for Building Change Detection in Remote Sensing Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Overall Framework -- 3.2 SCM Module -- 3.3 AFM Module -- 3.4 Loss Function -- 4 Experiment Results and Analysis -- 4.1 Datasets and Experimental Platform -- 4.2 Evaluation Metric -- 4.3 The Influence of Different Parameters in Gabor Convolution -- 4.4 Comparative Experiment -- 4.5 Ablation Experiment -- 5 Conclusion -- References -- Contextual Feature Modulation Network for Efficient Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning-Based Image Super-Resolution -- 2.2 Efficient Image Super-Resolution -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Multi-scale Feature Spatial Modulation -- 3.3 Channel Attention Fusion Module -- 3.4 Feature Fusion Module -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison to Other Methods -- 4.3 Ablation -- 5 Conclusion -- References -- Fine-Grained Image Editing Using ControlNet: Expanding Possibilities in Visual Manipulation -- 1 Introduction -- 2 Related Work -- 2.1 Image Editing -- 2.2 Image Editing -- 3 Method -- 3.1 Partial Mask -- 3.2 Spatial Features Injection -- 3.3 Classifier-Guidance-Based Editing Design -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Metrics -- 4.3 Ablation Study -- 4.4 Qualitative Evaluation -- 4.5 Quantitative Evaluation -- 5 Conclusion -- References -- MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction -- 1 Introduction -- 2 Problem Setup -- 3 Proposed Approach -- 3.1 Inter-class Feature Fusion Module -- 3.2 High-Level Feature Extraction Module -- 3.3 Feature Fusion and Decoded Output -- 3.4 Hybrid Loss -- 4 Experiments -- 4.1 Experimental Setting. 327 $a4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusion -- References -- A Large Model Assisted Remote Sensing Image Scene Understanding Algorithm Based on Object Detection -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Network Structure of Large Model Assisted Visual Scene Understanding Algorithm -- 3.2 Prompt Engineering Based on Large Language Modelg -- 4 Experiments -- 4.1 Scene Description and Visualization Analysis of QA -- 4.2 Design of Visual Scene Understanding System -- 5 Conclusion -- References -- PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Dynamic Mix Augmentation from Heterogeneous Views -- 2.3 Uncertainty-Guided Pixel-Level Contrastive Learning -- 2.4 Regularization of Supervision via Dual Consistency -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Quantitative Comparison -- 3.3 Ablation Study -- 3.4 Visualization Analysis -- 4 Conclusions -- References -- Few-Shot Domain Adaptation via Prompt-Guided Multi-prototype Alignment Network -- 1 Introduce -- 2 Relate Work -- 3 Method -- 3.1 Prompt-Based Prototype Alignment Network -- 3.2 Domain-Specific Feature Mapping Network -- 3.3 Design of Multi-text-Guided Soft Prompts -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Implementation Details. -- 4.3 Comparison with State-Of-The-Art DA Methods -- 4.4 Ablation Study and Analyse -- 5 Conclusion -- References -- Controllable Rain Image Generation: Balance Between Diversity and Controllability -- 1 Introduction -- 2 The Proposed Method -- 2.1 Generative Model -- 2.2 Training Strategy -- 3 Experimental Results -- 3.1 Rain Generation Experiments -- 3.2 Disentanglement Experiments -- 3.3 Rain Removal Experiments -- 4 Conclusion and Future Work -- References. 327 $aUnsupervised Domain Adaptation in Medical Image Segmentation via Fourier Feature Decoupling and Multi-teacher Distillation -- 1 Introduction -- 2 Related Work -- 2.1 Unsupervised Domain Adaptation -- 2.2 Multi-teacher Knowledge Distillation Models -- 3 Method Overview -- 3.1 Feature Decoupling Based on Fourier Transform -- 3.2 Multi-teacher Knowledge Distillation Module -- 3.3 Dynamic Weighting Adaptive Strategy -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Validation of Different Models Adaptability in the Target Domain -- 4.3 Validation of the Effectiveness of Fourier Feature Decoupling Method -- 4.4 Performance Validation of Multi-teacher Knowledge Distillation Network -- 5 Conclusion -- References -- DeCoGAN: Photo Cartoonization Based on Deformation Consistency GAN -- 1 Introduction -- 2 Methodology -- 2.1 Network Architecture -- 2.2 Loss Function -- 3 Experiments -- 3.1 Dataset -- 3.2 Parameters -- 3.3 Qualitative Evaluation -- 3.4 Quantitative Evaluation -- 3.5 Ablation Study -- 4 Conclusion and Limitation -- References -- Self-supervised Siamese Networks with Squeeze-Excitation Attention for Ear Image Recognition -- 1 Introduction -- 2 Classical Convolutional Neural Network -- 3 Methods -- 3.1 Siamese Network -- 3.2 SE-SiamNet -- 4 Experimental Result -- 4.1 Dataset Description -- 4.2 Experimental Setup -- 4.3 Result Analysis -- 5 Conclusion -- References -- CtF: Mitigating Visual Confusion in Continual Learning Through a Coarse-To-Fine Screening -- 1 Introduction -- 2 Related Work -- 2.1 Continual Learning -- 2.2 Vision-Language Model -- 3 Proposed Method -- 3.1 Preliminary Analysis -- 3.2 Coarse-To-Fine Screening Framework -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Quantitative Evaluation -- 4.3 Ablation Study -- 4.4 Sensitivity Study -- 5 Conclusion -- References -- HDR Video Coding Based on Perceptual Optimization. 327 $a1 Introduction -- 2 Perceptual Optimization Model -- 2.1 Perceptual Lossless Preprocessing Model -- 2.2 Visual Perceptual Saliency -- 2.3 CTU-Level Quantization Model -- 3 Results and Analysis -- 4 Conclusion -- References -- Infrared-Visible Light Image Fusion Method Based on Weighted Salience Detection and Visual Information Preservation -- 1 Introduction -- 2 Proposed Method -- 2.1 Two-Scale Image Decomposition Based on Average Filter -- 2.2 Weighted Visual Significance Feature Extraction -- 2.3 The Final Detail Layer and Visible Image Fusion Based on Grayscale Driven -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Comparative Experiment -- 4 Conclusions -- References -- CAT-DG: A Cross-Attention-Based Domain Generalization Model for Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Concepts Related to Domain Generalization -- 2.2 The FLLN-DG Model -- 2.3 Cross-Attention Mechanism -- 3 Cross-Attention-Based Segmentation Model for Domain Generalization -- 3.1 Model Architecture -- 3.2 Cross-Attention Module -- 3.3 Loss Function -- 4 Experiments and Results -- 4.1 Dataset and Parameter Settings -- 4.2 Performance Comparison of Different Models -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Superpixel-Based Dual-Neighborhood Contrastive Graph Autoencoder for Deep Subspace Clustering of Hyperspectral Image -- 1 Introduction -- 2 Proposed Methods -- 2.1 Construction of Dual-Neighborhood Graph -- 2.2 Superpixel Dual-Neighborhood Contrastive Graph Autoencoder -- 2.3 Contrastive Learning -- 2.4 Objective Function and Optimization -- 3 Experiment -- 3.1 Experiment Setup -- 3.2 Comparison with State-of-the-Art Methods -- 3.3 Parameter Analysis -- 3.4 Ablation Study -- 4 Conclusion -- References -- SMVT: Spectrum-Driven Multi-scale Vision Transformer for Referring Image Segmentation -- 1 Introduction. 327 $a2 Related Works -- 3 Method -- 3.1 Overview -- 3.2 Cross-modal Feature Encoder -- 3.3 Spectrum-Driven Fusion Attention -- 3.4 Cross-modal Feature Refinement Enhancement Module -- 3.5 Prediction Mask and Loss Function -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- GDCSF: Global Depth Convolution-Based Swin Framework for Electron Microscopy Pollen Image Classification -- 1 Introduction -- 2 Research Background -- 3 Methodology -- 3.1 Swin Transformer -- 3.2 Strip Convolution Scaling Module -- 3.3 Field Dual Bilateral Attention Module -- 3.4 Measurement -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Experimental Results and Inference -- 4.4 Ablation Experiments -- 5 Conclusions -- References -- Adversarial Attack Against Convolutional Neural Network via Gradient Approximation -- 1 Introduction -- 2 Related Work -- 2.1 Notions -- 2.2 Convolutional Neural Network -- 2.3 Threat Model -- 3 Methodologies -- 3.1 Model Framework -- 3.2 Gradient Optimization -- 3.3 Adversarial Sample Generation -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Hierarchical Cascaded Multi-Axis Window Self-Attention and Layer Feature Fusion for Brain Glioma Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method Overview -- 3.1 Model Architecture -- 3.2 Hierarchical Cascaded Multi-Axis Window Self-Attention -- 3.3 Layer Feature Fusion -- 4 Experiments -- 4.1 Dataset -- 4.2 Training and Parameter Settings -- 4.3 Validation of the Effectiveness of the Hierarchical Cascaded Multi-Axis Window Self-Attention Component -- 4.4 Validation of the Effectiveness of the Layer Feature Fusion Component -- 4.5 Performance Comparison of Different Models. 327 $a5 Conclusion. 330 $aThis 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14867 606 $aComputational intelligence 606 $aComputer networks 606 $aMachine learning 606 $aApplication software 606 $aComputational Intelligence 606 $aComputer Communication Networks 606 $aMachine Learning 606 $aComputer and Information Systems Applications 615 0$aComputational intelligence. 615 0$aComputer networks. 615 0$aMachine learning. 615 0$aApplication software. 615 14$aComputational Intelligence. 615 24$aComputer Communication Networks. 615 24$aMachine Learning. 615 24$aComputer and Information Systems Applications. 676 $a006.3 702 $aHuang$b De-Shuang 702 $aSi$b Zhanjun 702 $aGuo$b Jiayang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910878992303321 996 $aAdvanced Intelligent Computing Technology and Applications$94410353 997 $aUNINA