LEADER 01746nam 2200349 n 450 001 996388954803316 005 20221108042602.0 035 $a(CKB)1000000000639868 035 $a(EEBO)2240933935 035 $a(UnM)9959484200971 035 $a(EXLCZ)991000000000639868 100 $a20790322d1705 uh 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aBy the Queen, a proclamation. Anne R. Whereas it has been represented to us, that not only many inconveniencies have already happened, but that the like may hereafter attend the trade of our subjects, ..$b[electronic resource] 210 $aLondon $cprinted by Charles Bill, and the executrix of Thomas Newcomb, deceas'd; printers to the Queens most excellent Majesty$d1705 215 $a1 sheet ([1] p.) 300 $aMasters of ships travelling in convoy under naval protection to furnish themselves with copies of instructions issued to the commander in chief of the convoy. 300 $a"Given at our court at St. James's the third day of May, in the fourth year of our reign, annoq; Dom. 1705.". 300 $aSteele notation: Arms 160 many ly and. 300 $aReproduction of original in the British Library. 330 $aeebo-0018 607 $aGreat Britain$xHistory$yAnne, 1702-1714$vEarly works to 1800 701 $aAnne$cQueen of Great Britain,$f1665-1714.$01000904 801 0$bUk-ES 801 1$bUk-ES 801 2$bCStRLIN 801 2$bCu-RivES 906 $aBOOK 912 $a996388954803316 996 $aBy the Queen, a proclamation. 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Whereas it has been represented to us, that not only many inconveniencies have already happened, but that the like may hereafter attend the trade of our subjects, .$92333472 997 $aUNISA LEADER 04584nam 22009135 450 001 9910483038503321 005 20251226202851.0 010 $a3-540-76888-2 024 7 $a10.1007/978-3-540-76888-3 035 $a(CKB)1000000000490846 035 $a(SSID)ssj0000319268 035 $a(PQKBManifestationID)11236917 035 $a(PQKBTitleCode)TC0000319268 035 $a(PQKBWorkID)10338398 035 $a(PQKB)10182827 035 $a(DE-He213)978-3-540-76888-3 035 $a(MiAaPQ)EBC4435052 035 $a(MiAaPQ)EBC3062997 035 $a(PPN)123729114 035 $a(MiAaPQ)EBC337679 035 $a(BIP)34165139 035 $a(BIP)17712386 035 $a(EXLCZ)991000000000490846 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aOn the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops $eOTM Confederated International Workshops and Posters, AWeSOMe, CAMS, OTM Academy Doctoral Consortium, MONET, OnToContent, ORM, PerSys, PPN, RDDS, SSWS, and SWWS 2007, Vilamoura, Portugal, November 25-30, 2007, Proceedings, Part I /$fedited by Zahir Tari 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (XXXIV, 760 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4805 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-76887-4 320 $aIncludes bibliographical references and index. 327 $aPosters of the 2007 DOA (Distributed Objects and Applications) International Conference -- Posters of the 2007 ODBASE (Ontologies, Databases, and Applications of Semantics) International Conference -- Posters of the 2007 GADA (Grid Computing, High Performance and Distributed Applications) International Conference -- Posters of the 2007 IS (Information Security) International Conference -- Workshop on Agents, Web Services and Ontologies Merging (AweSOMe) -- Workshop on Context Aware Mobile Systems (CAMS) -- Doctoral Consortium -- Workshop on Mobile and Networking Technologies for Social Applications (MONET) -- Workshop on Ontology Content and Evaluation in Enterprise (OntoContent) -- Workshop on Object-Role Modeling (ORM). 330 $aThis two-volume set LNCS 4805/4806 constitutes the refereed proceedings of 10 international workshops and papers of the OTM Academy Doctoral Consortium held as part of OTM 2007 in Vilamoura, Portugal, in November 2007. The 126 revised full papers presented were carefully reviewed and selected from a total of 241 submissions to the workshops. The first volume begins with 23 additional revised short or poster papers of the OTM 2007 main conferences. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4805 606 $aData structures (Computer science) 606 $aInformation theory 606 $aDatabase management 606 $aData mining 606 $aApplication software 606 $aComputer networks 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aData Structures and Information Theory 606 $aDatabase Management 606 $aData Mining and Knowledge Discovery 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aUser Interfaces and Human Computer Interaction 615 0$aData structures (Computer science). 615 0$aInformation theory. 615 0$aDatabase management. 615 0$aData mining. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 14$aData Structures and Information Theory. 615 24$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a005.74 701 $aMeersman$b R$053963 701 $aTari$b Zahir$0845849 701 $aHerrero$b Pilar$g(Herrero Martin)$0739388 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483038503321 996 $aOn the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops$94520609 997 $aUNINA LEADER 12708nam 22007215 450 001 9910878055803321 005 20251225201945.0 010 $a981-9756-00-6 024 7 $a10.1007/978-981-97-5600-1 035 $a(MiAaPQ)EBC31572152 035 $a(Au-PeEL)EBL31572152 035 $a(CKB)33566468000041 035 $a(DE-He213)978-981-97-5600-1 035 $a(OCoLC)1450837519 035 $a(EXLCZ)9933566468000041 100 $a20240730d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Intelligent Computing Technology and Applications $e20th International Conference, ICIC 2024, Tianjin, China, August 5?8, 2024, Proceedings, Part VII /$fedited by De-Shuang Huang, Chuanlei Zhang, Qinhu Zhang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (492 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14868 300 $aIncludes index. 311 08$a981-9755-99-9 327 $aIntro -- Preface -- Organization -- Contents - Part VII -- Image Processing -- Light-Dark: A Novel Lightweight Self-supervised Monocular Depth Estimation in the Dark -- 1 Introduction -- 2 Method -- 2.1 Nighttime Self-supervised Depth Estimation Architecture -- 2.2 Lightweight DepthNet -- 2.3 Noise-Constrained Adaptive Image Enhancement -- 2.4 Loss Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Detail -- 3.3 Evaluation Results -- 3.4 Ablation Study -- 4 Conclusions -- References -- Non-homogeneous Image Dehazing with Edge Attention Based on Relative Haze Density -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Dual-Cycle Framework Based on Relative Haze Density -- 3.2 Multi-class Discriminator -- 3.3 Loss Function -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation of Supervised Learning -- 4.3 Ablation Study -- 4.4 Evaluation of Unsupervised Learning -- 5 Conclusion -- References -- Contrastive Learning for Silent Face Liveness Detection Based on A Hybrid Framework -- 1 Introduction -- 2 Related Work -- 2.1 RGB Image-Based FLD -- 2.2 Transformers and FLD -- 3 Method -- 3.1 Overview -- 3.2 Network Architecture -- 4 Experiment -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Domain Generalized Evaluation -- 4.4 Ablation Study -- 5 Conclusion -- References -- BS2CL: Balanced Self-supervised Contrastive Learning for Thyroid Cytology Whole Slide Image Multi-classification -- 1 Introduction -- 2 Related Work -- 2.1 Self-Supervised Contrastive Learning in MIL -- 2.2 Thyroid Cytology WSI Classification -- 3 Method -- 3.1 Preliminary: Problem Description -- 3.2 Balanced Self-Supervised Contrastive Learning -- 3.3 Bag-Level Data Augmentation Strategy -- 4 Experiments -- 4.1 Thyroid Cytology Dataset -- 4.2 Experiment Details and Evaluation Metrics -- 4.3 Classification Results. 327 $a4.4 Ablation Study -- 5 Conclusion -- References -- Unsupervised Domain Adaptation Method for Medical Image Segmentation Using Fourier Feature Decoupling and Multi-scale Feature Fusion -- 1 Introduction -- 2 Related Work -- 3 Method Overview -- 3.1 Fourier Transform-Based Feature Decoupling Method -- 3.2 Multi-scale Feature Fusion Strategy -- 3.3 Model Training and Testing -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Validation of Model Adaptability in the Target Domain -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling -- 1 Introduction -- 2 Methodology -- 2.1 Problem Description -- 2.2 SAM-URPG:SAM Unsupervised Region Proposal Generation -- 2.3 ORWM: Object Reconstruction Error Weibull Model -- 3 Experiment -- 3.1 Dataset -- 3.2 Metric -- 3.3 Details -- 3.4 Comparison with State-of-the-Art Models -- 3.5 Ablation Study -- 4 Conclusions -- References -- Implementation and Application of Violence Detection System Based on Multi-head Attention and LSTM -- 1 Introduction -- 2 Related Work -- 2.1 Abnormal Behavior Detection -- 2.2 Violence Detection -- 3 Method -- 3.1 Feature Extraction -- 3.2 Optimization Using Attention Mechanism -- 3.3 Classification -- 4 Experiment and Analysis -- 4.1 Datasets -- 4.2 Experiment -- 4.3 Application -- 5 Conclusion -- References -- GFFNet: An Efficient Image Denoising Network with Group Feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network -- 2.2 U-Net Network -- 3 Method -- 3.1 Network Architecture -- 3.2 Group Feature Fusion (GFF) Module -- 3.3 Cross-Information Integration (CII) Module -- 3.4 Network Optimization -- 4 Experiment -- 4.1 Experimental Details -- 4.2 Gaussian Noise Cancellation -- 4.3 Real Image Denoising -- 4.4 Ablation Experiment -- 5 Conclusion. 327 $aReferences -- End-to-End Object Detection with YOLOF -- 1 Introduction -- 2 Our Approach -- 2.1 Overall Architecture -- 2.2 Stop Gradient -- 2.3 Auxiliary Loss -- 2.4 Semantic Anchor Optimization -- 3 Experients -- 3.1 Baseline Settings -- 3.2 Main Experimental Results -- 3.3 Ablation Experiments -- 3.4 Comparison with Other NMS-Free Detectors -- 4 Conclusion -- References -- BiRGAN: Bi-directional Deep Image Retargeting -- 1 Introduction -- 2 Related Work -- 2.1 Retargeting Dataset -- 3 Retargeting TIReD ++?Dataset -- 4 Approach -- 4.1 BiRGAN Model -- 4.2 Loss Design -- 5 Experiment -- 5.1 Ablation Study -- 5.2 Comparison with Previous Methods -- 5.3 Quantitative Assessment -- 6 Conclusion -- References -- MulTIR: Deep Multi-Target Image Retargeting -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Formulation -- 3.2 Task Loss -- 3.3 Network Details -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Ablation Study -- 4.3 Performance Comparison -- 5 Conclusion -- References -- PAAM (Parameter-free Attentional Aggregation Model) -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Local Feature Enhancement Module -- 3.2 Global Feature Enhancement Module -- 3.3 Local-Global Feature Interaction Module (L-GFIM) -- 4 Experimental Results -- 4.1 Experimental Details -- 4.2 Experiment Comparing Parameter Count -- 4.3 Image Classification Experiments -- 5 Conclusion -- References -- FRFT Domain Watermarking Algorithm Based on GA Adaptive Optimization -- 1 Introduction -- 2 Main Related Technologies -- 2.1 Discrete Wavelet Transform (DWT) -- 2.2 Singular Value Decomposition (SVD) -- 2.3 Fractional Fourier Transform (FRFT) -- 2.4 Genetic Algorithm (GA) -- 3 Watermark Scheme -- 3.1 Watermark Embedding and Extraction Methods -- 3.2 GA-Based Digital Watermark Algorithm -- 4 Experimental Results and Performance Analysis -- 4.1 Evaluation Indicators. 327 $a4.2 Solution Performance Analysis and Comparison -- 4.3 Attack Experiment -- 5 Conclusion -- References -- Joint Semantic Feature and Optical Flow Learning for Automatic Echocardiography Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Overview of Framework Workflow -- 2.2 Segmentation Learning -- 2.3 Optical Flow Learning -- 2.4 Cooperation Mechanism and Joint Learning -- 3 Materials -- 3.1 Data -- 3.2 Implementation Details -- 4 Experiment -- 4.1 Evaluation of Introducing Optical Flow Branch -- 4.2 Affection of Training Sample Numbers -- 4.3 Comparison with Existing Methods -- 5 Conclusion -- References -- FMUnet: Frequency Feature Enhancement Multi-level U-Net for Low-Dose CT Denoising with a Real Collected LDCT Image Dataset -- 1 Introduction -- 2 Methods -- 2.1 FMUnet -- 2.2 Frequency Feature Attention (FFA) -- 2.3 Loss Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Performance Comparisons -- 3.4 Ablation Study -- 4 Discussion -- 5 Conclusion -- References -- Research on Intelligent Recognition Algorithm of Container Numbers in Ports Based on Deep Learning -- 1 Introduction -- 2 Container Number Localization and Recognition Workflow -- 3 Low-Light Enhancement Method Based on Retinex Theory -- 4 Character Super Resolution Reconstruction -- 5 Container Number Localization Method Based on YOLOv5 -- 5.1 MobileNetv3 Module -- 5.2 ECA -- 5.3 Improvements to the YOLOv5 Model -- 5.4 Generating Samples with DCGAN -- 6 Improved CRNN for Container Number Identification -- 7 Experimental Results and Analysis -- 7.1 Dataset -- 7.2 The Improved YOLOv5 Was Used for Experimental Analysis of Container Number Region Positioning -- 7.3 Analysis of the Improved CRNN for Container Number Recognition -- 8 Conclusion and Future Work -- References. 327 $aDr-SAM: U-Shape Structure Segment Anything Model for Generalizable Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 DrSAM Architecture Design -- 2.2 Training of DrSAM -- 2.3 Inference of DrSAM -- 2.4 Advantages of DrSAM vs. SAM -- 2.5 Advantages of DrSAM vs. MedSAM -- 3 Experiments -- 3.1 Datasets -- 3.2 Performance Comparisons -- 3.3 Ablation Studies -- 4 Conclusion -- References -- Aerial Multi-object Tracking via Information Weighting -- 1 Introduction -- 2 Introduction -- 2.1 Adaptive Weighting -- 2.2 Distribution Feature Extraction -- 2.3 Prediction Box Correction -- 2.4 Spatial-Temporal Feature Enhancement -- 3 Experimental Setup and Results -- 4 Conclusion -- References -- Optimization Method for Fractal Image Compression Based on Self-similarity Evaluation and Gradient Bisection Algorithm -- 1 Introduction -- 2 Related Work -- 2.1 Evaluation of Image Self-similarity -- 2.2 DCT-Based Fractal Coding -- 3 Method -- 3.1 Self-similarity Evaluation Algorithm Based on SSIM -- 3.2 Codebook Classification Based on Low-Frequency Coefficient Statistics -- 3.3 Adaptive Adjustment Methods for Inter-class Thresholds -- 4 Experiment -- 4.1 Data Sets and Evaluation Criteria -- 4.2 Results of Self-similarity Evaluation -- 4.3 Compression Performance Experiments -- 5 Conclusion -- References -- DiffGIC: Diffusion Prior Based Null-Space Correction for High Resolution Grayscale Image Colorization -- 1 Introduction -- 2 Related Work -- 2.1 Grayscale Image Colorization -- 2.2 Text-Driven Diffusion-Based Grayscale Image Colorization -- 2.3 Image Super-Resolution -- 3 DiffGIC -- 3.1 Preliminaries: Range-Null Space Decomposition -- 3.2 Color Image Decomposition for Hierarchical Image Colorization -- 3.3 Null-Space Diffusion Prior for Color Information Correction -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experiment Setup. 327 $a4.3 Comparing with SR-HIPS-Based Methods. 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,$x1611-3349 ;$v14868 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 $aZhang$b Chuanlei 702 $aZhang$b Qinhu 702 $aHuang$b De-Shuang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910878055803321 996 $aAdvanced Intelligent Computing Technology and Applications$94410353 997 $aUNINA