LEADER 11458nam 2200553 450 001 996565868803316 005 20231129122705.0 010 $a981-9980-70-4 024 7 $a10.1007/978-981-99-8070-3 035 $a(CKB)28853478400041 035 $a(MiAaPQ)EBC30943620 035 $a(Au-PeEL)EBL30943620 035 $a(DE-He213)978-981-99-8070-3 035 $a(EXLCZ)9928853478400041 100 $a20231129d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNeural Information Processing $e30th International Conference, ICONIP 2023, Changsha, China, November 20-23, 2023, Proceedings, Part IV /$fedited by Biao Luo [and four others] 205 $aFirst edition. 210 1$aSingapore :$cSpringer,$d[2024] 210 4$d©2024 215 $a1 online resource (594 pages) 225 1 $aLecture Notes in Computer Science Series ;$vVolume 14450 311 08$a9789819980697 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents - Part IV -- Human Centred Computing -- Cross-Modal Method Based on Self-Attention Neural Networks for Drug-Target Prediction -- 1 Instructions -- 2 Materials and Approaches -- 2.1 Benchmark Datasets -- 2.2 Implementation Process of SANN-DTI -- 2.3 Adjustment of Parameters -- 2.4 Evaluation Metrics -- 3 Experimental Results -- 3.1 Compared with Baseline Models -- 3.2 Impact of Each Component on Predicted Performance -- 4 Case Study -- 5 Conclusion -- References -- GRF-GMM: A Trajectory Optimization Framework for Obstacle Avoidance in Learning from Demonstration -- 1 Introduction -- 2 Problem Statement -- 3 Method -- 3.1 Gaussian Mixture Model/Gaussian Mixture Regression -- 3.2 Optimization Algorithm: GRF-GMM -- 4 Simulations and Experiments -- 4.1 2D Handwriting Letter Task -- 4.2 Experiment -- 4.3 Comparisons -- 5 Conclusions -- References -- SLG-NET: Subgraph Neural Network with Local-Global Braingraph Feature Extraction Modules and a Novel Subgraph Generation Algorithm for Automated Identification of Major Depressive Disorder -- 1 Introduction -- 2 Related Work -- 2.1 Construction of Braingraph -- 3 Method -- 3.1 Sub-braingraph Sampling and Encoding -- 3.2 Sub-braingraph Selection and Sub-braingraph's Node Selection by LFE Module -- 3.3 Sub-braingraph Sketching by GFE Module and Classification -- 4 Experiments -- 4.1 Dataset and Parameters Setting -- 4.2 Overall Evaluation -- 4.3 S-BFS, LFE, and GFE Modules Analysis -- 5 Conclusion -- References -- CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with Human-Environment-Robot Ternary Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Socially Aware Crowded Navigation -- 2.2 Simulator for Crowded Navigation -- 3 Problem Formulation -- 4 HRO Ternary Fusion Simulator -- 4.1 Simulator Setting. 327 $a4.2 Static Environment Construction and Collision Avoidance -- 4.3 Crowd Interaction Optimization -- 5 A Crowded Navigation Framework with HERO Ternary Feature Fusion -- 5.1 Spatial-Temporal Pedestrian Trajectory Prediction -- 5.2 Dual-Channel Value Estimation Network -- 6 Experiments -- 6.1 Experimental Settings -- 6.2 Quantitative Evaluations for Crowded Navigation -- 6.3 Quantitative Evaluation of Impact of Environment on Navigation -- 6.4 Quantitative Evaluations on Real Pedestrian Dataset -- 7 Conclusion -- References -- Modeling User's Neutral Feedback in Conversational Recommendation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Proposed Methods -- 4.1 Representation Learning -- 4.2 Action Decision -- 4.3 Selection Strategies -- 4.4 Update and Deduction -- 5 Experiments -- 5.1 DataSet -- 5.2 Experimental Settings -- 5.3 Performance Comparison of NFCR with Existing Models (RQ1) -- 5.4 Ablation Studies (RQ2) -- 5.5 Case Study on Neutral Feedback (RQ3) -- 6 Conclusions -- References -- A Domain Knowledge-Based Semi-supervised Pancreas Segmentation Approach -- 1 Introduction -- 2 Related Work -- 2.1 Semi-supervised Medical Image Segmentation -- 2.2 Domain Knowledge -- 3 Methodology -- 3.1 Loss Function -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison Study -- 5 Conclusion -- References -- Soybean Genome Clustering Using Quantum-Based Fuzzy C-Means Algorithm -- 1 Introduction -- 2 Preliminaries -- 2.1 Fuzzy C-Means -- 2.2 Quantum Computing Concept -- 3 Proposed Work -- 3.1 Dataset Preparation -- 3.2 Quantum Fuzzy C-Means (QFCM) Clustering Approach -- 4 Experiment and Result -- 4.1 Experimental Environment -- 4.2 Datasets Description -- 4.3 Performance Evaluation -- 4.4 Results and Discussion -- 5 Conclusion -- References. 327 $aDAMFormer: Enhancing Polyp Segmentation Through Dual Attention Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 Polyp Segmentation -- 2.2 Attention Mechanism -- 3 Proposed Method -- 3.1 Transformer Encoder -- 3.2 ConvBlock -- 3.3 Enhanced Dual Attention Module -- 3.4 Channel-Wise Scaling -- 3.5 Effective Feature Fusion -- 4 Experiments -- 5 Conclusion -- References -- BIN: A Bio-Signature Identification Network for Interpretable Liver Cancer Microvascular Invasion Prediction Based on Multi-modal MRIs -- 1 Introduction -- 2 Related Works -- 2.1 MVI Prediction Models Based on MRIs -- 2.2 MVI Interpretable Deep Models -- 3 The Proposed Multi-modal Fusion Based BIN Method -- 4 Experiment and Analysis -- 4.1 Performance Comparisons -- 4.2 Qualitative Experiment -- 5 Conclusion -- References -- Human-to-Human Interaction Detection -- 1 Introduction -- 2 Related Work -- 3 HID Task -- 3.1 Problem Definition -- 3.2 Evaluation Metrics -- 3.3 The AVA-Interaction Dataset -- 4 SaMFormer -- 4.1 Visual Feature Extractor -- 4.2 The Split Stage -- 4.3 The Merging Stage -- 4.4 Training and Inference -- 5 Experiments -- 5.1 Main Results on AVA-I -- 5.2 Ablation Study -- 5.3 Qualitative Results -- 5.4 Evaluation on BIT and UT -- 6 Conclusion -- References -- Reconstructing Challenging Hand Posture from Multi-modal Input -- 1 Introduction -- 2 Related Work -- 3 Capture -- 4 Skeleton-Shape Alignment -- 5 Data Evaluation and Applications -- 6 Conclusions and Future Work -- References -- A Compliant Elbow Exoskeleton with an SEA at Interaction Port -- 1 Introduction -- 2 Mechanical Design -- 2.1 Exoskeleton Design -- 2.2 SEA Analysis -- 3 SEA Modeling -- 3.1 NARMAX Model -- 3.2 T-S Fuzzy Model -- 3.3 LSTM Model -- 3.4 Model Training -- 3.5 Model Validation -- 4 Exoskeleton Flexible Control -- 5 Conclusion -- References -- Applications. 327 $aDifferential Fault Analysis Against AES Based on a Hybrid Fault Model -- 1 Introduction -- 2 DFA on AES State -- 2.1 Proposed Fault Model -- 2.2 The Analysis of Cracking AES -- 2.3 The Process of Cracking AES -- 3 Experimental Results and Comparisons -- 4 Conclusions -- References -- Towards Undetectable Adversarial Examples: A Steganographic Perspective -- 1 Introduction -- 2 Related Works -- 2.1 Adversarial Attack -- 2.2 Embedding Suitability Map -- 3 Proposed Scheme -- 3.1 Motivation -- 3.2 Embedding Suitability Map-Weighted Attack -- 3.3 Combination with CAM -- 4 Experimental Results -- 4.1 Attack Ability -- 4.2 Undetectability -- 4.3 Undetectability-Attack Ability Tradeoff -- 4.4 Visual Quality -- 5 Conclusion -- References -- On Efficient Federated Learning for Aerial Remote Sensing Image Classification: A Filter Pruning Approach -- 1 Introduction -- 2 Related Work -- 2.1 Efficient Federated Learning -- 2.2 Filter Pruning -- 3 Methodology -- 3.1 System Model -- 3.2 Cross-All-Layers Importance Measure for Pruning -- 3.3 CALIM-FL Work Process -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Result Discussion -- 5 Conclusion -- References -- ASGNet: Adaptive Semantic Gate Networks for Log-Based Anomaly Diagnosis -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Task Description -- 3.2 Definition of Terms -- 3.3 Log Statistics Information Representation -- 3.4 Log Deep Semantic Representation -- 3.5 Adaptive Semantic Threshold Mechanism -- 4 Experimental Setup -- 4.1 Dataset and Hyper-parameters -- 4.2 Training and Hyperparameters -- 5 Experimental Results -- 5.1 Model Comparisons (RQ1) -- 5.2 Ablation Study (RQ2) -- 5.3 Parameter Sensitivity (RQ3) -- 6 Conclusion -- References -- Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method. 327 $a3.1 Prophetic Teacher Learning -- 3.2 Propheter-Guided Long-Tailed Classification -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusions -- References -- Sequential Transformer for End-to-End Person Search -- 1 Introduction -- 2 Method -- 2.1 SeqTR Architecture -- 2.2 re-ID Transformer -- 2.3 Training and Inference -- 3 Experiments -- 3.1 Datasets and Settings -- 3.2 Implementation Details -- 3.3 Comparison to the State-of-the-arts -- 3.4 Ablation Study -- 4 Conclusion -- References -- Multi-scale Structural Asymmetric Convolution for Wireframe Parsing -- 1 Introduction -- 2 Methodology -- 2.1 Overall Network Architecture -- 2.2 Customized Backbone -- 2.3 Geometry Proposal Network -- 3 Experiments -- 3.1 Datasets and Metrics -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparison with Other Methods -- 4 Conclusions -- References -- S3ACH: Semi-Supervised Semantic Adaptive Cross-Modal Hashing -- 1 Introduction -- 2 Related Works -- 3 The Proposed Method -- 3.1 Notation and Problem Formulation -- 3.2 S3ACHMethod -- 3.3 Optimization -- 3.4 Hash Function Learning -- 3.5 Time Cost Analysis -- 4 Experiments -- 4.1 Datasets -- 4.2 Compared Baselines and Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Results -- 4.5 Ablation Experiments -- 4.6 Parameter Sensitivity Analysis -- 4.7 Convergence Analysis -- 5 Conclusion -- References -- Intelligent UAV Swarm Planning Based on Undirected Graph Model -- 1 Introduction -- 2 Methods -- 2.1 Improved MINCO Algorithm -- 2.2 UAV Cluster Modeling -- 3 Constraints in Cost Functions -- 3.1 Smoothness Penalty -- 3.2 Total Time Penalty -- 3.3 Collision Penalty -- 3.4 Cluster Formation Penalty -- 3.5 Penalty for Collisions Between Unmanned Aerial Vehicles -- 3.6 Dynamic Feasibility Penalty. 327 $a3.7 Penalty for Uniform Distribution of Constraint Points. 330 $aThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. . 410 0$aLecture notes in computer science ;$vVolume 14450. 606 $aNeural computers$vCongresses 606 $aNeural networks (Computer science)$vCongresses 615 0$aNeural computers 615 0$aNeural networks (Computer science) 676 $a745.05 702 $aLuo$b Biao 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996565868803316 996 $aNeural Information Processing$92554499 997 $aUNISA LEADER 02161nam0 22002531i 450 001 UON00425708 005 20231205104846.258 100 $a20130531d1665 |0itac50 ba 101 $alat 102 $aGB 140 $a||||||||| ||||||||| 200 1 $aAugustini Barbosae, I.V.D. Lusitani, Protonotarii Apostolici, olim S. Congregationis Indicis Consultoris, & insignis Ecclesia Vimaranensis Thesaurary maioris, Demum Episcopi Ugentini, et a Consiliss D. Philippi IV. Hispaniarum Regis Catholici: Pastoralis Solicitudinis sive De Officio, et Potestate parochi tripartita descriptio: Cuius partes singulas earumque materias, methodo nova, atque expedita pertractatas, sequens pagina declarabit. Ultima editio prioribus emendatior. Cum summariis, & indicibus locupletissimis / Augustinus Barbosa 210 $aLugduni$cSumptibus Philippi Borde, Laurentii Arnaud, Petri Borde, et Guill. Barbier$d1665 215 $a193 p.$eIndex Rerum$d34 cm 620 $aNL$dLeiden$3UONL003056 700 1$aBARBOSA$bAgostinho$3UONV216483$0233947 712 $aPhilippi Borde$3UONV280541$4650 801 $aIT$bSOL$c20240220$gRICA 856 4 $uhttp://next.unior.it/sebina/repository/catalogazione/documenti/Augustini Barbosae.pdf$zAugustini Barbosae_SiBA_Dig.Unior 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI$uhttp://next.unior.it/sebina/repository/catalogazione/documenti/Augustini Barbosae.pdf 912 $aUON00425708 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI RARI MC MC 022 3° PIANO - CORRIDOIO$eSI MR 66964 6 3° PIANO - CORRIDOIO 996 $aAugustini Barbosae, I.V.D. Lusitani, Protonotarii Apostolici, olim S. Congregationis Indicis Consultoris, & insignis Ecclesia Vimaranensis Thesaurary maioris, Demum Episcopi Ugentini, et a Consiliss D. Philippi IV. Hispaniarum Regis Catholici: Pastoralis Solicitudinis sive De Officio, et Potestate parochi tripartita descriptio: Cuius partes singulas earumque materias, methodo nova, atque expedita pertractatas, sequens pagina declarabit. Ultima editio prioribus emendatior. Cum summariis, & indicibus locupletissimis$91332987 997 $aUNIOR LEADER 03852nam 22008415 450 001 9910298619203321 005 20250609112035.0 010 $a3-319-14175-9 024 7 $a10.1007/978-3-319-14175-6 035 $a(CKB)3710000000378023 035 $a(EBL)2094252 035 $a(SSID)ssj0001465572 035 $a(PQKBManifestationID)11935261 035 $a(PQKBTitleCode)TC0001465572 035 $a(PQKBWorkID)11478165 035 $a(PQKB)10702302 035 $a(DE-He213)978-3-319-14175-6 035 $a(MiAaPQ)EBC2094252 035 $a(PPN)184888778 035 $a(MiAaPQ)EBC3108656 035 $a(EXLCZ)993710000000378023 100 $a20150318d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIsoconversional Kinetics of Thermally Stimulated Processes /$fby Sergey Vyazovkin 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (247 p.) 300 $aDescription based upon print version of record. 311 08$a3-319-14174-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aSome basics en route to isoconversional methodology -- Isoconversional methodology -- Physical Processes -- Chemical processes -- Epilogue. 330 $aThe use of isoconversional kinetic methods for analysis of thermogravimetric and calorimetric data on thermally stimulated processes is quickly growing in popularity. The purpose of this book is to create the first comprehensive resource on the theory and applications of isoconversional methodology. The book introduces the reader to the kinetics of physical and chemical condensed phase processes that occur as a result of changing temperature and discusses how isoconversional analysis can provide important kinetic insights into them. The book will help the readers to develop a better understanding of the methodology, and promote its efficient usage and successful development. 606 $aChemistry, Physical and theoretical 606 $aAnalytical chemistry 606 $aChemical engineering 606 $aPolymers 606 $aThermodynamics 606 $aHeat engineering 606 $aHeat$xTransmission 606 $aMass transfer 606 $aPhysical Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C21001 606 $aAnalytical Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C11006 606 $aIndustrial Chemistry/Chemical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/C27000 606 $aPolymer Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/C22008 606 $aEngineering Thermodynamics, Heat and Mass Transfer$3https://scigraph.springernature.com/ontologies/product-market-codes/T14000 615 0$aChemistry, Physical and theoretical. 615 0$aAnalytical chemistry. 615 0$aChemical engineering. 615 0$aPolymers. 615 0$aThermodynamics. 615 0$aHeat engineering. 615 0$aHeat$xTransmission. 615 0$aMass transfer. 615 14$aPhysical Chemistry. 615 24$aAnalytical Chemistry. 615 24$aIndustrial Chemistry/Chemical Engineering. 615 24$aPolymer Sciences. 615 24$aEngineering Thermodynamics, Heat and Mass Transfer. 676 $a54 676 $a541 676 $a541.2254 676 $a543 676 $a621.4021 676 $a660 700 $aVyazovkin$b Sergey$4aut$4http://id.loc.gov/vocabulary/relators/aut$0860916 906 $aBOOK 912 $a9910298619203321 996 $aIsoconversional Kinetics of Thermally Stimulated Processes$91921248 997 $aUNINA