LEADER 07753nam 22007935 450 001 9910983038503321 005 20241117120807.0 010 $a9789819601196$b(electronic bk.) 010 $z9789819601189 024 7 $a10.1007/978-981-96-0119-6 035 $a(MiAaPQ)EBC31784969 035 $a(Au-PeEL)EBL31784969 035 $a(CKB)36590111400041 035 $a(DE-He213)978-981-96-0119-6 035 $a(OCoLC)1472990534 035 $a(EXLCZ)9936590111400041 100 $a20241117d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPRICAI 2024: Trends in Artificial Intelligence $e21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18?24, 2024, Proceedings, Part II /$fedited by Rafik Hadfi, Patricia Anthony, Alok Sharma, Takayuki Ito, Quan Bai 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (482 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15282 311 08$aPrint version: Hadfi, Rafik PRICAI 2024: Trends in Artificial Intelligence Singapore : Springer,c2024 9789819601189 327 $a -- Deep Learning. -- STLB-GN: Spatio-Temporal Dual Graph Network with Learnable Bases. -- Rethinking the Reliability of Post-hoc Calibration Methods under Subpopulation Shift. -- Zero-shot Heterogeneous Graph Embedding via Semantic Extraction. -- TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data. -- Stock Market Index Movement Prediction using Partial Contextual Embedding BERT-LSTM. -- SCBC: A Supervised Single-cell Classification Method Based on Batch Correction for ATAC-seq Data. -- TS-CATMA: A Lung Cancer Electronic Nose Data Classification Method Based on Adversarial Training and Multi-Scale Attention. -- Visualizing the Unseen: Arabic Image-to-Story Generation Using Deep Learning Techniques. -- Federated Learning. -- Federated Prompt Tuning: When is it Necessary?. -- Dirichlet-Based Local Inconsistency Query Strategy for Active Domain Adaptation. -- FedSD: Cross-Heterogeneous Federated Learning Based on Self-Distillation. -- Personalized Federated Learning with Feature Alignment via Knowledge Distillation. -- Multi-Party Collaborative Hate Speech Study on Social Media via Personalized Federated Learning. -- Preserving Individual User?s Right to be Forgotten in Enterprise-Level Federated Learning. -- Generative AI. -- Dance Generation From Music with Enhanced Beat. -- Contrastive Prototype Network for Generative Zero-Shot learning. -- Steganography: An improved robust model for deep hidden network. -- Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and Detection. -- Natural Language Processing. -- Mongolian-Chinese Cross-lingual Topic Detection Based on Knowledge Distillation and Contrastive Learning Methods. -- Emergence of Grounded Language Representations for Continuous Object Properties through Decentralized Embodied Learning. -- AI-facilitation for consensus-building by virtual discussion using large language models. -- False Positive Detection for Text-based Person Retrieval. -- An End-to-End Method for Chinese Spelling Error Detection and Correction. -- Dialogue Summarization based on Feature Extraction and Commonsense Injection. -- SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq -- Personalized Generation with Causal Inference. -- Document-Level Relation Extraction Model Based On Boundary Distance Loss And Long-Tail Relation Enhancement. -- MCQG: Reading Comprehension Multiple Choice Questions Generation based on Pre-trained Language Models. -- ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification. -- EC-PEFT: An Expertise-Centric Parameter-Efficient Fine-Tuning Framework for Large Language Models. -- Enhanced Classification of Delay Risk Sources in Road Construction Using Domain- Knowledge-Driven. -- Modeling the Structural and Semantic Features for Japanese Lyrics Generation of J-pop Songs. -- FINE-LMT: Fine-grained Feature Learning for Multi-Modal Machine Translation. -- Segmentation Strategies and Data Enrichment for Improved Abstractive Summarization of Burmese Language. -- Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models. -- Spatial-Temporal Union Channel Enhancement for Continuous Sign Language Recognition. -- KLoB: a Benchmark for Assessing Knowledge Localization Methods in Language Models. -- Cross-lingual Entity Alignment Model based on Multi-entity Enhancement and Semantic Information. -- Large Language Models. -- A Decomposed-Distilled Sequential Framework for Text-to-Table Task with LLMs. -- Are Dense Retrieval Models Few-Shot Learners?. -- An Empirical Study of Leveraging PLMs and LLMs for Long-Text Summarization. -- A Novel MLLMs-based Two-stage Model for Zero-shot Multimodal Sentiment Analysis. -- DeepTTS: Enhanced Transformer-Based Text Spotter via Deep Interaction Between Detection and Recognition Tasks. 330 $aThe five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18?24, 2024. The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. The papers are organized in the following topical sections: Part I: Machine Learning, Deep Learning Part II: Deep Learning, Federated Learning, Generative AI, Natural Language Processing, Large Language Models, Part III: Large Language Models, Computer Vision Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15282 606 $aArtificial intelligence 606 $aComputers 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aPattern recognition systems 606 $aArtificial Intelligence 606 $aComputing Milieux 606 $aComputer Communication Networks 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aAutomated Pattern Recognition 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 14$aArtificial Intelligence. 615 24$aComputing Milieux. 615 24$aComputer Communication Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aAutomated Pattern Recognition. 676 $a006.3 700 $aHadfi$b Rafik$01782728 701 $aAnthony$b Patricia$01782729 701 $aSharma$b Alok$01782730 701 $aIto$b Takayuki$0908181 701 $aBai$b Quan$01782731 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910983038503321 996 $aPRICAI 2024: Trends in Artificial Intelligence$94309238 997 $aUNINA LEADER 05317nam 2200649 a 450 001 9910954318303321 005 20251117063830.0 010 $a1-84755-237-4 035 $a(CKB)1000000000791294 035 $a(EBL)1186226 035 $a(SSID)ssj0000379719 035 $a(PQKBManifestationID)11275619 035 $a(PQKBTitleCode)TC0000379719 035 $a(PQKBWorkID)10365449 035 $a(PQKB)10855081 035 $a(MiAaPQ)EBC1186226 035 $a(Au-PeEL)EBL1186226 035 $a(CaPaEBR)ebr10626378 035 $a(OCoLC)843642403 035 $a(PPN)198476469 035 $a(EXLCZ)991000000000791294 100 $a20121204d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aWheat gluten /$fedited by Peter R. Shewry, Arthur S. Tatham 205 $a1st ed. 210 $aCambridge [England] $cRSC$d2000 215 $a1 online resource (566 p.) 225 0 $aSpecial publication ;$vn. 261 300 $aDescription based upon print version of record. 311 08$a0-85404-865-0 320 $aIncludes bibliographical references and index. 327 $aBK9780854048656-FX001; BK9780854048656-FP001; BK9780854048656-FP005; BK9780854048656-FP006; BK9780854048656-00001; BK9780854048656-00003; BK9780854048656-00011; BK9780854048656-00016; BK9780854048656-00020; BK9780854048656-00025; BK9780854048656-00029; BK9780854048656-00034; BK9780854048656-00038; BK9780854048656-00043; BK9780854048656-00047; BK9780854048656-00051; BK9780854048656-00055; BK9780854048656-00061; BK9780854048656-00066; BK9780854048656-00071; BK9780854048656-00073; BK9780854048656-00077; BK9780854048656-00080; BK9780854048656-00084; BK9780854048656-00088; BK9780854048656-00093 327 $aBK9780854048656-00097BK9780854048656-00101; BK9780854048656-00105; BK9780854048656-00109; BK9780854048656-00113; BK9780854048656-00117; BK9780854048656-00123; BK9780854048656-00125; BK9780854048656-00132; BK9780854048656-00136; BK9780854048656-00140; BK9780854048656-00144; BK9780854048656-00149; BK9780854048656-00154; BK9780854048656-00158; BK9780854048656-00162; BK9780854048656-00166; BK9780854048656-00171; BK9780854048656-00175; BK9780854048656-00179; BK9780854048656-00183; BK9780854048656-00188; BK9780854048656-00192; BK9780854048656-00196; BK9780854048656-00200; BK9780854048656-00204 327 $aBK9780854048656-00209BK9780854048656-00211; BK9780854048656-00215; BK9780854048656-00219; BK9780854048656-00223; BK9780854048656-00227; BK9780854048656-00231; BK9780854048656-00235; BK9780854048656-00239; BK9780854048656-00244; BK9780854048656-00249; BK9780854048656-00254; BK9780854048656-00258; BK9780854048656-00262; BK9780854048656-00267; BK9780854048656-00271; BK9780854048656-00273; BK9780854048656-00277; BK9780854048656-00283; BK9780854048656-00287; BK9780854048656-00291; BK9780854048656-00296; BK9780854048656-00300; BK9780854048656-00305; BK9780854048656-00307; BK9780854048656-00313 327 $aBK9780854048656-00317BK9780854048656-00321; BK9780854048656-00326; BK9780854048656-00331; BK9780854048656-00335; BK9780854048656-00340; BK9780854048656-00347; BK9780854048656-00352; BK9780854048656-00356; BK9780854048656-00361; BK9780854048656-00363; BK9780854048656-00368; BK9780854048656-00372; BK9780854048656-00376; BK9780854048656-00380; BK9780854048656-00383; BK9780854048656-00387; BK9780854048656-00391; BK9780854048656-00396; BK9780854048656-00400; BK9780854048656-00404; BK9780854048656-00408; BK9780854048656-00413; BK9780854048656-00417; BK9780854048656-00421; BK9780854048656-00425 327 $aBK9780854048656-00430BK9780854048656-00435; BK9780854048656-00439; BK9780854048656-00442; BK9780854048656-00447; BK9780854048656-00451; BK9780854048656-00454; BK9780854048656-00460; BK9780854048656-00464; BK9780854048656-00469; BK9780854048656-00471; BK9780854048656-00475; BK9780854048656-00480; BK9780854048656-00484; BK9780854048656-00488; BK9780854048656-00492; BK9780854048656-00497; BK9780854048656-00499; BK9780854048656-00503; BK9780854048656-00507; BK9780854048656-00512; BK9780854048656-00519; BK9780854048656-00521; BK9780854048656-00526; BK9780854048656-00531; BK9780854048656-00535 327 $aBK9780854048656-00538 330 $aBread, pasta, noodles ... some of the many ways in which humans consume wheat after processing has taken place. The gluten proteins of wheat grain, which determine the processing properties of wheat flour, have been the subject of intensive study for many years. The structures, genetics and functional properties of this unique group of proteins are the focus of this book.Providing a unique ""snapshot"" of the most exciting current research in the area, this wide-ranging book encompasses topics such as biotechnology; analysis, purification and characterization; quality testing; and environmenta 410 0$aSpecial Publication 606 $aGluten$vCongresses 606 $aPlant proteins$vCongresses 615 0$aGluten 615 0$aPlant proteins 676 $a572.6 701 $aShewry$b P. R$g(Peter R.)$0893797 701 $aTatham$b Arthur S$01864672 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910954318303321 996 $aWheat gluten$94471560 997 $aUNINA