LEADER 00774nam0-22002771i-450- 001 990006340830403321 005 19980601 035 $a000634083 035 $aFED01000634083 035 $a(Aleph)000634083FED01 035 $a000634083 100 $a19980601d1939----km-y0itay50------ba 105 $a--------00-yy 200 1 $aDroit naturel et positivisme juridique$fHenri De Page. 210 $aBruxelles$cEtablissement Emile Bruylant$d1939 215 $a42 p.$d24 cm 676 $a340.1 700 1$aDe Page,$bHenri$0227575 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006340830403321 952 $aBUSTA 15 (6) 32$b30659$fFGBC 959 $aFGBC 996 $aDroit naturel et positivisme juridique$9657442 997 $aUNINA DB $aGIU01 LEADER 06678nam 22007095 450 001 996542671303316 005 20230707134312.0 010 $a3-031-36622-0 024 7 $a10.1007/978-3-031-36622-2 035 $a(MiAaPQ)EBC30621907 035 $a(Au-PeEL)EBL30621907 035 $a(DE-He213)978-3-031-36622-2 035 $a(PPN)272249882 035 $a(EXLCZ)9927531922700041 100 $a20230707d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Swarm Intelligence$b[electronic resource] $e14th International Conference, ICSI 2023, Shenzhen, China, July 14?18, 2023, Proceedings, Part I /$fedited by Ying Tan, Yuhui Shi, Wenjian Luo 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (502 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13968 311 08$aPrint version: Tan, Ying Advances in Swarm Intelligence Cham : Springer International Publishing AG,c2023 9783031366215 327 $aSwarm Robotics and UAV A Blockchain-Based Service-Oriented Framework to Enable Cooperation of Swarm Robots -- Collective Behavior for Swarm Robots with Distributed Learning -- Exploration of Underwater Environments with a Swarm of Heterogeneous Surface Robots -- Integrating Reinforcement Learning and Optimization Task: Evaluating an agent to dynamically select PSO communication topology -- Filho Swarm Multi-agent Trapping Multi-target Control with Obstacle Avoidance -- A Novel Data Association Method for Multi-target Tracking Based on IACA -- MACT: Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup -- Research on UAV Dynamic Target Tracking with Multi-sensor Position Feedback -- Multiple Unmanned Aerial Vehicles Path Planning Based on Collaborative Differential Evolution -- Design and Analysis of VLC-OCC-CDMA Rake System with Multiple Sources -- x Machine Learning Noise-tolerant Hardware-aware Pruning for Deep Neural Networks -- Nature Inspired Algorithm to Promoting Diversity in Recommender Systems -- Analysis of SIR Compartmental Model Results with Different Update Strategies -- Research on Location Selection of General Merchandise Store Based on Machine Learning -- CF-PMSS: Collaborative Filtering Based on Preference Model and Sparrow Search -- Asynchronous Federated Learning Framework Based on Dynamic Selective Transmission -- Data Mining Small Aerial Target Detection Algorithm Based on Improved YOLOv5 -- Secondary Pulmonary Tuberculosis Lesions Detection Based on Improved YOLOv5 Networks -- Abnormal Traffic Detection based on a Fusion BiGRU Neural Network -- A Fabric Defect Detection Model Based on Feature Extraction of Weak Sample Scene -- Intrusion Detection Method Based on Complementary Adversarial Generation Network -- EEG-Based Subject-Independent Depression Detection Using Dynamic Convolution and Feature Adaptation -- Multi-label Ensemble Defense Scheme Based on Negative Correlation -- Analysis of the Impact of Mathematics Courses on Professional Courses in Science and Engineering Majors -- Intelligent of Clustering Algorithms to Sparseness of One Correlation Network -- Routing and Scheduling Problems Monte Carlo Tree Search with Adaptive Estimation for DAG Scheduling -- Resource Allocation in Heterogeneous Network with Supervised GNNs -- Petrosian Satellite downlink scheduling under breakpoint resume mode -- A Repetitive Grouping Max-Min Ant System for Multi-Depot Vehicle Routing Problem with Time Window -- Secure Access Method of Power Internet of things based on Zero Trust Architecture -- On the Complete Area Coverage Problem of Painting Robots -- Reachability Map-based Motion Planning for Robotic Excavation -- Reinforced Vision-and-Language Navigation Based on Historical BERT -- Stock Prediction and Portfolio Optimization Meta-heuristics for Portfolio Optimization: Part I ? Review of Meta-heuristics -- Meta-heuristics for Portfolio Optimization: Part II - Empirical Analysis . Hierarchical Node Representation Learning for Stock Prediction -- Application of APSO-BP Neural Network Algorithm in Stock Price Prediction -- The Research in Credit Risk of Micro and Small Companies with Linear Regression Model -- Bo ICSI-Optimization Competition Deep-Layered Differential Evolution -- Dual-Population Differential Evolution L-NTADE for ICSI-OC?2023 Competition -- Group Simulated Annealing Algorithm for ICSI-OC. 330 $aThis two-volume set LNCS 13968 and 13969 constitutes the proceedings of the 14th International Conference on Advances in Swarm Intelligence, ICSI 2023, which took place in Shenzhen, China, China, in July 2023. The theme of this year?s conference was ?Serving Life with Swarm Intelligence?. The 81 full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized into 12 cohesive sections covering major topics of swarm intelligence research and its development and applications. The papers of the first part cover topics such as: Swarm Intelligence Computing; Swarm Intelligence Optimization Algorithms; Particle Swarm Optimization Algorithms; Genetic Algorithms; Optimization Computing Algorithms; Neural Network Search & Large-Scale Optimization; Multi-objective Optimization. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13968 606 $aComputer science 606 $aComputer engineering 606 $aComputer networks 606 $aMachine learning 606 $aComputer science?Mathematics 606 $aComputational intelligence 606 $aTheory of Computation 606 $aComputer Engineering and Networks 606 $aMachine Learning 606 $aMathematics of Computing 606 $aComputational Intelligence 615 0$aComputer science. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aMachine learning. 615 0$aComputer science?Mathematics. 615 0$aComputational intelligence. 615 14$aTheory of Computation. 615 24$aComputer Engineering and Networks. 615 24$aMachine Learning. 615 24$aMathematics of Computing. 615 24$aComputational Intelligence. 676 $a004.0151 700 $aTan$b Ying$0846863 701 $aShi$b Yuhui$0907797 701 $aLuo$b Wenjian$01372829 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996542671303316 996 $aAdvances in Swarm Intelligence$93403752 997 $aUNISA LEADER 03313nam 22005415 450 001 9910484192303321 005 20200703072730.0 010 $a3-030-12835-0 024 7 $a10.1007/978-3-030-12835-7 035 $a(CKB)4100000007810282 035 $a(DE-He213)978-3-030-12835-7 035 $a(MiAaPQ)EBC5941595 035 $a(PPN)24376913X 035 $a(EXLCZ)994100000007810282 100 $a20190316d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntegrated Model of Distributed Systems /$fby Wiktor B. Daszczuk 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XVIII, 238 p.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v817 311 $a3-030-12834-2 320 $aIncludes bibliographical references. 327 $aPreface -- Acknowledgments -- Chapter 1. Introduction -- Chapter 2. Related work on deadlock and termination detection techniques -- Chapter 3. Integrated Model of Distributed Systems -- Chapter 4. Model Checking of IMDS specifications in the Dedan environment -- etc. 330 $aIn modern distributed systems, such as the Internet of Things or cloud computing, verifying their correctness is an essential aspect. This requires modeling approaches that reflect the natural characteristics of such systems: the locality of their components, autonomy of their decisions, and their asynchronous communication. However, most of the available verifiers are unrealistic because one or more of these features are not reflected. Accordingly, in this book we present an original formalism: the Integrated Distributed Systems Model (IMDS), which defines a system as two sets (states and messages), and a relation of the "actions" between these sets. The server view and the traveling agent?s view of the system provide communication duality, while general temporal formulas for the IMDS allow automatic verification. The features that the model checks include: partial deadlock and partial termination, communication deadlock and resource deadlock. Automatic verification can support the rapid development of distributed systems. Further, on the basis of the IMDS, the Dedan tool for automatic verification of distributed systems has been developed. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v817 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a004.36 676 $a004.6 700 $aDaszczuk$b Wiktor B$4aut$4http://id.loc.gov/vocabulary/relators/aut$01225059 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484192303321 996 $aIntegrated Model of Distributed Systems$92844488 997 $aUNINA