LEADER 03432nam 2200529 450 001 996547965203316 005 20230526002821.0 010 $a981-19-8261-9 024 7 $a10.1007/978-981-19-8261-3 035 $a(MiAaPQ)EBC7212010 035 $a(Au-PeEL)EBL7212010 035 $a(CKB)26257503800041 035 $a(DE-He213)978-981-19-8261-3 035 $a(PPN)269098933 035 $a(EXLCZ)9926257503800041 100 $a20230526d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSampled-Data Control of Logical Networks /$fYang Liu, Jianquan Lu, and Liangjie Sun 205 $aFirst edition. 210 1$aSingapore :$cSpringer Nature Singapore Pte Ltd.,$d[2023] 210 4$d©2023 215 $a1 online resource (228 pages) 311 08$aPrint version: Liu, Yang Sampled-Data Control of Logical Networks Singapore : Springer Singapore Pte. Limited,c2023 9789811982606 320 $aIncludes bibliographical references. 327 $aChapter 1 Introduction -- Chapter 2 Stabilization of sampled-data Boolean control networks -- Chapter 3 Controllability, observability and synchronization of sampled-data Boolean control networks -- Chapter 4 Stabilization of probabilistic Boolean control networks under sampled-data control -- Chapter 5 Stabilization of aperiodic sampled-data Boolean control networks -- Chapter 6 Event-triggered control for logical control networks. 330 $aThis book mainly focuses on the sampled-data control of logical networks. We believe that the methods (semi-tensor product of matrices), results (recent results on Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control) and topics (logical networks) in this book have become of particular interest to readers recently. Firstly, logical networks are of interest due to their rich range of applications in biology, game theory, coding, finite automata, graph theory, and other fields. Secondly, semi-tensor product of matrices offers a useful tool for formulating, analyzing and designing controllers for logical networks. Moreover, this book is the first to introduce sampled-data control into the study of logical control networks. All research results in this book are novel and worthy of further study. The book?s content is divided into three parts (Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control), which essentially progress from easier to more difficult. In addition, corresponding examples and diagrams are included in each section to facilitate understanding. 606 $aComputational complexity 606 $aComputer science$xMathematics 606 $aMathematical statistics 615 0$aComputational complexity. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 676 $a511.3 700 $aLiu$b Yang$0651655 702 $aLu$b Jianquan 702 $aSun$b Liangjie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996547965203316 996 $aSampled-Data Control of Logical Networks$93374631 997 $aUNISA LEADER 03996nam 2200649 450 001 9910787387403321 005 20200520144314.0 010 $a0-8131-2668-1 010 $a0-8131-5644-0 035 $a(CKB)3710000000334077 035 $a(EBL)1915234 035 $a(SSID)ssj0001434850 035 $a(PQKBManifestationID)11814684 035 $a(PQKBTitleCode)TC0001434850 035 $a(PQKBWorkID)11427944 035 $a(PQKB)11684783 035 $a(OCoLC)891934178 035 $a(MdBmJHUP)muse44112 035 $a(Au-PeEL)EBL1915234 035 $a(CaPaEBR)ebr11009721 035 $a(CaONFJC)MIL691062 035 $a(OCoLC)900344566 035 $a(MiAaPQ)EBC1915234 035 $a(EXLCZ)993710000000334077 100 $a20150205h20042004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLucifer ascending $ethe occult in folklore and popular culture /$fBill Ellis 210 1$aLexington, Kentucky :$cThe University Press of Kentucky,$d2004. 210 4$d©2004 215 $a1 online resource (286 p.) 300 $aDescription based upon print version of record. 311 $a1-322-59780-4 311 $a0-8131-2289-9 320 $aIncludes bibliographical references and index. 327 $aCover; Half-title; Title; Copyright; Dedication; Contents; Acknowledgments; 1. Wizards vs. Muggles: A Long-Standing Debate; 2. What Were Witches Really Like?; Muth, Legend, and Fetish; Contemporary Descriptions of ""witches""; witchcraft in Rural Folklore; Burt E. Boch's Catalog of Modern Witchcraft; Folkloristic Approaches to Witchcraft Claims; How Do We Assess Witchcraft Reports?; 3. Black Books and Chain Letters; The Written word as Fetish; The Jewish Qabbalah and Anti-semitic Crusades; The Blood Libel; The Jewish Amulet Tradition as ""Satanism"" 327 $aLetters from Heaven and Chain Letters as Conjuring4. Satanic Bibles; Brauch Books and Evil Books ; Spellbinding: The Conjurer as Psuchic Cop; The Schemhamforas; Untitled; 5. Why Is a Lucky Rabbit's Foot Lucky?; The Background of the Rabbit's Foot Belief; Grauesite Fetishes; Animal Bodu Parts as Fetishes; Human Body Parts as Fetishes; Body Parts and Intercultural Space; 6. Visits to Forbidden Graveyards; Legend Tripping and its History; Living Rocks; Defying the Witch: Rites of Rebellion; The Self-healing Gravestone; The Legend-Trip-Ritual or ""Fun""?; 7. Table-Setting and Mirror-Gazing 327 $aWitchcraft and Occult PlayThe Dumb Supper; Untitled; The Witch in the Mirror; Erised Stra Chru Oy; Conclusions; 8. The @#%&! Ouija Board; Oringins of the Ouija Board; Ouija Board Groups: Cults or Communitas?; Suck the Greasu Cock of the Dark Lord; 9. The Welsh Revival: Evangelical Christianity Meets the Occult; Spiritualism and Revival; Feminism and Fire from Heaven; War by Satan upon the Womenhood of the World; Conclusions; 10. Learning from Lucifer; Notes; Sources Cited; Index 330 $aDespite their centuries-old history and traditions, witchcraft and magic are still very much a part of modern Anglo-American culture. In Lucifer Ascending, Bill Ellis looks at modern practices that are universally defined as ""occult,"" from commonplace habits such as carrying a rabbit's foot for good luck or using a Ouija board, to more esoteric traditions, such as the use of spell books. In particular, Ellis shows how the occult has been a common element in youth culture for hundreds of years.Using materials from little known publications and archives, Lucifer Ascending details the true so 606 $aSatanism 606 $aOccultism 606 $aSuperstition 615 0$aSatanism. 615 0$aOccultism. 615 0$aSuperstition. 676 $a133.4 700 $aEllis$b Bill$f1950-$01485504 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910787387403321 996 $aLucifer ascending$93704693 997 $aUNINA LEADER 12672nam 22007815 450 001 9910522573703321 005 20251113200927.0 010 $a3-030-95405-6 024 7 $a10.1007/978-3-030-95405-5 035 $a(MiAaPQ)EBC6877811 035 $a(Au-PeEL)EBL6877811 035 $a(CKB)21047946300041 035 $a(PPN)264961730 035 $a(OCoLC)1295275029 035 $a(DE-He213)978-3-030-95405-5 035 $a(EXLCZ)9921047946300041 100 $a20220131d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Data Mining and Applications $e17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2?4, 2022, Proceedings, Part I /$fedited by Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (449 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13087 311 08$aPrint version: Li, Bohan Advanced Data Mining and Applications Cham : Springer International Publishing AG,c2022 9783030954048 327 $aIntro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Healthcare -- Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Phase I: Model Training -- 3.2 Phase II: Model Testing -- 4 Experiments -- 4.1 Datasets -- 4.2 Competing Approaches -- 4.3 Parameter Configuration -- 4.4 Hardware and Software Configuration -- 5 Evaluation -- 5.1 Evaluation Metrics -- 5.2 Comparison Among Competing Approaches -- 5.3 Effect of Image Preprocessing -- 5.4 Evaluation of Parameter Sensitivity -- 5.5 Performance Analysis of the Custom Loss Function -- 5.6 Evidence of Generalization of DeepPhase -- 6 Conclusion -- References -- An Empirical Study on Human Flying Imagery Using EEG -- 1 Introduction -- 2 Method -- 2.1 Experiment and EEG Recording -- 2.2 Classification and Feature Analysis -- 3 Results -- 3.1 Overall Classification Results -- 3.2 Frequency Band Specific Classification Results -- 3.3 Time Window Specific Classification Results -- 3.4 Time-frequency Specific Classification Results -- 3.5 EEG Activity Patterns in Most Significant Time-frequency Bin -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Network Graph Analysis of Hospital and Health Services Functional Structures -- 1 Introduction -- 2 Subjects and Methods -- 3 Network Graphs -- 4 Results -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Feature Selection in Gene Expression Profile Employing Relevancy and Redundancy Measures and Binary Whale Optimization Algorithm (BWOA) -- 1 Introduction -- 1.1 Objective and Contributions -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Feature Scaling -- 3.2 Phase 1 Feature Selection: Relevance Analysis -- 3.3 Phase 2 Feature Selection: Redundancy Analysis -- 3.4 Phase 3 Feature Selection: Meta-heuristic Optimization. 327 $a3.5 Binary Whale Optimization Algorithm (BWOA) -- 3.6 BWOA for Gene Selection -- 3.7 Classification -- 4 Datasets and Baselines -- 4.1 Gene Expression Datasets -- 4.2 Performance Metrics -- 4.3 Baseline Methods -- 5 Results and Discussion -- 6 Summary and Conclusions -- References -- Hand Bone Age Estimation Using Deep Convolutional Neural Networks -- 1 Introduction -- 2 Background and Related Works -- 3 Materials and Methods -- 3.1 Normalization -- 3.2 Hand Detection -- 3.3 Vision Pipeline -- 3.4 Proposed Bone Age Prediction Model -- 3.5 Dataset -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- An Interpretable Machine Learning Approach for Predicting Hospital Length of Stay and Readmission -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition -- 1 Introduction -- 2 Related Works -- 2.1 Diagnosis of Cardiac Arrhythmias -- 2.2 Deep Learning-Based Cardiac Arrhythmias Diagnose -- 3 Methodology -- 3.1 Data Segmentation -- 3.2 the Proposed Model -- 4 Experiment -- 4.1 Datasets and Model Implementation -- 4.2 Comparison Model -- 4.3 Experimental Results -- 5 Conclusion -- References -- Education -- Augmenting Personalized Question Recommendation with Hierarchical Information for Online Test Platform -- 1 Introduction -- 2 Preliminary -- 2.1 Definitions and Problem Statement -- 2.2 Framework Overview -- 3 Method -- 3.1 Incorporating the Student and Question Hierarchical Information -- 3.2 Student Performance Predicting -- 3.3 The Framework APQR -- 4 Experiments -- 4.1 Online Test Dataset -- 4.2 Evaluation Metric -- 4.3 Baseline Algorithms -- 4.4 Overall Performance -- 4.5 Parameter Analysis -- 5 Related Work -- 5.1 Recommender System -- 5.2 Student Performance Modeling -- 6 Conclusion -- References. 327 $aSmart Online Exam Proctoring Assist for Cheating Detection -- 1 Intruduction -- 2 Related Work -- 3 Proposed Technique Highlight -- 3.1 Problem Statement -- 3.2 High Level Architecture -- 4 Proposed Work Details -- 4.1 Exam Recording -- 4.2 Video Characteristic Analysis -- 4.3 Videos Transformed to Feature Vector -- 4.4 Data Uniforming by Video Length Equalizing -- 4.5 Training -- 5 Experiments -- 5.1 Dataset -- 5.2 Competing Approaches -- 5.3 Parameters, Hardware and Software -- 5.4 Evaluation -- 6 Conclusion -- References -- Design and Development of Real-Time Barrage System for College Class -- 1 Introduction -- 2 System Design -- 2.1 System Framework and Function Design -- 2.2 System Flow Design -- 3 Analysis of Sensitive Word Filtering Algorithm -- 4 System Realization -- 4.1 PC Function Realization -- 4.2 Mobile Function Realization -- 4.3 Server-Side Function Realization -- 5 Conclusion -- References -- Recommendation for Higher Education Candidates: A Case Study on Engineering Programs -- 1 Introduction -- 2 Related Work -- 3 ESTHER -- 3.1 Students Profiler -- 3.2 Programs Recommender -- 3.3 System Dependencies and Limitations -- 4 Case Study -- 4.1 Students Profiler -- 4.2 Programs Recommender -- 4.3 ESTHER Overview -- 5 Conclusions -- References -- Web Application -- UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background -- 2.2 Related Work -- 3 The UQ-AAS21 Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 3.3 Dataset Features -- 4 Preliminary Studies Based on UQ-AAS21 Datasets -- 4.1 Demographic Study -- 4.2 Analysis of Privacy Policy and Term of Use Document -- 5 Potential Usage of UQ-AAS21 -- 6 Conclusion -- References -- Are Rumors Always False?: Understanding Rumors Across Domains, Queries, and Ratings -- 1 Introduction -- 2 Background. 327 $a2.1 Detecting Rumors on the Web -- 2.2 Actions Against Detected Rumors -- 2.3 Research Gap -- 3 Research Questions -- 4 Methodology -- 4.1 Data Collection -- 5 Empirical Analyses and Findings -- 5.1 What Are the Rumors About? -- 5.2 Where Do the Rumors Come From? -- 5.3 Who Contribute to Rumors? -- 5.4 When Are the Rumors Reported? -- 5.5 How Do Rumors Propagate? -- 6 Discussion -- 6.1 Key Findings -- 6.2 Implications for Public Trust and Explainable Rumor Detection -- 6.3 Limitations and Future Work -- 7 Conclusion -- References -- A Green Pipeline for Out-of-Domain Public Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Pre-trained Transformer Encoder -- 3.3 Pipeline Sentiment Analysis Model -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Sentiment Analysis Evaluation -- 4.3 Performance of Sub-models -- 4.4 Analysis and Case Study -- 5 Conclusion -- References -- Profiling Fake News: Learning the Semantics and Characterisation of Misinformation -- 1 Introduction -- 2 Experimental Dataset -- 2.1 Data Pre-processing -- 3 Proposed Solution Approach -- 3.1 Features Extraction and Selection -- 3.2 Classification Models -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model -- 1 Introduction -- 2 Proposed Model -- 2.1 Initialization -- 2.2 Propagation -- 3 Results and Discussions -- 4 Conclusion -- References -- Predicting Network Threat Events Using HMM Ensembles -- 1 Introduction -- 2 Background and Related Work -- 3 Ensemble of Hidden Markov Models -- 3.1 Hidden Markov Model Structure -- 3.2 Event Sequence Clustering -- 3.3 Ensemble Creation and Prediction Methods -- 4 Data Set -- 5 Evaluation -- 6 Conclusion -- References -- On-device Application. 327 $aGroup Trip Planning Queries on Road Networks Using Geo-Tagged Textual Information -- 1 Introduction -- 2 Background and Problem Definition -- 3 Proposed Solution Methodologies -- 3.1 Brute Force Approach with Precomputed Distance -- 3.2 Group Nearest Neighbor (GNN) to Compute GTP Queries -- 3.3 Using R-trees to Compute GTP Queries -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- Deep Reinforcement Learning Based Iterative Participant Selection Method for Industrial IoT Big Data Mobile Crowdsourcing -- 1 Introduction -- 2 System Model and Deep Neural Network -- 2.1 System Model -- 2.2 Participant Selection Problem -- 3 System Framework and Deep Q-Network -- 3.1 System Framework -- 3.2 Deep Q-Network -- 4 Evaluation -- 4.1 Dataset and Experiment Setups -- 4.2 BaseLine Method -- 4.3 The Performance Evaluation and Comparison -- 5 Related Work -- 6 Conclusion -- References -- Know Your Limits: Machine Learning with Rejection for Vehicle Engineering -- 1 Introduction -- 2 Background -- 2.1 Vehicle Engineering: The Need for Usage Profiling -- 2.2 Related Work on Machine Learning with a Reject Option -- 3 Usage Profiling: Data Science Challenge -- 4 Our Approach for Vehicle Usage Profiling -- 4.1 Predictor h -- 4.2 Rejector r -- 4.3 Combined Model h' -- 5 Use-Case: Road-Roughness Analysis -- 5.1 Data Collection and Preprocessing -- 5.2 Experimental Methodology -- 5.3 Results -- 5.4 Discussion and Lessons Learned -- 6 Conclusion -- References -- Towards Generalizable Machinery Prognostics -- 1 Introduction -- 2 Background and Related Work -- 3 A Generic Approach to Incipient Failure Prediction -- 4 Learning the Prediction Horizon -- 5 Ablation Study -- 6 Results and Discussion -- 7 Conclusion -- 8 Future Work -- References -- A Trust Management-Based Route Planning Scheme in LBS Network -- 1 Introduction -- 2 Related Work. 327 $a3 Framework of System. 330 $aThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022. . 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13087 606 $aArtificial intelligence 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aComputer engineering 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aArtificial Intelligence 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Engineering and Networks 606 $aComputer Engineering and Networks 606 $aComputer Application in Social and Behavioral Sciences 615 0$aArtificial intelligence. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 14$aArtificial Intelligence. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Engineering and Networks. 615 24$aComputer Engineering and Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a006.312 676 $a006.312 702 $aLi$b Bohan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522573703321 996 $aAdvanced Data Mining and Applications$92982700 997 $aUNINA