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[null,"1.","Record Nr.",null,"UNINA9910160959203321"]
[null,null,"Autore",null,"Thomson Joseph John"]
[null,null,"Titolo",null,"Notes on Recent Researches in Electricity and Magnetism : Intended as a Sequel to Professor Clerk-Maxwell's Treatise on Electricity and Magnetism / / Joseph John Thomson"]
[null,null,"Pubbl/distr/stampa",null,"Place of publication not identified : , : publisher not identified, , 1893"]
[{"$nulls":4},"Cambridge : , : Cambridge University Press"]
[null,null,"ISBN",null,"0-511-65903-2"]
[null,null,"Descrizione fisica",null,"1 online resource (600 pages) : digital, PDF file(s)"]
[null,null,"Collana",null,"Cambridge library collection. Physical  Sciences"]
[null,null,"Soggetti",null,"Atomic theory"]
[{"$nulls":4},"Electrons"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from publisher's bibliographic system (viewed on 05 Oct 2015)."]
[null,null,null,"Sommario/riassunto","This 1893 publication is a central text in the work of the Nobel prize winning physicist Sir Joseph John Thomson (1858–1940). Intended as an extension of James Clerk Maxwell's Treatise on Electricity and Magnetism, it documents the important shift in Thomson's thinking towards the model of the atomic electric field, a theory that would eventually lead to his discovery of the electron. In Chapter 1, Thomson documents his experiments with Faraday tubes, using them to physically demonstrate a 'molecular theory of electricity'. Chapter 2 considers the discharge of electricity through gases, Chapter 3 theories of electrostatics, and Chapters 4–6 are primarily concerned with alternating currents. In addition to providing crucial insight into Thomson's evolving theory of the atom, Recent Researches underscores his commitment to experimental physics, which offers 'all the advantages in vividness which arise from concrete qualities rather than abstract symbols'."]
[null,"2.","Record Nr.",null,"UNINA9910586578803321"]
[null,null,"Titolo",null,"Data Science : 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022, Chengdu, China, August 19–22, 2022, Proceedings, Part I / / edited by Yang Wang, Guobin Zhu, Qilong Han, Hongzhi Wang, Xianhua Song, Zeguang Lu"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"981-19-5194-2"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (455 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 1628"]
[null,null,"Disciplina",null,"005.7"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Big Data Mining and Knowledge Management -- Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction -- 1 Introduction -- 2 Spatiotemporal Prediction in Deep Learning -- 2.1 Time Correlation Research -- 2.2 Time Correlation Research -- 3 Prediction Model of Traffic Flow Based on Multi-module Fusion -- 3.1 Model Frame Diagram -- 3.2 Space-Time Decoupling -- 3.3 Spatial Convolution -- 3.4 Spatial Self-attention -- 3.5 Temporal Convolution -- 3.6 Time Self-attention -- 3.7 Information Fusion and GRU -- 4 Experimental Analysis -- 4.1 Dataset -- 4.2 Analysis of Results -- 5 Conclusion -- References -- Data Analyses and Parallel Optimization of the Tropical-Cyclone Coupled Numerical Model -- 1 Introduction -- 1.1 A Subsection Sample -- 2 Model Setup -- 2.1 "]
[{"$nulls":4},"Atmospheric Model Setup -- 2.2 Hydrodynamic Model Setup -- 2.3 Ocean Wave Model Setup -- 2.4 HPC Facilities -- 2.5 Coupled Variables -- 3 Scaling Experiments -- 3.1 Parallel Tests Analysis -- 3.2 SWAN Model Parallel Algorithm Optimization -- 3.3 Ocean Model Grid Optimization -- 4 Parallel Test Results -- 5 Model Results Discussion -- 6 Conclusion -- References -- Factorization Machine Based on Bitwise Feature Importance for CTR Prediction -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Embedding Layer -- 3.2 Learning -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Hyperparameter Study -- 4.3 Ablation Study -- 4.4 Performance Comparison -- 5 Conclusion -- References -- Focusing on the Importance of Features for CTR Prediction -- 1 Introduction -- 2 ECABiNet Model -- 2.1 Sparse Input and Embedding Layer -- 2.2 Layer Norm -- 2.3 ECANET Layer -- 2.4 Feature Cross Layer -- 2.5 DNN Layer -- 2.6 Output -- 3 Experiment -- 3.1 Experimental Setup."]
[{"$nulls":4},"3.2 LayerNorm Effect Comparison -- 3.3 Comparison of the Effects of Different Attention Modules -- 3.4 Comparison of the Classic Model -- 3.5 Study HyperParameter -- 4 Related Work -- 5 Conclusions -- References -- Active Anomaly Detection Technology Based on Ensemble Learning -- 1 Introduction -- 2 Problem Statement -- 3 Proposed Model -- 3.1 Supervised Ensemble Learning Model -- 3.2 Human Participation -- 3.3 Model Self-training -- 3.4 Experiment -- 3.5 Conclusion -- References -- Automatic Generation of Graduation Thesis Comments Based on Multilevel Analysis -- 1 Introduction -- 2 Technical Principle -- 2.1 BERT Model Introduced -- 2.2 Basic Structure of the BERT Model -- 2.3 Comparison with Other Algorithms -- 3 Project Analysis -- 3.1 Technical Route -- 3.2 Technical Analysis -- 4 Project Implementation -- 4.1 Database Established Modification -- 4.2 Student Information Input -- 4.3 Neural Network Training -- 4.4 Automatically Generate Comments -- 5 Conclusion -- References -- A Survey of Malware Classification Methods Based on Data Flow Graph -- 1 Introduction -- 2 Data Flow Graph -- 2.1 Basic Concepts of the Data Flow Graph -- 2.2 Data Flow Graph Corresponding to Common APIs -- 2.3 Extension of Data Flow Graphs -- 3 Malware Classification Based on Data Flow Graph -- 3.1 User-Defined Data Flow Graph Feature-based Malware Classification -- 3.2 Data Flow Graph Similarity-Based Malware Classification -- 3.3 Graph Neural Network-Based Malware Classification -- 4 Discussion -- 5 Conclusion -- References -- Anomaly Detection of Multivariate Time Series Based on Metric Learning -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Model -- 3.1 Preprocessing -- 3.2 Encoder for High-Dimensional Time Series Data -- 3.3 Attentional Center Learning -- 3.4 Loss Function -- 3.5 Semisupervised Learning -- 4 Experiments -- 4.1 Dataset -- 4.2 Setup -- 4.3 Result."]
[{"$nulls":4},"5 Conclusion -- References -- Social Network Analysis of Coauthor Networks in Inclusive Finance in China -- 1 Introduction and Motivation -- 2 Data Collection and Preprocessing -- 3 Results -- 3.1 General Characteristics of the Coauthor Network -- 3.2 Ego Characteristics of the Coauthor Network -- 3.3 The Evolution of Cohesive Subgroups in the Coauthor Network -- 4 Conclusions -- References -- Multirelationship Aware Personalized Recommendation Model -- 1 Introduction -- 2 Preliminary Preparation -- 2.1 Problem Definition -- 2.2 Data Preprocessing -- 2.3 User Relationship Graphs -- 3 Modeling and Training -- 3.1 MrAPR Model -- 3.2 Model Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Baselines and Evaluation Metrics -- 4.3 Parameter Settings -- 4.4 Ablation Experiments -- 5 Conclusion -- References -- Machine Learning for Data Science -- Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML -- 1 "]
[{"$nulls":4},"Introduction -- 2 Related Work -- 2.1 Few-Shot Learning -- 2.2 Interpretability -- 3 Proposed Methods -- 3.1 Adapting ProtoPNet to MAML -- 3.2 Evaluating Models -- 4 Experiments -- 4.1 Datasets -- 4.2 Experiment 1: Omniglot Few-Shot Classification -- 4.3 Experiment 2: MiniImagenet Few-Shot Classification -- 4.4 Experiment 3: Interpretability Analysis on Omngilot -- 4.5 Experiment 4: Preliminary Interpretability Analysis on MiniImagenet -- 5 Conclusion and Future Work -- References -- A Preliminary Study of Interpreting CNNs Using Soft Decision Trees -- 1 Introduction -- 2 Related Work -- 3 Proposed Methods -- 3.1 Model Foundations -- 3.2 Using Normal/Soft Decision Trees to Interpret CNNs -- 3.3 Evaluating Interpretability -- 4 Experiments -- 4.1 Dataset and Experimental Setup -- 4.2 Experiment 1: Classification Performance -- 4.3 Experiment 2: Visualization of Normal/Soft Decision Trees' Top Features."]
[{"$nulls":4},"4.4 Experiment 3: Interpretability Performance -- 4.5 Experiment 4: Scores of Human Experts on Tag Clarity -- 5 Conclusion and Future Work -- References -- Deep Reinforcement Learning with Fuse Adaptive Weighted Demonstration Data -- 1 Introduction -- 2 Related Work -- 2.1 Deep Reinforcement Learning -- 2.2 Multiagent Reinforcement Learning -- 3 Methods -- 4 Experimental Results and Analysis -- 4.1 Experimental Environment and Data -- 4.2 Results and Analysis -- 5 Discussion -- References -- DRIB: Interpreting DNN with Dynamic Reasoning and Information Bottleneck -- 1 Introduction -- 2 Related Works -- 2.1 Explain the Existing Deep Learning Models -- 2.2 Construction of Interpretable Deep Learning Models -- 3 Method -- 3.1 Dynamic Reasoning Decision Module -- 3.2 Information Bottleneck Verification Module -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Interpretability of Calculation in Dynamic Reasoning Decision -- 4.3 Explainability of Attribution in the Information Bottleneck -- 4.4 Visualization of Understandability -- 5 Conclusion -- References -- Multimedia Data Management and Analysis -- Advanced Generative Adversarial Network for Image Superresolution -- 1 Introduction -- 2 Related Work -- 3 GAN and SRGAN -- 4 Proposed Method -- 4.1 Generator Network Structure -- 4.2 Discriminator Network Structure -- 4.3 Loss Function -- 5 Experiment Results and Analysis -- 5.1 Implementation Details -- 5.2 Datasets and Evaluation Metrics -- 5.3 Experimental Results and Analysis -- 5.4 Ablation Study -- 6 Conclusions -- References -- Real-World Superresolution by Using Deep Degradation Learning -- 1 Introduction -- 2 Related Work -- 2.1 Real-World Superresolution -- 2.2 Contrastive Learning -- 3 PurPosed Method -- 3.1 Overview of the Unsupervised Framework -- 3.2 Degradation Model -- 3.3 Reconstruction Model -- 4 Experiments -- 4.1 Training Data."]
[{"$nulls":4},"4.2 Training Details -- 4.3 Training Details -- 5 Conclusion -- References -- Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Fisher Vector Generation -- 3.2 Probability Visual Vocabulary -- 3.3 Loop Closure Detection -- 4 Results and Discussion -- 4.1 Dataset and Preprocessing -- 4.2 Evaluation Metrics -- 4.3 2D Motion -- 4.4 3D Motion -- 4.5 Bidirectional Loops -- 4.6 Ablation Study -- 5 Conclusions -- References -- A Complex Background Image Registration Method Based on the Optical Flow Field Algorithm -- 1 Introduction -- 2 The Proposed Method -- 3 Evaluation Functions -- 4 Experimental Results -- 5 Conclusion -- References -- Collaborative Learning Method for Natural Image Captioning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 P2PM: Pix2Pix Inverting Module -- 3.2 NLGM: Natural Language Generation Module -- 3.3 Collaborative Learning Framework -- 4 Experiments -- 4.1 Experimental Setup -- "]
[{"$nulls":4},"4.2 Main Results -- 5 Conclusion -- References -- Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph -- 1 Introduction -- 2 Related Studies -- 3 Construction of the COVID-19 Vaccine Knowledge Graph -- 3.1 Data Acquisition -- 3.2 Entity Extraction to Construct a Relational Model -- 3.3 Knowledge Graph Establishment -- 4 Visual Analysis of the National Characteristics of the COVID-19 Vaccine -- 5 Conclusions and Recommendations -- References -- Speech Recognition for Parkinson's Disease Based on Improved Genetic Algorithm and Data Enhancement Technology -- 1 Introduction -- 2 The Proposed Methods -- 2.1 Method -- 3 Speech Recognition and Diagnosis -- 3.1 Data Preprocessing -- 3.2 Improved GA-SVM Model -- 3.3 Speech Recognition Algorithm -- 4 Experiment and Evaluation -- 4.1 Experiment Setup."]
[{"$nulls":4},"4.2 Comparison and Analysis of Results."]
[null,null,null,"Sommario/riassunto","This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge Management; Machine Learning for Data Science; Multimedia Data Management and Analysis."]