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[null,"1.","Record Nr.",null,"UNISA996500062003316"]
[null,null,"Titolo",null,"Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II / / Weitong Chen [and five others] editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-22137-0"]
[null,null,"Descrizione fisica",null,"1 online resource (500 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 13726"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Text Mining -- Towards Idea Mining: Problem-Solution Phrase Extraction from Text -- 1 Introduction -- 2 Related Work -- 2.1 Problem Formation -- 3 Methodology -- 3.1 Models for Extracting Problem-Solution Phrases -- 4 Experiment -- 4.1 Dataset UCCL1000 -- 4.2 Dataset NIPS488 -- 4.3 Dataset Summary -- 4.4 Text Preprocessing -- 4.5 Input Representations -- 4.6 Training and Evaluation -- 4.7 Result Analysis -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Spam Email Categorization with NLP and Using Federated Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Federated Phishing Filter (FPF) -- 3.1 Natural Language Processing -- 3.2 Deep Learning Model for Spam Categorization -- 3.3 Spam Detection and Categorization Model -- 3.4 Federated Learning -- 3.5 Federated Training Models -- 3.6 Federated Averaging (FA) -- 3.7 Federated Averaging Strategies -- 3.8 Equal Weighting (EWS) -- 3.9 Weighted Average (WAS) -- 3.10 Datasets -- 4 Empirical Evaluation -- 4.1 Comparison of EWS and AWS Averaging Strategies -- 4.2 Features Performance Comparison -- 5 Conclusion and Future Work -- References -- SePass: Semantic Password Guessing Using k-nn Similarity Search in Word Embeddings*-12pt -- 1 Introduction -- 2 Related Work -- 3 Semantic Password Guessing -- 3.1 Generation of New Password Candidates -- 3.2 Sorting of the Password Candidates -- 4 Test Bed -- 4.1 Data Sets -- 4.2 Compared Methods -- 4.3 Experimental Set-Up and Evaluation Metric -- 5 Results and Discussion "]
[{"$nulls":4},"-- 5.1 Accuracy Results -- 5.2 Unseen Base Words -- 6 Conclusion -- References -- DeMRC: Dynamically Enhanced Multi-hop Reading Comprehension Model for Low Data -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Sentence Filtering Model -- 3.2 Answer Prediction Model."]
[{"$nulls":4},"3.3 Self-training Augmentation Based on External Data -- 4 Experiments -- 4.1 Data Set -- 4.2 Implementation Details -- 5 Results -- 6 Conclusion -- References -- ESTD: Empathy Style Transformer with Discriminative Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 NLP for Online Mental Health Assistance -- 2.2 Text Style Transfer -- 2.3 Discriminatory Mechanism -- 3 Methodology -- 3.1 Empathic Expression Calculation -- 3.2 ESTD Framework -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Evaluation Metrics -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Detection Method of User Behavior Transition on Computer -- 1 Introduction -- 2 Related Work -- 2.1 Image Classification and Clustering -- 2.2 Search and Operation Automation -- 2.3 User Behavior Analytics -- 3 Detection Method of User Behavior Transition -- 3.1 Overview -- 3.2 Feature Extraction -- 3.3 Time-Series Grouping Function -- 3.4 Time-Series Features Grouping Function -- 3.5 User Behavior Transition Detection Function -- 4 Experiment -- 4.1 Our Dataset -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion -- References -- Image, Multimedia and Time Series Data Mining -- Ensemble Image Super-Resolution CNNs for Small Data and Diverse Compressive Models -- 1 Introduction -- 1.1 Contribution -- 2 Foundational Work and Background -- 2.1 Sparse Representations -- 2.2 Miralon Areal Density Maps -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Training Details -- 4.2 Reconstruction Quality on Testing Images -- 4.3 Application of Miralon Areal Density Maps -- 5 Conclusion -- References -- Optimizing MobileNetV2 Architecture Using Split Input and Layer Replications for 3D Face Recognition Task -- 1 Introduction -- 2 Backgrounds -- 2.1 Related Works -- 2.2 Convolutional Neural Network (CNN) -- 3 Methodology -- 3.1 Data Gathering."]
[{"$nulls":4},"3.2 Preprocessing -- 3.3 Model Overview -- 3.4 Metrics -- 3.5 Training Configuration -- 3.6 Automatic Model Finding -- 4 Experimental Results -- 4.1 Comparison Between 2D and 3D Face Recognition Models -- 4.2 Comparison Between RGBD and RGB+D Face Recognition Models -- 4.3 Comparison Between Baseline MobileNetV2 and RGB+D MobileNetV2 with Layer Replication -- 4.4 Comparison Between Our Baseline Model and EffiencientNet on CelebA Dataset -- 5 Conclusion and Future Work -- References -- GANs for Automatic Generation of Data Plots -- 1 Introduction -- 2 Generative Adversarial Networks -- 3 Related Work -- 4 Methodology -- 5 Results -- 6 Conclusion -- References -- An Explainable Approach to Semantic Link Mining in Multi-sourced Dynamic Data -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Graph Link Prediction -- 2.2 Semantic Data Integration -- 3 Preliminaries -- 4 Our Approach -- 4.1 Our Framework -- 4.2 KG-Based Integration -- 4.3 Rule-Based Link Prediction -- 5 An Application Case -- 6 Evaluation -- 6.1 Static Link Prediction -- 6.2 Dynamic Link Prediction -- 7 Conclusion -- References -- Information Mining from Images of Pipeline Based on Knowledge Representation and Reasoning -- 1 Introduction -- 2 Related Work -- 2.1 Pipeline Defects Identification -- 2.2 Ontology for Knowledge Formalization -- 3 PDI Ontology Construction -- 3.1 Knowledge Resource -- 3.2 Ontology Development for PDI -- 3.3 Reasoning Rules for PDI -- 4 Case Study -- 4.1 Selected Pipeline Images with Common Defect Types -- 4.2 The Attribute Information of Pipeline Images -- 4.3 Mapping "]
[{"$nulls":4},"Rules for Images Instantiation in PDI Ontology -- 4.4 Knowledge Reasoning -- 4.5 Discussion -- 5 Conclusion -- References -- Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation."]
[{"$nulls":4},"4 Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 4.1 Subspace Search with Adapted Binary GSA -- 4.2 Solution Overview -- 5 Experimental Study and Results Analysis -- 5.1 Experimentation Setting -- 5.2 Results and Analysis -- 6 Conclusion and Future Works -- References -- Classification, Clustering and Recommendation -- Signal Classification Using Smooth Coefficients of Multiple Wavelets to Achieve High Accuracy from Compressed Representation of Signal -- 1 Introduction -- 2 Wavelets -- 2.1 DWT -- 2.2 MDWT -- 2.3 Energy Distribution -- 3 Proposed Technique -- 3.1 Advantages -- 3.2 Steps in the Proposed Technique: MWCSC -- 4 Experimental Results -- 4.1 Classification Methods Used -- 4.2 Arrowhead Data -- 4.3 Mallat Data -- 4.4 Ford Data -- 5 Conclusion -- References -- On Reducing the Bias of Random Forest -- 1 Introduction -- 2 The Proposed Technique -- 3 Experimental Results -- 4 Conclusion -- References -- A Collaborative Filtering Recommendation Method with Integrated User Profiles*-12pt -- 1 Introduction -- 2 Proposed Method -- 2.1 User Profile Labeling System -- 2.2 User Profile Construction and Similarity Calculation -- 2.3 User Clustering -- 2.4 Collaborative Filtering -- 3 Performance Analysis -- 3.1 Experimental Method -- 3.2 Experimental Result -- 4 Conclusion -- References -- A Quality Metric for K-Means Clustering Based on Centroid Locations -- 1 Introduction -- 2 Related Work -- 3 New Quality Metrics -- 3.1 Reduced 2R Metric -- 3.2 Implicit Assumptions in K-Means Algorithm -- 3.3 Covariant Metric (MC) -- 3.4 Quantifying Index Performance -- 4 Experiments on Synthetic Data -- 4.1 Data Generation -- 4.2 Analysis of Synthetic Data -- 4.3 Results and Discussion -- 5 Experiments on Real Data -- 5.1 Variable Selection -- 5.2 Data Sets -- 6 Comparison with Other Indexes -- 7 Limitations."]
[{"$nulls":4},"8 Conclusion -- References -- Clustering Method for Touristic Photographic Spots Recommendation -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Global Clustering -- 3.2 Local Clustering -- 3.3 Indexes and Validation -- 3.4 TPS Qualification -- 4 Experiments -- 4.1 Data Processing -- 4.2 Global Clustering Comparison -- 4.3 Local Clustering Comparison -- 4.4 Spot Qualification -- 5 Conclusion and Future Work -- References -- Personalized Federated Learning with Robust Clustering Against Model Poisoning -- 1 Introduction -- 2 Related Work -- 2.1 PFL -- 2.2 Robust Clustering -- 2.3 Model Poisoning and Anomaly Detection -- 3 Methodology -- 3.1 PFL -- 3.2 LOF -- 3.3 Proposed Method -- 4 Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Study -- 6 Conclusion -- References -- A Data-Driven Framework for Driving Style Classification -- 1 Introduction -- 2 State of the Art -- 3 Problem Statement -- 4 Proposed Solution -- 4.1 Dataset Description -- 4.2 Pre-processing -- 4.3 Feature Engineering -- 4.4 Neural Architecture Search -- 5 Results -- 5.1 Selection of Time-Window for Aggregation -- 5.2 Comparison of Different Models -- 6 Conclusion and Future Work -- References -- Density Estimation in High-Dimensional Spaces: A Multivariate Histogram Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Basic Concepts -- 2.2 Approaches to Density Estimation -- 2.3 Applications in Research -- 2.4 Example: Density of the Old Faithful Dataset -- 3 A Multivariate Histogram-Based Approach -- 3.1 Define Hypergrid -- 3.2 Calculate Relative Frequencies -- 3.3 Calculate Hypervolumes and "]
[{"$nulls":4},"Density Estimates -- 3.4 Estimate Density for Datasets with Missing Values -- 4 Evaluation and Results -- 4.1 Computational Performance -- 4.2 Measuring Density with Categorical Variables -- 4.3 Measuring Density with Missing Values."]
[{"$nulls":4},"5 Conclusions."]
[null,"2.","Record Nr.",null,"UNINA9910409666803321"]
[null,null,"Autore",null,"Mishra Mukunda"]
[null,null,"Titolo",null,"Contouring Human Development : Methods and Applications Using an Indian District as Case Study / / by Mukunda Mishra, Soumendu Chatterjee"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"981-15-4083-7"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (387 pages)"]
[null,null,"Disciplina",null,"330.954"]
[null,null,"Soggetti",null,"Regional economics"]
[{"$nulls":4},"Space in economics"]
[{"$nulls":4},"Economic geography"]
[{"$nulls":4},"Development economics"]
[{"$nulls":4},"Regionalism"]
[{"$nulls":4},"Developmental psychology"]
[{"$nulls":4},"Regional and Spatial Economics"]
[{"$nulls":4},"Economic Geography"]
[{"$nulls":4},"Development Economics"]
[{"$nulls":4},"Developmental Psychology"]
[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,"Chapter 1. Does geography matter in human development? -- Chapter 2. On contouring human development -- Chapter 3. Briefing the area for case study -- Chapter 4. Mapping the components of human development -- Chapter 5. Mapping economic inequality -- Chapter 6. Drawing the contours of educational attainment -- Chapter 7. Mapping public health scenario -- Chapter 8. Strategy mapping with predictive modelers."]
[null,null,null,"Sommario/riassunto","This book acquaints readers with a range of techniques to help them effectively identify, record, map, analyze and report on patterns in various dimensions of human development (HD) with spatial scales down to the village level. It is impossible to capture HD at the local and global scale with only a single index, because differences in HD at the international scale are caused by ‘general’ factors, whereas local-scale differences are influenced by ‘specific’ factors. This book offers a variety of methods for scientifically mapping HD at any spatial scale. It "]
[{"$nulls":4},"covers how to rationally select variables; how to test the models; how to validate the results, and how to analyze them. For this purpose, it employs a case study on an Indian district. The socio-economic factors regulating the patterns of HD are now more complex than they were only a few decades ago, making it essential to incorporate newer models in order to successfully ‘replicate’ the real-world situation. Accordingly, the book offers essential methodological tools & techniques for mapping HD. It sheds new light on a handful of statistical multivariate analysis and machine learning algorithms that are rarely used in the social sciences when dealing with HD, yet have sound mathematical and statistical bases. These techniques can be successfully used for predictive analysis in the earth & natural sciences, decision sciences and management disciplines, and are equally effective in terms of capturing, predicting and projecting the composite HD ‘landscape.’ This book will especially benefit two groups of readers: firstly, HD practitioners who want to find out ‘why some areas are doing better than others’ by exploring the complex interactions of spatially linked variables with different HD parameters. And secondly, practitioners in other branches of the social sciences who are not concerned with HD but are looking for ‘hands-on training’ with techniques they can apply in their respectivefield of spatial investigations. ."]