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Biblioteca

MARC Lista (tabellare)
Text Analytics with Python [[electronic resource] ] : A Practical Real-World Approach to Gaining Actionable Insights from your Data / / by Dipanjan Sarkar
Text Analytics with Python [[electronic resource] ] : A Practical Real-World Approach to Gaining Actionable Insights from your Data / / by Dipanjan Sarkar
Autore Sarkar Dipanjan
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016
Descrizione fisica 1 online resource (XXI, 385 p. 54 illus., 33 illus. in color.)
Disciplina 004
Soggetto topico Big data
Database management
Data mining
Programming languages (Electronic computers)
Big Data
Database Management
Data Mining and Knowledge Discovery
Programming Languages, Compilers, Interpreters
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1:Natural Language Basics -- Chapter 2:Python Refresher for Text Analytics -- Chapter 3:Text Processing -- Chapter 4:Text Classification -- Chapter 5:Text summarization and topic modeling -- Chapter 6:Text Clustering and Similarity analysis -- Chapter 7:Sentiment Analysis.-.
Record Nr. UNINA-9910154842603321
Sarkar Dipanjan  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Thinking in Pandas [[electronic resource] ] : How to Use the Python Data Analysis Library the Right Way / / by Hannah Stepanek
Thinking in Pandas [[electronic resource] ] : How to Use the Python Data Analysis Library the Right Way / / by Hannah Stepanek
Autore Stepanek Hannah
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020
Descrizione fisica 1 online resource (xi, 186 pages) : illustrations
Disciplina 005.1068
Soggetto topico Python (Computer program language)
Open source software
Computer programming
Machine learning
Big data
Python
Open Source
Machine Learning
Big Data
ISBN 1-4842-5839-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Introduction -- Chapter 2: Basic Data Access and Merging -- Chapter 3: How Pandas Works Under the Hood -- Chapter 4: Loading and Normalizing Data in pandas -- Chapter 5: Basic Data Transformation in pandas -- Chapter 6: The Apply Method -- Chapter 7: Groupby -- Chapter 8: Performance Improvements Beyond pandas -- Chapter 9: The Future of Pandas -- Appendix.-.
Record Nr. UNINA-9910409987703321
Stepanek Hannah  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data [[electronic resource] /] / by L. Octavio Lerma, Vladik Kreinovich
Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data [[electronic resource] /] / by L. Octavio Lerma, Vladik Kreinovich
Autore Lerma L. Octavio
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (141 pages) : illustrations
Disciplina 004.36
Collana Studies in Big Data
Soggetto topico Computational intelligence
Data mining
Big data
Artificial intelligence
Computational Intelligence
Data Mining and Knowledge Discovery
Big Data
Big Data/Analytics
Artificial Intelligence
ISBN 3-319-61349-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction --  Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing --  Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
Record Nr. UNINA-9910739409603321
Lerma L. Octavio  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Trends in Communication, Cloud, and Big Data [[electronic resource] ] : Proceedings of 3rd National Conference on CCB, 2018 / / edited by Hiren Kumar Deva Sarma, Bhaskar Bhuyan, Samarjeet Borah, Nitul Dutta
Trends in Communication, Cloud, and Big Data [[electronic resource] ] : Proceedings of 3rd National Conference on CCB, 2018 / / edited by Hiren Kumar Deva Sarma, Bhaskar Bhuyan, Samarjeet Borah, Nitul Dutta
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (XIII, 168 p.)
Disciplina 004
Collana Lecture Notes in Networks and Systems
Soggetto topico Electrical engineering
Big data
Input-output equipment (Computers)
Communications Engineering, Networks
Big Data
Input/Output and Data Communications
ISBN 981-15-1624-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910373896703321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Ubiquitous Computing and Computing Security of IoT [[electronic resource] /] / edited by N. Jeyanthi, Ajith Abraham, Hamid Mcheick
Ubiquitous Computing and Computing Security of IoT [[electronic resource] /] / edited by N. Jeyanthi, Ajith Abraham, Hamid Mcheick
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (132 pages)
Disciplina 005.8
Collana Studies in Big Data
Soggetto topico Computational intelligence
Data protection
Big data
Artificial intelligence
Computational Intelligence
Security
Big Data
Artificial Intelligence
ISBN 3-030-01566-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Security Protocols for IoT -- Security in Ubiquitous Computing Environment: Vulnerabilities, Attacks and Defences -- Security of Big Data in Internet of Things -- Trust Management Approaches in Mobile Adhoc Networks -- IoT for Ubiquitous Learning Applications: Current Trends and Future.
Record Nr. UNINA-9910737299303321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Using R for Statistics [[electronic resource] /] / by Sarah Baldock
Using R for Statistics [[electronic resource] /] / by Sarah Baldock
Autore Baldock Sarah
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2014
Descrizione fisica 1 online resource (232 p.)
Disciplina 570.1
570.1/5195
Collana The expert's voice in R
Soggetto topico Big data
Software engineering
R (Computer program language)
Big Data
Software Engineering/Programming and Operating Systems
ISBN 1-4842-0139-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents at a Glance; Introduction; Chapter 1: R Fundamentals; Downloading and Installing R; Getting Orientated; The R Console and Command Prompt; Functions; Objects; Simple Objects; Vectors; Data Frames; The Data Editor; Workspaces; Error Messages; Script Files; Summary; Chapter 2: Working with Data Files; Entering Data Directly; Importing Plain Text Files; CSV and Tab-Delimited Files; DIF Files; Other Plain Text Files; Importing Excel Files; Importing Files from Other Software; Using Relative File Paths; Exporting Datasets; Summary; Chapter 3: Preparing and Manipulating Your Data; Variables
Rearranging and Removing VariablesRenaming Variables; Variable Classes; Calculating New Numeric Variables; Dividing a Continuous Variable into Categories; Working with Factor Variables; Manipulating Character Variables; Concatenating Character Strings; Extracting a Substring; Searching a Character Variable; Working with Dates and Times; Adding and Removing Observations; Adding New Observations; Removing Specific Observations; Removing Duplicate Observations; Selecting a Subset of the Data; Selecting a Subset According to Selection Criteria; Selecting a Random Sample from a Dataset
Sorting a DatasetSummary; Chapter 4: Combining and Restructuring Datasets; Appending Rows; Appending Columns; Merging Datasets by Common Variables; Stacking and Unstacking a Dataset; Stacking Data; Unstacking Data; Reshaping a Dataset; Summary; Chapter 5: Summary Statistics for Continuous Variables; Univariate Statistics; Statistics by Group; Measures of Association; Covariance; Pearson's Correlation Coefficient; Spearman's Rank Correlation Coefficient; Hypothesis Test of Correlation; Comparing a Sample with a Specified Distribution; Shapiro-Wilk Test; Kolmogorov-Smirnov Test
Confidence Intervals and Prediction IntervalsSummary; Chapter 6: Tabular Data; Frequency Tables; Creating Tables; Displaying Tables; Creating Tables from Count Data; Creating a Table Directly; Chi-Square Goodness-of-Fit Test; Tests of Association Between Categorical Variables; Chi-Square Test of Association; Fisher's Exact Test; Proportions Test; Summary; Chapter 7: Probability Distributions; Probability Distributions in R; Probability Density Functions and Probability Mass Functions; Finding Probabilities; Finding Quantiles; Generating Random Numbers; Summary; Chapter 8: Creating Plots
Simple PlotsHistograms; Normal Probability Plots; Stem-and-Leaf Plots; Bar Charts; Pie Charts; Scatter Plots; Scatterplot Matrices; Box Plots; Plotting a Function; Exporting and Saving Plots; Summary; Chapter 9: Customizing Your Plots; Titles and Labels; Axes; Colors; Plotting Symbols; Plotting Lines; Shaded Areas; Adding Items to Plots; Adding Straight Lines; Adding a Mathematical Function Curve; Adding Labels and Text; Adding a Grid; Adding Arrows; Overlaying Plots; Adding a Legend; Multiple Plots in the Plotting Area; Changing the Default Plot Settings; Summary
Chapter 10: Hypothesis Testing
Record Nr. UNINA-9910300462103321
Baldock Sarah  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
UX Optimization [[electronic resource] ] : Combining Behavioral UX and Usability Testing Data to Optimize Websites / / by W. Craig Tomlin
UX Optimization [[electronic resource] ] : Combining Behavioral UX and Usability Testing Data to Optimize Websites / / by W. Craig Tomlin
Autore Tomlin W. Craig
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018
Descrizione fisica 1 online resource (205 pages)
Disciplina 005.437
Soggetto topico Big data
Big Data
Big Data/Analytics
ISBN 1-4842-3867-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: UX Optimization Overview -- Chapter 2: What’s a Persona? -- Chapter 3: Types of Personas -- Chapter 4: Why Personas Matter -- Chapter 5: How to Create a Persona -- Chapter 6: Behavioral UX Data -- Chapter 7: UX and Usability Testing Data -- Chapter 8: Putting It All Together: Behavioral UX Data Analysis and Recommendations -- Chapter 9: Putting It All Together: Usability Testing Data Analysis and Recommendations -- Chapter 10: Conclusion: The Big Picture.-.
Record Nr. UNINA-9910300363903321
Tomlin W. Craig  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Veracity of Big Data [[electronic resource] ] : Machine Learning and Other Approaches to Verifying Truthfulness / / by Vishnu Pendyala
Veracity of Big Data [[electronic resource] ] : Machine Learning and Other Approaches to Verifying Truthfulness / / by Vishnu Pendyala
Autore Pendyala Vishnu
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018
Descrizione fisica 1 online resource (XIV, 180 p. 41 illus.)
Disciplina 005.74
Soggetto topico Big data
Artificial intelligence
Big Data
Artificial Intelligence
ISBN 1-4842-3633-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 The Big Data Phenomenon -- 2 Veracity of Web Information -- 3 Approaches to Big Data Veracity -- 4 Change Detection Techniques -- 5 Machine Learning Algorithms -- 6 Formal Methods and Knowledge Representation -- 7 Medley of More Methods -- 8 The Future: Blockchain and Beyond.-.
Record Nr. UNINA-9910300751503321
Pendyala Vishnu  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part II / / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min
Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part II / / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min
Autore Song Xiangyu
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (536 pages)
Disciplina 005.7
Altri autori (Persone) FengRuyi
ChenYunliang
LiJianxin
MinGeyong
Collana Lecture Notes in Computer Science
Soggetto topico Big data
Data structures (Computer science)
Information theory
Application software
Image processing - Digital techniques
Computer vision
Data mining
Big Data
Data Structures and Information Theory
Computer and Information Systems Applications
Computer Imaging, Vision, Pattern Recognition and Graphics
Data Mining and Knowledge Discovery
ISBN 981-9723-90-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Computing Maximal Likelihood Subset Repair for Inconsistent Data -- 1 Introduction -- 2 Problem Statement -- 2.1 Function Dependency -- 2.2 Subset Repair -- 2.3 Problem Definition -- 3 Statistical Learning and Inference -- 3.1 Probability Modeling -- 3.2 Scalable Inference -- 4 Subset Repair with Maximum Likelihood -- 4.1 From Maximum Likelihood to Minimum Cost -- 4.2 Approximate Algorithm -- 5 Experiments -- 5.1 Experimental Steup -- 5.2 Performance Evaluation -- 5.3 Runtime Evaluation -- 6 Related Work -- 7 Conclusions -- References -- Design of Data Management System for Sustainable Development of Urban Agglomerations' Ecological Environment Based on Data Lake Architecture -- 1 Introduction -- 2 Related Work -- 3 System Architecture Design -- 4 System Implementation -- 4.1 Metadata Design -- 4.2 Data Management -- 4.3 Data Product Production -- 4.4 System Presentation -- 5 Future Work -- 6 Conclusion -- References -- P-QALSH+: Exploiting Multiple Cores to Parallelize Query-Aware Locality-Sensitive Hashing on Big Data -- 1 Introduction -- 1.1 Our Contribution -- 2 Preliminaries -- 2.1 c-ANN Search Problem -- 2.2 Framework of QALSH -- 3 Parallel Table Design -- 3.1 Inter-Table Parallel Design -- 3.2 Intra-table Parallel Design -- 4 Parallel Query Design -- 4.1 Overview of Parallel Query -- 4.2 Parallel Collision Counting Technology -- 4.3 Search Radius Estimation Strategy -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Results and Analysis of the Index Phase -- 5.3 Results and Analysis of the Query Phase -- 6 Conclusion -- References -- Face Super-Resolution via Progressive-Scale Boosting Network -- 1 Introduction -- 2 Face Super-Resolution -- 3 Our Methods -- 3.1 Network Architectures -- 3.2 Attention Feature Fusion Block -- 4 Experimental Results and Analysis.
4.1 Datasets and Implementation Details -- 4.2 Compared with State-of-the-Arts -- 4.3 Ablation Study -- 4.4 Effectiveness of the Proposed Method -- 5 Conclusion -- References -- An Investigation of the Effectiveness of Template Protection Methods on Protecting Privacy During Iris Spoof Detection -- 1 Introduction -- 2 Related Work -- 2.1 Iris Spoof Detection -- 2.2 Iris Template Protection Methods -- 3 Methodology -- 3.1 TPISD -- 3.2 Image Pre-processing -- 3.3 Iris Template Protection Methods -- 3.4 Spoof Detection Model -- 3.5 Security Analysis -- 4 Experiment -- 4.1 Dataset and Evaluation Metrics -- 4.2 Experimental Setup -- 4.3 Transformation Parameter Experiment -- 5 Conclusion -- References -- Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Basic Theory of Volatility -- 3.2 Definition of Indicators -- 3.3 Prediction Method -- 4 Empirical Analysis -- 4.1 Experiment Setup -- 4.2 Experiment Result -- 5 Conclusion -- References -- A Hierarchy-Based Analysis Approach for Blended Learning: A Case Study with Chinese Students -- 1 Introduction -- 2 Related Work -- 2.1 Elements Regarding Evaluating Blended Learning -- 2.2 Evaluation Frameworks -- 3 Method -- 3.1 Gradient Boosting Regression -- 3.2 Gini Importance and Permutation Importance -- 3.3 Analytic Hierarchy Process -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results and Analysis -- 5 Conclusion -- References -- A Multi-teacher Knowledge Distillation Framework for Distantly Supervised Relation Extraction with Flexible Temperature -- 1 Introduction -- 2 Related Work -- 2.1 Distantly Supervised Relation Extraction -- 2.2 Knowledge Distillation -- 3 Method -- 3.1 Task Definition -- 3.2 Model Overview -- 3.3 Flexible Temperature Regulation.
3.4 Multi-view Knowledge Distillation -- 3.5 Total Loss of Student Model -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics and Settings -- 4.3 Baselines -- 4.4 Main Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- PAEE: Parameter-Efficient and Data-Effective Image Captioning Model with Knowledge Prompter and Cross-Modal Representation Aligner -- 1 Introduction -- 2 Related Work -- 2.1 Frozen Parameters Captioning Models -- 2.2 Knowledge Retrieval-Based Prompting -- 2.3 Visual and Language Connection -- 2.4 Prompting Caption Generation -- 3 Method -- 3.1 Architecture -- 3.2 Pre-trained Image Encoder -- 3.3 Pre-trained Language Model -- 3.4 Prompter-Based Caption Generation -- 3.5 Cross-Modal Representation Aligner -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Data Utilization Capabilities -- 4.4 Exploration of Small-Data Learning -- 4.5 Ablation Analysis -- 4.6 Qualitative Analysis -- 5 Conclusion and Future Work -- References -- TSKE: Two-Stream Knowledge Embedding for Cyberspace Security -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Representation Models -- 2.2 Knowledge Embedding Methods -- 3 Preliminaries -- 3.1 System Model -- 3.2 Problem Definition -- 4 TSKE: A Two-Stream Knowledge Embedding Method Based on the MDATA Model -- 4.1 Static Stream Model -- 4.2 Spatio-Temporal Stream Model -- 4.3 Weighted Fusion -- 4.4 Learning -- 5 Experiment Results -- 5.1 Implementation -- 5.2 Baselines -- 5.3 Attack Link Prediction -- 5.4 Results -- 6 Conclusion and Future Work -- References -- Research on the Impact of Executive Shareholding on New Investment in Enterprises Based on Multivariable Linear Regression Model -- 1 Introduction -- 2 Related Work -- 2.1 Executive Shareholding and Corporate Innovation Investment -- 2.2 Two Types of Agency Costs -- 3 Method.
3.1 Data Sources and Variable Definition -- 3.2 Research Hypothesis -- 3.3 Research Model Design -- 4 Analysis of Empirical Test Results -- 4.1 Descriptive Statistics -- 4.2 Correlation Analysis -- 4.3 Analysis of Regression Results -- 4.4 Robustness Test -- 5 Conclusion -- References -- MCNet: A Multi-scale and Cascade Network for Semantic Segmentation of Remote Sensing Images -- 1 Introduction -- 2 Methods -- 2.1 Overall -- 2.2 Multi-scale Feature Extraction Module -- 2.3 Channel Activation Module -- 2.4 Cross-Layer Feature Selection Module -- 2.5 Multi-scale Object Guidance Module -- 2.6 Loss Function -- 3 Datasets and Experimental Implementation -- 3.1 Dataset Description -- 3.2 Implementation Details -- 3.3 Evaluation Indicators -- 4 Experimental Results and Analysis -- 4.1 Results -- 4.2 Analysis -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Proposed Approach -- 4.1 Overview of WikiCPRL -- 4.2 Weak Label Generation -- 4.3 Concept Feature Acquisition -- 4.4 Graph Attentional Layer -- 4.5 Encoding-Decoding Layer -- 4.6 Edge Direction Inferring -- 5 Performance Analysis -- 5.1 Datasets -- 5.2 Compare with Baselines -- 5.3 Case Study -- 6 Conclusion -- References -- An Effective Privacy-Preserving and Enhanced Dummy Location Scheme for Semi-trusted Third Parties -- 1 Introduction -- 2 Model and Design Goal -- 2.1 System Model -- 2.2 Security Model -- 2.3 Design Goal -- 3 EPED Scheme Design -- 3.1 Preliminaries -- 3.2 Location Anonymization Model -- 3.3 Optimization Based on the Stackelberg Game -- 4 Performance Evaluation and Security Analysis -- 4.1 Performance Analysis -- 4.2 Security Analysis -- 5 Related Work -- 6 Conclusion -- References.
W-MRI: A Multi-output Residual Integration Model for Global Weather Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Numerical Weather Prediction -- 2.2 Deep Learning Weather Forecasting Methods -- 2.3 Residual Network -- 3 Preliminaries -- 3.1 Dataset -- 3.2 Multi-variable Forecasting Problems -- 4 Method -- 4.1 ViT and Residual Model -- 4.2 Integration and Constraint of Residual -- 5 Experiments -- 5.1 Evaluation Metrics -- 5.2 Quantitative Forecasting Performance of W-MRI -- 5.3 Effect of Integration Constraint Module -- 6 Conclusion -- References -- HV-Net: Coarse-to-Fine Feature Guidance for Object Detection in Rainy Weather -- 1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Single Image Deraining -- 3 Proposed Method -- 3.1 Generate the Edge Map -- 3.2 From Edge-Attentional Features to Image -- 3.3 Object Detection Stage -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Qualitative and Quantitative Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Vehicle Collision Warning System for Blind Zone in Curved Roads Based on the Spatial-Temporal Correlation of Coordinate -- 1 Introduction -- 2 Materials and Methods -- 2.1 Target Tracking Method -- 2.2 Traffic Condition Analysis -- 2.3 Module of Communication -- 3 Results -- 3.1 Software Testing -- 3.2 Field Application -- 4 Conclusions -- References -- Local-Global Cross-Fusion Transformer Network for Facial Expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Facial Expression Recognition -- 2.2 Transformer -- 3 Method -- 3.1 Overall Framework -- 3.2 Local Feature Decomposition (LFD) -- 3.3 Cross-Fusion Transformer -- 3.4 Loss Function -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Comparison with the State-of-the-Art Methods -- 4.3 Param and FLOPs Comparison -- 4.4 Ablation Study -- 5 Conclusions -- References.
Answering Spatial Commonsense Questions by Learning Domain-Invariant Generalization Knowledge.
Record Nr. UNINA-9910855375403321
Song Xiangyu  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part III / / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min
Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part III / / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min
Autore Song Xiangyu
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (540 pages)
Disciplina 005.7
Altri autori (Persone) FengRuyi
ChenYunliang
LiJianxin
MinGeyong
Collana Lecture Notes in Computer Science
Soggetto topico Big data
Data structures (Computer science)
Information theory
Application software
Image processing - Digital techniques
Computer vision
Data mining
Big Data
Data Structures and Information Theory
Computer and Information Systems Applications
Computer Imaging, Vision, Pattern Recognition and Graphics
Data Mining and Knowledge Discovery
ISBN 981-9723-87-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part III -- Adaptive Graph Attention Hashing for Unsupervised Cross-Modal Retrieval via Multimodal Transformers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation and Problem Definition -- 3.2 Framework Overview -- 3.3 Objective Function -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison Results and Analysis -- 4.4 Ablation Study -- 4.5 Parameter Sensitivity Analysis -- 4.6 Convergence Testing -- 5 Conclusion -- References -- Answering Property Path Queries over Federated RDF Systems -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 The Proposed Method -- 4.1 Query Decomposition and Source Localization -- 4.2 Thompson-Based MinDFA Construction -- 4.3 Query Execution Strategy Base on B-DFS -- 5 Evaluation -- 5.1 Experimental Environment -- 5.2 Performance Comparison of Five Property Path Query Symbols -- 5.3 Performance and Resource Consumption of Different Matching Strategies of MinDFA -- 5.4 Performance Robustness of Five Property Path Query Symbols -- 6 Conclusion -- References -- Distributed Knowledge Graph Query Acceleration Algorithm -- 1 Introduction -- 2 Related Works -- 3 Offline Module for Distributed Construction of Indexes -- 3.1 MapReduce-Based Data Pre-processing -- 3.2 Coding-Oriented Construction of Distributed Hierarchical Clustering -- 4 Online Module for Distributed Parallel Processing of SPARQL Queries -- 4.1 Splitting and Loading of BitSet-Tree -- 4.2 Candidate Solution Acquisition -- 4.3 Shuffle -- 4.4 Merge Splicing of Candidate Vertices -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Results and Discussion -- 6 Conclusions -- References -- Truth Discovery of Source Dependency Perception in Dynamic Scenarios -- 1 Introduction -- 2 Problem Setting -- 2.1 Notation Definition -- 2.2 Task Description.
3 Preliminary -- 3.1 Source Dependency Detection Based on Bayesian Model -- 3.2 Truth Discovery Framework Based on Optimization Model -- 4 Methodology -- 4.1 Source Dependency Detection in Dynamic Scenarios -- 4.2 Dynamic Incremental Model Framework -- 4.3 Truth Discovery with Source Dependency Perception -- 5 Experiment -- 5.1 Experimental Setup -- 5.2 The Results on Real-World Datasets -- 5.3 The Results on Synthetic Datasets -- 6 Related Work -- 7 Conclusion -- References -- Truth Discovery Against Disguised Attack Mechanism in Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Disguised Attack Mechanism -- 3.2 Problem Formulation -- 3.3 Truth Discovery -- 4 Methodology -- 4.1 Behavior-Based Truth Discovery -- 4.2 Task Assignment Based on WAM -- 4.3 TD-DA Framework -- 5 Experiments -- 5.1 Experiment Setting -- 5.2 Verification of the Proportion of Malicious Workers -- 5.3 Experiment on Real-World Datasets -- 5.4 Experiment on Synthetic Datasets -- 6 Conclusion -- References -- Continuous Group Nearest Group Search over Streaming Data -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works -- 2.2 Problem Definition -- 3 The Framework KMPT -- 3.1 The Basic Idea -- 3.2 The Initialization Algorithm -- 3.3 The Incremental Maintenance Algorithm -- 4 The Experiment -- 4.1 Experiment Settings -- 4.2 Performance Comparison -- 5 Conclusion -- References -- Approximate Continuous Skyline Queries over Memory Limitation-Based Streaming Data -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works -- 2.2 Problem Definition -- 3 The Self-adaptive-based Framework -SEAK -- 3.1 The -CSS Definition -- 3.2 The Initialization Algorithm -- 3.3 The Incremental Maintenance Algorithm -- 3.4 The Partition-Based Optimization Algorithm -- 4 Performance Evaluation -- 4.1 Experiment Settings -- 4.2 Experimental Evaluation -- 5 Conclusion -- References.
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Attacks on Model Faithfulness -- 2.2 Defenses Against Backdoor Attack -- 3 Preliminaries and Attack Formulation -- 3.1 Federated Learning -- 3.2 Threat Model -- 3.3 Choice of Backdoor Triggers -- 4 Methodology -- 4.1 Motivation -- 4.2 Segmenting Local Updates -- 4.3 Identifying Outliers in Fragments -- 4.4 Pruning Backdoored Participants -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Effectiveness of Our Defense -- 5.3 Comparison with Prior Arts -- 5.4 Effectiveness on Advanced Attacks -- 5.5 Ablation Study -- 6 Conclusion -- References -- PaTraS: A Path-Preserving Trajectory Simplification Method for Low-Loss Map Matching -- 1 Introduction -- 2 Related Work -- 2.1 Error-Bounded Line Simplification -- 2.2 Semantic-Preserving Trajectory Simplification -- 2.3 Analysis of Existing Work -- 3 Preliminaries -- 3.1 Basic Definitions -- 3.2 Methodology Analysis -- 4 Path-Preserving Trajectory Simplification -- 4.1 Overview of PaTraS -- 4.2 Preserving Shortest Paths -- 4.3 Candidates Pairing -- 4.4 Similarity Computation -- 4.5 Pairing Optimization -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Oriented to Map-Matching -- 5.3 Parameter Sensitivity Study -- 6 Conclusion -- References -- Coordinate Descent for k-Means with Differential Privacy -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 k-Means -- 3.2 Coordinate Descent for k-Means -- 3.3 A Fast Version of CDKM -- 3.4 Differential Privacy -- 4 Proposed Our Method -- 4.1 Approximate CDKM -- 4.2 Proposed DP-ACDKM -- 4.3 Privacy Analysis -- 5 Experiments -- 5.1 Privacy-Utility Trade-Off -- 5.2 Convergence -- 6 Conclusion -- References -- DADR: A Denoising Approach for Dense Retrieval Model Training -- 1 Introduction -- 2 Related Work -- 3 Method.
3.1 Task Formulation -- 3.2 Denoising Approach Based on Dynamical Weight -- 4 Experiment -- 4.1 Dataset and Metrics -- 4.2 Experiment Settings -- 4.3 Experiment Results -- 5 Conclusions -- References -- Multi-pair Contrastive Learning Based on Same-Timestamp Data Augmentation for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 2.1 Self-supervised Learning -- 2.2 Sequential Recommendation -- 3 The Proposed Model -- 3.1 Problem Definition -- 3.2 Model Framework -- 3.3 Data Augmentaion -- 3.4 Masking Operation -- 3.5 Embedding Layer -- 3.6 BERT Encoder -- 3.7 Prediction Layer -- 3.8 Multi-pair Contrastive Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Hyperparameter Experiments -- 4.4 Ablation Study -- 5 Conclusion -- References -- Enhancing Collaborative Features with Knowledge Graph for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 KG Explore Module -- 4.2 Multi-IMP-GCN -- 4.3 Model Prediction -- 4.4 Model Optimization -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Performance Comparison -- 5.3 Ablation Studies -- 5.4 Impacts of Multi-IMP-GCN -- 6 Conclusion and Future Work -- References -- PageCNNs: Convolutional Neural Networks for Multi-label Chinese Webpage Classification with Multi-information Fusion -- 1 Introduction -- 2 Multi-label Chinese Webpage Classification Models -- 3 Experimental Results and Discussions -- 3.1 Multi-label Chinese Webpage Dataset -- 3.2 Implementation Details and Evaluation Metrics -- 3.3 Multi-label Chinese Webpage Classification Results -- 4 Conclusion -- References -- MFF-Trans: Multi-level Feature Fusion Transformer for Fine-Grained Visual Classification -- 1 Introduction -- 2 Related Works -- 2.1 CNN-Based FGVC Methods -- 2.2 ViT-Based FGVC Methods -- 3 Proposed Method -- 3.1 Vision Transformer Encoder.
3.2 Important Token Election Module -- 3.3 Semantic Connection Enhancing Module -- 4 Experiments -- 4.1 DataSets and Implement Details -- 4.2 Comparisons with Advanced Methods -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Summarizing Doctor's Diagnoses and Suggestions from Medical Dialogues -- 1 Introduction -- 2 Related Work -- 3 Model -- 3.1 Pointer Generator Network as Backbone -- 3.2 Input Token Enhancement by Speaker-level Embedding -- 3.3 Input Token Enhancement by Utterance-level Embedding -- 4 Experiment -- 4.1 Dataset -- 4.2 Baseline Models -- 4.3 Settings -- 4.4 Evaluation Metrics -- 4.5 Automatic Evaluation -- 4.6 Doctor Evaluation -- 4.7 Case Study -- 5 Conclusion -- References -- HSA: Hyperbolic Self-attention for Sequential Recommendation -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Empirical Analysis of Datasets -- 2.2 Lorentz Model of Hyperbolic Space -- 2.3 Self-attention Mechanism for Sequential Recommendation -- 3 Proposed Approach -- 3.1 Problem Formulation and Approach Overview -- 3.2 Item Embeddings in Hyperbolic Space -- 3.3 Sequence Learning with Self-attention Mechanism -- 3.4 Prediction Layer -- 3.5 Model Training -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Performance as a Plugin on Baselines -- 5 Conclusion -- References -- CFGCon: A Scheme for Accurately Generating Control Flow Graphs of Smart Contracts -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Scheme -- 4.1 Overview of the System Model -- 4.2 Transform Module -- 4.3 Division Module -- 4.4 Connection Module -- 5 Experiment and Performance Evaluation -- 5.1 Dataset -- 5.2 General Test for CFGCon -- 5.3 Performance Comparision with Existing Approaches -- 6 Conclusion -- References -- Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning -- 1 Introduction.
2 Related Work.
Record Nr. UNINA-9910855389603321
Song Xiangyu  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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