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[null,"1.","Record Nr.",null,"UNIPARTHENOPE000016497"]
[null,null,"Autore",null,"Commissione d'indagine sulla povertà"]
[null,null,"Titolo",null,"Secondo rapporto sulla povertà in Italia / Commissione d'indagine sulla povertà e l'emarginazione"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Franco Angeli, c1992"]
[null,null,"ISBN",null,"88-204-7226-0"]
[null,null,"Descrizione fisica",null,"169 p. ; 25 cm"]
[null,null,"Collana",null,"Sociologia ; 194"]
[null,null,"Disciplina",null,"339.460945"]
[null,null,"Collocazione",null,"126/153"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996464435403316"]
[null,null,"Titolo",null,"Web information systems and applications : 18th international conference, WISA 2021, Kaifeng, China, September 24-26, 2021 : proceedings / / Chunxiao Xing [and four others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-87571-7"]
[null,null,"Descrizione fisica",null,"1 online resource (782 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; 12999"]
[null,null,"Disciplina",null,"004.678"]
[null,null,"Soggetti",null,"World Wide Web"]
[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","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- World Wide Web -- An Active Learning Approach for Identifying Adverse Drug Reaction-Related Text from Social Media Using Various Document Representations -- 1 Introduction -- 2 Related Work -- 2.1 Identifying Adverse Drug Reactions from Social Media -- 2.2 Document Representation -- 3 Our Proposed Approach -- 3.1 Framework -- 3.2 View-Generation Mechanism Using Various Document Representations -- 3.3 Selection Strategy in Active Learning -- 4 Experimental Dataset and Settings -- 5 Results and Discussion -- 5.1 Effectiveness of Different View Configurations -- 5.2 Effectiveness of Different Selection Strategies -- 5.3 Comparison between the Proposed Approach and Other Methods -- 5.4 Performance Comparison with Baselines -- 6 Conclusions -- References -- DualLink: Dual Domain Adaptation for User Identity Linkage Across Social Networks -- 1 Introduction -- 2 Related Work -- 3 DualLink: The Proposed Model -- 4 DualLink-Based User Identity Linkage Algorithm -- 4.1 Node Embedding Based on Adversarial Domain Adaptation -- 4.2 Node Matching Based on Back-Propagation Domain Adaptation -- 5 Experiments -- 5.1 Datasets -- 5.2 Baselines and Evaluation Metrics -- 5.3 Parameter Setup -- 5.4 Performance Evaluation -- 6 Conclusions -- References -- Multimodal Topic Detection in Social Networks with Graph Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Topic Detection -- 2.2 Multimodal Fusion -- 3 Methodology -- 3.1 Overview -- 3.2 Feature Embedding Module -- 3.3 Graph Fusion Module -- 3.4 Clustering Module -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 "]
[{"$nulls":4},"Baselines -- 4.3 Results -- 5 Conclusion -- References -- A New Novel Label Propagation Algorithm -- 1 Introduction -- 2 Related Work -- 3 KC_LPA -- 3.1 Movitation of KC_LPA -- 3.2 KC_LPA Model -- 4 Experimental Results and Analysis:."]
[{"$nulls":4},"4.1 Real Network Experiment -- 4.2 Synthetic Network Experiment -- 5 Summary -- References -- Query Processing and Algorithm -- A Coreset Based Approach for Continuous k-regret Minimization Set Queries over Sliding Windows -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 k-Regret Minimization Set Queries -- 3.2 Sliding Window Model -- 3.3 Coreset -- 4 The Coreset Based Algorithm -- 4.1 Coreset Construction -- 4.2 Coreset Maintaining -- 4.3 Result Set Computation -- 5 Experiments -- 6 Conclusion -- References -- Configurable In-Database Similarity Search of Electronic Medical Records -- 1 Introduction -- 2 Problem Definition -- 3 System Architecture -- 4 Basic Design -- 4.1 Non-metric Space Library (nmslib) for Similarity Search -- 4.2 External Program Embedding -- 5 Advanced Design -- 5.1 User-Defined Function (UDF) for Similarity Search -- 5.2 UDF Embedding and Combination -- 6 Implementation -- 6.1 Environment -- 6.2 Dataset -- 6.3 Interface -- 7 Evaluation -- 7.1 Running Time Evaluation -- 7.2 Interface Evaluation -- 8 Related Work -- 9 Conclusion -- References -- Database Native Approximate Query Processing Based on Machine-Learning -- 1 Introduction -- 2 Problem Definition -- 3 System Overview -- 4 Machine Learning Based AQP -- 4.1 Neural Network Training -- 4.2 Queries Process -- 5 Embedding in Database by UDF -- 5.1 ETEQ -- 5.2 ETIQ -- 6 Implementation and Evaluation -- 6.1 Settings -- 6.2 Datasets -- 6.3 Training Process -- 6.4 Experiment Results -- 7 Related Work -- 8 Conclusion -- References -- Improving Medical Record Search Performance by Particle Swarm Optimization Based Data Fusion Techniques -- 1 Introduction -- 2 Related Work -- 2.1 EMR Retrieval -- 2.2 Data Fusion -- 3 Data Fusion for Information Search -- 3.1 Several Fusion Methods -- 3.2 Weight Assignment for the Linear Combination Method."]
[{"$nulls":4},"3.3 Particle Swarm Optimization Based Linear Combination -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Analysis of the Four Learning Algorithms -- 5 Conclusion -- References -- Real-Time Aggregation Approach for Power Quality Data -- 1 Introduction -- 2 Data Aggregation Computing Framework -- 3 Data Aggregation Approach -- 3.1 Aggregate Query by Incremental Computing -- 3.2 95% Probability Value Aggregation by Approximate Computing -- 4 Experiments -- 4.1 Comparation of Incremental and Full Computing -- 4.2 Comparison of Approximate Computing and Full Computation -- 5 Conclusions and Future Work -- References -- Data Mining -- Decision Behavior Based Private Vehicle Trajectory Generation Towards Smart Cities -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Framework -- 3.2 Regional Destination Choice Game Model -- 3.3 Predict the Private Vehicle Traffic -- 3.4 Trajectory Simulation and Data Generation -- 3.5 Optimization of Private Vehicle Trajectory Data -- 4 Experiments -- 4.1 Dataset Preprocessing and Region Division -- 4.2 Traffic Forecast -- 4.3 Trajectory Simulation -- 4.4 Trajectory Optimization -- 5 Results Verification -- 5.1 Evaluation of Macroscopic Experimental Results -- 5.2 Evaluation of Microscopic Experimental Results -- 6 Conclusions -- References -- Self-learning Tags and Hybrid Responses for Deep Knowledge Tracing -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Model Student Response Sequences -- 3.2 Knowledge State -- 3.3 Prediction Output -- 3.4 Model Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Discussion -- 5 Conclusion "]
[{"$nulls":4},"-- References -- Shortest Path Distance Prediction Based on CatBoost -- 1 Introduction -- 2 Related Work -- 2.1 Shortest Path Distance Prediction -- 2.2 CatBoost -- 3 Shortest Distance Prediction Based on CatBoost."]
[{"$nulls":4},"3.1 Problem Formulation -- 3.2 CatBoost Based Shortest Distance Prediction Model -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Hyperparameters -- 4.3 Evaluation Metrics -- 4.4 Baselines -- 4.5 Results -- 5 Conclusion -- References -- Traffic Prediction Based on Multi-graph Spatio-Temporal Convolutional Network -- 1 Introduction -- 2 Related Work -- 2.1 Statistical Models -- 2.2 Machine Learning Methods -- 3 Methodology -- 3.1 Architecture -- 3.2 Multi-graph Generation -- 3.3 Spatial Dependence Modeling -- 3.4 Temporal Dependence Modeling -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics and Baselines -- 4.3 Experimental Results -- 5 Conclusion and Future Work -- References -- Online Runtime Prediction Method for Distributed Iterative Jobs -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Background -- 3 Approach -- 3.1 Data Collection -- 3.2 Key Input Features -- 3.3 Estimate the Number of Iterations -- 3.4 Online Runtime Prediction Model -- 4 Evaluation -- 4.1 Experimental Environment -- 4.2 Dataset -- 4.3 Parameter Selection -- 4.4 Prediction Accuracy -- 4.5 Performance Comparison -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Workload Prediction of Cloud Workflow Based on Graph Neural Network -- 1 Introduction -- 2 Cloud Workflow Data Analysis -- 2.1 Dataset Selection -- 2.2 Dataset Introduction -- 2.3 Dataset Extraction -- 3 Algorithm Introduction -- 3.1 Graph Convolution Networks (GCN) -- 3.2 Graph Attention Networks -- 4 Study Design -- 4.1 Data Preparation -- 4.2 Data Preprocessing -- 4.3 Label Value Processing -- 4.4 Feature Value Processing -- 4.5 The Construction of Homogeneous Graph and Heterogeneous Graph -- 4.6 Data Set Split -- 5 Result -- 6 Discussion and Concluding Remarks -- References -- Prognosis Prediction of Breast Cancer Based on CGAN -- 1 Introduction -- 2 Data and Methods."]
[{"$nulls":4},"2.1 Dataset Processing -- 2.2 Conditional Generation Adversarial Network -- 2.3 Cox Proportional Hazards Model -- 3 Results and Discussion -- 3.1 Prognosis of Breast Cancer Based on Multivariate Cox Model -- 3.2 Prognosis of Breast Cancer Based on the Improved CGAN -- 3.3 Comparison of Improved CGAN-Based and Multi-factor Cox Prognostic Hazards Models -- 4 Conclusions -- References -- Few-Shot Learning for Time Series Data Generation Based on Distribution Calibration -- 1 Introduction -- 2 Main Approach -- 2.1 Pre-training of the Auto-Encoder -- 2.2 Similar Class Searching for Few-Shot Samples -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Data Generation for Few-Shot Samples -- 3.3 Introduction of Auto-Encoder -- 3.4 Data Generation for Sufficient Sample -- 3.5 Generate Visualization of the Sample -- 4 Conclusion -- References -- Sleep Analysis During Light Sleep Based on K-means Clustering and BiLSTM -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Sequence Segmentation -- 3.2 Unsupervised Learning Module -- 3.3 BiLSTM Sleep Staging -- 4 Experiments -- 4.1 Experimental Results -- 5 Conclusion -- References -- Prognosis Analysis of Breast Cancer Based on DO-UniBIC Gene Screening Method -- 1 Introduction -- 2 Methods -- 2.1 Screening for Differentially Expressed Genes -- 2.2 Selection of Breast Cancer Related Genes -- 2.3 Prognosis Analysis -- 3 Results -- 3.1 Differentially Expressed Genes -- 3.2 GO Enrichment Analysis and KEGG Pathway Analysis -- 3.3 DO-UniBIC Analysis -- 3.4 Prognosis Analysis -- 4 Discussion -- References -- Data Privacy and Security -- A Frequent Itemset Mining Method Based on Local Differential Privacy "]
[{"$nulls":4},"-- 1 Introduction -- 2 Related Work -- 3 Problem Description and Related Definitions -- 3.1 Problem Description -- 3.2 Related Description -- 4 Frequent Itemset Mining Based on LDP -- 4.1 Basic."]
[{"$nulls":4},"4.2 Data Disturbing."]