Intro
Preface
Organization
Abstracts Keynotes
Responsible AI and Data Science for Social Good
Forecasting Inflation Rates: A Large Panel of Micro-Level Data Approach
Encouraging the Development of Knowledge Systems
Analysis, Visualization and Improvement of Human Collaboration Dynamics Using Computational Methods
Contents
Data Mining, Machine Learning and Deep Learning
A Hybrid Supervised Learning Approach for Intrusion Detection Systems
1 Introduction
2 Related Work
3 Cyber Security Dataset
4 Proposed Framework
4.1 Data Pre-processing

4.2 Feature Engineering
4.3 Supervised Learning Algorithms for IDS
4.4 Validation Metrics
5 Experimental Study
5.1 Experimental Setup
5.2 Performance Analysis
6 Conclusions
References
A Novel Approach for Fake Review Detection Based on Reviewing Behavior and BERT Fused with Cosine Similarity
1 Introduction
2 Related Works
3 Fake Review Detection Design
3.1 Fusing Cosine Similarity into BERT
3.2 Reviewer Behavioral Feature Extractor
3.3 Fake Review Detection
4 Experiments
4.1 Dataset and Pre-processing
4.2 Evaluation Metrics

4.3 Experimental Settings
4.4 Experimental Results
4.5 Dimensionality Reduction Analysis of Text Feature Vectors
4.6 Ablation Study
5 Conclusions
References
LinkEE: Linked List Based Event Extraction with Attention Mechanism for Overlapping Events
1 Introduction
2 Related Work
3 Our Approach
3.1 BERT Encoder
3.2 Event Type Decoder
3.3 Trigger Word Decoder
3.4 Event Arguments Decoder
4 Experiments
4.1 Dataset and Evaluation Metric
4.2 Implementation Details
4.3 Comparison Methods
4.4 Comparative Results
4.5 Analysis on Overlap/Normal Data

4.6 Discussion for Model Variants
5 Conclusion
References
End-to-End Aspect-Based Sentiment Analysis Based on IDCNN-BLSA Feature Fusion
1 Introduction
2 Model Overview
2.1 Model Architecture
2.2 BERT as Embedding Layer
2.3 Downstream Model Design
3 Experiment Results and Discussions
3.1 Dataset and Parameters Settings
3.2 Performances Comparison
3.3 Ablation Experiments
3.4 Generalization Issue
3.5 Case Studies
4 Conclusions
References
How to Quantify Perceived Quality from Consumer Big Data: An Information Usefulness Perspective

1 Introduction
2 Related Works
2.1 Perceived Quality
2.2 Information Usefulness Analysis
3 Methodology
3.1 Information Usefulness Evaluation
3.2 Perceived Quality Quantification
4 Results
4.1 Data Collection and Description
4.2 Results of Information Usefulness Analysis
4.3 Results of Perceived Quality Quantification
5 Discussion and Conclusion
References
Chinese Medicinal Materials Price Index Trend Prediction Using GA-XGBoost Feature Selection and Bidirectional GRU Deep Learning
1 Introduction
2 Related Works