Intro
Preface
Conference Organization
Contents
Data Science and Artificial Intelligence
A New ConvMixer-Based Approach for Diagnosis of Fault Bearing Using Signal Spectrum
1 Introduction
2 Related Work
2.1 Conv-Mixer Neural Networks
2.2 Siamese Neural Networks
3 Methodology
3.1 General Architecture for Failure Diagnosis
3.2 The Proposed Siamese-Based Conv-Mixer Model
3.3 Diagnosis Network
4 Experiment
4.1 Datasets
4.2 Training
4.3 Results
4.4 Comparison
5 Conclusion
References

Differentially-Private Distributed Machine Learning with Partial Worker Attendance: A Flexible and Efficient Approach
1 Introduction
1.1 Background
1.2 Our Contributions
1.3 Paper Organization
2 Preliminaries
3 Our Proposed Algorithm
4 Experiments
5 Conclusion
References
Building Legal Knowledge Map Repository with NLP Toolkits
1 Introduction
2 Legal Knowledge Maps
2.1 Modeling
2.2 Hierarchy of Legislation
2.3 Legal Ontology Design
2.4 Legal Knowledge Map Construction
3 Implementation
3.1 Materials
3.2 Vietnamese NLP Toolkits

3.3 Building VLegalKMaps as Linked Data
4 Validations
4.1 Experiment Setup
4.2 Case Study
4.3 Statistics and Discussions
5 Conclusions and Future Works
References
Classification of Ransomware Families Based on Hashing Techniques
1 Introduction
2 Overview of Hashing Techniques in Malware Analysis
3 The Proposed Method
3.1 The Combined Analysis Method of Imphash, File Level Ssdeep Hashing, and Section Level Ssdeep Hashing
3.2 Preparing the Database
3.3 Predictive Model
3.4 Evaluation Criteria
4 Evaluations and Results

4.1 Experiment with Test Set Containing only Ransomware
4.2 Experiment with Test Set Containing both Malicious and Benign Samples
4.3 Advantages and Limitations of the Proposed Method
5 Conclusions
References
Towards a New Multi-tasking Learning Approach for Human Fall Detection
1 Introduction
2 Related Work
2.1 Simulated Falls
2.2 Real Falls
2.3 Multi-task Learning
2.4 Summary
3 Multi-task Deep Neural Network for Fall Detection
3.1 Multiple Classifiers
3.2 Temporal Convolutional Network as a Feature Extractor
3.3 Re-sampling for Class and Task Balance

3.4 Data Augmentation
4 Experiments
4.1 Datasets
4.2 Experiment Settings
4.3 Evaluation and Discussion
5 Conclusion and Future Work
References
A Comparative Study of Wrapper Feature Selection Techniques in Software Fault Prediction
1 Introduction
2 Related Work
3 Feature Selection
3.1 Software Metrics
3.2 Genetic Algorithm
3.3 Particle Swarm Optimization
3.4 Whale Optimization Algorithm
3.5 Cuckoo Search
3.6 Mayfly Algorithm
3.7 Binary Bat Algorithm
3.8 Feature Selection Details
4 Methodology
4.1 Proposed Approach