Advances in Computational Collective Intelligence

Name: Advances in Computational Collective Intelligence | 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11, 2024, Proceedings, Part I
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16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11, 2024, Proceedings, Part I

Ngoc-Than Nguyen Bogdan Franczyk André Ludwig Manuel Nunez Jan Treur Gottfried Vossen Adrianna Kozierkiewicz(Editor)

Springer (Publisher)

Published on 7. September 2024

XXVIII, 409 pages

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978-3-031-70248-8 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Collective Intelligence and Collective Decision-Making
Formalization of Agent-Based Model of Group Learning
1 Introduction
2 Environment
2.1 Students and Teachers
2.2 Phases of Learning
3 Model
3.1 Agents
3.2 Groups
3.3 Agent's Knowledge
3.4 Agent Characteristics
4 Example
5 Conclusion
References
Music Genre Classification Using Hybrid Committees and Voting Mechanisms
1 Introduction
1.1 Related Work
1.2 Contribution
2 Outline of the System
2.1 Used Dataset
2.2 Used Classifiers
3 Voting Systems
4 Experimental Results
4.1 Classifiers
4.2 Classifiers Committee
5 Conclusions and Future Work
References
Towards Practical Large Scale Traffic Model of Electric Transportation
1 Introduction
2 Related Works
3 Basic Concepts in Simulation
4 Agent Model
5 Results
6 Conclusions
References
A Systematic Literature Review on Affective Computing Techniques for Workplace Stress Detection
1 Introduction
2 Background and Related Works
2.1 Stress at Work
2.2 Related Works
3 Systematic Research Papers Collection Methodology
4 SLR Results
4.1 RQ1: Which Context for Stress at Work Assessment?
4.2 RQ2: Which Method of Data Collection for Stress Assessment?
4.3 RQ3: How is Collected Stress-Related Data Analyzed?
5 RQ4: What Challenges and How Are They Addressed?
6 Conclusion
References
Rough Set Decision Rules for Usage-Based Churn Modeling in Mobile Telecommunications
1 Introduction
2 Methodology and Basic Concepts
3 Experiments Results and Comparisons
3.1 Source Data and Features
3.2 Classification Quality Evaluation
3.3 Analysis of Rough Set Rules Generation
4 Conclusions
References
Deep Learning Techniques
CNN Classifier for Helicobacter Pylori Detection in Immunohistochemically Stained Gastric WSI
1 Introduction
1.1 State-of-the-Art
2 Detection of H. Pylori Using CNN
3 Experiments
3.1 Results
4 Conclusions
References
Complete Convolutional Neural Networks Environment for Computer Vision Problems With Nvidia Drive AGX Xavier
1 Introduction
2 Related Work
3 Problem Description
4 Methods
4.1 Environment Concept
4.2 Working Hours
4.3 Outside Working Hours
5 Experiment
6 Discussion
7 Future Work
8 Conclusions
References
The Development of an Application-Specific Instruction Set Processor Specialized on a Convolutional Neural Network Trained on MNIST
1 Introduction
2 Related Work
3 Methods
4 Obtained Results
5 Future Work
6 Conclusions
References
Detection and Localization of Covid-19 on Chest Radiographs by Deep Learning Algorithms
1 Introduction
2 Related Word
3 Materials and Methods
3.1 Dataset
3.2 Proposed Method
4 Results
4.1 Experimentation 1: Binary Classification
4.2 Experimentation 2: Three-Class Classification
4.3 Experimentation 3: Ensemble Learning
4.4 Localization and Visualization of Lesions for Radiographs Classified as COVID and Pneumonia
5 Conclusions
References
Big Textual Data Analytics Using Transformer-Based Deep Learning for Decision Making
1 Introduction
1.1 Context and Issues
1.2 Contribution
1.3 Paper Organization
2 Releated Work
3 Proposed Decision-Making Approach
3.1 Pre-Processing : Big Data Analytics
3.2 Classification : Deep Learning
4 Experimental Study of the Proposed Approach
4.1 Evaluation Environment
4.2 Experimental Study and Results Study
5 Conclusions
5.1 Summary
5.2 Prospects
References
Multistep Time Series Forecasting of Energy Consumption Based on Stacked Deep LSTM Network Architecture
1 Introduction
2 Deep Neural Networks for Time-Series Forecasting: Related Work
3 Stacking LSTMs for Time Series Forecasting
4 Model Optimization
5 Experiments and Results
5.1 Model Architecture
5.2 Training and Validation
5.3 Future Forecasting
6 Conclusion
References
On the Effect of Quantization on Deep Neural Networks Performance
1 Introduction
2 Related Work
2.1 Neural Network Quantization
2.2 Performance of Quantized Models
3 Performance Evaluation
3.1 Evaluation Performance Techniques
4 Experiments
4.1 Evaluation Metrics
4.2 Clean Accuracy
4.3 Uncertainty Quantification
4.4 Sensitivity Analysis
4.5 Adversarial Attacks
5 Conclusion
References
Natural Language Processing
Three-Stage Extraction of Spatial Relationships Using Markers
1 Introduction
2 Related Works
3 Datasets
4 Approach Used
4.1 Selection of SIs
4.2 SI-Driven Selection of TRs and LDs
4.3 Building Sample Representation
4.4 Classification
5 Discussion and Extension
6 Experiments
6.1 Evaluation Settings
6.2 CLEF2017-mSpRL
6.3 SpaceEval
6.4 PST
7 Results
8 Ablation Study
9 Limitations
10 Conclusion
References
A Quadruplication Multilingual and Multilevel Topic Seeding Approach Towards a Bottom-Up Graph Generation and Enhancement
1 Introduction
2 Related Work
3 Methodology
3.1 Step 1: Data Preparation
3.2 Step 2: Initial Training
3.3 Steps 3: Second Training
3.4 Step 4 and 5: Third and Fourth Training
4 Experimentation
4.1 Experimentation Settings
4.2 Experimental Results and Discussion
5 Conclusion and Future Work
References
Question Answering System to Answer Questions About Technical Documentation
1 Introduction
2 Related Work
3 Dataset
4 Proposed Solution
4.1 Indexing Pipeline
4.2 Query Pipeline
5 Results
5.1 Retriever Evaluation
5.2 Reader Evaluation
6 Solution Variants
7 Qualitative Analysis
8 Discussion
References
Interpretable Dense Embedding for Large-Scale Textual Data via Fast Fuzzy Clustering
1 Introduction
2 Related Works
3 Description of the Proposed Text Vectorization Method
3.1 Forming the Target Dictionary
3.2 Grouping Words According to Their Semantic and Thematic Relatedness
3.3 Fuzzy Clustering of Word Vectors
3.4 The Process of Text Embedding Construction
4 Experimental Setup
4.1 Description of the Text Corpus
4.2 First Step: Text Preprocessing and Dictionary Construction
4.3 Second Step: Grouping of the Dictionary Words According to Their Thematic Relatedness
4.4 Third Step: Merging of the Clusters to Obtain Thematic Categories
4.5 Fourth Step: Construction of Text Embeddings
4.6 Assessing the Quality of the Proposed Embeddings in Solving the Task of Thematic Categorization of Publications
5 Experimental Results
5.1 Random Simulation of k Neighbors
5.2 Sparse Vector Representation Based on the Word Frequencies
5.3 Text Embeddings Based on Neural Networks Model
5.4 Proposed Text Embeddings
5.5 Conclusions
References
M2DS: Multilingual Dataset for Multi-document Summarisation
1 Introduction
2 Related Work
2.1 Major MDS Datasets Across Diverse Domains
2.2 Existing MDS Models
2.3 Prior Work on Multilingual MDS
2.4 Existing Multilingual Text Summarisation Datasets
3 M2DS Dataset
3.1 Dataset Development
3.2 Dataset Composition
3.3 Dataset Comparison
4 Experiments
4.1 Pre-trained Model Selection
4.2 Baselines
5 Analysis and Discussion
6 Conclusion and Future Directions
References
uMentor: LLM-Powered Chatbot for Harnessing Technology Books in Digital Library
1 Introduction
2 Background and Related Works
3 Proposed Method
3.1 Approach Direction
3.2 Instruction Dataset Preparation
3.3 LLM Fine-Tuning Process
3.4 AI-Powered Academic Mentor
4 Experiments and Evaluation
4.1 Experiment Environment
4.2 Experiment Results
4.3 Evaluation and Discussion
4.4 LLM Ablation Study and Analysis
5 Conclusions
References
Advancements in Text Subjectivity Analysis: From Simple Approaches to BERT-Based Models and Generalization Assessments
1 Introduction
2 Related Work
3 Models
3.1 Our Pipeline
3.2 Pre-built Models
4 Experimental Results
4.1 Datasets
4.2 Implementation Details
4.3 Experimental Results
5 Discussion
6 Conclusion
7 Appendix
References
Hybrid Approach Text Generation for Low-Resource Language
1 Introduction
2 Related Works
3 Problems of Text Generation in the Turkish and Kazakh Languages
4 Description of the Hybrid Text Generation Approach in the Low Resource Language
4.1 Data Collection and Processing
4.2 Structural and Semantic Properties of Text in Kazakh Language
5 Practical Results
6 Conclusion and Future Work
References
Data Mining and Machine Learning
ROCKET with Dynamic Convolution for Time Series Classification
1 Introduction
2 Background
2.1 Problem Formulation
2.2 Dynamic Time Warping
2.3 Dynamic Convolution
2.4 ROCKET
3 DynamicROCKET
4 Experimental Evaluation
4.1 Data
4.2 Baselines
4.3 Experimental Protocol
4.4 Implementation
4.5 Results
4.6 Discussion
5 Conclusions and Outlook
References
Prediction of the Delay Time of Public Transportation Using Machine Learning
1 Introduction
2 Related Works
3 Research Methodology
3.1 Data Processing
3.2 Methods
4 Results
5 Conclusion
References
Processing the 3D Heritage Data Samples Based on Combination of GNN and GAN
1 Introduction
2 Related Work
3 Proposed Method
4 Implementation and Results
5 Discussion, Evaluation and Comparison
6 Conclusion
References
Testing the Robustness of Machine Learning Models Through Mutations
1 Introduction
2 Related Work
3 Description of Dataset and Models Employed
4 Mutations
5 Experiments
5.1 Linear Regression
5.2 Random Forest
5.3 LSTM Neural Network
5.4 BiLSTM Neural Network
5.5 CNN-BiLSTM Neural Network
5.6 Discussion
6 Conclusions and Future Work
References
Outlier Detection in Human Activity Recognition Systems
1 Introduction
2 Outliers in the Context of Human Movement Phases
3 Approaches Proposed for Outlier Activity Detection
3.1 Supervised Learning Algorithms
3.2 Nested Binary Classifier
4 Experimental Studies
4.1 Characteristics of Data Sets
4.2 Experimental Research and Tests
5 Results
6 Conclusion
References
Using Brain-Computer Interface and Artificial Intelligence Algorithms for Language Learning
1 Introduction
2 Related Works
3 Methodology
3.1 Multilayer Perceptron
3.2 Recurrent Neural Network
4 Conclusion and Future Work
References
Social Networks and Intelligent Systems
From Detection Through Display to Understanding: Bridging AI and UI in Disinformation and Fake News Analysis
1 Introduction and Rationale
2 Relevant Systems and Prototypes
3 SocialTruth Approach to User Interface in Fake News Detection System
3.1 SocialTruth UI/UX Design Choices and Examples
3.2 Clarification on What is Evaluated and How?
4 SWAROG Approach to the User Interface in Fake News Detection System
5 Discussion and Future Work
6 Conclusions
References
Social Attraction Mutation: A Novel Method for Mutation Based on Attraction
1 Introduction and Related Works
2 Proposed Algorithm
2.1 Mutation Variants Used
3 Methodology
3.1 Selection and Crossover Operations Used Throughout the Experiment
3.2 Used Benchmark Functions
3.3 Used Classical Control Problems
4 Results
4.1 Preliminary Results
4.2 Results Obtained on the Continuous Benchmarks
4.3 Results Obtained on the Classical Control Problems
5 Summary
References
Extracting Common DNA Segments from the Complete Genomes of 7538 Viruses and Five Selected Mammals
1 Introduction
1.1 Viruses Vs. Genomic Fossils
1.2 The Limitations of Multiple Sequence Alignment for Large and Diverse Genome Comparisons
1.3 A Scalable Maximal Repeat Extraction Approach
2 Method
2.1 Packing and Tagging Complete Genomic Sequences of Mammals and Viruses
2.2 Extracting Common DNA Segments Appearing in both of Viruses and Mammals
2.3 Analyzing the Taxonomy and Hosts of Selected Viruses with Five Selected Mammals
3 Experimental Results
3.1 Tagged Whole Genomic Sequences for Each of Species
3.2 The Longest Common DNA Segments in Each of Viruses
3.3 Inspecting the Connections Linked by These Common DNA Segments
4 Conclusion
References
Strategies to Use Harvesters in Trustworthy Fake News Detection Systems
1 Introduction
2 State of the Art
3 Test-Bed and Experimental Evaluation
3.1 Test-Bed Design and Experimental Setup
3.2 Developed Harvester
3.3 Experiments Description
4 Results
5 Conclusions
References
Time Series Analysis of Sentiment Polarity Trends: A Case Study
1 Introduction
2 Related Works
2.1 Single User Opinion Evolution
2.2 Opinion Dynamic in a Group of Users
3 A Method for Time Series Analysis of Sentiment Polarity
3.1 Time Series Analysis Methods
3.2 Opinion Evolution Model of a Group of Users
4 Experimental Evaluation
4.1 Sentiment Score Dynamics for a Single User
4.2 Seasonal Decomposition (Time Series of Tweets Cardinality)
4.3 Seasonal Decomposition (Time Series of Average Sentiment Score of Tweets)
4.4 Results for Forecast Model
5 Summary and Future Works
References
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Advances in Computational Collective Intelligence

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