Advances in Integrations of Intelligent Methods: Post-workshop volume of the 8th International Workshop CIMA 2018, Volos, Greece, November 2018

Preface
Reviewers
Contents
About the Editors
1 Aligning Learning Materials and Assessment with Course Learning Outcomes in MOOCs Using Data Mining Techniques
	1.1 Introduction
	1.2 Related Work
		1.2.1 ILOs and Assessments
		1.2.2 Data Mining Techniques
		1.2.3 Adaptation in MOOCs
	1.3 Model Construction
		1.3.1 Research Method
		1.3.2 Hybrid Adaptation Framework
		1.3.3 Validation
	1.4 Experiment and Results
		1.4.1 Matching Learning Materials with ILOs
		1.4.2 Generating Examination
	1.5 Conclusion
	References
2 Edge-Centric Queries\' Stream Management Based on an Ensemble Model
	2.1 Introduction
	2.2 Prior Work
	2.3 Problem Definition and High-Level Description
		2.3.1 Data Processing at the Edge of the Network
		2.3.2 Matching Queries and Processors
		2.3.3 Delivering the Query Complexity Class
	2.4 Allocating Queries to Processors
		2.4.1 The Ensemble Scheme
		2.4.2 The Matching Process
	2.5 Experimental Evaluation
		2.5.1 Experimentation Setup
		2.5.2 Performance Assessment
	2.6 Conclusions and Future Work
	References
3 Bitcoin Price Prediction Combining Data and Text Mining
	3.1 Introduction
	3.2 Related Works
	3.3 Proposed Methodology and Dataset Creation
		3.3.1 LSTM Networks
		3.3.2 Gradient Boosting
		3.3.3 Gradient Boosted Trees and XGBoost
	3.4 Experiment
	3.5 Future Work
	3.6 Appendix
	References
4 Toward New Evaluation Metrics  for Relational Learning
	4.1 Introduction
	4.2 Bayesian Networks
		4.2.1 Definition
		4.2.2 Evaluation of BNs Structure Learning Algorithms
	4.3 Probabilistic Relational Models
		4.3.1 Definition
		4.3.2 Evaluation of PRM Structure Learning Algorithms
	4.4 New Relational Evaluation Metrics
		4.4.1 Penalization for Relational Models
		4.4.2 Relational Precision and Recall
	4.5 Experiments
		4.5.1 Networks and Datasets
		4.5.2 Learning Algorithms
		4.5.3 Evaluation Metrics
		4.5.4 Results and Interpretation
	4.6 Discussion and Conclusion
	References
5 Color Models for Skin Lesion Classification from Dermatoscopic Images
	5.1 Introduction
	5.2 Skin Lesion Classification from Dermatoscopic Images
		5.2.1 Dermatoscopic Image Preprocessing
		5.2.2 Image Segmentation
		5.2.3 Color Model-Based Feature Extraction
		5.2.4 Classification
	5.3 Experimental Setup
		5.3.1 Dataset
		5.3.2 Classification Algorithms
	5.4 Experimental Results
	5.5 Conclusion
	References
6 Methods of Statistical Analysis and Machine Learning for the Evaluation of Generated Hardware and Firmware Designs
	6.1 Introduction
	6.2 Background and Motivation
		6.2.1 The MetaRTL Design Flow
		6.2.2 Open Problem: Determining the Desired Configuration
	6.3 Proposed Solution
		6.3.1 The Problem of Area Forecast
		6.3.2 Data Generation and Features Space Exploration
		6.3.3 Analysis of the Generated Data
		6.3.4 Machine Learning for Area Forecast
	6.4 Results Evaluation
		6.4.1 Assessment of Hardware Area
		6.4.2 Assessment of Firmware Metircs
		6.4.3 Relation Between Hardware Area and Firmware Metrics
	6.5 Conclusion and Future Works
	References
7 Genetic Algorithms for Creating Large Job Shop Dispatching Rules
	7.1 Introduction
	7.2 Architecture
		7.2.1 Simulation Engine
		7.2.2 Basic Dispatching Rules
		7.2.3 Genetic Algorithm
	7.3 Experimental Setup
		7.3.1 Algorithmic Configuration and Testing Environment
		7.3.2 Problem Instances
	7.4 Results and Discussion
	7.5 Conclusions
	References
8 o-LPMLN: A Combination of LPMLN   and LPOD
	8.1 Introduction
	8.2 Preliminaries
		8.2.1 LPMLN
		8.2.2 LPOD
	8.3 o-LPMLN
		8.3.1 Syntax of o-LPMLN
		8.3.2 Semantics of o-LPMLN
	8.4 Implementing o-LPMLN
		8.4.1 Translating o-LPMLN into LPOD
		8.4.2 Translating o-LPMLN into LPMLN
		8.4.3 Discussion
	8.5 A Prototype Application: The System AS
		8.5.1 System Description
		8.5.2 Experiments
	8.6 Conclusion and Future Work
	References