Artificial Intelligence Applications and Innovations

Name: Artificial Intelligence Applications and Innovations | 14th IFIP WG 12.5 International Conference, AIAI 2018, Rhodes, Greece, May 25-27, 2018, Proceedings
Brand: Springer
Price: 96.29 EUR
Availability: OnlineOnly

14th IFIP WG 12.5 International Conference, AIAI 2018, Rhodes, Greece, May 25-27, 2018, Proceedings

Lazaros Iliadis Ilias Maglogiannis Vassilis Plagianakos(Editor)

Springer (Publisher)

Published on 21. May 2018

XXIV, 649 pages

E-Book

PDF with digital watermarking

System requirements

978-3-319-92007-8 (ISBN)

€96.29incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
Preface
Organization
Abstracts
Empirical Approach to Learning from Data (Streams): Fast and Interpretable Deep Learning
AI Designing Games for Us. with (or Without) Us
Deep Learning and Robotics: Perception, Control, and Innovations
Tutorial
Contents
Social Media - Games - Ontologies
On Addressing the Challenges of Complex Stochastic Games Using ``Representative'' Moves
1 Introduction
2 Background
3 Representative Moves
4 Game Model
5 Experimental Design
6 Results
7 Discussion
8 Conclusions and Future Work
References
Finding Influential Users in Twitter Using Cluster-Based Fusion Methods of Result Lists
1 Introduction
2 Related Work
3 System Description
3.1 Modular Architecture
3.2 Feature Extraction
3.3 Clustering and Ranking
4 Experimental Evaluation
4.1 Dataset
4.2 Top-K Users
4.3 User Evaluation
5 Conclusions and Future Work
References
Studying the Dissemination of the K-core Influence in Twitter Cascades
Abstract
1 Introduction
2 Related Work
3 Relationship Between K-core Size and Graph Size
3.1 Conditions for the Presence of Correlation
3.2 Correlation on Synthetic Scale-Free Power-Law Degree Graphs
3.3 DD Similarity in Real-Life Dynamic Cascades
4 Experiments and Results
4.1 Datasets Description
4.2 Correlation on Twitter Datasets
4.3 The Spam Effect on the Correlation
5 Discussion and Conclusion
References
Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning
Abstract
1 Introduction
2 Social Network Spam Filtering - A Literature Review
3 Dataset
4 Methods
5 Experimental Results
6 Conclusion
Acknowledgments.
References
Sub-event Detection on Twitter Network
Abstract
1 Introduction
2 Background
3 Methodology
3.1 Kalman Filter (KF)
3.2 Gaussian Process (GP)
3.3 Probabilistic Principle Component Analysis (PPCA)
4 Experiments
5 Results
6 Conclusion
References
Ontology-Based Spatial Pattern Recognition in Diagrams
1 Introduction
2 Ontology-Based Spatial Pattern Recognition
3 Prototype: Automated Extraction of UML Concepts from Class Diagrams in SVG Format
3.1 An Ontology of Spatial Patterns for Class Diagrams
3.2 Prototype Design and Implementation
4 Prototype Performance
5 Discussion
6 Related Work
7 Conclusion
References
Deep Learning
Improving Deep Models of Person Re-identification for Cross-Dataset Usage
Abstract
1 Introduction
2 Metric Embedding Learning for Person Re-ID
2.1 Loss Function
2.2 Model
3 Improvements
3.1 Embedding Learning on Multiple Datasets
3.2 Practically Unsupervised Fine-Tuning
3.2.1 Extracting Positive Samples
3.2.2 Extracting Negative Samples
3.2.3 Batch Hard Modification
4 Experiments
4.1 Multiple Datasets
4.2 Unsupervised Fine-Tuning
5 Conclusion
References
Keywords-To-Text Synthesis Using Recurrent Neural Network
Abstract
1 Introduction
2 Previous Work and Motivation
3 Preprocessing Methodology
3.1 Detecting Verbs and Nouns with Parts of Speech Tagging
3.2 Unifying Synonyms
3.3 Other Keyword Elimination Measures
3.4 Segmentation of Texts
3.5 Data Reformulation
4 Recurrent Neural Network Training and Results
4.1 Training Details
4.2 Evaluation
4.3 Selecting Recurrent Neural Network Mapping Model
4.4 Results and Discussion
5 Conclusion and Future Work
Acknowledgements
References
Attention-Based Temporal Weighted Convolutional Neural Network for Action Recognition
Abstract
1 Introduction
2 Related Works
3 Formulation
3.1 Temporally Structured Representation of Action
3.2 Temporal Attention Model
3.3 Implementation Details
4 Experiments
5 Conclusion
Acknowledgment
References
Content-Aware Attention Network for Action Recognition
1 Introduction
2 Related Work
2.1 Hand-Crafted Feature Based Methods
2.2 Deep Learning Based Methods
3 Content-Aware Attention Network
3.1 Frame-Level Feature Embedding
3.2 Adaptive Attention Weighting Block
3.3 Content-Aware Weighting Block
3.4 Two-Stream Structure
4 Experiments and Discussions
4.1 Implementation Details
4.2 Evaluation of the Proposed Attention Module
4.3 Evaluation of the Proposed CatNet for Action Recognition
5 Conclusion and Future Work
References
Cognition-Based Deep Learning: Progresses and Perspectives
Abstract
1 Introduction
2 Fundamental Concepts
2.1 Deep Learning
2.2 Cognitive Mechanisms
2.3 Combination
3 Deep Learning Inspired by Memory Mechanism
3.1 RNNs-Based Memory Model
3.2 Memory Model with Importance
4 Attention Mechanism Applied to Deep Learning
4.1 Natural Language Processing
4.2 Object Detection
4.3 Deep Reinforcement Learning
5 Deep Neural Networks with Knowledge
5.1 Intuitive Psychology
5.2 Intuitive Physics
5.3 Domain Knowledge
6 Perspectives
6.1 General Framework of Cognition-Based Deep Learning
6.2 Key Problems and Potential Solutions
6.3 Associative Memory
6.4 Interpretable Network with Cognitive Mechanisms
6.5 Cognition-Based Deep Reinforcement Learning
7 Conclusion
Acknowledgment
References
A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages
Abstract
1 Introduction
2 Method
2.1 Date Code Region Identification
2.2 Expiry Date Recognition
3 Experimental Results
3.1 Expiry Date Region Detection Evaluation
3.2 Expiry Date Recognition Evaluation
4 Conclusions
References
Spatial-Temproal Based Lane Detection Using Deep Learning
Abstract
1 Introduction
2 Related Work
3 Spatial and Temporal Based Lane Boundary Detection
3.1 Spatial-Temporal Based Lane Boundary Position Estimation in Top View
3.2 CNN for Boundary Type Classification and Lane Boundary Regression
3.3 Lane Fitting and Optimization
4 Experiments
4.1 Model Training
4.2 Evaluation
4.3 Implementation on Embedded System
5 Conclusion and Future Work
Acknowledge
References
Support Vector Machines
An Investigation into the Effects of Multiple Kernel Combinations on Solutions Spaces in Support Vector Machines
1 Introduction
2 Related Work
2.1 Hyperparameter Tuning
2.2 Genetic Algorithm Approach to Kernel Weights
2.3 Computational Cost
3 Design and Methodology
3.1 Business Understanding
3.2 Data Understanding
3.3 Data Preparation
3.4 Modeling
3.5 Evaluation
4 Results
4.1 Cross Validation
4.2 Kruskal Wallis Analysis
4.3 Summary of Findings, Strengths and Limitations
5 Conclusion
References
A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization
Abstract
1 Introduction
2 Proposed Algorithm and Probabilistic Properties
2.1 Sequential Execution
2.2 Parallel Execution
2.3 The Inverse Problem
3 Experiments
3.1 Accuracy Estimation
3.2 Efficiency of the Stopping Condition
3.3 Scalability
4 Conclusions
References
Constraints
Greedy Heuristics for Automatic Synthesis of Efficient Block-Structured Scheduling Processes from Declarative Specifications
1 Introduction
2 Process Models
3 Declarative Specification of Ordering Constraints
3.1 Activity Ordering Graph
3.2 Optimal Scheduling Processes
4 Heuristics for Suboptimal Processes
4.1 Hierarchical Decomposition Heuristic
4.2 Critical Path Heuristic
4.3 Reducing the Duration of Execution
4.4 Automatic Synthesis Algorithm
5 Experimental Evaluation
6 Conclusions
References
Corpus Based Machine Translation for Scientific Text
Abstract
1 Introduction
1.1 Machine Translation
1.2 English to Urdu Machine Translation
1.3 Machine Translation Service for Scientific Text
1.4 Problems in Automated Translation of Scientific Text
2 Shortfalls in Existing MT Techniques for Scientific Text
3 Methodology
3.1 Overview of Proposed Scientific Text Translator
3.2 Module 1: Preprocessing
3.3 Module 2: Sentence Fragmentation
3.4 Module 3: CBR (Case Based Reasoning) Trainer
3.5 Module 4: Reordering
4 Experimental Studies
4.1 Experimental Corpus
4.2 Experiments
5 Conclusion
References
Machine Learning - Regression - Classification
The Regularization of CSPs for Rostering, Planning and Resource Management Problems
Abstract
1 Introduction
2 Basic Principles of Constraint Programming
3 Transformation from a CSP into a RCSP
3.1 Theoretical Considerations
3.2 Transformations from Special Global Constraints into Regular Constraints
4 The Use of Regular CSPs
4.1 Advantages and Disadvantages
4.2 A Rostering Example
5 Conclusion and Future Work
References
An Evaluation of Regression Algorithms Performance for the Chemical Process of Naphthalene Sublimation
Abstract
1 Introduction
2 Related Work
3 The Naphthalene Sublimation Dataset
4 Regression Algorithms
4.1 Classical Algorithms
4.2 The Large Margin Nearest Neighbor Regression Algorithm
5 Results and Discussion
5.1 Parameters of Regression Methods
5.2 A Comparison Between Algorithm Performance
6 Conclusions
Acknowledgments
References
Using Decision Trees to Extract Patterns for Dairy Culling Management
Abstract
1 Introduction
2 Related Work
3 Modelling the Culling Task
3.1 The Data Base
4 Results
5 Discussion
6 Conclusions
Acknowledgments
References
Wind Energy Forecasting at Different Time Horizons with Individual and Global Models
Abstract
1 Introduction
2 Data
3 Individual and Global Approaches for Energy Forecasting
4 Experimental Results
5 Conclusions
Acknowledgements
References
Entropy-Assisted Emotion Recognition of Valence and Arousal Using XGBoost Classifier
Abstract
1 Introduction
2 Common Approaches of Emotion Recognition Framework
2.1 Pre-processing
2.2 Feature Extraction
2.3 Machine Learning Engine
3 Proposed Framework with Entropy Domain Features and XGBoost Classifier
3.1 Entropy Domain Features
3.2 Extreme Gradient Boosting (XGBoost)
4 Experimental Settings and Results
5 Conclusion
Acknowledgements
References
Evaluating Sequence Discovery Systems in an Abstraction-Aware Manner
1 Introduction
2 Prior Work
2.1 Stability-Based Metrics
2.2 Ward et al.'s Error Analysis Technique
3 Ground Truth-Based Metrics
4 Instances, Types and Abstractions
5 A Proposal for a New Metric
6 Experiments and Results
7 Conclusion
References
PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches
Abstract
1 Introduction
2 Impurity Measures
2.1 Mathematical Formulations
2.2 Parameterised Impurity Measures
3 S-pruning
4 Comparison of Decision Tree Classifiers with Various Impurity Measures
4.1 Experimental Analysis
5 Conclusion and Directions for Future Work
References
Smoothness Bias in Relevance Estimators for Feature Selection in Regression
Abstract
1 Introduction
2 Feature Selection with Filters
2.1 Feature Selection with Mutual Information
2.2 Feature Selection with Noise Variance
3 Behaviour of kNN-Based Estimators of Relevance Criteria in Small Sample Scenarios
3.1 Mutual Information Analysis
3.2 Delta Test Analysis
3.3 Discussion
4 Experimental Settings
5 Experimental Results
6 Conclusion
References
Neural Networks
The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment
Abstract
1 Introduction
2 Related Work
3 The Random Neural Network Genetic Deep Learning Model
3.1 The Random Neural Network
3.2 The Random Neural Network with Multiple Clusters
3.3 Deep Learning Management Cluster
3.4 Genetic Learning Algorithm Model
4 Smart Investment Model
4.1 Asset Banker Reinforcement Learning
5 Experimental Results
5.1 Asset Banker Reinforcement Learning Validation
5.2 Market Banker Deep Learning Management Cluster Validation
5.3 CEO Banker Deep Learning Management Cluster Validation
5.4 Genetic Algorithm Validation
6 Conclusions
Appendix: Smart Investment Model - Neural Schematic
References
Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use?
Abstract
1 Introduction
2 Building Prediction Models
2.1 Data Preparation
2.2 Missing Values Treatment
2.3 Training and Tuning Feed-Forward Artificial Neural Networks
2.4 Treating Unbalanced Classes
2.5 Increasing Model Performance via Optimized Cut-off Point Selection on the ROC Curve
2.6 Monte Carlo and Models' Stability
3 Cannabis Use Attributes' Predictive Power
3.1 Student's t-Test
3.2 Ranking Attributes' Importance with the ROC Curve Approach
4 Conclusion and Future Work
References
Cost-Sensitive Decision Making for Online Fraud Management
Abstract
1 Introduction
2 Related Work
3 Problem Definition
4 Methodology
4.1 Feature Extraction
4.2 Fraud Classification Model and Risk Score Calculation
4.3 Cost-Sensitive Label Derivation
4.4 Profit-Optimizing Neural Risk Manager
5 Experiments
5.1 Evaluation Metrics
5.2 Dataset and Parameter Settings
5.3 PONRM vs. Cost-Sensitive and Cost-Insensitive Baselines
5.4 PONRM vs. Risk Managers Under Different Review Capacities
5.5 Which Classifier to Use as the Fraud Classification Model
6 Conclusion and Future Work
Acknowledgements
References
Echo State Network for Classification of Human Eye Movements During Decision Making
Abstract
1 Introduction
2 Clustering Algorithm
2.1 Echo State Network and IP Tuning
2.2 Classification Approach for Dynamic Data Series
3 Experimental Set-Up
4 Classification Results and Discussion
5 Conclusions
Acknowledgment
References
Medical Intelligence
Iliou Machine Learning Data Preprocessing Method for Stress Level Prediction
Abstract
1 Introduction
2 Diagnostic Criteria
3 Cognitive Models
4 Beck Anxiety Inventory
5 Data Collection
6 Data Preprocessing
7 Experimental Results
8 Conclusions
References
A Temporal-Causal Network Model for the Internal Processes of a Person with a Borderline Personality Disorder
Abstract
1 Introduction
2 Neuropsychological Background
3 The Temporal-Causal Network Model
3.1 The Conceptual Representation of the Network Model
3.2 Numerical Representation of the Network Model
4 Simulation Results
5 Verification by Mathematical Analysis
6 Discussion
References
Content Based Image Retrieval in Digital Pathology Using Speeded Up Robust Features
Abstract
1 Introduction
2 Related Work
3 Methodology
4 The System in Practice
5 Experimental Results
6 Conclusion
References
An Investigation of Argumentation Theory for the Prediction of Survival in Elderly Using Biomarkers
1 Introduction
2 Related Work
3 Design and Methodology
3.1 Layer 1 - Definition of the Structure of Arguments
3.2 Layer 2 - Definition of the Conflicts of Arguments
3.3 Layer 3 - Evaluation of the Conflicts of Arguments
3.4 Layer 4 - Definition of the Dialectical Status of Arguments
3.5 Layer 5 - Accrual of Acceptable Arguments
4 Results
4.1 Accuracy
4.2 Sensitivity
4.3 Discussion
5 Conclusion and Future Work
References
Recommender Systems
Reproducibility of Experiments in Recommender Systems Evaluation
Abstract
1 Introduction
2 Background
3 Comparing Experimental Results Using Different Libraries
3.1 Settings
3.2 Recommendation Methods
3.3 Accuracy Measure
3.4 Results
4 Proposed Approach
4.1 Guidelines
4.2 Replication
5 Conclusions and Future Work
References
Optimization
Advertiser Bidding Prediction and Optimization in Online Advertising
1 Introduction
1.1 Our Contribution
2 Model
2.1 Setup
2.2 Dataset and Linear Regression Models
3 Problem Statement and Formulation
3.1 Bidding for Maximizing Total Number of Clicks for Ads
3.2 Solution
4 Data Experiments
4.1 Dataset and Regression Models
4.2 Results
5 Related Work
6 Conclusion
References
A Multi-objective Data Mining Approach for Road Traffic Prediction
Abstract
1 Introduction
2 Related Work
3 Motivation and Contribution
4 Proposed Approach
4.1 Problem Formulation
4.2 Road Segment Distance Measures
4.3 Multi-objective Multidimensional Scaling
5 Evaluation of the Proposed Approach
5.1 Dataset Description
5.2 Experimental Results
6 Conclusions and Next Steps
Acknowledgments
References
A Simulation-Based Analysis of Interdependent Populations in a Dynamic Ecological Environment
1 Introduction
2 Problem Description
3 Implementation of the Problem
3.1 Application Architecture and Representation
3.2 Defining the Simulation Model
4 Performed Tests and Results Obtained
4.1 Model Accuracy
4.2 Ecosystem's Capability to Self-Sustain
4.3 Input/Output Relation
4.4 Approach to Efficiently Optimize Input Parameters
5 Conclusion and Future Work
References
Learning - Intelligence
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
1 Introduction
1.1 Contributions of the Paper
2 The HCPA LA
2.1 Rationale for Our Solution
2.2 Construction of the Hierarchy
2.3 The Proposed Solution
2.4 The Algorithm of the Proposed Solution
3 Experimental Results
3.1 The Data Sets for the Environment
3.2 Convergence of the HCPA Algorithm
3.3 Average Convergence Iterations
3.4 Environment with 128 Actions
4 Conclusions
References
Speedup of Network Training Process by Eliminating the Overshoots of Outputs
Abstract
1 Introduction
2 The Overshoot Definition and Mathematical Method
3 Numerical Simulation and Result Analysis
4 Conclusion
Acknowledgements
References
Collective Intelligence for Decision-Making in Complex Environments: Literature Review
Abstract
1 Introduction
2 Methodology
3 Collective Intelligence in Natural and Artificial Social Systems
4 Collective Intelligence in Human Systems
5 Gap Between Natural and Artificial Systems Compared to Human Systems
6 Discussions and Future Research Lines
References
Quantile Estimation Based on the Principles of the Search on the Line
1 Introduction
1.1 Legacy SPL Solutions
2 Discretized Estimator
3 Experimental Results
3.1 Comparison in Stationary Environments for Different Distributions
4 Conclusion
References
Heuristic approaches - Cloud
Improved Cuckoo Search with Luus-Jakoola Heuristics for the IFS Inverse Problem of Binary Self-Similar Fractal Images
1 Introduction
2 Basic Concepts and Definitions
2.1 Iterated Function Systems
2.2 The Collage Theorem
2.3 The IFS Inverse Problem
3 The Cuckoo Search Algorithms
3.1 Original Cuckoo Search (CS)
3.2 Improved Cuckoo Search (ICS)
4 Proposed Approach
4.1 Hybrid ICS with Luus-Jakoola Heuristics
4.2 Application to the IFS Inverse Problem
4.3 Parameter Tuning
5 Experimental Results
6 Conclusions and Future Work
References
A Scatter Search Based Heuristic for Reliable Clustering in Vehicular Ad Hoc Networks
Abstract
1 Introduction
2 Prior Works
3 Theoretical Foundation of the Proposed Model
3.1 Weighted k-Medoids Clustering Approach (WKCA)
3.2 Scatter Search (SS)
3.3 Tabu Search (TS)
4 Proposed Model
4.1 Initialization (by User)
4.2 Reference Set Generation
4.3 Evaluation
4.4 Subset Generation
4.5 Combination Method
4.6 Improvement Method
5 Experimental Results and Analysis
5.1 Simulation Setup
5.2 Simulation Results
6 Conclusion
References
Providing Mission-Critical Services over 5G Radio Access Network
Abstract
1 Introduction
2 Description of the Public Safety Use Case
3 5G Cloud-Enabled RAN with MEC Capabilities
3.1 Network Slicing Within the Radio Access Network
3.2 Radio Resource Management Within the Radio Access Network
3.3 Management and Orchestration of Edge Services
4 Deploying Mission-Critical Service on MEC Infrastructure
4.1 Distributing Core Operator Infrastructure at the Edge of the Network
4.2 Isolated E-UTRAN Operation for Public Safety
5 Conclusions
Acknowledgement
References
Fuzzy
A Study of Heuristic Evaluation Measures in Fuzzy Rule Induction
Abstract
1 Introduction
2 FuzzyRULES
3 Evaluation Measures
3.1 Purity
3.2 Information Content
3.3 Entropy
3.4 Information Gain
3.5 Accuracy
3.6 Laplace
3.7 m-estimate
3.8 H Measure
3.9 AQ18 Measure
4 Experimental Evaluation
5 Conclusions and Future Work
Acknowledgements
References
A Visual Quality Index for Fuzzy C-Means
1 Introduction
2 Principles of Fuzzy Clustering
3 Fuzzy Clustering Quality Indices
4 An Index Associated with a Visual Solution
5 Experimental Validation
5.1 Datasets
5.2 Experimental Results
6 Conclusion and Perspectives
References
Evaluation of the Linked Open Data Quality Based on a Fuzzy Logic Model
Abstract
1 Introduction
2 Theoretical Background and Related Work
2.1 Quality Dimensions
2.2 Fuzzy Logic
2.3 Related Work
3 The Proposed Methodology
4 The Infrastructure of the Implemented Model
5 Results
6 Conclusion and Discussion
References
A Hybrid Fuzzy Regression-Based Methodology for Normal Distribution (Case Study: Cumulative Annual Precipitation)
Abstract
1 ?ntroduction
2 Basic Notions
3 Proposed Methodology
3.1 Modulating the Independent and Dependent Variables
3.2 Applying Fuzzy Regression
3.3 Evaluation of the Solution Achieved
4 Application in Case of Annual Rainfall Time Series
5 Concluding Remarks
References
Fuzzy Approach for Bibliometric Analysis of Publication Trends on Intragastric Balloon as a Minimally Invasive Procedure for Weight Loss in Obese Individuals
Abstract
1 Introduction
2 Materials and Methods
2.1 Data Source and Description
2.2 Data Analysis Techniques
3 Results and Discussion
3.1 Time Series Components
3.2 Trend Extraction and Fitting
3.3 Verification of Lotka's Law with Fuzzy Estimators
4 Conclusion
Appendix
References
Temporal Modeling of Invasive Species' Migration in Greece from Neighboring Countries Using Fuzzy Cognitive Maps
Abstract
1 Introduction
1.1 Invasive Species
1.2 Related Literature - Innovations of the Proposed Methodology
1.3 Description of Data
2 Theoretical Frameworks and Methodology
2.1 Correlation Analysis
2.2 Fuzzy Cognitive Maps
2.3 Climate Change Scenarios Employed
3 Description of the Proposed Methodology
4 Results and Discussion
5 Conclusions and Future Work
References
An Approach to Modelling User Interests Using TF-IDF and Fuzzy Sets Qualitative Comparative Analysis
Abstract
1 Introduction
2 User Profiling in Tourism
3 Methodology
4 Data Analysis: Illustrative Example
5 Conclusions-Future Research
References
Human & Computer Interaction - Sound - Video - Processing
Spatial-Temporal Neural Networks for Action Recognition
Abstract
1 Introduction
1.1 Related Work
2 Spatial-Temporal Neural Network Model
3 Data Processing
3.1 RGB Multi Frame Sequence Input Processing
3.2 Multi Optical Flow Calculation
4 Experiments
4.1 Action Recognition on MSR DailyActivity 3D Dataset
4.2 Action Recognition on UCF101 Dataset
5 Conclusions
Acknowledgement
References
Building Trust Between Users and Telecommunications Data Driven Virtual Assistants
Abstract
1 Introduction
2 What Does Trust Mean and Why Is It Important?
3 Key Challenges in Trusting an AI
3.1 A Lack of Knowledge of AI Does not Help Build Reliance
3.2 Mistrusting AI to Solve Complex Problems
3.3 Managing and Handling Personal and Private Data
4 Best Practices in AI: What to Do and What to Avoid
5 The Context
6 Methodology and Sample
7 Findings
7.1 How Should a Telecoms Data Driven Virtual Assistant Be?
7.2 Transferring Human Roles to AI
7.3 The Importance of the Decision-Making Process
7.4 Gathering and Handling Personal Data
8 Conclusions
References
Voice Separation in Polyphonic Music: Information Theory Approach
Abstract
1 Introduction
2 Information Theory
3 The Voice Separation Model
4 Obtained Results
5 Discussion and Conclusions
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Artificial Intelligence Applications and Innovations

Description

More details

Other editions

Additional editions

Content

System requirements