%{search_type} search results

• A Study of Energy Reduction Strategies in Renewable Hybrid Grid.- Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks.- A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout.- EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions.- SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks.- Toward Intelligent Solution to Identify Learner Attitude from Source Code.
• (source: Nielsen Book Data)

This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. The 29 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• Minimum Diameter $k$-Steiner Forest.- Factors Impacting the Label Denoising of Neural Relation Extraction.- Makespan Minimization on Unrelated Parallel Machines with a Few Bags.- Channel Assignment with r-Dynamic Coloring.- Profit Maximization Problem with Coupons in Social Networks.- A Bicriteria Approximation Algorithm for Minimum Submodular Cost Partial Multi-Cover Problem.- A Novel Approach to Verifying Context Free Properties of Programs.- Determination of Dual Distances for a Kind of Perfect Mixed Codes.- Approximation and Competitive Algorithms for Single-Minded Selling Problem.- An Empirical Analysis of Feasibility Checking Algorithms for UTVPI Constraints.- Quality-aware Online Task Assignment Using Latent Topic Model.- Calibration Scheduling with Time Slot Cost.- The k-power domination problem in weighted trees.- General Rumor Blocking: An Efficient Random Algorithm with Martingale Approach.- A Robust Power Optimization Algorithm to Balance Base Stations' Load in LTE-A Network.- Faster Compression of Patterns to Rectangle Rule Lists.- Algorithm Designs for Dynamic Ridesharing System.- New LP Relaxations for Minimum Cycle/Path/Tree Cover Problems.- Computation of Kullback-Leibler Divergence between Labeled Stochastic Systems with Non-Identical State Spaces.- Order preserving barrier coverage with weighted sensors on a line.- Achieving Location Truthfulness in Rebalancing Supply-Demand Distribution for Bike Sharing.- Approximation algorithms and a hardness result for the three-machine proportionate mixed-shop problem.- A New Algorithm Design Technique for Hard Problems, Building on Methods of Complexity Theory.- Community-based Acceptance Probability Maximization for Target Users on Social Networks.- Knowledge Graph Embedding Based on Subgraph-aware Proximity.
• (source: Nielsen Book Data)

This volume constitutes the proceedings of the 12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018, held in Dallas, Texas, USA, in December 2018. The 25 full papers presented were carefully reviewed and selected. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents; Time Series Representation and Compression; Symbolic Representation of Time Series: A Hierarchical Coclustering Formalization; 1 Introduction; 2 Related Work; 3 Formalization of the SAXO Approach; 3.1 Prior Distribution of the SAXO Models; 3.2 Likelihood of Data Given a SAXO Model; 3.3 Evaluation Criterion; 4 Comparative Experiments on Real Datasets; 4.1 Coding Length Evaluation; 4.2 Supervised Learning Evaluation; 5 Conclusion and Perspectives; References; Dense Bag-of-Temporal-SIFT-Words for Time Series Classification; 1 Introduction; 2 Related Work

• 2.1 Distance-Based Time Series Classification2.2 Bag-of-Words for Time Series Classification; 2.3 Ensemble Classifiers for Time Series; 3 Bag-of-Temporal-SIFT-Words (BoTSW); 3.1 Keypoint Extraction in Time Series; 3.2 Description of the Extracted Keypoints; 3.3 Bag-of-Temporal-SIFT-Words for Time Series Classification; 4 Experiments and Results; 4.1 Experimental Setup; 4.2 Dense Extraction vs. Scale-Space Extrema Detection; 4.3 Impact of the BoW Normalization; 4.4 Comparison with State-of-the-Art Methods; 5 Conclusion; References

• Dimension Reduction in Dissimilarity Spaces for Time Series Classification1 Introduction; 2 Related Work; 3 Dissimilarity Representations of Time Series; 3.1 The Basic Idea; 3.2 Dynamic Time Warping Distance; 3.3 Dissimilarity Representations; 3.4 Learning Classifiers in Dissimilarity Space; 3.5 Prototype Dependent Kernels; 4 Experiments; 4.1 Data; 4.2 Classifiers; 4.3 Experimental Protocol; 4.4 Results; 5 Conclusion; A Performance Profiles; References; Time Series Classification and Clustering; Fuzzy Clustering of Series Using Quantile Autocovariances; 1 Introduction

• 2 A Dissimilarity Based on Quantile Autocovariances3 Fuzzy Clustering Based on Quantile Autocovariances; 4 Simulation Study; 5 A Case Study; 6 Concluding Remarks; References; A Reservoir Computing Approach for Balance Assessment; 1 Introduction; 2 Balance Assessment Using Reservoir Computing; 3 Experimental Results; 4 Conclusions; References; Learning Structures in Earth Observation Data with Gaussian Processes; 1 Introduction; 2 Gaussian Process Regression; 2.1 Gaussian Processes: A Gentle Introduction; 2.2 On the Model Selection; 2.3 On the Covariance Function

• 2.4 Gaussian Processes Exemplified3 Advances in Gaussian Process Regression; 3.1 Structured, Non-stationary and Multiscale; 3.2 Time-based Covariance for GPR; 3.3 Heteroscedastic GPR: Learning the Noise Model; 3.4 Warped GPR: Learning the Output Transformation; 3.5 Source Code and Toolboxes; 4 Analysis of Gaussian Process Models; 4.1 Ranking Features Through the ARD Covariance; 4.2 Uncertainty Intervals; 5 Conclusions and Further Work; References; Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes; 1 Introduction; 2 Methods Used

This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The 11 full papers presented were carefully reviewed and selected from 22 submissions. The first part focuses on learning new representations and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.

• Robust Functional Regression for Outlier Detection
• Transform Learning Based Function Approximation for Regression and Forecasting
• Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data
• A fully automated periodicity detection in time series
• Conditional Forecasting of Water Level Time Series with RNNs
• Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories
• Localized Random Shapelets
• Feature-Based Gait Pattern Classification for a Robotic Walking Frame
• How to detect novelty in textual data streams? A comparative study of existing methods
• Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model
• Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
• Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
• Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning
• Learning Stochastic Dynamical Systems via Bridge Sampling
• Quantifying Quality of Actions Using Wearable Sensor
• An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis.

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.

• Intro; Preface; Organization; Contents; On the Trustworthy Fulfillment of Commitments; 1 Motivation; 2 Computational Models of Commitment; 3 Problem Formulation; 4 Commitment Semantics; 4.1 Relationship to Other Commitment Semantics; 4.2 Semantics-Respecting Algorithms; 4.3 Semantics with Other Kinds of Uncertainty; 5 Implications for Non-Decision-Theoretic Agents; 6 Conclusions; References; Evaluating the Efficiency of Robust Team Formation Algorithms; 1 Introduction; 2 Problem Definition; 3 Related Work; 4 Approximations of the TORTF Problem; 4.1 Greedy Algorithms; 4.2 Genetic Algorithm

• 4.3 Linear Programming Approach5 Results and Discussion; 5.1 Datasets; 5.2 Results; 6 Conclusions; References; Social Welfare in One-Sided Matching Mechanisms; 1 Introduction; 1.1 Our Results; 1.2 Discussion and Related Work; 2 Preliminaries; 3 Price of Anarchy Guarantees; 4 Lower Bounds; 5 General Solution Concepts; 6 Extensions; 6.1 Price of Stability; 6.2 Unit-Range Representation; 7 Conclusion and Future Work; References; Using Multiagent Negotiation to Model Water Resources Systems Operations; 1 Introduction; 2 Related Work; 3 The Case Study; 4 The Negotiation Protocols

• 4.1 Point-Based Protocol4.2 Set-Based Protocol; 5 Simulations; 6 Conclusions; References; To Big Wing, or Not to Big Wing, Now an Answer; 1 Introduction; 1.1 The Battle of Britain; 1.2 The Lanchester Model; 1.3 Agent Based Models; 2 Model Design; 2.1 RAF Forces; 2.2 German Forces; 2.3 Model Functionality; 3 Experiments; 4 Results; 5 Conclusion; References; How Testable Are BDI Agents? An Analysis of Branch Coverage; 1 Introduction; 2 Belief-Desire-Intention (BDI) Agents; 3 All-Edge Coverage Analysis; 3.1 Removing Failure Handling; 3.2 Simplifying for Uniform Programs

• 4 All-Edges vs. All-Paths5 BDI vs. Procedural; 6 Conclusion; References; Dynamics of Fairness in Groups of Autonomous Learning Agents; 1 Introduction; 2 Multiplayer Ultimatum Game; 2.1 Sub-game Perfect Equilibrium; 3 Learning Model; 4 Results; 5 Discussion and Conclusion; References; Using Stackelberg Games to Model Electric Power Grid Investments in Renewable Energy Settings; 1 Introduction; 2 Related Work; 3 Curtailment Rules; 3.1 Effects of Curtailment Strategies on Renewable Capacity Utilisation
• An Illustration; 4 Renewable Investment in Single Locations

• 4.1 Individual Generator Incentives4.2 Total Generation Capacity; 5 Transmission Investment in Multiple Locations; 5.1 Implementation in Areas with High Curtailment; 5.2 Transmission Investment as a Stackelberg Game; 6 Network Upgrade Case Study; 7 Conclusions and Future Work; References; Multi-scale Simulation for Crowd Management: A Case Study in an Urban Scenario; 1 Introduction; 2 Related Works; 3 A Multi-scale Model for the Simulation of Urban Scenarios; 3.1 The Discrete Microscopic Model; 3.2 The Mesoscopic Model; 3.3 Strategic Model; 4 Analysis of an Urban Scenario

This book features a selection of best papers from 11 workshops held at the International Conference on Autonomous Agents and Multiagent Systems, in Singapore in May 2016. The 11 full papers were carefully reviewed and selected for inclusion in this volume. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.

• A Language for Trust Modelling (TRUST workshop)
• Abstraction Methods for Solving Graph-Based Security Games (SECMAS workshop)
• Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning (ARMS workshop)
• Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network (OPTMAS workshop)
• POMDPs for Assisting Homeless Shelters
• Computational and Deployment Challenges (IDEAS workshop)
• Summarizing simulation results using causally-relevant states (MABS workshop)
• Augmenting Agent Computational Environments with Quantitative Reasoning Modules and Customisable Bridge Rules (EMAS workshop)
• Using Awareness to Promote Richer, More Human-Like Behaviors in Artificial Agents (ALA workshop)
• Using GDL to Represent Domain Knowledge for Automated Negotiations (ACAN workshop)
• Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam (ABMUS workshop)
• Gamification of Multi-Agent Systems Theory Classes (COIN/CARE workshop)
• Analysis of Market Trend Regimes for March 2011 USDJPY Exchange Rate Tick Data (WEIN workshop).

This book constitutes the most visionary papers of the AAMAS 2016 Workshops, held in Singapore, Singapore, in May 2016. The 12 revised full papers presented were carefully reviewed and selected from the 12 workshops. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.

• Urban Systems Modelling.- Towards an Agent-Based Simulation of Housing in Urban Beirut.- Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam.- Integrating Behavior and Microsimulation Models.- Agent-Based Modelling for Urban Planning Current Limitations and Future Trends.- Traffic Simulation in Urban Modelling.- Software Architecture for a Transparent and Versatile Traffic Simulation.- A Generic Software Framework for Carsharing Modelling based on a Large-Scale Multi-Agent Traffic Simulation Platform.- Mapping bicycling patterns with an agent-based model, census and crowdsourced data.- Transportation in Agent-Based Urban Modelling.- Applications.- Simulation-aided Crowd Management: a Multi-scale Model for an Urban Case Study.- A National Heat Demand Model for Germany.- How Smart is the Smart City? Assessing the Impact of ICT on Cities.
• (source: Nielsen Book Data)

This book constitutes revised, selected, and invited papers from the First International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016, held in conjunction with AAMAS 2016 in Singapore in May 2016. The 11 papers presented in this volume were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: urban systems modeling; traffic simulation in urban modeling; and applications. .
(source: Nielsen Book Data)

• Mathematical Modeling and Theory
• Fractal Dimension
• A Spatial and Visual Design Technique for the Creation of Lifelike Artificial Forms
• Using Closed Sets to Model Cognitive Behavior
• Learning and Optimization
• Solving dynamic optimisation problem with known changeable boundaries
• Compaction for Code Fragment Based Learning Classifier Systems
• The Boon of Gene-Culture Interaction for Effective Evolutionary Multitasking
• A Study on Performance Metrics to Identify Solutions of Interest From a Trade-off Set
• Dynamic Configuration of Differential Evolution Control Parameters and Operators
• Exploring the Feasible Space using Constraint Consensus in Solving Constrained Optimization Problems
• A Nested Differential Evolution based Algorithm for Solving Multi-objective Bilevel Optimization Problems
• Parkinson's Disease Data Classification Using Evolvable Wavelet Neural Networks
• GO-PEAS: A Scalable Yet Accurate Grid-based Outlier Detection Method Using Novel Pruning Searching Techniques
• Multi-objective Genetic Programming for Figure-ground Image Segmentation
• A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation
• Competitive Island Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction
• Reverse Neuron Level Decomposition for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction
• A Delaunay Triangulation Based Density Measurement for Evolutionary Multi-objective Optimization
• Use of Infeasible Solutions During Constrained Evolutionary Search: A Short Survey
• Planning and Scheduling
• A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems
• A hybrid imperialist competitive algorithm for flexible job shop problem
• Parallel Multi-objective Job Shop Scheduling Using Genetic Programming
• Optimization of Location Allocation of Web Services Using A Modified Non-dominated Sorting Genetic Algorithm
• Double Action Genetic Algorithm for Scheduling the Wind-Thermal Generators
• Feature Selection
• Investigating Multi-operator Differential Evolution for Feature Selection
• Coevolutionary Feature Selection and Reconstruction in Neuro-Evolution for Time Series Prediction
• A Subset Similarity Guided Method for Multi-objective Feature Selection
• Applications and Games
• An Evolutionary Optimization Approach to Maximize Runway Throughput Capacity for Hub and Spoke Airports
• Finite Population Trust Game Replicators
• Towards Evolved Time to Contact Neurocontrollers for Quadcopters
• The Effect of Risk Perceived Payoffs in Iterated Interdependent Security Games
• Genetic Algorithm Based Trading System Design.

This book constitutes the proceedings of the Second Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2016, held in Canberra, ACT, Australia, in February 2016. The 30 full papers presented in this volume were carefully reviewed and selected from 41 submissions. They are organized in topical sections named: mathematical modeling and theory; learning and optimization; planning and scheduling; feature selection; and applications and games.

• Artificial Life and Computational Intelligence.- Extending the Delaunay Triangulation Based Density Measurement to Many-objective Optimization.- Emotion, Trustworthiness and Altruistic Punishment in a Tragedy of the Commons Social Dilemma.- Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm.- Co-Evolving Line Drawings with Hierarchical Evolution.- Reliability estimation of individual multi-target regression predictions.- Feedback Modulated Attention Within a Predictive Framework.- A Batch Infill Strategy for Computationally Expensive Optimization Problems.- Automatic Clustering and Summarisation of Microblogs: A Multi-Subtopic Phrase Reinforcement Algorithm.- Generation and exploration of architectural form using a composite Cellular Automata.- Wrapper Feature Construction for Figure-ground Image Segmentation Using Genetic Programming.- Surrogate-assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils.- Applying Dependency Patterns in Causal Discovery of Latent Variable Models.- An Evolutionary Multi-criteria Journey Planning Algorithm for Multi-modal Transportation Networks.- Estimating Passenger Preferences Using Implicit Relevance Feedback for Personalized Journey.- Quantitative Assessment of Hearts Function: A Hybrid Mechanism for Left Ventricles Segmentation from Cine MRI Sequences.- A Hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance.- Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN.- Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution.- Detectable Genetic Algorithms-based techniques for solving Dynamic Optimisation Problem with Unknown Active Variables.- Neighbourhood analysis: a case study on Google Machine Reassignment Problem.- Optimisation Algorithms and Applications.- Multi-Objective Optimisation with Multiple Preferred Regions.- An Adaptive Memetic Algorithm for the Architecture Optimisation Problem.- Resource Constrained Job Scheduling with Parallel Constraint-based ACO.- An Iterated Local Search with Guided Perturbation for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints.- A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization.- Investigating the Generality of Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown.- Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution.- A PSO-based Reference Point Adaption Method for Genetic Programming Hyper-heuristic in Many-Objective Job Shop Scheduling.- Optimal power allocation of wireless sensor networks with multi-operator based constrained differential evolution.- CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed Bandits.- Binary PSO for Web Service Location-Allocation.- A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017, held in Geelong, VIC, Australia, in January/February 2017. The 32 papers presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: artificial life and computational intelligence and optimization algorithms and applications. .
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (source: Nielsen Book Data)

This book presents the outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (source: Nielsen Book Data)

• Proposed Framework Application for a Quality Mobile Application Measurement and Evaluation.- Proposal and Development of Artificial Personality (AP) application using the "Requesting" Mechanism.- Load Experiment of the vDACS Scheme in case of the 300 Simultaneous Connection.- Hearing-Dog Robot to wake People up using its Bumping Action.- Implementation of Document Production Support System with Obsession Mechanism.- Detecting Outliners in Terms of Errors in Embedded Software Development Projects Using Imbalance Data Classification.- Development of Congestion State Guiding System for University Cafeteria.- Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model.- Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning.- Improvement of "Multiple Sightseeing Spot Scheduling System".- Advertising in the Webtoon of Cosmetics Brand -Focusing on 'tn' Youth Cosmetics Brands
• Testing Driven Development of Mobile Application using Automatic Bug Management Systems.- Shape Recovery of Polyp from Endoscope Image Using Blood Vessel Information.- Design of Agent Development Framework for RoboCupRescue Simulation.
• (source: Nielsen Book Data)

This book presents the outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(source: Nielsen Book Data)
This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Proposed Framework Application for a Quality Mobile Application Measurement and Evaluation.- Proposal and Development of Artificial Personality (AP) application using the "Requesting" Mechanism.- Load Experiment of the vDACS Scheme in case of the 300 Simultaneous Connection.- Hearing-Dog Robot to wake People up using its Bumping Action.- Implementation of Document Production Support System with Obsession Mechanism.- Detecting Outliners in Terms of Errors in Embedded Software Development Projects Using Imbalance Data Classification.- Development of Congestion State Guiding System for University Cafeteria.- Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model.- Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning.- Improvement of "Multiple Sightseeing Spot Scheduling System".- Advertising in the Webtoon of Cosmetics Brand -Focusing on 'tn' Youth Cosmetics Brands
• Testing Driven Development of Mobile Application using Automatic Bug Management Systems.- Shape Recovery of Polyp from Endoscope Image Using Blood Vessel Information.- Design of Agent Development Framework for RoboCupRescue Simulation.
• (source: Nielsen Book Data)

This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Gradients versus Grey Values for Sparse Image Reconstruction and Inpainting-Based Compression
• Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms
• Neural Network Boundary Detection for 3D Vessel Segmentation
• A Simple Human Activity Recognition Technique using DCT
• Hand Gesture Recognition using Infrared Imagery Provided by Leap Motion Controller
• Horizon line detection from fisheye images using color local image region descriptors and Bhattacharyya coefficient-based distance
• Joint Segmentation Of Myocardium On Multi State Spect Images
• Parallel Hough space image generation method for real time lane detection
• A Novel Decentralised System Architecture for Multi-Camera Target Tracking
• Intramolecular FRET efficiency measures for time-lapse fluorescence microscopy images
• Predicting Image Aesthetics with Deep Learning
• Automatic Image Splicing Detection Based On Noise Density Analysis In Raw Images
• Breast Shape Parametrization through Planar Projections
• Decreasing Time Consumption of Microscopy Image Segmentation through Parallel Processing on the GPU
• Coral reef fish detection and recognition in underwater videos by supervised machine learning : Comparison between Deep Learning and hog+svm methods
• A Real-time Eye Gesture Recognition System Based on Fuzzy Inference System for Mobile Devices Monitoring
• Spatially Varying Weighting Function-based Global and Local Statistical Active Contours. Application to X-ray Images
• Vegetation segmentation in cornfield images using bag of words
• Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks
• The Orlando Project: a 28 nm FDSOI Low Memory Embedded Neural Network ASIC
• Factor Analysis of Dynamic Sequence with Spatial Prior for 2D Cardiac Spect Sequences Analysis
• Soccer Player Detection with Only Color Features Selected Using Informed Haar-like Features
• Person Re-identification in frontal gait sequences via Histogram of Optic flow Energy Image
• A Bayesian approach to linear unmixing in the presence of highly mixed spectra
• Key frames extraction based on local features for efficient video summarization
• A simple evaluation procedure for range camera measurement quality
• Accordion Representation based Multi-scale Covariance Descriptor for Multi-shot Person Re-identification
• Jensen Shannon divergence as reduced reference measure for image denoising
• Visual Localization using Sequence Matching Based on Multi-feature Combination
• Towards Automated Drone Surveillance in Railways: State-of-the-Art and Future Directions
• Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
• Multimodal Registration of PET/MR Brain Images based on Adaptive Mutual Information
• Aerial detection in maritime scenarios using convolutional neural networks
• R3P: Real-time RGB-D Registration Pipeline
• Vector Quantization Enhancement for Computer Vision Tasks
• Learning Approaches for Parking Lots Classification
• Video event detection based non-stationary Bayesian networks
• Optimized Connected Components Labeling with Pixel Prediction
• Hierarchical Fast Mean-Shift Segmentation in Depth Images
• Robust Color Watermarking Method Based On Clifford Transform
• Action-02MCF: A robust space-time Correlation Filter for Action Recognition in clutter and adverse lighting conditions
• An Image Quality Metric With Reference For Multiply Distorted Image
• 3D Planar RGB-D SLAM System
• Towards a generic m-svm parameters estimation using overlapping swarm intelligence for handwritten characters recognition
• Human Action Recognition Based on Temporal Pyramid of Key Poses Using RGB-D Sensors
• Multi-layer Dictionary Learning for Image Classification
• Intelligent Vision System for ASD Diagnosis and Assessment
• Visual Target Detection and Tracking in UAV EO/IR Videos by Moving Background Subtraction
• A Multiphase Level Set Method on Graphs for Hyperspectral Image Segmentation
• A Mobile Application for Leaf Detection in Complex Background using Saliency Maps
• Content-based mammogram retrieval using mixed kernel PCA and curvelet transform
• Combination of RGB-D Features for Head and Upper Body Orientation Classification
• A parametric algorithm for skyline extraction
• Quaternion linear color edge-glowing filter using genetic algorithm
• Scalable Vision System for Mouse Homecage Ethology
• Spatio-Temporal Features Learning with 3DPyraNet
• Automatic segmentation of tv news into stories using visual and temporal information
• Wavelet neural network initialization using LTS for DNA Sequence Classification
• Collection of Visual Data in Climbing Experiments for Addressing the Role of Multi-Modal Exploration in Motor Learning Efficiency
• Fog Augmentation of Road Images for Performance Analysis of Traffic Sign Detection Algorithms
• Statistical Modeling based Adaptive Parameter Setting for Random Walk Segmentation
• On-the-y Architecture Design and Implementation of a Real-Time Stereovision System
• Complex Image Processing Using Correlated Color Information
• Using PNU-based Techniques to Detect Alien Frames in Videos.

This book constitutes the refereed proceedings of the 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016, held in Lecce, Italy, in October 2016. The 64 revised full papers presented in this volume were carefully selected from 137 submissions. They deal with classical low-level image processing techniques; image and video compression; 3D; security and forensics; and evaluation methodologies.

• Human-computer interaction.- Classification and recognition.- Navigation, mapping, robotics, and transports.- Video processing and retrieval.- Security, forensics, surveillance.- Image processing.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 18th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2017, held in Antwerp, Belgium, in September 2017. The 63 full papers presented in this volume were carefully selected from 134 submissions. They deal with human-computer interaction; classification and recognition; navigation, mapping, robotics, and transports; video processing and retrieval; security, forensics, surveillance; and image processing.
(source: Nielsen Book Data)

• Video Analysis.- Improving a Switched Vector Field Model for Pedestrian Motion Analysis.- Matrix Descriptor of Changes (MDC): Activity Recognition Based on
• Skeleton.- Person Re-identification with a Body Orientation-Specific Convolutional
• Neural Network.- Distributed Estimation of Vector Fields.- Clustering Based Reference Normal Pose for Improved Expression Recognition.- Detecting and Recognizing Salient Object in Videos.- Directional Beams of Dense Trajectories for Dynamic Texture Recognition.- Intrinsic Calibration of a Camera to a Line-Structured Light using a Single View of Two Spheres.- 3D Object-Camera and 3D Face-Camera Pose Estimation For Quadcopter Control: Application To Remote Labs.- Orthogonally-Divergent Fisheye Stereo.- Two-Camera Synchronization and Trajectory Reconstruction for a
• Touch Screen Usability Experiment.- Segmentation and classification.- Comparison of Co-segmentation Methods for Wildlife Photo-identification.- An Efficient Agglomerative Algorithm Cooperating with Louvain Method for Implementing Image Segmentation.- Robust Feature Descriptors For Object Segmentation Using Active Shape Models.- Foreground Background Segmentation in Front of Changing Footage on a Video Screen.- Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet.- Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative Targeting Using Fuzzy Logic.- Identification of Saimaa rRnged Seal Individuals using Transfer Learning.- Remote sensing.- Enhanced Codebook Model and Fusion for Object Detection with Multispectral Images.- Unsupervised Perception Model for UAVs Landing Target Detection and Recognition.- Parallel and Distributed Local Fisher Discriminant Analysis to reduce Hyperspectral Images on Cloud Computing Architectures.- Bayesian Vehicle Detection using Optical Remote Sensing Images .- Integrating UAV in IoT for RoI Classification in Remote Images.- Biometrics.- Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image
• Representation for Face Recognition.- Single Sample Face Recognition by Sparse Recovery of Deep-learned Ida Features.- Recursive Chaining of Reversible Image-to-image Translators for Face
• Aging.- Automatically Selecting the Best Pictures for an Individualized Child Photo Album.- Face Detection in Painting using Deep Convolutional Neural Networks.- Robust Geodesic Skeleton Estimation from Body Single Depth.- Deep Learning.- Analysis of Neural Codes for Near-Duplicate Detection.- Optimum Network/Framework Selection from High-Level Specifications in Embedded Deep Learning Vision Applications.- Contour Propagation in CT scans with Convolutional Neural Networks.- Person Re-identification using Group Context.- Fingerprint Classification using Conic Radon Transform and Convolutional Neural Networks.- NoiseNet: Signal-dependent Noise Variance Estimation with Convolutional Neural Network.- Effective Training of Convolutional Neural Networks for Insect Image Recognition.- A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images.- Learning Morphological Operators for Depth Completion.- Dealing with Topological Information within a Fully Convolutional Neural Network.- Coding and Compression.- L-infinite Predictive Coding of Depth.- An Application of Data Compression Models to Handwritten Digit Classification.- A Global Decoding Strategy with a Reduced-reference Metric Designed for the Wireless Transmission of JPWL .- Reconfigurable FPGA Implementation of the AVC Quantiser and Dequantiser Blocks.- Image Restauration and Reconstruction.- Large Parallax Image Stitching Using an Edge-Preserving Diffeomorphic Warping Process.- A Wavelet Based Image Fusion Method using Local Multiscale Image Regularity.- Optimising Data for Exemplar-based Inpainting.- Fast Light Field inpainting Propagation using Angular Warping and Color-guided Disparity Interpolation.- Fusing Omnidirectional Visual Data for Probability Matching Prediction.- Derivative Half Gaussian Kernels and Shock Filter.- Scanner Model Identification of Official Documents Using Noise Parameters Estimation in the Wavelet Domain.- Relocated Colour Contrast Occurrence Matrix and Adapted Similarity Measure for Colour Texture Retrieval.- I-HAZE: a Dehazing Benchmark with Real Hazy and Haze-free Indoor Images.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018. The 52 full papers presented in this volume were carefully reviewed and selected from 91 submissions. They were organized in topical sections named: video analysis; segmentation and classification; remote sending; biometrics; deep learning; coding and compression; and image restauration and reconstruction.
(source: Nielsen Book Data)

• Theory, Methods and Tools to support Computer Science
• Computer Security and Cryptography
• Computer Network and Communication
• Real World Application in Information Science and Technology.

This book constitutes the refereed proceedings of the First International Conference on Applied Computing to Support Industry: Innovation and Technology, ACRIT 2019, held in Ramadi, Iraq, in September 2019. The 38 revised full papers and 1 short paper were carefully reviewed and selected from 159 submissions. The papers of this volume are organized in topical sections on theory, methods and tools to support computer science; computer security and cryptography; computer network and communication; real world application in information science and technology.

• Invited Papers.- Information extraction and Integration.- Data Mining and Knowledge Discovery.- Indexing, Query Processing and Optimization.- Data Quality and Data Cleansing.- Distributed Data Platforms, Including Cloud Data Systems, Key-Value Stores, and Big Data Systems.- Streaming Data Analysis.- Web, XML and Semi-Structured Databases.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 22nd European Conference on Advances in Databases and Information Systems, ADBIS 2018, held in Budapest, Hungary, in September 2018.The 17 regular papers presented together with two invited papers were carefully selected and reviewed from numerous submissions. The papers are organized in topical sections such as information extraction and integration; data mining and knowledge discovery; indexing, query processing and optimization; data quality and data cleansing; distributed data platforms, including cloud data systems, key-value stores, and big data systems; and streaming data analysis; web, XML and semi-structured databases.
(source: Nielsen Book Data)

• Keynote Extended Abstracts.- Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless.- Processing Temporal and Time Series Data: Present State and Future Challenges.- Integrating (Very) Heterogeneous Data Sources: a Structured and an Unstructured Perspective.- Data Access and Database Performance.- Upper Bound on the Size of FP-tree.- An Efficient Index for Reachability Queries in Public Transport Networks.- Context-Free Path Querying by Kronecker Product.- Pattern Sampling in Distributed Databases.-Can We Probabilistically Generate Uniformly Distributed Relation Instances Efficiently?.- Machine Learning.- Towards Proximity Graph Auto-Configuration: an Approach Based on Meta-Learning.- Fake News Detection Based on Subjective Opinions.- Improving on Coalitional Prediction Explanation.- Data Processing.- JSON Functionally.- Semantic Web.- Template-Based Multi-Solution for Data-to-Text Generation (on RDF).- Distributed Tree-Pattern Matching in Big Data Analytics Systems.- Data Analytics.- Iterations and Propensity Score Matching in MonetDB.- The Tell-Tale Cube.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in Lyon, France, in August 2020.* The 13 full papers presented were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They are organized in the following topical sections: keynote extended abstracts; data access and database performance; machine learning; semantic web; data processing; and data analytics. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Data Access and Database Performance.- Machine Learning.- Data Processing.- Semantic Web.- Data Analytics.
• (source: Nielsen Book Data)

This book constitutes thoroughly refereed short papers of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in August 2020. ADBIS 2020 was to be held in Lyon, France, however due to COVID-19 pandemic the conference was held in online format. The 18 presented short research papers were carefully reviewed and selected from 69 submissions. The papers are organized in the following sections: data access and database performance; machine learning; data processing; semantic web; data analytics.
(source: Nielsen Book Data)

This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.
(source: Nielsen Book Data)

• Database and Distributed Machine Learning.- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures.- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks.- Discovering Group Skylines with Constraints by Early Candidate Pruning.- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data.- A Higher-Fidelity Frugal Quantile Estimator.- Recommender System.- Fair Recommendations Through Diversity Promotion.- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data.- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities.- Group Recommender Model Based on Preference Interaction.- Identification of Grey Sheep Users By Histogram Inter
• section In Recommender Systems.- Social Network and Social Media.- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks.- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach.- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises.- Efficient Revenue Maximization for Viral Marketing in Social Networks.- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage.- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia.- A Solution to Tweet-Based User Identification across Online Social Networks.- Machine Learning.- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning.- Mixed Membership Sparse Gaussian Conditional Random Fields.- Effects of Dynamic Subspacing in Random Forest.- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach.- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions.- Classification and Clustering Methods.- StruClus: Scalable Structural Graph Set Clustering with Representative Sampling.- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification.- Environmental Sound Recognition using Masked Conditional Neural Networks.- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure.- A Framework for Clustering and Dynamic Maintenance of XML Documents.- Language-independent Twitter Classification using Character-based Convolutional Networks.- Behavior Modeling and User Profiling.- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues.- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors.- An Approach for Identifying Author Profiles of Blogs.- Generating Topics of Interests for Research Communities.- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples.- Bioinformatic and Medical Data Analysis.- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks.- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties.- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets.- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers.- Spatio-temporal Data.- People-Centric Mobile Crowdsensing Platform for Urban Design.- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining.- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.- Mobile Robot Scheduling with Multiple Trips and Time Windows.- Natural Language Processing and Text Mining.- Feature Analysis for Duplicate Detection in Programming QA Communities.- A Joint Human/Machine Process for Coding Events and Conflict Drivers.- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning.- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN.- Entity Recognition by Distant Supervision with Soft List Constraint.- Structured Sentiment Analysis.- Data Mining Applications.- Improving Real-Time Bidding Using a Constrained Markov Decision Process.- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network.- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments.- Color-sketch simulator: a guide for color-based visual known-item search.- Applications.- Making Use of External Company Data to Improve the Classification of Bank Transactions.- Mining Load Profile Patterns for Australian Electricity Consumers.- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing.- Privacy and Utility Preservation for Location Data Using Stay Region Analysis.- Location-aware Human Activity Recognition.- Demos.- SWYSWYK: a new Sharing Paradigm for the Personal Cloud.- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs.- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data.- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media.- A system for Querying and Analyzing Urban Regions.- Detect tracking behavior among trajectory data.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. .
(source: Nielsen Book Data)

• Learning Agents' Relations in Interactive Multiagent Dynamic Influence Diagrams.- Agent-Based Customer Profile Learning in 3G Recommender Systems: Ontology-Driven Multi-source Cross-Domain Case.- Modeling Temporal Propagation Dynamics in Multiplex Networks.- Mining Movement Patterns from Video Data to Inform Multi-agent Based Simulation.- Accessory-Based Multi-agent Simulating Platform on the Web.- Performance Evaluation of Agents and Multi-agent Systems Using Formal Specifications in Z Notation.- Reputation in Communities of Agent-Based Web Services Through Data Mining.- Data Mining Process Optimization in Computational Multi-agent Systems.- Diversifying the Storytelling Using Bayesian Networks.- A Coupled Similarity Kernel for Pairwise Support Vector Machine.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Workshop on Agents and Data Mining Interactions, ADMI 2014, held in Paris, France, in May 2014. The 11 papers presented were carefully reviewed and selected for inclusion in this volume.
(source: Nielsen Book Data)

• Preferences.- Manipulation.- Learning and other issues.- Utility and decision theory.- Agumentation.- Bribery and control.- Social choice.- Allocation and other problems.- Doctoral consortium.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA. The 32 full papers presented were carefully selected from 76 submissions. The papers are organized in topical sections such as preferences; manipulation, learning and other issues; utility and decision theory; argumentation; bribery and control; social choice; allocation and other problems; doctoral consortium.
(source: Nielsen Book Data)

• Preferences and multi-criteria decision aiding.- Decision making and voting.- Game theory and decision theory.- Allocation and matching. .
• (source: Nielsen Book Data)

This book constitutes the conference proceedings of the 5th International Conference on Algorithmic Decision Theory , ADT 2017, held in Luxembourg, in October 2017.The 22 full papers presented together with 6 short papers, 4 keynote abstracts, and 6 Doctoral Consortium papers, were carefully selected from 45 submissions. The papers are organized in topical sections on preferences and multi-criteria decision aiding; decision making and voting; game theory and decision theory; and allocation and matching. .
(source: Nielsen Book Data)

• Monte-Carlo Tree Search and its enhancements
• Theoretical aspects and complexity.-Analysis of game characteristics
• Search algorithms
• Machine learning.

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Advances in Computer Games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers cover a wide range of topics such as Monte-Carlo Tree Search and its enhancements; theoretical aspects and complexity; analysis of game characteristics; search algorithms; and machine learning.

• Analytical Solution for "EinStein wurfelt nicht!" with One Stone.- Toward Solving EinStein wurfelt nicht!.- Analysis of Fred Horn's Gloop Puzzle.- Set Matching: An Enhancement of the Hales-Jewett Pairing Strategy Playing Hanabi Near-Optimally Optimal Play of the Farkle Dice GameDeep df-pn and its Efficient Implementations.- Improved Policy Networks for Computer Go.- Exploring Positional Linear Go.- Influence of Search Depth on Position Evaluation.- Evaluating Chess-like Games Using Generated Natural Language Descriptions.- Machine Learning in the Game of Breakthrough.- A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action among Similar States.- Exploration Bonuses Based on Upper Confidence Bounds for Sparse Developing a 2048 Player with Backward Temporal Coherence Learning and Restart.- A Little Bit of Frustration Can Go a Long Way.- Automated Adaptation and Assessment in Serious Games: A Portable Tool for Supporting Learning.- An Analysis of Majority Voting in Homogeneous Groups for Checkers: Understanding Group Performance through Unbalance.- Yasol: An Open Source Solver for Quantified Mixed Integer Programs.
• (source: Nielsen Book Data)

• Analytical Solution for EinStein w⦥lt nicht!" with One Stone
• Toward Solving EinStein w⦥lt nicht!
• Analysis of Fred Horn's Gloop Puzzle
• Set Matching: An Enhancement of the Hales-Jewett Pairing Strategy Playing Hanabi Near-Optimally Optimal Play of the Farkle Dice GameDeep df-pn and its Efficient Implementations
• Improved Policy Networks for Computer Go
• Exploring Positional Linear Go
• Influence of Search Depth on Position Evaluation
• Evaluating Chess-like Games Using Generated Natural Language Descriptions
• Machine Learning in the Game of Breakthrough
• A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action among Similar States
• Exploration Bonuses Based on Upper Confidence Bounds for Sparse Developing a 2048 Player with Backward Temporal Coherence Learning and Restart
• A Little Bit of Frustration Can Go a Long Way
• Automated Adaptation and Assessment in Serious Games: A Portable Tool for Supporting Learning
• An Analysis of Majority Voting in Homogeneous Groups for Checkers: Understanding Group Performance through Unbalance
• Yasol: An Open Source Solver for Quantified Mixed Integer Programs.

This book constitutes the refereed conference proceedings of the 15th International Conference, ACG 2017, held in Leiden, The Netherlands, in July 2017.The 19 revised full papers were selected from 23 submissions and cover a wide range of computer games. They are grouped in four classes according to the order of publication: games and puzzles, go and chess, machine learning and MCTS, and gaming.
(source: Nielsen Book Data)

• Analysis of the impact of permissions on the vulnerability of mobile applications
• On the Relevance of Using Multi-layered Security in the Opportunistic Internet-of-Things
• Analysis of Software Vulnerabilities Using Machine Learning Techniques
• Africas Multilateral Legal Framework on Personal Data Security: What Prospects for the Digital Environment?
• A LoRaWAN coverage testBed and a multi-optional communication architecture for smart city feasibility in developing countries
• Effective Management of Delays at Road Intersections using Smart Traffic Light System
• Binary Search Based PSO for Master Node Enumeration and Placement in a Smart Water Metering Network
• State of Internet Measurement in Africa
• A Survey
• I2PA, U-prove, and Idemix: An Evaluation of Memory Usage and Computing Time Efficiency in an IoT Context
• A Hybrid Network Model embracing NB-IoT and D2D Communications : Stochastic Geometry Analysis
• Data Management and IT Applications
• Laws and Regulations on Big Data Management: The Case of South Africa
• Big Data Processing Using Hadoop and Spark: The Case of Meteorology Data
• Mobile health applications future trends and challenges
• AmonAI: a students academic performances prediction system
• Factors Influencing the Adoption of M-Government: Perspectives from a Namibian Marginalised Community
• Recent Approaches to Drift Effects in Credit Rating Models.

This book constitutes the thoroughly refereed proceedings of the 11th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2019, held in Porto-Novo, Benin, in December 2019. The 19 full papers were carefully selected from 46 submissions. The accepted papers provide a wide range of research topics including targeted infrastructures, Internet of Things (IoT), wireless and mobile networks, intelligent transportation systems (ITS), software and network security, cloud and virtualization, data analytics, and machine learning.

• Preface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.
• (source: Nielsen Book Data)

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution's manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The "parent problem" of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
(source: Nielsen Book Data)

• An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
• (source: Nielsen Book Data)

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
(source: Nielsen Book Data)

• Architectures.- Mathematical foundations.- Algorithms.- Safety.- Understanding.- Human cognition.- Philosophy.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Conference on Artificial General Intelligence, AGI 2017, held in Melbourne, VIC, Australia, in August 2017. The 24 regular papers presented in this book together with 1 short paper were carefully reviewed and selected from 35 submissions. They cover topics such as architectures; mathematical foundations; algorithms; safety; understanding; human cognition; and philosophy.
(source: Nielsen Book Data)

• Hybrid Strategies Towards Safe "Self-Aware" Super-intelligent Systems.- Request Confirmation Networks in MicroPsi 2.- Task Analysis for Teaching Cumulative Learners.- Associative Memory: A Spiking Neural Network Robotic Implementation.- A Comprehensive Ethical Framework for AI Entities: Foundations.- Partial Operator Induction with Beta Distributions.- Solving Tree Problems with Category Theory.- Goal-directed Procedure Learning.- Can Machines Design? An Artificial General Intelligence Approach.- Resource-constrained Social Evidence Based Cognitive Model for Empathy-driven Artificial Intelligence.- Unsupervised Language Learning in OpenCog.- Functionalist Emotion Model in NARS.- Towards a Sociological Conception of Artificial Intelligence.- Efficient Concept Formation in Large State Spaces.- DSO Cognitive Architecture: Implementation and Validation of the Global Workspace Enhancement.- The Foundations of Deep Learning with a Path Towards General Intelligence.- Zeta Distribution and Transfer Learning Problem.- Vision System for AGI: Problems and Directions.- Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures.- The Temporal Singularity: Time-accelerated Simulated Civilizations and Their Implications.- A Computational Theory for Life-Long Learning of Semantics.- Cumulative Learning with Causal-Relational Models.- Transforming Kantian Aesthetic Principles into Qualitative Hermeneutics for Contemplative AGI Agents.- Towards General Evaluation of Intelligent Systems: Using Semantic Analysis to Improve Environments in the AIQ Test.- Perception from an AGI Perspective.- A Phenomenologically Justifiable Simulation of Mental Modeling.- A Time-critical Simulation of Language Comprehension.- How Failure Facilitates Success.- Adaptive Compressed Search.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Artificial General Intelligence, AGI 2018, held in Prague, Czech Republic, in August 2018. The 19 regular papers and 10 poster papers presented in this book were carefully reviewed and selected from 52 submissions. The conference encourage interdisciplinary research based on different understandings of intelligence, and exploring different approaches. As the AI field becomes increasingly commercialized and well accepted, maintaining and emphasizing a coherent focus on the AGI goals at the heart of the field remains more critical than ever.
(source: Nielsen Book Data)

• AGI and the Knight-Darwin Law: why idealized AGI reproduction requires collaboration.- Error-Correction for AI Safety.- Artificial Creativity Augmentation.- The hierarchical memory based on compartmental spiking neuron model.- The Dynamics of Growing Symbols: A Ludics Approach to Language Design by Autonomous Agents.- Approach for development of engineering tools based on knowledge graphs and context separation.- Towards Dynamic Process Composition in the DSO Cognitive Architecture.- SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners.- Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence.- Self-explaining AI as an alternative to interpretable AI.- AGI needs the Humanities.- A report of a recent book: "AI and Human Thought and Emotion".- Cognitive Machinery and Behaviours.- Combinatorial Decision Dags: A Natural Computational Model for General Intelligence.- What Kind of Programming Language Best Suits Integrative AGI?.- Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities.- Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences.- Delta Schema Network in Model-based Reinforcement Learning.- Information Digital Twin{Enabling Agents to Anticipate Changes in their Tasks.- 'OpenNARS for Applications': Architecture and Control.- Towards AGI Agent Safety by Iteratively Improving the Utility Function.- Learning to Model Another Agent's Beliefs: A Preliminary Approach.- An Attentional Control Mechanism for Reasoning and Learning.- Hyperdimensional Representations in Semiotic Approach to AGI.- The Conditions of Artificial General Intelligence: Logic, Autonomy, Resilience, Integrity, Morality, Emotion, Embodiment, and Embeddedness.- Position paper: The use of engineering approach in creation of artificial general intelligence.- How do you test the strength of AI?.- Omega: An Architecture for AI Unification.- Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI.- The meaning of things as a concept in a strong AI architecture.- Toward a General Believable Model of Human-Analogous Intelligent Socially Emotional Behavior.- Autonomous Cumulative Transfer Learning.- New Brain Simulator II Open-Source Software.- Experience-specific AGI Paradigms.- Psychological portrait of a virtual agent in the Teleport game paradigm.- Logical probabilistic biologically inspired cognitive architecture.- An Architecture for Real-time Reasoning and Learning.- A Model for Artificial General Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020.The 30 full papers and 8 short papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover topics such as AGI architectures, artificial creativity and AI safety, transfer learning, AI unification and benchmarks for AGI.
(source: Nielsen Book Data)

• Self modification of policy and utility function in rational agents
• Avoiding wireheading with value reinforcement learning
• Death and suicide in universal Artificial Intelligence
• Ultimate Intelligence: Physical complexity and limits of inductive systems
• Open ended intelligence
• The AGI containment problem
• Imitation learning as cause-effect reasoning
• Some theorems in incremental compression
• Rethinking sigma's graphical architecture: An extension to neural networks.

This book constitutes the refereed proceedings of the 9th International Conference on Artificial General Intelligence, AGI 2016, held in New York City, NY, USA, in July 2016 as part of HLAI 2016, the Joint Multi-Conference on Human-Level Artificial Intelligence 2016. The 24 full papers, 2 short papers, and 10 poster papers presented were carefully reviewed and selected from 67 submissions. AGI research differs from the ordinary AI research by stressing on the versatility and wholeness of intelligence, and by carrying out the engineering practice according to an outline of a system comparable to the human mind inSelf a certain sense.

• Title page; Preface; Contents; Introduction: Aspects of Artificial General Intelligence; A Collection of Definitions of Intelligence; Four Contemporary AGI Designs: A Comparative Treatment; A Foundational Architecture for Artificial General Intelligence; A Working Hypothesis for General Intelligence; From NARS to a Thinking Machine; Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence; Cognitive Map Dimensions of the Human Value System Extracted from Natural Language; Program Evolution for General Intelligence.

Examines the creation of software programs displaying broad, deep, human-style general intelligence. This work features papers presented at the 2006 AGIRI (Artificial General Intelligence Research Institute) workshop, which illustrates that it is a fit and proper subject for serious science and engineering exploration.

• A Two-Stage Bi-LSTM Model for Chinese Company Name Recognition.- Multi-modal Multi-scale Speech Expression Evaluation in Computer-Assisted Language Learning.- From Global to Local: Local Popularity Prediction Using Context-embedded LSTM Recurrent Network.- Matching Low-Quality photo to DSLR-Quality with Deep Convolutional Networks.- Learning Frame-Level Recurrent Neural Networks Representations for Query-by-Example Spoken Term Detection on Mobile Devices.- Plant Identification based on Image Set Analysis.- Economic Index Forecasting via Multi-Scale Recursive Dynamic Factor Analysis.- Sub-Goal Oriented A* Search.- Towards Efficient Mobile Augmented Reality in Indoor Environments.- MAD-API: Detection, Correction and Explanation of API Misuses in Distributed Android Applications.- Relaxed Event-triggered Control of Networked Control Systems under Denial of Service Attacks.- Sentiment Analysis Based on Hybrid Bi-Attention mechanism in Mobile Application.- Automotive Diagnostics as a Service: An Artificially Intelligent Mobile Application for Tire Condition Assessment.- AICDS: An infant crying detection system based on lightweight convolutional neural network.- Exploring Trends of Lung Cancer Research Based on Word Representation.- Effective Facial Obstructions Removal With Enhanced Cycle-Consistent Generative Adversarial Networks.- Applied Analysis of Social Network Data in Personal Credit Evaluation.- Deep Neural Network Based Frame Reconstruction For Optimized Video Coding.- Detection and tracking of moving objects system for indoor mobile robots with a low-cost laser scanner.- Using IT/IS Applications to Empower Physically Challenged Individuals to Enjoy a High Quality of Life.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the International Conference on Artificial Intelligence and Mobile Services, AIMS 2018, held as part of SCF 2018, in Seattle, WA, USA, in June 2018. The 20 papers presented in this volume were carefully reviewed and selected from numerous submissions. The papers cover different aspects of mobile services from business management to computing systems, algorithms and applications. They promote technological technological innovations in research and development of mobile services, including, but not limited to, wireless and sensor networks, mobile and wearable computing, mobile enterprise and eCommerce, ubiquitous collaborative and social services, machine-to-machine and Internet-of-things, clouds, cyber-physical integration, and big data analytics for mobility-enabled services.
(source: Nielsen Book Data)

• Research Track.- Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge.- Building Vector Representations for Candidates and Projects in a CV Recommender System.- Candidate Classification and Skill Recommendation in a CV Recommender System.- A Novel Method to Estimate Students' Knowledge Assessment.- Answer Selection Based on Mixed Embedding and Composite Features.- A Neural Framework for Chinese Medical Named Entity Recognition.- An Annotated Chinese Corpus for Rumor Veracity Detection.- Attention-based Asymmetric Fusion Network for Saliency Prediction in 3D Images.- Review Spam Detection Based on Multidimensional.- Application Track.- Rehabilitation XAI to Predict Outcome with Optimal Therapies.- A Mobile Application using Deep Learning to Automatically Classify Adult-only Images.- Short Paper Track.- OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area.- Attention-based Interaction Trajectory Prediction.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 11 full and 2 short papers presented were carefully reviewed and selected from 42 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, Mobile backend as a service (MBaaS), User experience of AI and mobile services.
(source: Nielsen Book Data)

• Adaptive Instruction Design and Authoring.- Interoperability and Standardization in Adaptive Instructional Systems.- Instructional Theories in Adaptive Instruction.- Learner Assessment and Modelling.- AI in Adaptive Instructional Systems, Conversational Tutors.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the First International Conference on Adaptive Instructional Systems, AIS 2019, held in July 2019 as part of HCI International 2019 in Orlando, FL, USA. HCII 2019 received a total of 5029 submissions, of which 1275 papers and 209 posters were accepted for publication after a careful reviewing process. The 50 papers presented in this volume are organized in topical sections named: Adaptive Instruction Design and Authoring, Interoperability and Standardization in Adaptive Instructional Systems, Instructional Theories in Adaptive Instruction, Learner Assessment and Modelling, AI in Adaptive Instructional Systems, Conversational Tutors.
(source: Nielsen Book Data)

• Designing and Developing Adaptive Instructional Systems.- Sensor-based Adaptive Instructional Systems in Live Simulation Training.- An Ambient & Pervasive Personalized Learning Ecosystem: "Smart Learning" in the Age of the Internet of Things.- Bridging Conceptual Models and Architectural Interchange for Adaptive Instructional Systems.- Dewey's Ethics of Moral Principles and Deliberation: Extending IEEE's Ethics Initiative for Adaptive Instructional Systems.- Realistic and Relevant Role-Players for Experiential Learning.- Learning Traces, Measurement and Assessment Templates for AIS Interoperability.- Supporting different Roles and Responsibilities in Developing and Using Context-based Adaptive Personalized Collaboration Environments Compliant to the Law.- Experiential Instruction of Metacognitive Strategies.- Falling Forward: Lessons Learned from Real-Life Implementation of Adaptive Learning Solutions.- Usability Dimensions of Simulated Trainers for Buried Explosives.- Toward Zero Authoring: Considering How to Maximize Courseware Quality and Affordability Simultaneously.- Agent-Based Methods in Support of Adaptive Instructional Decisions.- Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model.- Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence.- User Rights and Adaptive A/IS - From Passive Interaction to Real Empowerment.- Supporting Metacognitive Learning Strategies through an Adaptive Application.- Towards Iteration by Design: An Interaction Design Concept for Safety Critical Systems.- Learner Modelling and Methods of Adaptation.- Bayesian Student Modelling in the AC&NL Tutor.- Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized Support.- Adapting Instruction by Measuring Engagement with Machine Learning in Virtual Reality Training.- Realizing the Promise of AI-Powered, Adaptive, Automated, Instant Feedback on Writing for Students in Grade 3-8 with an IEP.- Declarative Knowledge Extraction in the AC&NL Tutor.- On the Importance of Adaptive Operator Training in Human-Swarm Interaction.- The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate- measures Using Machine Learning.- Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems.- Pilot State Monitoring for Cursus Recommendation.- Experimental Evaluation of Heart-based Workload Measures as Related to Their Suitability for Real-time Applications.- EEG Covariance-based Estimation of Cooperative States in Teammates.- Adapting the Zone of Proximal Development to the Wicked Environments of Professional Practice.- An Adaptive Instructional System for the Retention of Complex Skills.- Learner Modeling in the Context of Caring Assessments.- Evaluating the Effectiveness of Adaptive Instructional Systems.- The Evolving Assessment Landscape and Adaptive Instructional Systems: Moving Beyond Good Intentions.- Contextual Barriers to Validity in Adaptive Instruction and Assessment.- Does Time Matter in Learning? A Computer Simulation of Carroll's Model of Learning.- Competency Development through Experiential Training: Mapping Scenarios with Assessments.- Does Gamification Work? Analyzing Effects of Game Features on Learning in an Adaptive Scenario-Based Trainer.- From "Knowing What" to "Knowing When": Exploring a Concept of Situation Awareness Synchrony for Evaluating SA Dynamics in Teams.- Exploring Video Engagement in an Intelligent Tutoring System.- Using a Non-Player Character to Improve Training Outcomes for Submarine Electronic Warfare Operators.- The Impact of Adaptive Activities in Acrobatiq Courseware: Investigating the Efficacy of Formative Adaptive Activities on Learning Estimates and Summative Assessment Scores.- A Mastery Approach to Flashcard-based Adaptive Training. .
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the Second International Conference on Adaptive Instructional Systems, AIS 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic. A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 41 papers presented in this volume were organized in topical sections as follows: designing and developing adaptive instructional systems; learner modelling and methods of adaptation; evaluating the effectiveness of adaptive instructional systems. Chapter "Exploring Video Engagement in an Intelligent Tutoring System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• Invited Talks.- Peer-to-Peer Data Mining, Privacy Issues, and Games.- Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics.- Robust Agent Communities.- WI Based Multi-aspect Data Analysis in a Brain Informatics Portal.- Agent and Data Mining.- Agent-Mining Interaction: An Emerging Area.- Evaluating Knowledge Intensive Multi-agent Systems.- Towards an Ant System for Autonomous Agents.- Semantic Modelling in Agent-Based Software Development.- Combination Methodologies of Multi-agent Hyper Surface Classifiers: Design and Implementation Issues.- Security in a Mobile Agent Based DDM Infrastructure.- Automatic Extraction of Business Rules to Improve Quality in Planning and Consolidation in Transport Logistics Based on Multi-agent Clustering.- Intelligent Agents for Real Time Data Mining in Telecommunications Networks.- Architecture of Typical Sensor Agent for Learning and Classification Network.- Self-organizing Multi-agent Systems for Data Mining.- Role-Based Decision Mining for Multiagent Emergency Response Management.- Agent Competition and Data Mining.- Virtual Markets: Q-Learning Sellers with Simple State Representation.- Fusion of Dependence Networks in Multi-agent Systems - Application to Support Net-Enabled Littoral Surveillance.- Multi-agent Framework for Simulation of Adaptive Cooperative Defense Against Internet Attacks.- On Competing Agents Consistent with Expert Knowledge.- On-Line Agent Teamwork Training Using Immunological Network Model.- Text Mining, Semantic Web, and Agents.- Combination of Rough Sets and Genetic Algorithms for Text Classification.- Multi-agent Meta-search Engine Based on Domain Ontology.- Efficient Search Technique for Agent-Based P2P Information Retrieval.- Classification of Web Documents Using Concept Extraction from Ontologies.- Emotional Cognitive Agents with Adaptive Ontologies.- Viral Knowledge Acquisition Through Social Networks.- Chinese Weblog Pages Classification Based on Folksonomy and Support Vector Machines.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Second International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, held in St. Petersburg, Russia in June 2007. The 17 revised full papers and six revised short papers presented together with four invited lectures cover agent and data mining, agent competition and data mining, as well as text mining, semantic Web, and agents.
(source: Nielsen Book Data)

• Artificial Intelligence, Theorem Proving and SAT Solving.- Symbolic and Numerical Computation.- Intelligent Documents and Collective Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Artificial Intelligence and Symbolic Computation, AISC 2018, held in Suzhou, China, in September 2018. The 13 full papers presented together with 5 short and 2 invited papers were carefully reviewed and selected from 31 submissions. The AISC conference is an important forum when it comes to ensuring that ideas, theoretical insights, methods and results from traditional AI can be discussed and showcased, while fostering new links with other areas of AI such as probabilistic reasoning and deep learning. .
(source: Nielsen Book Data)

• Machine generated contents note: Invited Papers
• A Probabilistic Rating System for Team Competitions with Individual Contributions / Sergey Nikolenko
• Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples / Natalya S. Belova
• Discerning Depression Propensity Among Participants of Suicide and Depression-Related Groups of Vk.com / Maxim Kharchenko
• Tutorial
• Normalization of Non-standard Words with Finite State Transducers for Russian Speech Synthesis / Artem Lukanin
• Analysis of Images and Videos
• Transform Coding Method for Hyperspectral Data: Influence of Block Characteristics to Compression Quality / Ruslan Yuzkiv
• Frechet Filters for Color and Hyperspectral Images Filtering / Tat'yana Fedorova
• Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model / Olga Krasotkina.

• Note continued: Theoretical Approach to Developing Efficient Algorithms of Fingerprint Enhancement / Maxim Pasynkov
• Remote Sensing Data Verification Using Model-Oriented Descriptors / Vladislav Myasnikov
• New Bi-, Tri-, and Fourlateral Filters for Color and Hyperspectral Images Filtering / Ivan Artemov
• Frequency Analysis of Gradient Descent Method and Accuracy of Iterative Image Restoration / Vladislav Kuznetsov
• Shape Matching Based on Skeletonization and Alignment of Primitive Chains / Oleg Seredin
• Color Image Restoration with Fuzzy Gaussian Mixture Model Driven Nonlocal Filter / Radhakrishnan Delhibabu
• A Phase Unwrapping Algorithm for Interferometric Phase Images / Andrey Sosnovsky
• Robust Image Watermarking on Triangle Grid of Feature Points / Victor Fedoseev
• Pattern Recognition and Machine Learning
• Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors / Vladislav Myasnikov.

• Note continued: Analysis of the Adaptive Nature of Collaborative Filtering Techniques in Dynamic Environment / Sheikh Muhammad Sarwar
• A Texture Fuzzy Classifier Based on the Training Set Clustering by a Self-Organizing Neural Network / Dmitry Lykom
• Learning Representations in Directed Networks / Sergey O. Bartunov
• Distorted High-Dimensional Binary Patterns Search by Scalar Neural Network Tree / Magomed Malsagov
• Hybrid Classification Approach to Decision Support for Endoscopy in Gastrointestinal Tract / Olga A. Buntseva
• User Similarity Computation for Collaborative Filtering Using Dynamic Implicit Trust / Mahamudul Hasan
• Similarity Aggregation for Collaborative Filtering / Dmitry I. Ignatov
• Distributed Coordinate Descent for L1-regularized Logistic Regression / Alexander Genkin
• Social Network Analysis
• Building Profiles of Blog Users Based on Comment Graph Analysis: The Habrahabr.ru Case / Rostislav Yavorskiy.

• Note continued: Formation and Evolution Mechanisms in Online Network of Students: The Vkontakte Case / Maria Yudkevich
• Large-Scale Parallel Matching of Social Network Profiles / Sergei Obiedkov
• Identification of Autopoietic Communication Patterns in Social and Economic Networks / Olga M. Zvereva
• Text Mining and Natural Language Processing
• A Heuristic Strategy for Extracting Terms from Scientific Texts / Natalia E. Efremova
• Text Analysis with Enhanced Annotated Suffix Trees: Algorithms and Implementation / Mikhail Dubov
• Morphological Analyzer and Generator for Russian and Ukrainian Languages / Mikhail Korobov
• Semantic Role Labeling for Russian Language Based on Russian FrameBank / Ilya Kuznetsov
• Supervised Approach to Finding Most Frequent Senses in Russian / Ilia Chetviorkin
• FrameBank: A Database of Russian Lexical Constructions / Egor Kashkin
• TagBag: Annotating a Foreign Language Lexical Resource with Pictures ... / Dmitry Ustalov.

• Note continued: BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections / Marina Dudarenko
• Industry Talk
• ATM Service Cost Optimization Using Predictive Encashment Strategy / Alois Knoll
• Industry Papers
• Comparison of Deep Learning Libraries on the Problem of Handwritten Digit Classification / Pavel Druzhkov
• Methods of Localization of Some Anthropometric Features of Face / Svetlana Volkova
• Ontological Representation of Networks for IDS in Cyber-Physical Systems / Vasily A. Sartakov
• Determination of the Relative Position of Space Vehicles by Detection and Tracking of Natural Visual Features with the Existing TV-Cameras / Filipp Gundelakh
• Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses / Nataly Zhukova.

This book constitutes the proceedings of the Fourth International Conference on Analysis of Images, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015. The 24 full and 8 short papers were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on analysis of images and videos; pattern recognition and machine learning; social network analysis; text mining and natural language processing.
(source: Nielsen Book Data)

• Natural language processing
• General topics of data analysis
• Analysis of images and video
• Optimization problems on graphs and network structures
• Analysis of dynamic behavior through event data
• Social network analysis.

This book constitutes the proceedings of the 6th International Conference on Analysis of Images, Social Networks and Texts, AIST 2017, held in Moscow, Russia, in July 2017. The 29 full papers and 8 short papers were carefully reviewed and selected from 127 submissions. The papers are organized in topical sections on natural language processing; general topics of data analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behavior through event data; social network analysis.

• Introduction.-
• On the Role of Compliance and Geometry in Mechanical Stability of the Humans and Robots.-
• Teleimpedance Control of a Robotic Arm.-
• Human-like Impedance Control of a Dual-Arm Manipulator.-
• Teleimpedance Control of a Robotic Hand.-
• Teleimpedance Control of a Compliant Knee Exoskeleton.
• (source: Nielsen Book Data)

This book introduces novel thinking and techniques to the control of robotic manipulation. In particular, the concept of teleimpedance control as an alternative method to bilateral force-reflecting teleoperation control for robotic manipulation is introduced. In teleimpedance control, a compound reference command is sent to the slave robot including both the desired motion trajectory and impedance profile, which are then realized by the remote controller. This concept forms a basis for the development of the controllers for a robotic arm, a dual-arm setup, a synergy-driven robotic hand, and a compliant exoskeleton for improved interaction performance.
(source: Nielsen Book Data)

• Preface.- Introduction.- Data Analytics.- Basic Learning Algorithms.- Fuzzy Logic.- Artificial Neural Networks.- Genetic Algorithms and Evolutionary Computing.- Other Metaheuristics and Classification Approaches.- Analytics and Big Data.- Data Analytics Using R.-
• Appendix I: Tools for Data Science.-
• Appendix II: Tools for Computational Intelligence.
• (source: Nielsen Book Data)

This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real-world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
(source: Nielsen Book Data)

Within artificial intelligence, the need to create sophisticated, intelligent behaviour based on common-sense reasoning has long been recognized. Research has demonstrated that formalism for dealing with common sense reasoning require nonmonotonic capabilities where, typically, inferences based on incomplete knowledge need to be revised in light of later information which fills in some of the gaps.
(source: Nielsen Book Data)

• Machine generated contents note: pt. 1 State of the Art
• ch. 1 Introduction
• 1.1. Organization of the book
• 1.2. Utterance corpus
• 1.3. Datasets from the UCI repository
• 1.3.1. Wine dataset (wine)
• 1.3.2. Wisconsin breast cancer dataset (breast)
• 1.3.3. Handwritten digits dataset (Pendig)
• 1.3.4. Pima Indians diabetes (diabetes)
• 1.3.5. Iris dataset (Iris)
• 1.4. Microarray dataset
• 1.5. Simulated datasets
• 1.5.1. Mixtures of Gaussians
• 1.5.2. Spatial datasets with non-homogeneous inter-cluster distance
• ch. 2 State of the Art in Clustering and Semi-Supervised Techniques
• 2.1. Introduction
• 2.2. Unsupervised machine learning (clustering)
• 2.3. A brief history of cluster analysis
• 2.4. Cluster algorithms
• 2.4.1. Hierarchical algorithms
• 2.4.1.1. Agglomerative clustering
• 2.4.1.2. Divisive algorithms
• 2.4.2. Model-based clustering
• 2.4.2.1. The expectation maximization (EM) algorithm
• 2.4.3. Partitional competitive models.

• 2.4.3.1. K-means
• 2.4.3.2. Neural gas
• 2.4.3.3. Partitioning around Medoids (PAM)
• 2.4.3.4. Self-organizing maps
• 2.4.4. Density-based clustering
• 2.4.4.1. Direct density reachability
• 2.4.4.2. Density reachability
• 2.4.4.3. Density connection
• 2.4.4.4. Border points
• 2.4.4.5. Noise points
• 2.4.4.6. DBSCAN algorithm
• 2.4.5. Graph-based clustering
• 2.4.5.1. Pole-based overlapping clustering
• 2.4.6. Affectation stage
• 2.4.6.1. Advantages and drawbacks
• 2.5. Applications of cluster analysis
• 2.5.1. Image segmentation
• 2.5.2. Molecular biology
• 2.5.2.1. Biological considerations
• 2.5.3. Information retrieval and document clustering
• 2.5.3.1. Document pre-processing
• 2.5.3.2. Boolean model representation
• 2.5.3.3. Vector space model
• 2.5.3.4. Term weighting
• 2.5.3.5. Probabilistic models
• 2.5.4. Clustering documents in information retrieval
• 2.5.4.1. Clustering of presented results
• 2.5.4.2. Post-retrieval document browsing (Scatter-Gather)
• 2.6. Evaluation methods.

• 2.7. Internal cluster evaluation
• 2.7.1. Entropy
• 2.7.2. Purity
• 2.7.3. Normalized mutual information
• 2.8. External cluster validation
• 2.8.1. Hartigan
• 2.8.2. Davies Bouldin index
• 2.8.3. Krzanowski and Lai index
• 2.8.4. Silhouette
• 2.8.5. Gap statistic
• 2.9. Semi-supervised learning
• 2.9.1. Self training
• 2.9.2. Co-training
• 2.9.3. Generative models
• 2.10. Summary
• pt. 2 Approaches to Semi-Supervised Classification
• ch. 3 Semi-Supervised Classification Using Prior Word Clustering
• 3.1. Introduction
• 3.2. Dataset
• 3.3. Utterance classification scheme
• 3.3.1. Pre-processing
• 3.3.1.1. Utterance vector representation
• 3.3.2. Utterance classification
• 3.4. Semi-supervised approach based on term clustering
• 3.4.1. Term clustering
• 3.4.2. Semantic term dissimilarity
• 3.4.2.1. Term vector of lexical co-occurrences
• 3.4.2.2. Metric of dissimilarity
• 3.4.3. Term vector truncation
• 3.4.4. Term clustering
• 3.4.5. Feature extraction and utterance feature vector.

• 3.4.6. Evaluation
• 3.5. Disambiguation
• 3.5.1. Evaluation
• 3.6. Summary
• ch. 4 Semi-Supervised Classification Using Pattern Clustering
• 4.1. Introduction
• 4.2. New semi-supervised algorithm using the cluster and label strategy
• 4.2.1. Block diagram
• 4.2.1.1. Dataset
• 4.2.1.2. Clustering
• 4.2.1.3. Optimum cluster labeling
• 4.2.1.4. Classification
• 4.3. Optimum cluster labeling
• 4.3.1. Problem definition
• 4.3.2. The Hungarian algorithm
• 4.3.2.1. Weighted complete bipartite graph
• 4.3.2.2. Matching, perfect matching and maximum weight matching
• 4.3.2.3. Objective of Hungarian method
• 4.3.2.4. Complexity considerations
• 4.3.3. Genetic algorithms
• 4.3.3.1. Reproduction operators
• 4.3.3.2. Forming the next generation
• 4.3.3.3. GAs applied to optimum cluster labeling
• 4.3.3.4. Comparison of methods
• 4.4. Supervised classification block
• 4.4.1. Support vector machines
• 4.4.1.1. The kernel trick for nonlinearly separable classes
• 4.4.1.2. Multi-class classification
• 4.4.2. Example.

• 4.5. Datasets
• 4.5.1. Mixtures of Gaussians
• 4.5.2. Datasets from the UCI repository
• 4.5.2.1. Iris dataset (Iris)
• 4.5.2.2. Wine dataset (wine)
• 4.5.2.3. Wisconsin breast cancer dataset (breast)
• 4.5.2.4. Handwritten digits dataset (Pendig)
• 4.5.2.5. Pima Indians diabetes (diabetes)
• 4.5.3. Utterance dataset
• 4.6. An analysis of the bounds for the cluster and label approaches
• 4.7. Extension through cluster pruning
• 4.7.1. Determination of silhouette thresholds
• 4.7.2. Evaluation of the cluster pruning approach
• 4.8. Simulations and results
• 4.9. Summary
• pt. 3 Contributions to Unsupervised Classification -- Algorithms to Detect the Optimal Number of Clusters
• ch. 5 Detection of the Number of Clusters through Non-Parametric Clustering Algorithms
• 5.1. Introduction
• 5.2. New hierarchical pole-based clustering algorithm
• 5.2.1. Pole-based clustering basis module
• 5.2.2. Hierarchical pole-based clustering
• 5.3. Evaluation
• 5.3.1. Cluster evaluation metrics
• 5.4. Datasets.

• 5.4.1. Results
• 5.4.2. Complexity considerations for large databases
• 5.5. Summary
• ch. 6 Detecting the Number of Clusters through Cluster Validation
• 6.1. Introduction
• 6.2. Cluster validation methods
• 6.2.1. Dunn index
• 6.2.2. Hartigan
• 6.2.3. Davies Bouldin index
• 6.2.4. Krzanowski and Lai index
• 6.2.5. Silhouette
• 6.2.6. Hubert's & gamma;
• 6.2.7. Gap statistic
• 6.3. Combination approach based on quantiles
• 6.4. Datasets
• 6.4.1. Mixtures of Gaussians
• 6.4.2. Cancer DNA-microarray dataset
• 6.4.3. Iris dataset
• 6.5. Results
• 6.5.1. Validation results of the five Gaussian dataset
• 6.5.2. Validation results of the mixture of seven Gaussians
• 6.5.3. Validation results of the NCI60 dataset
• 6.5.4. Validation results of the Iris dataset
• 6.5.5. Discussion
• 6.6. Application of speech utterances
• 6.7. Summary.

"This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described"-- Provided by publisher.

• Machine generated contents note: pt. 1 State of the Art
• ch. 1 Introduction
• 1.1. Organization of the book
• 1.2. Utterance corpus
• 1.3. Datasets from the UCI repository
• 1.3.1. Wine dataset (wine)
• 1.3.2. Wisconsin breast cancer dataset (breast)
• 1.3.3. Handwritten digits dataset (Pendig)
• 1.3.4. Pima Indians diabetes (diabetes)
• 1.3.5. Iris dataset (Iris)
• 1.4. Microarray dataset
• 1.5. Simulated datasets
• 1.5.1. Mixtures of Gaussians
• 1.5.2. Spatial datasets with non-homogeneous inter-cluster distance
• ch. 2 State of the Art in Clustering and Semi-Supervised Techniques
• 2.1. Introduction
• 2.2. Unsupervised machine learning (clustering)
• 2.3. A brief history of cluster analysis
• 2.4. Cluster algorithms
• 2.4.1. Hierarchical algorithms
• 2.4.1.1. Agglomerative clustering
• 2.4.1.2. Divisive algorithms
• 2.4.2. Model-based clustering
• 2.4.2.1. The expectation maximization (EM) algorithm
• 2.4.3. Partitional competitive models.

• 2.4.3.1. K-means
• 2.4.3.2. Neural gas
• 2.4.3.3. Partitioning around Medoids (PAM)
• 2.4.3.4. Self-organizing maps
• 2.4.4. Density-based clustering
• 2.4.4.1. Direct density reachability
• 2.4.4.2. Density reachability
• 2.4.4.3. Density connection
• 2.4.4.4. Border points
• 2.4.4.5. Noise points
• 2.4.4.6. DBSCAN algorithm
• 2.4.5. Graph-based clustering
• 2.4.5.1. Pole-based overlapping clustering
• 2.4.6. Affectation stage
• 2.4.6.1. Advantages and drawbacks
• 2.5. Applications of cluster analysis
• 2.5.1. Image segmentation
• 2.5.2. Molecular biology
• 2.5.2.1. Biological considerations
• 2.5.3. Information retrieval and document clustering
• 2.5.3.1. Document pre-processing
• 2.5.3.2. Boolean model representation
• 2.5.3.3. Vector space model
• 2.5.3.4. Term weighting
• 2.5.3.5. Probabilistic models
• 2.5.4. Clustering documents in information retrieval
• 2.5.4.1. Clustering of presented results
• 2.5.4.2. Post-retrieval document browsing (Scatter-Gather)
• 2.6. Evaluation methods.

• 2.7. Internal cluster evaluation
• 2.7.1. Entropy
• 2.7.2. Purity
• 2.7.3. Normalized mutual information
• 2.8. External cluster validation
• 2.8.1. Hartigan
• 2.8.2. Davies Bouldin index
• 2.8.3. Krzanowski and Lai index
• 2.8.4. Silhouette
• 2.8.5. Gap statistic
• 2.9. Semi-supervised learning
• 2.9.1. Self training
• 2.9.2. Co-training
• 2.9.3. Generative models
• 2.10. Summary
• pt. 2 Approaches to Semi-Supervised Classification
• ch. 3 Semi-Supervised Classification Using Prior Word Clustering
• 3.1. Introduction
• 3.2. Dataset
• 3.3. Utterance classification scheme
• 3.3.1. Pre-processing
• 3.3.1.1. Utterance vector representation
• 3.3.2. Utterance classification
• 3.4. Semi-supervised approach based on term clustering
• 3.4.1. Term clustering
• 3.4.2. Semantic term dissimilarity
• 3.4.2.1. Term vector of lexical co-occurrences
• 3.4.2.2. Metric of dissimilarity
• 3.4.3. Term vector truncation
• 3.4.4. Term clustering
• 3.4.5. Feature extraction and utterance feature vector.

• 3.4.6. Evaluation
• 3.5. Disambiguation
• 3.5.1. Evaluation
• 3.6. Summary
• ch. 4 Semi-Supervised Classification Using Pattern Clustering
• 4.1. Introduction
• 4.2. New semi-supervised algorithm using the cluster and label strategy
• 4.2.1. Block diagram
• 4.2.1.1. Dataset
• 4.2.1.2. Clustering
• 4.2.1.3. Optimum cluster labeling
• 4.2.1.4. Classification
• 4.3. Optimum cluster labeling
• 4.3.1. Problem definition
• 4.3.2. The Hungarian algorithm
• 4.3.2.1. Weighted complete bipartite graph
• 4.3.2.2. Matching, perfect matching and maximum weight matching
• 4.3.2.3. Objective of Hungarian method
• 4.3.2.4. Complexity considerations
• 4.3.3. Genetic algorithms
• 4.3.3.1. Reproduction operators
• 4.3.3.2. Forming the next generation
• 4.3.3.3. GAs applied to optimum cluster labeling
• 4.3.3.4. Comparison of methods
• 4.4. Supervised classification block
• 4.4.1. Support vector machines
• 4.4.1.1. The kernel trick for nonlinearly separable classes
• 4.4.1.2. Multi-class classification
• 4.4.2. Example.

• 4.5. Datasets
• 4.5.1. Mixtures of Gaussians
• 4.5.2. Datasets from the UCI repository
• 4.5.2.1. Iris dataset (Iris)
• 4.5.2.2. Wine dataset (wine)
• 4.5.2.3. Wisconsin breast cancer dataset (breast)
• 4.5.2.4. Handwritten digits dataset (Pendig)
• 4.5.2.5. Pima Indians diabetes (diabetes)
• 4.5.3. Utterance dataset
• 4.6. An analysis of the bounds for the cluster and label approaches
• 4.7. Extension through cluster pruning
• 4.7.1. Determination of silhouette thresholds
• 4.7.2. Evaluation of the cluster pruning approach
• 4.8. Simulations and results
• 4.9. Summary
• pt. 3 Contributions to Unsupervised Classification -- Algorithms to Detect the Optimal Number of Clusters
• ch. 5 Detection of the Number of Clusters through Non-Parametric Clustering Algorithms
• 5.1. Introduction
• 5.2. New hierarchical pole-based clustering algorithm
• 5.2.1. Pole-based clustering basis module
• 5.2.2. Hierarchical pole-based clustering
• 5.3. Evaluation
• 5.3.1. Cluster evaluation metrics
• 5.4. Datasets.

• 5.4.1. Results
• 5.4.2. Complexity considerations for large databases
• 5.5. Summary
• ch. 6 Detecting the Number of Clusters through Cluster Validation
• 6.1. Introduction
• 6.2. Cluster validation methods
• 6.2.1. Dunn index
• 6.2.2. Hartigan
• 6.2.3. Davies Bouldin index
• 6.2.4. Krzanowski and Lai index
• 6.2.5. Silhouette
• 6.2.6. Hubert's & gamma;
• 6.2.7. Gap statistic
• 6.3. Combination approach based on quantiles
• 6.4. Datasets
• 6.4.1. Mixtures of Gaussians
• 6.4.2. Cancer DNA-microarray dataset
• 6.4.3. Iris dataset
• 6.5. Results
• 6.5.1. Validation results of the five Gaussian dataset
• 6.5.2. Validation results of the mixture of seven Gaussians
• 6.5.3. Validation results of the NCI60 dataset
• 6.5.4. Validation results of the Iris dataset
• 6.5.5. Discussion
• 6.6. Application of speech utterances
• 6.7. Summary.

"This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described"-- Provided by publisher

• Phylogenetics.- Sequence Rearrangement and Analysis.- Systems Biology and Other Biological Processes.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 5th InternationalConference on Algorithms for Computational Biology, AlCoB 2018, held in Hong Kong, China, in June 2018. The 11 full papers presented together with 1 invited paper were carefully reviewed and selected from 20 submissions. They are organized in the following topical sections: Phylogenetics, Sequence Rearrangement and Analysis, Systems Biology and Other Biological Processes. .
(source: Nielsen Book Data)

• AUTHORS' NOTES AND ACKNOWLEDGEMENTS; INTRODUCTION; 1.1. Objectives of this Book; 1.2. The Seeing Eye and the Knowing Eye
• 1 IMAGE AND COMPUTER; 1.1. A Short History; 1.2. The Computer's Eye; 1.3. A Beetle and an Ant-Hill; 1.4. Features and Models; 2 HOW HUMANS SEE THE WORLD; 2.1. The Eye and the Brain; 2.2. The Level of Preattention; 2.3. Right and Left Vision; 2.4. Images and Words; 3 CONVERSATIONS WITH A COMPUTER; 3.1. From a Point to a Region; 3.2. From a Region to an Object; 3.3. From an Object to a Situation; 4 AN APOLOGIA FOR VISION; 4.1. The Evolution of Vision.

• 4
• .2. Vision and Thinking4
• .3. Recollection of the Future; 4
• .4. Cognition through Vision; 5 CREATING A NEW WORLD; 5
• .1. From Elements to the System; 5
• .2. Back to Nature; 5
• .3. Who Do We Think They Are?; CONCLUSIONS; PLATES; REFERENCES; ILLUSTRATIONS; INDEX.

This book considers computer vision to be an integral part of the artificial intelligence system. The core of the book is an analysis of possible approaches to the creation of artificial vision systems, which simulate human visual perception. Much attention is paid to the latest achievements in visual psychology and physiology, the description of the functional and structural organization of the human perception mechanism, the peculiarities of artistic perception and the expression of reality. Computer vision models based on these data are investigated. They include the processes of external data analysis, internal environmental model synthesis, and the generating of behavioristic responses based on external and internal models comparison. Computer vision system evolution resulting from environmental effects is also considered. A unique feature of this book is the authors' use of black and white, and colour prints of traditional and contemporary Russian art to illustrate their principal theses. In doing so, they introduce the reader to a particularly Russian view of the world.
(source: Nielsen Book Data)

• Tutorials
• Algorithmic Aspects of Large-Scale Data Stores
• Software Tools and Distributed Architectures for Cloud-based Data Management.

This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2015, held in Patras, Greece, in September 2015 in conjunction with ALGO 2015. The 13 revised full papers presented together with 2 tutorial papers were carefully reviewed and selected from 37 initial submissions. They cover a wide range of topics in two main tracks: algorithmic aspects of large-scale data stores, and software tools and distributed architectures for cloud-based data management.