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• 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.

• Plane and Planarity Thresholds for Random Geometric Graphs.- Connectivity of a dense mesh of randomly oriented directional antennas under a realistic fading model.- Maintaining Intruder Detection Capability in a Rectangular Domain with Sensors.- The Weakest Oracle for Symmetric Consensus in Population Protocols.- Exact and Approximation Algorithms for Data Mule Scheduling in a Sensor Network.- Limitations of Current Wireless Scheduling Algorithms.- Deterministic rendezvous with detection using beeps.- Minimizing total sensor movement for barrier coverage by non-uniform sensors on a line.- A comprehensive and lightweight security architecture to secure the IoT throughout the lifecycle of a device based on HIMMO.- Maximizing Throughput in Energy-Harvesting Sensor Nodes.- On verifying and maintaining connectivity of interval temporal networks.- Beachcombing on Strips and Islands.- Radio Aggregation Scheduling.- Gathering of Robots on Meeting-Points.- Mutual Visibility with an Optimal Number of Colors.- Mobile Agents Rendezvous in spite of a Malicious Agent.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the 11th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2015, held in Patras, Greece, in September 2015. The 16 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The deal with algorithms analysis and problem complexity; computer communication networks; computation by abstract devices; and mathematics of computing.
(source: Nielsen Book Data)

• Multi-Message Broadcast in Dynamic Radio Networks.- Global Synchronization and Consensus Using Beeps in a Fault-Prone MAC 16.- Vertex Coloring with Communication and Local Memory Constraints in Synchronous Broadcast Networks.- A New Kind of Selectors, and Their Applications to Conflict Resolution in Wireless Multi-channels Networks.- The Impact of the Gabriel Sub-graph of the Visibility Graph on the Gathering of Mobile Autonomous Robots.- Search-and-Fetch with One Robot on a Disk.- A 2-Approximation Algorithm for Barrier Coverage by Weighted Non-uniform Sensors on a Line.- Flexible Cell Selection in Cellular Networks.- The Euclidean k-Supplier Problem in IR2.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the 12th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2016, held in Aarhus, Denmark, in August 2016. The 9 full papers presented in this volume were carefully reviewed and selected from 19 submissions. This year papers were solicited into three tracks: Distributed and Mobile, Experiments and Applications, and Wireless and Geometry.
(source: Nielsen Book Data)

• Learning and evolution
• Human interaction
• Robotic simulation.

This book constitutes the refereed proceedings of the First International Symposium on Artificial Life and Intelligent Agents, ALIA 2014, held in Bangor, UK, in November 2014. The 10 revised full papers were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections on learning and evolution; human interaction; robotic simulation.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

• Error bounds, sample compression schemes
• Statistical learning, theory, evolvability
• Exact and interactive learning
• Complexity of teaching models
• Inductive inference
• Online learning
• Bandits and reinforcement learning
• Clustering.

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

• Introduction to Swarm Search.- Cooperative "Swarm Cleaning" of Stationary Domains.- Swarm Search of Expanding Regions in Grids: Lower Bounds.- Swarm Search of Expanding Regions in Grids: Upper Bounds.- The Search Complexity of Collaborative Swarms Expanding Z2 Grid Regions.
• (source: Nielsen Book Data)

This book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling unmanned aerial vehicle (UAV) swarms, focusing on its applications for tactical intelligence drones. It discusses frameworks for robustly and near-optimally analyzing flocks of semi-autonomous vehicles designed to efficiently perform the ongoing dynamic patrolling and scanning of pre-defined "search regions". It discusses the theoretical limitations of such systems, as well as the trade-offs between the systems' various economic and operational parameters. Current UAV systems rely mainly on human operators for the design and adaptation of drones' flying routes. However, recent technological advances have introduced new systems, comprised of a small number of self-organizing vehicles, manually guided at the swarm level by a human operator. With the growing complexity of such man-supervised architectures, it is becoming increasingly harder to guarantee a pre-defined level of performance. The use of large scale swarms of UAVs as a combat and reconnaissance platform therefore necessitates the development of an efficient optimization mechanism of their utilization, specifically in the design and maintenance of their patrolling routes. The book is intended for researchers and engineers in the fields of swarms systems and autonomous drones. .
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Mammographic Mass Segmentation Using Fuzzy C-means and Decision Trees
• 1 Introduction
• 2 Segmentation of Masses in Mammograms Using Fuzzy C-means and Decision Trees
• 2.1 Fuzzy C-means Based on Gray Levels Histogram
• 2.2 Reduction of False Positive ROIs
• 2.3 Feature Extraction
• 2.4 Binary Decision Tree
• 3 Experimentation
• 3.1 Results and Discussion
• 4 Conclusions
• References
• Refining the Pose: Training and Use of Deep Recurrent Autoencoders for Improving Human Pose Estimation
• 1 Introduction

• 2 Deep Architecture for 3D Human Pose Refinement
• 2.1 Denoising Recurrent Autoencoder
• 2.2 Convolutional Network for Pose Prediction
• 2.3 Pose Refinement Training
• 2.4 Cost Function
• 3 Experiments
• 3.1 Evaluation on HumanEva-I
• 3.2 Evaluation on Human 3.6 Million
• 3.3 Ablation Experiments
• 3.4 Conclusions
• References
• How Can Deep Neural Networks Be Generated Efficiently for Devices with Limited Resources?
• 1 Introduction
• 2 Background
• 3 Parameter Pruning
• 4 Quantization
• 5 Low-Rank Factorization
• 6 Compact Network Design
• 7 Neural Model Deployment

• 7.1 Compact Network Design
• 7.2 Training and Pruning
• 7.3 Quantize Model
• 7.4 Inference Optimization
• 8 Conclusion
• References
• Controlling a Smartphone with Brain-Computer Interfaces: A Preliminary Study
• 1 Introduction
• 2 Subjects and Methods
• 2.1 Acquisition
• 2.2 Processing
• 2.3 Application
• 2.4 Evaluation Procedure
• 3 Results
• 4 Discussion
• 5 Conclusion
• References
• Capturing Industrial Machinery into Virtual Reality
• 1 Introduction
• 2 Related Work
• 3 System Design
• 3.1 Initialisation
• 3.2 Capturing Images
• 3.3 Calibration
• 3.4 Visualisation

• 4 Results
• 5 Conclusion
• References
• Leishmaniasis Parasite Segmentation and Classification Using Deep Learning
• 1 Introduction
• 2 Data
• 3 Method
• 4 Results
• 5 Conclusions
• References
• Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset
• 1 Introduction
• 2 The JNU Dataset
• 3 Automatic Pedestrian Detection
• 4 Automatic Data Association
• 5 Evaluation
• 6 Conclusion
• References
• Shape and Appearance Based Sequenced Convnets to Detect Real-Time Face Attributes on Mobile Devices
• 1 Introduction
• 2 Related Work

• 3 Datasets and Data Preparation
• 3.1 FER-2013 and FER+ Datasets
• 3.2 Data Preprocessing
• 4 Proposed CNN Architecture
• 4.1 Sequenced CNN Models
• 4.2 Face Heatmap Image Construction
• 4.3 CNN Models
• 4.4 Learning with a Shape Heatmap Image
• 5 Results and Applications
• 5.1 Effects of Data Preparation and Alignment
• 5.2 Combining Face Shape and Appearance with VGG
• 5.3 Combining Face Shape and Appearance with Mobilenet
• 5.4 Implementation
• 6 Conclusions
• References
• Image Colorization Using Generative Adversarial Networks
• 1 Introduction

This book constitutes the refereed proceedings of the 10th International Conference on Articulated Motion and Deformable Objects, AMDO 2018, held in Palma de Mallorca, Spain, in July 2018. The 12 papers presented were carefully reviewed and selected from 26 submissions. The papers address the following topics: advanced computer graphics and immersive videogames; human modeling and animation; human motion analysis and tracking; 3D human reconstruction and recognition; multimodal user interaction and applications; ubiquitous and social computing; design tools; input technology; programming user interfaces; 3D medical deformable models and visualization; deep learning methods for computer vision and graphics; and multibiometric.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents; An Ecological View of Smart Home Technologies; Abstract; 1 Introduction; 2 Domotics as Home Automation; 3 The Ecology of the Smart Home; 3.1 A Smart Home Is not a HaaS; 3.2 Traditional Home Services; 3.3 The Smart Home as an Inside-Out Autonomous Robot; 4 Intelligent Services for the Smart Home; 4.1 Tool Services; 4.2 Housekeeping Services; 4.3 Advisor Services; 4.4 Media Services; 4.5 Categories of Service Are Based on Interaction; 5 Qualities and Show Stoppers for Smart Home Services; 5.1 Controllability; 5.2 Reliability and Maintainability

• 5.3 Usability5.4 Durability; 5.5 Security, Privacy and Trustworthiness; 6 Concluding Remarks; References; Modeling and Assessing Young Children Abilities and Development in Ambient Intelligence; Abstract; 1 Introduction; 2 Related Work; 2.1 Modelling User Abilities and Performance in Ambient Intelligence; 2.2 Software Assessment Tools; 3 Background; 3.1 Play and Its Contribution to Child's Development; 3.2 Knowledge Models and Assessment Tools; 4 The BEAN Framework; 4.1 Bean Model: A Knowledge-Based Data Model; 4.2 Reasoning Mechanism; 4.3 Reporting Facilities; 5 A Case Study: The Tower Game

• 6 Conclusions and Future WorkAcknowledgments; References; Augmented Home Inventories; Abstract; 1 Introduction; 2 Home Inventories: A Brief History; 3 New Household Items; 4 Emerging Home Entities and Societies; 5 Challenging the Home Inventory; 6 Conclusions; References; Ambient Intelligence from Senior Citizens' Perspectives: Understanding Privacy Concerns, Technology Acceptance, and Expectations; 1 Introduction; 2 Related Work; 3 Methods; 3.1 Sample; 3.2 Questionnaire Design; 4 Results; 4.1 Importance of Ambient Intelligence Features; 4.2 Acceptable System Limitations

• 4.3 Fears Associated with the Use of Ambient Intelligence Technologies4.4 Detailed Feature Comparison; 4.5 Comparison of Four Ambient Intelligence System Types; 5 Discussion and Summary; 5.1 Limitations; 5.2 Main Findings; 6 Summary; References; Person Identification by Analyzing Door Accelerations in Time and Frequency Domain; Abstract; 1 Introduction; 2 Background; 2.1 Physics and Acceleration Signal Description; 2.2 Related Work; 3 Time Domain Identification; 3.1 Feature-Based Identification; 3.2 Signal-Based Identification; 4 Frequency Domain Identification

• 4.1 Feature-Based Identification4.2 Signal-Based Identification; 5 Experiments; 5.1 Time Domain; 5.2 Frequency Domain; 5.3 Combining the Time and Frequency Domain Methods; 6 Conclusions; Acknowledgements; References; Design Factors for Flexible Capacitive Sensors in Ambient Intelligence; 1 Introduction; 2 Related Work; 3 Evaluating Flexible Capacitive Sensors; 4 Electrode Material Evaluation; 4.1 Measurement Setup; 4.2 Electrode Materials; 5 Results; 5.1 Self Capacitance Measurements; 5.2 Mutual Capacitance Measurements; 6 Design Factors; 6.1 On Materials; 6.2 On Size; 6.3 On Modes

This book constitutes the refereed proceedings of the 12th European Conference on Ambient Intelligence, AmI 2015, held in Athens, Greece, in November 2015. The 21 revised full papers presented together with 5 short papers were carefully reviewed and selected from 48 submissions. Over the past 20 years, the vision of Ambient Intelligence has gradually materialized into a plethora of technologies and devices, which are being introduced into almost every aspect of everyday life, thus affecting our abilities, activities, behavior and in the end, shaping a new way of thinking.
(source: Nielsen Book Data)

• Technologies for implementing AmIHealth environments.- Frameworks related with AmIHealth environments.- Applied algorithms in e-Health systems.- Interactions within the AmIHealth environments.- Applications and case studies of AmIHealth environments.- Metrics for Health environments.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the First International Conference on Ambient Intelligence for Health, AmIHEALTH 2015, held in Puerto Varas, Chile, in December 2015. The 20 revised full papers and 9 short papers were reviewed and selected from 32 submissions and cover topics on technologies for implementing AmIHealth environments; frameworks related with AmIHealth environments; applied algorithms in e-Health systems; interactions within the AmIHealth environments; applications and case studies of AmIHealth environments; and metrics for health environments.
(source: Nielsen Book Data)

• Fractional Polya Integral Inequality.- Univariate Fractional Polya Integral Inequalities.- About Multivariate General Fractional Polya Integral Inequalities.- Balanced Canavati Fractional Opial Inequalities.- Fractional Representation Formulae and Fractional Ostrowski Inequalities.- Basic Fractional Integral Inequalities.- Harmonic Multivariate Ostrowski and Gruss Inequalities.- Fractional Ostrowski and Gruss Inequalities Using Several Functions.- Further Interpretation of Some Fractional Ostrowski and Gruss Type Inequalities.- Multivariate Fractional Representation Formula and Ostrowski Inequality.- Fractional Representation Formulae and Ostrowski Inequalities.- About Multivariate Lyapunov Inequalities.- Ostrowski Type Inequalities for Semigroups.- About Ostrowski Inequalities for Cosine and Sine Operator Functions.- About Hilbert-Pachpatte Inequalities.- About Ostrowski and Landau Type Inequalities.- Multidimensional Ostrowski Type Inequalities.- About Fractional Representation Formulae and Right Fractional Inequalities.- About Canavati fractional Ostrowski inequalities.- The Most General Fractional Representation Formula.- Rational Inequalities for Integral Operators Using Convexity.- Fractional Integral Inequalities with Convexity.- Vectorial Inequalities for Integral Operators.- Vectorial Splitting Rational Lp Inequalities for Integral Operators.- Separating Rational Lp Inequalities for Integral Operators.- About Vectorial Hardy Type Fractional Inequalities.- About Vectorial Fractional Integral Inequalities Using Convexity.
• (source: Nielsen Book Data)

This monograph presents recent and original work of the author on inequalities in real, functional and fractional analysis. The chapters are self-contained and can be read independently, they include an extensive list of references per chapter. The book's results are expected to find applications in many areas of applied and pure mathematics, especially in ordinary and partial differential equations and fractional differential equations. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, as well as Science and Engineering University libraries.
(source: Nielsen Book Data)

• A strong left Fractional Calculus for Banach space valued functions.- Strong Right Abstract Fractional Calculus.- Strong mixed and generalized Abstract Fractional Calculus.- Foundations of General Fractional Analysis for Banach space valued functions.- Vector abstract fractional Korovkin Approximation.- Basic Abstract Korovkin theory.- High Approximation for Banach space valued functions.- Vectorial abstract fractional approximation using linear operators.- Abstract fractional trigonometric Korovkin approximation.- Multivariate Abstract Approximation for Banach space valued functions.- Arctangent function based Abstract Neural Network approximation. .
• (source: Nielsen Book Data)

This brief book presents the strong fractional analysis of Banach space valued functions of a real domain. The book's results are abstract in nature: analytic inequalities, Korovkin approximation of functions and neural network approximation. The chapters are self-contained and can be read independently. This concise book is suitable for use in related graduate classes and many research projects. An extensive list of references is provided for each chapter. The book's results are relevant for many areas of pure and applied mathematics. As such, it offers a unique resource for researchers, and a valuable addition to all science and engineering libraries.
(source: Nielsen Book Data)

• Newton-Like Methods on Generalized Banach Spaces and Fractional Calculus
• Semilocal Convegence of Newton-Like Methods and Fractional Calculus
• Convergence of Iterative Methods and Generalized Fractional Calculus
• Fixed Point Techniques And Generalized Right Fractional Calculus
• Approximating Fixed Points And K-Fractional Calculus
• Iterative Methods And Generalized G-Fractional Calculus
• Unified Convergence Analysis For Iterative Algorithms And Fractional Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional And Vector Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional Calculus
• Secant-Like Methods And Fractional Calculus
• Secant-Like Methods And Modified G- Fractional Calculus
• Secant-Like Algorithms And Generalized Fractional Calculus
• Secant-Like Methods And Generalized G-Fractional Calculus Of Canavati-Type
• Iterative Algorithms And Left-Right Caputo Fractional Derivatives
• Iterative Methods On Banach Spaces With A Convergence Structure And Fractional Calculus
• Inexact Gauss-Newton Method For Singular Equations
• The Asymptotic Mesh Independence Principle
• Ball Convergence Of A Sixth Order Iterative Method
• Broyden's Method With Regularily Continuous Divided Differences
• Left General Fractional Monotone Approximation
• Right General Fractional Monotone Approximation Theor
• Left Generalized High Order Fractional Monotone Approximation
• Right Generalized High Order Fractional Monotone Approximation
• Advanced Fractional Taylor's Formulae
• Generalized Canavati Type Fractional Taylor's Formulae.

In this monograph the authors present Newton-type, Newton-like and other numerical methods, which involve fractional derivatives and fractional integral operators, for the first time studied in the literature. All for the purpose to solve numerically equations whose associated functions can be also non-differentiable in the ordinary sense. That is among others extending the classical Newton method theory which requires usual differentiability of function. Chapters are self-contained and can be read independently and several advanced courses can be taught out of this book. An extensive list of references is given per chapter. The book's results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also to be in all science and engineering libraries.

• Fixed Point Results and Applications in Left Multivariate Fractional Calculus
• Fixed Point Results and Applications in Right Multivariate Fractional Calculus
• Semi-local Iterative Procedures and Applications In K-Multivariate Fractional Calculus
• Newton-like Procedures and Applications in Multivariate Fractional Calculus
• Implicit Iterative Algorithms and Applications in Multivariate Calculus
• Monotone Iterative Schemes and Applications in Fractional Calculus
• Extending the Convergence Domain of Newton?s Method
• The Left Multidimensional Riemann-Liouville Fractional Integral
• The Right Multidimensional Riemann-Liouville Fractional Integral.

In this short monograph Newton-like and other similar numerical methods with applications to solving multivariate equations are developed, which involve Caputo type fractional mixed partial derivatives and multivariate fractional Riemann-Liouville integral operators. These are studied for the first time in the literature. The chapters are self-contained and can be read independently. An extensive list of references is given per chapter. The book?s results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this short monograph is suitable for researchers, graduate students, to be used in graduate classes and seminars of the above subjects, also to be in all science and engineering libraries.

• Rate of Convergence of Basic Neural Network Operators to the Unit.- Rate of Convergence of Basic Multivariate Neural Network Operators.- Fractional Neural Network Operators Approximation.- Fractional Approximation Using Cardaliaguet-Euvrard Neural Networks.- Fractional Asymptotic Expansions for Quasi-interpolation neural Networks.- Voronovskaya Type Asymptotic Expansions for Multivariate Neural Networks.- Fractional Approximation by Bell and Squashing Neural Networks.- Fractional Asymptotic Expansions For Bell And Squashing Neural Networks.- Multivariate Asymptotic Expansions for Bell and Squashing Neural Networks.- Multivariate Fuzzy-Random Normalized Neural Network Approximation.- Fuzzy Fractional Approximations by Fuzzy Bell and Squashing Neural Networks.- Fuzzy Fractional Neural Network Approximation.- Multivariate Fuzzy Approximation Using Basic Neural Network Operators.- Multivariate Fuzzy Approximation Using Quasi-Interpolation Neural Networks.- Multivariate Fuzzy-Random Neural Networks Approximation.- Approximation by Kantorovich and Quadrature type neural Networks.- Univariate Error Function Based Neural Network Approximations.- Multivariate Error Function Based Neural Network Operators Approximation.- Asymptotic Expansions for Error Function Based Neural Networks.- Fuzzy Fractional Error Function Relied Neural Network Approximations.- Multivariate Fuzzy Approximation by Neural Networks.- Fuzzy-Random Error Function Relied Neural Network Approximations.- Approximation by Perturbed Neural Networks.- Approximations by Multivariate Perturbed Neural Networks.- Voronovskaya type Asymptotic Expansions for Perturbed Neural Networks.- Approximation using Fuzzy Perturbed Neural Networks.- Multivariate Fuzzy Perturbed Neural Network Approximations.- Multivariate Fuzzy-Random Perturbed Neural Network Approximations.
• (source: Nielsen Book Data)

This monograph is the continuation and completion of the monograph, "Intelligent Systems: Approximation by Artificial Neural Networks" written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book's results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.
(source: Nielsen Book Data)

• Prologue 7 1. Introduction 10 1.1 Autonomous Systems 13 1.2 The Role of Machine Learning in Autonomous Systems 15 1.3 System Identification 20 1.3.3 Novelty detection, outliers and the link to structure innovation 20 1.4 On-line versus Off-line Identification 21 1.5 Adaptive and Evolving Systems 22 1.6 Evolving or Evolutionary Systems 23 1.7 Supervised versus Un-supervised Learning 25 1.8 Structure of the Book 26 PART I: Fundamentals 29 2. Fundamentals of Probability Theory 29 2.1 Randomness and Determinism 30 2.2 Frequentistic versus Belief-based Approach 33 2.3 Probability Densities and Moments 33 2.4 Density Estimation
• Kernel-based Approach 37 2.5 Recursive Density Estimation (RDE) 40 2.6 Detecting Novelties/Anomalies/Outliers using RDE 45 2.7 Conclusion 49 3. Fundamentals of Machine Learning and Pattern Recognition 51 3.1 Pre-processing 51 3.1.1 Normalisation and standardisation 52 3.1.2 Orthogonalization of inputs/features
• rPCA method 53 3.2 Clustering 56 3.2.1 Proximity measures and clusters shape 59 3.2.2 Off-line methods 60 3.2.3 Evolving clustering methods 65 3.3 Classification 73 3.3.1 Recursive LDA, rLDA 74 3.4 Conclusion 74 4. Fundamentals of Fuzzy Systems Theory 77 4.1 Fuzzy Sets 77 4.2 Fuzzy Systems, Fuzzy Rules 80 4.2.1 Fuzzy Systems of Zadeh-Mamdani Type 81 4.2.2 Takagi-Sugeno Fuzzy Systems 83 4.3 Fuzzy Systems with Non-parametric Antecedents (AnYa) 87 4.3.1 Architecture 87 4.3.2 Analysis of AnYa 90 4.4 FRB (Off-line) classifiers 91 4.5 Neuro-Fuzzy Systems 94 4.5.1 Neuro-fuzzy system architecture 94 4.5.2 Evolving NFS as a framework for autonomous learning and knowledge extraction from data streams 97 4.5.3 Linguistic interpretation of the NFS 98 4.6 State Space Perspective 99 4.7 Conclusions 100 Part II Methodology of Autonomous Learning Systems 101 5 Evolving System Structure from Streaming Data 101 5.1 Defining system structure based on prior knowledge 101 5.2 Data Space Partitioning 102 5.2.1 Regular partitioning of the data space 103 5.2.2 Data space partitioning through clustering 104 5.2.3. Data space partitioning based on data clouds 105 5.2.4. Importance of partitioning the joint input-output data space 105 5.2.5 Principles of data space partitioning for autonomous machine learning 107 5.2.6 Dynamic data space partitioning
• evolving system structure autonomously 108 5.3 Normalisation and Standardisation of Streaming Data in Evolving Environments 114 5.3.1 Standardization in an Evolving Environment 115 5.3.2 Normalisation in an Evolving Environment 116 5.4 Autonomous Monitoring of the Structure Quality 117 5.4.1 Autonomous Input Variables Selection 117 5.4.2 Autonomous Monitoring of the Age of the Local Sub-model 120 5.4.3 Autonomous Monitoring of the Utility of the Local Sub-model 122 5.4.4 Update of the Cluster Radii 123 5.5 Short- and Long-term Focal Points and Sub-models 124 5.6 Simplification and Interpretability Issues 125 5.7 Conclusion 127 6 Autonomous Learning Parameters of the Local Sub-models 129 6.1 Learning Parameters of Local Sub-models 130 6.2 Global versus Local Learning 131 6.3 Evolving Systems Structure Recursively 133 6.4 Learning Modes 137 6.5 Robustness to Outliers in Autonomous Learning 140 6.6 Conclusions 140 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 142 7.1 Predictors, Estimators, Filters
• Problem Formulation 142 7.2 Non-linear Regression 144 7.3 Time series 145 7.4 Autonomous Learning Sensors 146 7.4.1 Autonomous Sensors
• Problem Definition 146 7.4.2 A brief Overview of Soft/Intelligent/Inferential Sensors 147 7.4.3 Autonomous Intelligent Sensors (AutoSense) 149 7.4.4 AutoSense Architecture 151 7.4.5 Modes of Operation of AutoSense 152 7.4.6 Autonomous Input Variable Selection 152 7.5 Conclusions 153 8. Autonomous Learning Classifiers 155 8.1 Classifying data streams 155 8.2 Why adapt the classifier structure? 155 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 157 8.3.1 AutoClassify0 159 8.3.2 AutoClassify1 159 8.4 Learning AutoClassify from Streaming Data 162 8.4.1 Learning AutoClassify0 162 8.4.2 Learning AutoClassify1 163 8.5 Analysis of AutoClassify methods 163 8.6 Conclusions 164 9. Autonomous Learning Controllers 166 9.1 Indirect Adaptive Control Scheme 167 9.2 Evolving Inverse Plant Model from On-line Streaming Data 169 9.3 Evolving Fuzzy Controller Structure from On-line Streaming Data 170 9.4 Examples of using AutoControl 172 9.5 Conclusion 177 10. Collaborative Autonomous Learning Systems 179 10.1 Distributed Intelligence Scenarios 179 10.2 Autonomous Collaborative Learning 181 10.3 Collaborative Autonomous Clustering, AutoCluster by a team of ALSs 183 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a team of ALSs 184 10.5 Collaborative Autonomous Classifiers AutoClassify by a team of ALSs 184 10.6 Superposition of Local Sub-models 185 10.7 Conclusion 186 PART III: Applications of ALS 187 11. Autonomous Learning Sensors for Chemical and Petro-chemical Industries 187 11.1 Case Study 1: Quality of the Products in an Oil Refinery 187 11.1.1 Introduction 187 11.1.2 The current state of the art 188 11.1.3 Problem description 189 11.1.4 The data set 189 11.1.5 AutoSesnse for kerosene quality prediction 191 11.1.6 AutoSense for Abel inflammability test 193 11.2 Case Study 2: Polypropylene Manufacturing 194 11.2.1 Problem description 194 11.2.2 Drift and shift detection by cluster age derivatives 198 11.2.3 Input variables selection 200 11.3 Conclusion 201 12. Autonomous Learning Systems in Mobile Robotics 203 12.1 The mobile robot Pioneer 3DX 203 12.2 Autonomous Classifier for Landmark Recognition 205 12.2.1 Corner detection and simple mapping of an indoor environment through wall following 207 12.2.2 Outdoor landmark detection based on visual input information 210 12.2.3 VideoDiaries 214 12.2.4 Collaborative scenario 217 12.3 Autonomous Leader Follower 220 12.4 Results Analysis 223 13. Autonomous Novelty Detection and Object Tracking in Video Streams 224 13.1 Problem Definition 224 13.2 Background subtraction and KDE for detecting visual novelties 225 13.2.1 Background subtraction method 225 13.2.2 Challenges 226 13.2.3 Parametric versus non-parametric approaches 229 13.2.4 Kernel Density Estimation method 230 13.3 Detecting Visual novelties with RDE Method 231 13.4 Object Identification in Image Frames using RDE 232 13.5 Real-time Tracking in Video Streams using ALS 234 13.6 Conclusion 237 14. Modelling Evolving User Behaviour with ALS 239 14.1 User Behaviour as an evolving phenomenon 239 14.2 Designing the User Behaviour Profile 241 14.3 Applying AutoClassify0 for modelling evolving user behaviour 244 14.4 Case studies 245 14.4.1 Users of UNIX commands 245 14.4.2 Modelling activity of people in a smart home environment 247 14.4.3 Automatic scene recognition 249 14.5 Conclusions 252 15. Epilogue 254 15.1 Conclusions 254 15.2 Open Problems 258 15.3 Future Directions 259 Bibliography 261 Index 274 Glossary 291 Appendices 295 A. Mathematical Foundations 296 A1 Probability distributions 297 A2 Basic matrix properties 300 B. Pseudo-code of the basic algorithms 302 B1 Mean shift with Epanechnikov kernel 302 B2 AutoCluster 304 B3 ELM 305 B4 AutoPredict 307 B5 AutoSense 308 B6 AutoClassify0 309 B7 AutoClassify1 311 B8 AutoControl 313.
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• Forewords xi Preface xix About the Author xxiii 1 Introduction 1 1.1 Autonomous Systems 3 1.2 The Role of Machine Learning in Autonomous Systems 4 1.3 System Identification an Abstract Model of the Real World 6 1.4 Online versus Offline Identification 9 1.5 Adaptive and Evolving Systems 10 1.6 Evolving or Evolutionary Systems 11 1.7 Supervised versus Unsupervised Learning 13 1.8 Structure of the Book 14 PART I FUNDAMENTALS 2 Fundamentals of Probability Theory 19 2.1 Randomness and Determinism 20 2.2 Frequentistic versus Belief-Based Approach 22 2.3 Probability Densities and Moments 23 2.4 Density Estimation Kernel-Based Approach 26 2.5 Recursive Density Estimation (RDE) 28 2.6 Detecting Novelties/Anomalies/Outliers using RDE 32 2.7 Conclusions 36 3 Fundamentals of Machine Learning and Pattern Recognition 37 3.1 Preprocessing 37 3.2 Clustering 42 3.3 Classification 56 3.4 Conclusions 58 4 Fundamentals of Fuzzy Systems Theory 61 4.1 Fuzzy Sets 61 4.2 Fuzzy Systems, Fuzzy Rules 64 4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69 4.4 FRB (Offline) Classifiers 73 4.5 Neurofuzzy Systems 75 4.6 State Space Perspective 79 4.7 Conclusions 81 PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS 5 Evolving System Structure from Streaming Data 85 5.1 Defining System Structure Based on Prior Knowledge 85 5.2 Data Space Partitioning 86 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96 5.4 Autonomous Monitoring of the Structure Quality 98 5.5 Short- and Long-Term Focal Points and Submodels 104 5.6 Simplification and Interpretability Issues 105 5.7 Conclusions 107 6 Autonomous Learning Parameters of the Local Submodels 109 6.1 Learning Parameters of Local Submodels 110 6.2 Global versus Local Learning 111 6.3 Evolving Systems Structure Recursively 113 6.4 Learning Modes 116 6.5 Robustness to Outliers in Autonomous Learning 118 6.6 Conclusions 118 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121 7.1 Predictors, Estimators, Filters Problem Formulation 121 7.2 Nonlinear Regression 123 7.3 Time Series 124 7.4 Autonomous Learning Sensors 125 7.5 Conclusions 131 8 Autonomous Learning Classifiers 133 8.1 Classifying Data Streams 133 8.2 Why Adapt the Classifier Structure? 134 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 135 8.4 Learning AutoClassify from Streaming Data 139 8.5 Analysis of AutoClassify 140 8.6 Conclusions 140 9 Autonomous Learning Controllers 143 9.1 Indirect Adaptive Control Scheme 144 9.2 Evolving Inverse Plant Model from Online Streaming Data 145 9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147 9.4 Examples of Using AutoControl 148 9.5 Conclusions 153 10 Collaborative Autonomous Learning Systems 155 10.1 Distributed Intelligence Scenarios 155 10.2 Autonomous Collaborative Learning 157 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs 158 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs 159 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs 160 10.6 Superposition of Local Submodels 161 10.7 Conclusions 161 PART III APPLICATIONS OF ALS 11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165 11.1 Case Study
• 1: Quality of the Products in an Oil Refinery 165 11.2 Case Study
• 2: Polypropylene Manufacturing 172 11.3 Conclusions 178 12 Autonomous Learning Systems in Mobile Robotics 179 12.1 The Mobile Robot Pioneer 3DX 179 12.2 Autonomous Classifier for Landmark Recognition 180 12.3 Autonomous Leader Follower 193 12.4 Results Analysis 196 13 Autonomous Novelty Detection and Object Tracking in Video Streams 197 13.1 Problem Definition 197 13.2 Background Subtraction and KDE for Detecting Visual Novelties 198 13.3 Detecting Visual Novelties with the RDE Method 203 13.4 Object Identification in Image Frames Using RDE 204 13.5 Real-time Tracking in Video Streams Using ALS 206 13.6 Conclusions 209 14 Modelling Evolving User Behaviour with ALS 211 14.1 User Behaviour as an Evolving Phenomenon 211 14.2 Designing the User Behaviour Profile 212 14.3 Applying AutoClassify0 for Modelling Evolving User Behaviour 215 14.4 Case Studies 216 14.5 Conclusions 221 15 Epilogue 223 15.1 Conclusions 223 15.2 Open Problems 227 15.3 Future Directions 227 APPENDICES Appendix A Mathematical Foundations 231 Appendix B Pseudocode of the Basic Algorithms 235 References 245 Glossary 259 Index 263.
• (source: Nielsen Book Data)

Addresses the techniques and challenges of autonomous learning systems and illustrates the practical relevance of the approach with a wide range of applications Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic and fuzzy systems, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, and research-driven - there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Including a wide range of applications, illustrations and software code (MatLAB(r)) that can be used for further research it constitutes a valuable 'one-stop-shop' for academics, researchers, practicing engineers, computer specialists and defence and industry experts.
(source: Nielsen Book Data)
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: * Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. * Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. * Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. * Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a one-stop shop on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
(source: Nielsen Book Data)

• Table of Contents Introduction to Markov Process Hidden Markov Models State Inference: Predicting the states Parameter Inference using Maximum Likelihood Parameter Inference using Bayesian Approach Time Series: Predicting Stock Prices Natural Language Processing: Teaching machines to talk 2D-HMM for Image Processing Reinforcement Learning: Teaching a robot to cross a maze.
• (source: Nielsen Book Data)

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you'll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book.
(source: Nielsen Book Data)

• Learning sequential data with the help of linear systems
• A spiking neural network for personalised modelling of Electrogastogrophy (EGG)
• Improving generalization abilities of maximal average margin classifiers
• Finding small sets of random Fourier features for shift-invariant kernel approximation
• Incremental construction of low-dimensional data representations
• Soft-constrained nonparametric density estimation with artificial neural networks
• Density based clustering via dominant sets
• Co-training with credal models
• Interpretable classifiers in precision medicine: feature selection and multi-class categorization
• On the evaluation of tensor-based representations for optimum-pathforest classification
• On the harmony search using quaternions
• Learning parameters in deep belief networks through firefly algorithm
• Towards effective classification of imbalanced data with convolutional neural networks
• On CPU performance optimization of restricted Boltzmann machine and convolutional RBM
• Comparing incremental learning strategies for convolutional neural networks
• Approximation of graph edit distance by means of a utility matrix
• Time series classification in reservoir- and model-space: a comparison
• Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks
• Background categorization for automatic animal detection in aerial videos using neural networks
• Predictive segmentation using multichannel neural networks in Arabic OCR system
• Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters
• A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins
• Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals
• Active learning for speech event detection in HCI
• Emotion recognition in speech with deep learning architectures
• On gestures and postural behavior as a modality in ensemble methods
• Machine learning driven heart rate detection with camera photoplethysmography in time domain.

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

• Intro
• Preface
• Organization
• Contents
• Invited Papers
• What's Wrong with Computer Vision?
• 1 Introduction
• 2 Top Ten Questions a Theory on Vision Should Address
• 3 Hierarchical Description of Visual Tasks
• 3.1 Pixel-Wise and Abstract Visual Interpretations
• 3.2 The Interwound Story of Vision and Language
• 3.3 When Vision Collapses to Classification
• 4 Conclusions
• References
• Deep Learning in the Wild
• 1 Introduction
• 2 Face Matching
• 3 Print Media Monitoring
• 4 Visual Quality Control
• 5 Music Scanning
• 6 Game Playing
• 7 Automated Machine Learning

• 8 Conclusions
• References
• Learning Algorithms and Architectures
• Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines
• 1 Introduction
• 2 Least Squares Support Vector Machines
• 3 Large Margin Distribution Machines and Their Variants
• 3.1 Large Margin Distribution Machines
• 3.2 Least Squares Large Margin Distribution Machines
• 3.3 Unconstrained Large Margin Distribution Machines
• 4 Performance Evaluation
• 4.1 Conditions for Experiment
• 4.2 Results for Two-Class Problems
• 5 Conclusions
• References
• DLL: A Fast Deep Neural Network Library

• 1 Introduction
• 2 DLL: Deep Learning Library
• 2.1 Performance
• 2.2 Example
• 3 Experimental Evaluation
• 4 MNIST
• 4.1 Fully-Connected Neural Network
• 4.2 Convolutional Neural Network
• 5 CIFAR-10
• 6 ImageNet
• 7 Conclusion and Future Work
• References
• Selecting Features from Foreign Classes
• 1 Introduction
• 2 Methods
• 2.1 Learning from Context Classes
• 2.2 Foreign Class Combinations
• 3 Experiments
• 3.1 Datasets
• 4 Results
• 5 Discussion and Conclusion
• References
• A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

• 1 Introduction
• 2 Error-Driven Target Propagation: Formalization of the Algorithms
• 2.1 The Inversion Net
• 2.2 Refinement of Deep Learning via Target Propagation
• 3 Experiments
• 4 Conclusions
• References
• Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text
• 1 Introduction
• 2 Model
• 2.1 Semantic Features
• 2.2 Logic Constraints
• 2.3 Segmentation
• 3 Experiments
• 4 Conclusions
• References
• SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
• 1 Introduction
• 2 Related Work

• 3 Proposed Method
• 4 Experiments
• 4.1 Network Architecture
• 4.2 Training Methodology
• 4.3 Isolated Learning
• 4.4 Adding New Tasks to the Models
• 4.5 Three Tasks Scenario
• 5 Conclusion
• References
• Classification Uncertainty of Deep Neural Networks Based on Gradient Information
• 1 Introduction
• 2 Entropy, Softmax Baseline and Gradient Metrics
• 3 Meta Classification
• A Benchmark Between Maximum Softmax Probability and Gradient Metrics
• 4 Recognition of Unlearned Concepts
• 5 Meta Classification with Known Unknowns
• 6 Conclusion and Outlook
• References

This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search.- Sttructured (De)composable Representations Trained with Neural Networks.- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling.- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins.- Abstract Echo State Networks.- Minimal Complexity Support Vector Machines.- Named Entity Disambiguation at Scale.- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds.- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database.- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario.- A deep learning approach for efficient registration of dual view mammography.- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology.- Applications of Generative Adversarial Networks to Dermatologic Imaging.- Typing Plasmids with Distributed Sequence Representation.- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes.- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools.- A Hybrid Deep Learning Approach For Forecasting Air Temperature.- Using CNNs to optimize numerical simulations in geotechnical engineering.- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification.- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture.- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis.- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
(source: Nielsen Book Data)

• Network and Cyber-physical Systems Security.- Modeling and Mitigating Security Threats in Network Functions Virtualization (NFV).- Managing Secure Inter-slice Communication in 5G Network Slice Chains.- Proactively Extracting IoT Device Capabilities: An Application to Smart Homes.- Security Enumerations for Cyber-Physical Systems.- Information Flow and Access Control.- Inference-Proof Monotonic Query Evaluation and View Generation Reconsidered.- Network Functions Virtualisation Access Control as a Service.- Effective Access Control in Shared-Operator Multi-tenant Data Stream Management Systems.- Information Flow Security Certification for SPARK Programs.- Privacy-preserving Computation.- Provably Privacy-Preserving Distributed Data Aggregation in Smart Grids.- Non-Interactive Private Decision Tree Evaluation.- Privacy-preserving Anomaly Detection using Synthetic Data.- Local Differentially Private Matrix Factorization with MoG for Recommendations.- Visualization and Analytics for Security.- Designing a Decision-Support Visualization for Live Digital Forensic Investigations.- Predictive Analytics to Prevent Voice Over IP International Revenue Sharing Fraud.- PUA Detection Based on Bundle Installer Characteristics.- ML-supported Identification and Prioritization of Threats in the OVVL Threat Modelling Tool.- Spatial Systems and Crowdsourcing Security.- Enhancing the Performance of Spatial Queries on Encrypted Data through Graph Embedding.- Crowdsourcing under Data Poisoning Attacks: A Comparative Study.- Self-Enhancing GPS-based Authentication Using Corresponding Address.- Secure Outsourcing and Privacy.- GOOSE: A Secure Framework for Graph Outsourcing and SPARQL Evaluation.- SGX-IR: Secure Information Retrieval with Trusted Processors.- Measuring Readability of Privacy Policies.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• A New Method of Breakpoint Connection for Human Skeleton Image
• Insult Detection in Social Network Comments using Possibilistic based Fusion Approach
• What Information in Software Historical Repositories Do We Need to Support Software Maintenance Tasks? An Approach Based on Topic Model
• Evaluation Framework for the Dependability of Ubiquitous Learning Environment
• Improving Content Recommendation in Social Streams via Interest Model
• Performance Evaluation of Unsupervised Learning Techniques for Intrusion Detection in Mobile Ad Hoc Networks
• Live Migration Performance Modelling for Virtual Machines with Resizable Memory
• A Heuristic Algorithm forWorkflow-Based Job Scheduling in Decentralized Distributed Systems with Heterogeneous Resources
• Novel Data Integrity Verification Schemes in Cloud Storage
• Generation of Assurance Cases For Medical Devices
• A Survey on the Categories of Service Value/Quality/Satisfactory Factors
• Effective Domain Modeling for Mobile Business AHMS (Adaptive Human Management Systems) Requirements.

• Intro; Foreword; Contents; Contributors; 1 A New Method of Breakpoint Connection for Human Skeleton Image; 1 Introduction; 2 Image Preprocessing; 3 Basic Elements; 3.1 Neighborhood Layer; 3.2 Upper Left Neighborhood and Lower Right Neighborhood; 3.3 Breakpoint Type; 3.4 Available Connection Point; 4 Breakpoint Connection Function; 4.1 ΔW = 0; 4.2 ΔW neq 0; 5 Procedure of Breakpoint Connection; 5.1 Search Skeleton Breakpoint; 5.2 Search Available Connection Point; 5.3 Connect Breakpoint; 6 Experimental Results; 7 Conclusion; References

This edited book presents scientific results of the 13th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2014) which was held on June 4-6, 2014 in Taiyuan, China. 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 and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rigorous rounds of review. This publication captures 14 of the conference's most promising papers, and we impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
• (source: Nielsen Book Data)

A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• A Bearing-only Pattern Formation Algorithm for Swarm Robotics
• A Macroscopic Privacy Model for Heterogeneous Robot Swarms
• A New Continuous Model for Segregation Implemented and Analyzed on Swarm Robots
• A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking
• Ant Colony Optimisation-based Classification using Two-dimensional Polygons
• Collective Perception of Environmental Features in a Robot Swarm
• Communication Diversity in Particle Swarm Optimizers
• Continuous Time Gathering of Agents with Limited Visibility and Bearing-only Sensing
• Design and Analysis of Proximate Mechanisms for Cooperative Transport in Real Robots
• Dynamic Task Partitioning for Foraging Robot Swarms
• Human-robot Swarm Interaction with Limited Situational Awareness
• Monotonicity in Ant Colony Classification Algorithms
• Observing the Effects of Overdesign in the Automatic Design of Control Software for Robot Swarms
• Parameter Selection in Particle Swarm Optimisation from Stochastic Stability Analysis
• Population Coding: A New Design Paradigm for Embodied Distributed Systems
• Random Walks in Swarm Robotics: An Experiment with Kilobots
• Synthesizing Rulesets for Programmable Robotic Self-assembly: A Case Study using Floating Miniaturized Robots
• Using Ant Colony Optimization to Build Cluster-based Classification Systems
• A Swarm Intelligence Approach in Undersampling Majority Class
• Optimizing PolyACO Training with GPU-based Parallelization
• Motion Reconstruction of Swarm-like Self-organized Motor Bike Traffic from Public Videos
• On Heterogeneity in Foraging by Ant-like Colony: How Local Affects Global and Vice Versa
• On Stochastic Broadcast Control of Swarms
• Route Assignment for Autonomous Vehicles
• Stealing Items More Efficiently with Ants: A Swarm Intelligence Approach to the Travelling Thief Problem
• Achieving Synchronisation in Swarm Robotics: Applying Networked Q-Learning to Production Line Automata
• Autonomous Task Allocation for Swarm Robotic Systems Using Hierarchical Strategy
• Avoidance Strategies for Particle Swarm Optimisation in Power Generation Scheduling
• Clustering with the ACOR Algorithm
• Consideration Regarding the Reduction of Reality Gap in Evolutionary Swarm Robotics
• Hybrid Deployment Algorithm of Swarm Robots for Wireless Mesh Network
• On the Definition of Self-organizing Systems: Relevance of Positive/Negative Feedback and Fluctuations
• Particle Swarm Optimisation with Diversity Influenced Gradually Increasing Neighbourhoods.

This book constitutes the proceedings of the 10th International Conference on Swarm Intelligence, ANTS 2016, held in Brussels, Belgium, in September 2016. The 18 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 47 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.

• Full Papers.- Short Papers.- Extended Abstracts.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018. The 24 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 69 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.
(source: Nielsen Book Data)

• Types.- Program Analysis.- Tools.- Functional Programs and Probabilistic Programs.- Verification.- Logic.- Continuation and Model Checking.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 16th Asian Symposium on Programming Languages and Systems, APLAS 2018, held in Wellington, New Zealand, in December 2018.The 22 papers presented in this volume were carefully reviewed and selected from 51 submissions. They are organized in topical sections named: types; program analysis; tools; functional programs and probabilistic programs; verification; logic; and continuation and model checking.
(source: Nielsen Book Data)

• Mobile web data analytics.- Big spatial data and urban computing.- Graph data management and analytics.- Mobility analytics from spatial and social data.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2017 Workshops, held jointly with the First International Joint Conference APWeb-WAIM 2017, held in Beijing, China, in July 2017. The 25 full papers presented were carefully reviewed and selected from 40 submissions. The papers originating from six workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; big spatial data and urban computing; graph data management and analytics; mobility analytics from spatial and social data.
(source: Nielsen Book Data)

• Spatial data processing and data quality.- Graph data processing.- Data mining, privacy and semantic analysis.- Text and log data management.-Social networks.- Data mining and data streams.- Query processing.- Topic modeling.- Machine learning.- Recommendation systems.- Distributed data processing and applications.- Machine learning and optimization.
• (source: Nielsen Book Data)

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
(source: Nielsen Book Data)

• Spatial data processing and data quality.- Graph data processing.- Data mining, privacy and semantic analysis.- Text and log data management.-Social networks.- Data mining and data streams.- Query processing.- Topic modeling.- Machine learning.- Recommendation systems.- Distributed data processing and applications.- Machine learning and optimization. .
• (source: Nielsen Book Data)

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
(source: Nielsen Book Data)

• Mobile web data analytics.- Big data analytics for healthcare.- Knowledge graph management and analysis.- Data management and mining on MOOCs.- Data science. .
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2018 Workshops, held jointly with the Second International Joint Conference APWeb-WAIM 2018 in Macau, China, in July 2018. The 31 full papers presented were carefully reviewed and selected from 44 submissions. The papers originating from five workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; knowledge graph management and analysis; data management and mining on MOOCs; Big data analytics for healthcare; data science.
(source: Nielsen Book Data)

• Text Analysis.- Social Networks.- Recommender Systems.- Information Retrieval.- Machine Learning.- Knowledge Graphs.- Database and Web Applications.- Data Streams.- Data Mining and Application.- Query Processing.- Big Data and Blockchain.
• (source: Nielsen Book Data)

This two-volume set, LNCS 10987 and 10988, constitutes the thoroughly refereed proceedings of the Second International Joint Conference, APWeb-WAIM 2018, held in Macau, China in July 2018. The 40 full papers presented together with 30 short papers, 6 demonstration papers and 3 keynotes were carefully reviewed and selected from 168 submissions. The papers are organized around the following topics: Text Analysis, Social Networks, Recommender Systems, Information Retrieval, Machine Learning, Knowledge Graphs, Database and Web Applications, Data Streams, Data Mining and Application, Query Processing, Big Data and Blockchain.
(source: Nielsen Book Data)

• Video and image processing
• Fault-tolerant systems
• Tools and architectures
• Signal processing
• Multicore systems
• Funded RD running and completed projects.

This book constitutes the refereed proceedings of the 12th International Symposium on Applied Reconfigurable Computing, ARC 2016, held in Rio de Janeiro, Brazil, in March 2016. The 20 full papers presented in this volume were carefully reviewed and selected from 47 submissions. They are organized in topical headings named: video and image processing; fault-tolerant systems; tools and architectures; signal processing; and multicore systems. In addition, the book contains 3 invited papers and 8 poster papers on funded RD running and completed projects.

This book constitutes the refereed proceedings of the 13th International Symposium on Applied Reconfigurable Computing, ARC 2017, held in Delft, The Netherlands, in April 2017. The 17 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They are organized in topical sections on adaptive architectures, embedded computing and security, simulation and synthesis, design space exploration, fault tolerance, FGPA-based designs, neural neworks, and languages and estimation techniques.
(source: Nielsen Book Data)

• Machine Learning and Neural Networks.- Approximate FPGA-based LSTMs under Computation Time Constraints.- Redundancy-reduced MobileNet Acceleration on Reconfigurable Logic For ImageNet Classification.- Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic.- Deep Learning on High Performance FPGA Switching Boards: Flow-in-Cloud.- SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks.- Efficient hardware acceleration of recommendation engines: a use case on collaborative filtering.- FPGA-based Design and CGRA Optimizations.- VerCoLib: Fast and Versatile Communication for FPGAs via PCI Express.- Performance Estimation of FPGA Modules for Modular Design Methodology using Artificial Neural Network.- Achieving Efficient Realization of Kalman Filter on CGRA through Algorithm-Architecture Co-design.- FPGA-based Memory Efficient Shift-And Algorithm for Regular Expression Matching.- Towards an optimized multi FPGA architecture with STDM network: a preliminary study.- Applications and Surveys.- An FPGA/HMC-based Accelerator for Resolution Proof Checking.- An Efficient FPGA Implementation of the Big Bang-Big Crunch Optimization Algorithm.- ReneGENE-GI: Empowering Precision Genomics with FPGAs on HPCs.-FPGA-based Parallel Pattern Matching.- Embedded Vision Systems: A Review of the Literature.- A Survey of Low Power Design Techniques for Last Level Caches.- Fault-Tolerance, Security and Communication Architectures.- ISA-DTMR: Selective Protection in Configurable Heterogeneous Multicores.- Analyzing AXI Streaming Interface for Hardware Acceleration in AP-SoC under Soft Errors.- High Performance UDP/IP 40Gb Ethernet Stack for FPGAs.- Tackling Wireless Sensor Network Heterogeneity Through Novel Reconfigurable Gateway Approach.- A Low-Power FPGA-Based Architecture for Microphone Arrays in Wireless Sensor Networks.- A Hybrid FPGA Trojan Detection Technique Based-on Combinatorial Testing and On-chip Sensing.- HoneyWiN: Novel Honeycomb-based Wireless NoC Architecture in Many-Core Era.- Reconfigurable and Adaptive Architectures.- Fast Partial Reconfiguration on SRAM-based FPGAs: A Frame-Driven Routing Approach.- A Dynamic Partial Reconfigurable Overlay Framework for Python.- Runtime Adaptive Cache for the LEON3 Processor.- Exploiting Partial Reconfiguration on a Dynamic Coarse Grained Reconfigurable Architecture.- DIM-VEX: Exploiting Design Time Configurability and Runtime Reconfigurability.- The use of HACP+SBT lossless compression in optimizing memory bandwidth requirement for hardware implementation of background modelling algorithms.- A Reconfigurable PID Controller.- Design Methods and Fast Prototyping.- High-Level Synthesis of Software-defined MPSoCs.- Improved High-Level Synthesis for Complex CellML Models.- An Intrusive Dynamic Reconfigurable Cycle-accurate Debugging System for Embedded Processors.- Rapid prototyping and verification of hardware modules generated using HLS.- Comparing C and SystemC Based HLS Methods for Reconfigurable Systems Design.- Fast DSE for Automated Parallelization of Embedded Legacy Applications.- Control Flow Analysis for Embedded Multi-Core Hybrid Systems.- FPGA-based Design and Applications.- A Low-Cost BRAM-based Function Reuse for Configurable Soft-Core Processors in FPGAs.- A Parallel-Pipelined OFDM Baseband Modulator with Dynamic Frequency Scaling for 5G Systems.- Area-Energy Aware Dataow Optimisation of Visual Tracking Systems.- Fast Carry Chain based Architectures for Two's Complement to CSD Recoding on FPGAs.- Exploring Functional Acceleration of OpenCL on FPGAs and GPUs Through Platform-Independent Optimizations.- ReneGENE-Novo: Co-designed Algorithm-Architecture for Accelerated Preprocessing and Assembly of Genomic Short Reads.- An OpenCL Implementation of WebP Accelerator on FPGAs.- Efficient Multitasking on FPGA Using HDL-based Checkpointing.- High Level Synthesis Implementation of Object Tracking Algorithm on Reconfigurable Hardware.- Reconfigurable FPGA-Based Channelization Using Polyphase Filter Banks for Quantum Computing Systems.- Reconfigurable IP-Based Spectral Interference Canceller.- FPGA-Assisted Distribution Grid Simulator.- Analyzing the Use of Taylor Series Approximation in Hardware and Embedded Software for Good Cost-Accuracy Tradeoffs.- Special Session: Research Projects.- CGRA Tool Flow for Fast Run-Time Reconfiguration.- Seamless FPGA deployment over Spark in cloud computing: A use case on Machine learning hardware acceleration.- The ARAMiS Project Initiative: Multicore Systems in Safety- and Mixed-Critical Applications.- Mapping and scheduling hard real time applications on multicore systems - The ARGO approach.- Robots in assisted living environments as an unobtrusive, efficient, reliable and modular solution for independent ageing: The RADIO Experience.- HLS Algorithmic Explorations for HPC Execution on Reconfigurable Hardware ECOSCALE.- Supporting uTilities for Heterogeneous EMbedded image processing platforms (STHEM): An Overview.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 14th International Conference on Applied Reconfigurable Computing, ARC 2018, held in Santorini, Greece, in May 2018. The 29 full papers and 22 short presented in this volume were carefully reviewed and selected from 78 submissions. In addition, the volume contains 9 contributions from research projects. The papers were organized in topical sections named: machine learning and neural networks; FPGA-based design and CGRA optimizations; applications and surveys; fault-tolerance, security and communication architectures; reconfigurable and adaptive architectures; design methods and fast prototyping; FPGA-based design and applications; and special session: research projects. .
(source: Nielsen Book Data)

• Discovering Lisp
• Lists everywhere.- Functions in Lisp
• Lisp Programming
• From Crisp Sets to Fuzzy Sets
• From Fuzzy Sets to Linguistic Variables
• Fuzzy Logic
• Practical Projects using FuzzyLisp.-

This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to "teach" the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book's website

• Motivation.- Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle.- Ontology Usage Analysis Framework (OUSAF).- Identification Phase : Ontology Usage Network Analysis Framework (OUN-AF).- Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF).
• (source: Nielsen Book Data)

This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders. Where possible it improves and reuses terms in existing vocabularies/ontologies, as recommended by the Linked Data community. Recent advances in the Semantic Web have led to a proliferation of Resource Description Framework (RDF) data, which employ ontologies to semantically describe the information on the Web making it equally understandable for both humans and machines. However, to create a network effect, it is important that selective ontologies are used by more data publishers to improve the value of that ontology. For this to happen, it is vital to discover what is being used from an ontology to semantically annotate the information on the Web specific to a given domain. Answers to such basic but crucial questions can only be achieved by ascertaining how ontologies in the current semantic web are being utilized and adopted. The proposed frameworks to obtain such insights are explained with real-world examples to provide a clear and detailed description of ontology usage analysis. Both theoretical and practical, the book is of value to academics and professionals working in industry. Specifically, it is of primary interest to researchers, graduate students and practitioners in the area of the Semantic Web and its various real-world applications.
(source: Nielsen Book Data)

• Keynotes.- Efficiency.- Graphs, Knowledge Bases and Taxonomies.- Recommendations.- Twitter and Social Media.- Web Search.- Text Processing, Understanding and Categorization.- Topics and Models.- Clustering.- Evaluation.- Social Media and Recommendation.- Short Papers.- Demonstrations.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 11th Information Retrieval Societies Conference, AIRS 2015, held in Brisbane, QLD, Australia, in December 2015. The 29 full papers presented together with 11 short and demonstration papers, and the abstracts of 2 keynote lectures were carefully reviewed and selected from 92 submissions. The final programme of AIRS 2015 is divided in 10 tracks: Efficiency, Graphs, Knowledge Bases and Taxonomies, Recommendation, Twitter and Social Media, Web Search, Text Processing, Understanding and Categorization, Topics and Models, Clustering, Evaluation, and Social Media and Recommendation.
(source: Nielsen Book Data)

• IR Infrastructure and Systems.- IR Models and Theories.- Personalization and Recommendation.- Data Mining for IR.- IR Evaluation.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th Information Retrieval Societies Conference, AIRS 2017, held in Jeju, Korea, in November 2017. The 17 full papers presented were carefully reviewed and selected from numerous submissions. The final program of AIRS 2017 is divided in the following tracks: IR Infrastructure and Systems; IR Models and Theories; Personalization and Recommendation; Data Mining for IR; and IR Evaluation.
(source: Nielsen Book Data)

• Information retrieval.- Natural language processing.- Social networks.- Search methodologies.- Neural networks.- Web searching.- Information discovery.- Retrieval tasks and goals.- Machine learning.- Medical technologies.- Multimedia information systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 14th Information Retrieval Societies Conference, AIRS 2018, held in Taipei, Taiwan, in November 2018. The 8 full papers presented together with 9 short papers and 3 session papers were carefully reviewed and selected from 41 submissions. The scope of the conference covers applications, systems, technologies and theory aspects of information retrieval in text, audio, image, video and multimedia data.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• Knowledge Engineering and Semantic Web.- Social Networks and Recommender Systems.- Text Processing and Information Retrieval.- Machine Learning and Data Mining
• Decision Support and Control Systems.- Computer Vision Techniques.- Advanced Data Mining Techniques and Applications.- Multiple Model Approach to Machine Learning.- Sensor Networks and Internet of Things.- Intelligent Information Systems.- Data Structures Modeling for Knowledge Representation.- Modeling, Storing, and Querying of Graph Data.- Data Science and Computational Intelligence.- Design Thinking Based R&D, Development Technique, and Project Based Learning.- Intelligent and Contextual Systems.- Intelligent Systems and Algorithms in Information Sciences.- Intelligent Applications of Internet of Thing and Data Analysis Technologies.- Intelligent Systems and Methods in Biomedicine.- Intelligent Biomarkers of Neurodegenerative Processes in Brain.- Analysis of Image, Video and Motion Data in Life Sciences.- Computational Imaging and Vision.- Computer Vision and Robotics.- Intelligent Computer Vision Systems and Applications.- Intelligent Systems for Optimization of Logistics and Industrial Applications.
• (source: Nielsen Book Data)

The two-volume set LNAI 10751 and 10752 constitutes the refereed proceedings of the 10th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2018, held in Dong Hoi City, Vietnam, in March 2018. The total of 133 full papers accepted for publication in these proceedings was carefully reviewed and selected from 423 submissions. They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Machine Learning and Data Mining; Decision Support and Control Systems; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Multiple Model Approach to Machine Learning; Sensor Networks and Internet of Things; Intelligent Information Systems; Data Structures Modeling for Knowledge Representation; Modeling, Storing, and Querying of Graph Data; Data Science and Computational Intelligence; Design Thinking Based R&D, Development Technique, and Project Based Learning; Intelligent and Contextual Systems; Intelligent Systems and Algorithms in Information Sciences; Intelligent Applications of Internet of Thing and Data Analysis Technologies; Intelligent Systems and Methods in Biomedicine; Intelligent Biomarkers of Neurodegenerative Processes in Brain; Analysis of Image, Video and Motion Data in Life Sciences; Computational Imaging and Vision; Computer Vision and Robotics; Intelligent Computer Vision Systems and Applications; Intelligent Systems for Optimization of Logistics and Industrial Applications.
(source: Nielsen Book Data)

• Knowledge Engineering and Semantic Web.- Social Networks and Recommender Systems.- Text Processing and Information Retrieval.- Intelligent Database Systems.- Intelligent Information Systems.- Decision Support and Control Systems.- Machine Learning and Data Mining.- Computer Vision Techniques.- Advanced Data Mining Techniques and Applications.- Intelligent and Context Systems.- Multiple Model Approach to Machine Learning.- Applications of Data Science.- Artificial Intelligence Applications for E-services.- Automated Reasoning and Proving Techniques with Applications in Intelligent Systems.- Collective Intelligence for Service Innovation, Technology Opportunity, E-Learning and Fuzzy Intelligent Systems.- Intelligent Computer Vision Systems and Applications.- Intelligent Data Analysis, Applications and Technologies for Internet of Things.- Intelligent Algorithms and Brain Functions.- Intelligent Systems and Algorithms in Information Sciences.- IT in Biomedicine.- Intelligent Technologies in the Smart Cities in the 21st Century.- Analysis of Image, Video and Motion Data in Life Sciences.- Modern Applications of Machine Learning for Actionable Knowledge Extraction.- Mathematics of Decision Sciences and Information Science.- Scalable Data Analysis in Bioinformatics and Biomedical Informatics.- and Technological Perspective of Agile Transformation in IT organizations.
• (source: Nielsen Book Data)

The two-volume set LNAI 10191 and 10192 constitutes the refereed proceedings of the 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, held in Kanazawa, Japan, in April 2017. The total of 152 full papers accepted for publication in these proceedings was carefully reviewed and selected from 420 submissions.They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Intelligent Database Systems; Intelligent Information Systems; Decision Support and Control Systems; Machine Learning and Data Mining; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Intelligent and Context Systems; Multiple Model Approach to Machine Learning; Applications of Data Science; Artificial Intelligence Applications for E-services; Automated Reasoning and Proving Techniques with Applications in Intelligent Systems; Collective Intelligence for Service Innovation, Technology Opportunity, E-Learning and Fuzzy Intelligent Systems; Intelligent Computer Vision Systems and Applications; Intelligent Data Analysis, Applications and Technologies for Internet of Things; Intelligent Algorithms and Brain Functions; Intelligent Systems and Algorithms in Information Sciences; IT in Biomedicine; Intelligent Technologies in the Smart Cities in the 21st Century; Analysis of Image, Video and Motion Data in Life Sciences; Modern Applications of Machine Learning for Actionable Knowledge Extraction; Mathematics of Decision Sciences and Information Science; Scalable Data Analysis in Bioinformatics and Biomedical Informatics; and Technological Perspective of Agile Transformation in IT organizations.
(source: Nielsen Book Data)

• Knowledge Engineering and Semantic Web.- Social Networks and Recommender Systems.- Text Processing and Information Retrieval.- Intelligent Database Systems.- Intelligent Information Systems.- Decision Support and Control Systems.- Machine Learning and Data Mining.- Computer Vision Techniques.- Advanced Data Mining Techniques and Applications.- Intelligent and Context Systems.- Multiple Model Approach to Machine Learning.- Applications of Data Science.- Artificial Intelligence Applications for E-services.- Automated Reasoning and Proving Techniques with Applications in Intelligent Systems.- Collective Intelligence for Service Innovation, Technology Opportunity, E-Learning and Fuzzy Intelligent Systems.- Intelligent Computer Vision Systems and Applications.- Intelligent Data Analysis, Applications and Technologies for Internet of Things.- Intelligent Algorithms and Brain Functions.- Intelligent Systems and Algorithms in Information Sciences.- IT in Biomedicine.- Intelligent Technologies in the Smart Cities in the 21st Century.- Analysis of Image, Video and Motion Data in Life Sciences.- Modern Applications of Machine Learning for Actionable Knowledge Extraction.- Mathematics of Decision Sciences and Information Science.- Scalable Data Analysis in Bioinformatics and Biomedical Informatics.- and Technological Perspective of Agile Transformation in IT organizations.
• (source: Nielsen Book Data)

The two-volume set LNAI 10191 and 10192 constitutes the refereed proceedings of the 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, held in Kanazawa, Japan, in April 2017. The total of 152 full papers accepted for publication in these proceedings was carefully reviewed and selected from 420 submissions. They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Intelligent Database Systems; Intelligent Information Systems; Decision Support and Control Systems; Machine Learning and Data Mining; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Intelligent and Context Systems; Multiple Model Approach to Machine Learning; Applications of Data Science; Artificial Intelligence Applications for E-services; Automated Reasoning and Proving Techniques with Applications in Intelligent Systems; Collective Intelligence for Service Innovation, Technology Opportunity, E-Learning and Fuzzy Intelligent Systems; Intelligent Computer Vision Systems and Applications; Intelligent Data Analysis, Applications and Technologies for Internet of Things; Intelligent Algorithms and Brain Functions; Intelligent Systems and Algorithms in Information Sciences; IT in Biomedicine; Intelligent Technologies in the Smart Cities in the 21st Century; Analysis of Image, Video and Motion Data in Life Sciences; Modern Applications of Machine Learning for Actionable Knowledge Extraction; Mathematics of Decision Sciences and Information Science; Scalable Data Analysis in Bioinformatics and Biomedical Informatics; and Technological Perspective of Agile Transformation in IT organizations. .
(source: Nielsen Book Data)