Man-Machine Interactions 3

Höfundur: Dr. Aleksandra Gruca (Útgáfa: 0)
Man-Machine Interactions 3

Kaup valmöguleikar

Man-Machine Interaction is an interdisciplinary field of research that covers many aspects of science focused on a human and machine in conjunction. Basic goal of the study is to improve and invent new ways of communication between users and computers, and many different subjects are involved to reach the long-term research objective of an intuitive, natural and multimodal way of interaction with machines.

The rapid evolution of the methods by which humans interact with computers is observed nowadays and new approaches allow using computing technologies to support people on the daily basis, making computers more usable and receptive to the user's needs. This monograph is the third edition in the series and presents important ideas, current trends and innovations in the man-machine interactions area.

The aim of this book is to introduce not only hardware and software interfacing concepts, but also to give insights into the related theoretical background. Reader is provided with a compilation of high-quality original papers covering a wide scope of research topics divided into eleven sections, namely: human-computer interactions, robot control, embedded and navigation systems, bio data analysis and mining, biomedical signal processing, image and sound processing, decision support and expert systems, rough and fuzzy systems, pattern recognition, algorithms and optimization, computer networks and mobile technologies and data management systems.

Nánar um bókina

Útgefandi
Springer Nature
ISBN
9783319023090
Print ISBN
9783319023083
Format
Page Fidelity (PDF)
Útgáfa
0
Höfundar
Dr. Aleksandra Gruca
Tungumál
English
Útgefið
2013-10-01
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Preface
  • Contents
  • Part I Invited Papers
  • Pattern Recognition with Non-Euclidean Similarities
  • 1 Introduction
  • 1.1 Related Literature
  • 1.2 Contribution
  • 2 Ricci Flow in Constant Curvature Riemannian Space
  • 3 The Algorithm
  • 4 Experiments
  • 5 Conclusion
  • References
  • Case-Based Reasoning and the Statistical Challenges II
  • 1 Introduction
  • 2 Case-Based Reasoning
  • 3 Case-Based Reasoning Process Model
  • 4 Similarity
  • 5 Organization of Case Base
  • 6 The Incremental Aspect of CBR
  • 7 The Life-Cycle of a CBR System
  • 8 Applications
  • 8.1 Meta-learning for Parameter Selection of Data/SignalProcessing Algorithms
  • 8.2 Case-Based Image Interpretation
  • 8.3 Incremental Prototype-Based Classification
  • 8.4 Novelty Detection by Case-Based Reasoning
  • 9 Conclusion
  • References
  • Robust Adaptive Predictive Modeling and Data Deluge (Extended Abstract)
  • References
  • A Visual Excursion into Parallel Coordinates(Extended Abstract)
  • References
  • Part II Human-Computer Interactions
  • SOM Based Segmentation of Visual Stimuli in Diagnosis and Therapy of Neuropsychological Disorders
  • 1 Introduction
  • 2 Multiple Levels of the Visual Art Perception Approach
  • 3 Informational Aesthetics Measures
  • 4 The Clustering Procedure Using Self Organizing FeatureMaps
  • 5 Experiments
  • 6 Conclusions and Further Works
  • References
  • Independent Interactive Testingof Interactive Relational Systems
  • 1 Introduction and Background
  • 2 Related Works
  • 2.1 Asymmetric Ring Topology
  • 2.2 Symmetric Ring Topology
  • 2.3 Cycles
  • 3 The Heart-Shaped Substructure
  • 3.1 An Example
  • 4 Discussion and Conclusion
  • References
  • Hypothesis-Driven Interactive ClassificationBased on AVO
  • 1 Introduction
  • 2 Attribute Value Ontology
  • 2.1 Levels of Abstraction
  • 2.2 Increasing the Precision
  • 2.3 Ontological Bayes Classifier
  • 3 Hypothesis-Driven Interactive Classification
  • 3.1 Hypothesis-Driven Approach
  • 3.2 Test Selection Based on Information Gain
  • 3.3 Test Selection Based on OBC
  • 4 Discussion
  • References
  • Wrist Localization in Color Imagesfor Hand Gesture Recognition
  • 1 Introduction
  • 2 Algorithm Outline
  • 3 Ground-Truth Data Analysis
  • 4 Experimental Study
  • 5 Conclusions and Future Work
  • References
  • Bimodal Speech Recognition for RobotApplications
  • 1 Introduction
  • 2 Face Recognition
  • 2.1 Face and Lip Detection
  • 2.2 User Recognition
  • 3 Bimodal Speech Recognition
  • 3.1 Audio Speech Processing
  • 3.2 Visual Speech Procession (VSR)
  • 3.3 Audio Visual Speech Integration
  • 4 Database and Experimental Results
  • 4.1 Database
  • 4.2 Experimental Results
  • 4.3 Comparison with Other Reported Systems
  • 5 Conclusion and Future Work
  • References
  • Part III Robot Control, Embedded and NavigationSystems
  • Developing and Implementationof theWalking Robot Control System
  • 1 Introduction
  • 2 The Walking Robot Control System
  • 2.1 Robot Controller
  • 3 The Control Program and Experiments
  • 4 Conclusions
  • References
  • Programming of Industrial Object Simulatorsin Proficy HMI/SCADA iFIX System
  • 1 Introduction
  • 2 Structure of the Program and Placement of the SimulatorProcedures
  • 3 Variables of VBA Language and Process Database BlocksNames in Scripts
  • 4 Control of the Work of the Simulator
  • 5 Exiting the Application
  • 6 Example
  • 7 Conclusions
  • References
  • KUKA Robot Motion Planning Using the 1742 NI Smart Camera
  • 1 Introduction
  • 2 Robot’s Workstation and Its Peripherals
  • 3 Robot-Camera Interface and Its Implementation
  • 4 Summary
  • References
  • Visual Simultaneous Localizationand Mapping with Direct OrientationChange Measurements
  • 1 Introduction
  • 2 VisualSLAMSystem
  • 2.1 Environment
  • 2.2 Prediction and Measurement
  • 2.3 Incorporating Orientation Measurements
  • 3 Experiments
  • 4 Conclusions
  • References
  • Managing System Architecturefor Multi-Rotor Autonomous FlyingPlatform-Practical Aspects
  • 1 Introduction
  • 2 Ground Control Station
  • 3 Ground Control Station Software
  • 4 Summary
  • References
  • Calculation of the Location Coordinates of an Object Observed by a Camera
  • 1 Introduction
  • 2 Perspective Model of Camera
  • 3 The Algorithm Camera for Calculations the LocationCoordinates of the Object
  • 4 The Examples of Calculations
  • 4.1 The Example 1 – Computing of Camera Location
  • 4.2 The Example 2 – Computing of Body Car Displacement andReorientation
  • 5 Summary
  • References
  • SMAC-GPS and Radar Data Integrationto Set the Status of the Objects in Secure Areas
  • 1 Introduction
  • 2 Characteristic of Modern Security Systems
  • 3 Description of the SMAC System Concept
  • 4 Mobile Location Devices
  • 5 Radar Subsystem
  • 6 Summary
  • References
  • Part IV Bio-Data Analysis and Mining
  • Comparison of Connectionist and Rough Set Based Knowledge Discovery Methods in Search for Selection
  • 1 Introduction
  • 2 Materials and Methods
  • 2.1 Genetic Material
  • 2.2 Connectionist Approach: MLP and PNN
  • 2.3 Rough Set Based Approaches: QDRSA, DRSA, and CRSA
  • 3 Experiments and Results
  • 4 Conclusions
  • References
  • Bit-Parallel Algorithm for the Block Variantof the Merged Longest Common SubsequenceProblem
  • 1 Introduction
  • 2 Definitions and Background
  • 3 The Algorithm
  • 4 Experimental Results
  • 5 Conclusions
  • References
  • Improvement of FP-Growth Algorithm for Mining Description-Oriented Rules
  • 1 Introduction
  • 2 Finding Logical Rules for Describing Biological Datasets
  • 2.1 Rule Quality Assessment
  • 3 Rule Induction
  • 4 Reduction of FP-Tree
  • 5 Experimental Results
  • 6 Conclusions
  • References
  • Comparison of Algorithms for Profile-BasedAlignment of Low Resolution MALDI-ToFSpectra
  • 1 Introduction
  • 2 Methods
  • 2.1 Data
  • 2.2 Spectra Pre-processing
  • 2.3 Alignment Algorithms
  • 2.4 Alignment and Peak Detection Quality Indices
  • 3 Results and Discussion
  • 4 Conclusions
  • References
  • Stochastic Fluctuationsin the Mathematical-Simulation Approachto the Protocell Model of RNAWorld
  • 1 Introduction
  • 2 Model of Protocells with Random Assortment of Genes
  • 3 Computer Simulations
  • 4 Mathematical Analysis
  • 5 Fluctuation of NORM Parameter
  • 6 Results
  • 7 Discussion
  • References
  • Evaluation of Machine Learning Algorithmson Protein-Protein Interactions
  • 1 Introduction
  • 2 Methods
  • 2.1 Description of Gold and Silver Datasets
  • 2.2 Performance Evaluation
  • 2.3 Machine Learning Methods
  • 3 Discussion
  • 3.1 Statistical Significance Test
  • 4 Conclusion
  • References
  • Investigation for Genetic Signature of Radiosensitivity – Data Analysis
  • 1 Introduction
  • 2 Materials and Methods
  • 2.1 Materials
  • 2.2 Mathematical Modelling and Hierarchical Clustering
  • 2.3 SNP Selection
  • 2.4 The Distribution of Relevant SNPs along the Chromosomes
  • 2.5 Analysis of Nonsynonymous SNPs
  • 2.6 In Silico Prediction for Other Radiosensitive Strains of Mice
  • 3 Results and Discussion
  • 4 Conclusions
  • References
  • Part V Biomedical Signal Processing
  • Fuzzy Approach to Saccades Detection in Optokinetic Nystagmus
  • 1 Introduction
  • 2 The Method of Saccades Detection
  • 2.1 Calculation of the Amplitude Threshold Using FuzzyClustering
  • 3 Results and Discussion
  • 4 Conclusions
  • References
  • Design of Linear-Phase FIR Filters with Time and Frequency Domains Constraints by Means of AI Based
  • 1 Introduction
  • 2 Preliminaries
  • 3 New Method for Low-Pass FIR Filter Design
  • 4 Design Examples
  • 5 Conclusion
  • References
  • Identification of SlowWave Propagationin the Multichannel (EGG)Electrogastrographical Signal
  • 1 Introduction
  • 2 Methods
  • 2.1 Independent Component Analysis
  • 2.2 Adaptive Filtering
  • 2.3 Cross-Covariance
  • 3 Results
  • 3.1 Simulation Results
  • 3.2 Conclusions
  • References
  • An Application of Fuzzy C-Regression Models to Characteristic Point Detection in Biomedical Signals
  • 1 Introduction
  • 2 Fuzzy C-regression Models
  • 3 Numerical Experiments and Discussion
  • References
  • An Application of Myriad M-Estimatorfor Robust Weighted Averaging
  • 1 Introduction
  • 2 The Weighted Averaging Method
  • 2.1 Idea of Criterion Function Minimization
  • 2.2 Myriad Weighted Averaging
  • 3 Numerical Experiments and Results
  • 4 Conclusion
  • References
  • Evolutionary Computation for Design of Preprocessing Filters in QRS Detection Algorithm
  • 1 Introduction
  • 2 QRS Detection Algorithm
  • 3 Filter Design Algorithm
  • 3.1 Covariance Matrix Adaptation Evolution Strategy
  • 3.2 Objective Function Definition
  • 4 Results
  • 5 Summary
  • References
  • Part VI Image and Sound Processing
  • InFeST – ImageJ Plugin for Rapid Developmentof Image Segmentation Pipelines
  • 1 Introduction
  • 2 Plugin
  • 3 Evaluation
  • 4 Conclusions and Future Work
  • References
  • Visualization of Heterogenic Images of 3D Scene
  • 1 Introduction
  • 2 Incoming Data
  • 3 Image Visualization
  • 4 Conclusion
  • References
  • Application of the Cellular Automata for Obtaining Pitting Images during Simulation Process of Their
  • 1 Introduction
  • 2 Cellular Automata Approach
  • 3 The Mechanism of Pit Growth
  • 4 Basic Model of Simulation Procedure
  • 5 Results and Discussion
  • 6 Conclusions
  • References
  • Metaheuristic Optimization of MultipleFundamental Frequency Estimation
  • 1 Introduction
  • 2 Estimation of the Multiple Fundamental Frequencies
  • 2.1 Spectral Analysis Approaches
  • 3 Metaheuristic Optimization
  • 4 Proposed Approach
  • 4.1 The Database
  • 4.2 The Optimized Parameters
  • 5 Results
  • 6 Conclusions
  • References
  • Implementation of Registration Algorithmsfor Multiple Views
  • 1 Introduction
  • 2 Overview of Registration Techniques
  • 3 Automatic Determination of the Rotation Axis
  • 4 Optimizations of ICP Algorithm
  • 5 Testing the Registration Algorithms
  • 6 Strategies of Registration from Multiple Views
  • 7 Testing the Influence of Scanning Order
  • 8 Summary
  • References
  • Multimodal Speech Synthesis for PolishLanguage
  • 1 Introduction
  • 2 Corpus Preparation
  • 3 Recordings
  • 4 Automated Segmentation of the Corpus
  • 5 Cost Function and Its Optimization
  • 6 Avatar
  • 6.1 Data Flow between Speech Synthesis System and Avatar
  • 6.2 Creating the Avatar Visems
  • 7 Conclusions and Further Research Work
  • References
  • Part VII Decision Support and Expert Systems
  • Intuitionistic Notice Boards for Expert Systems
  • 1 Introduction
  • 2 Notices and Statements
  • 3 Intuitionistic Notices
  • 4 Notice Network
  • 5 Example
  • 6 Conclusions
  • References
  • Multi-Domain Data Integrationfor Criminal Intelligence
  • 1 Introduction
  • 2 Criminal Analysis and Support Tools
  • 4 Multi-Domain Data Integration in LINK
  • 5 Example of Integration – Case Study
  • 6 Conclusion and Further Research
  • References
  • Preference Models and Their Elicitationand Analysis for Context-Aware Applications
  • 1 Introduction
  • 2 UML Diagrams
  • 3 Preference Models
  • 4 Illustration of the Approach
  • 5 Conclusions
  • References
  • Deduction-Based Modelling and Verificationof Agent-Based Systems for Data Integration
  • 1 Introduction
  • 2 Agent-Based Data Integration Infrastructure
  • 3 Deduction System
  • 4 Sample Application and Scenario
  • 5 Formal Analysis of the Scenario
  • 6 Conclusions
  • References
  • Relevance Prevails: Missing Data Treatment in Intelligent Lighting
  • 1 Introduction
  • 2 Problem Formulation
  • 3 Proposed Scheme for Finding (p0,h0)
  • 3.1 Treatment of Missing Data
  • 3.2 Computation of Relevance Score
  • 4 Experiments and Results
  • 5 Conclusion
  • References
  • Some Remarks on Complex Information Systems over Ontological Graphs
  • 1 Introduction
  • 2 Basic Definitions
  • 3 Complex Information and Decision Systems over OntologicalGraphs
  • 4 Conclusions and Further Works
  • References
  • Generic Framework for Simulation of CognitiveSystems: A Case Study of Color Category Boundaries
  • 1 Introduction
  • 2 Cognitive Systems Simulation Framework
  • 2.1 Agent’s Cognitive System
  • 2.2 Interaction in the Population of Agents
  • 3 The Case Study of Color Categorization
  • 3.1 Synthetic Perceptual Space
  • 3.2 Real Perceptual Space
  • 4 Conclusion
  • References
  • Part VIII Rough and Fuzzy Systems
  • Application of the Conditional Fuzzy Clusteringwith Prototypes Pairs to Classification
  • 1 Introduction
  • 2 Linear and Nonlinear IRLS Classifier
  • 3 New Conditional Fuzzy Clustering with Pairs of Prototypes
  • 4 Determining Values of the Conditional Variable
  • 5 Results
  • 6 Conclusions
  • References
  • Environmental Modelling Basedon Rough-Fuzzy Approach
  • 1 Introduction
  • 2 Problem Formulation
  • 3 Suggestion and Model Analysis
  • 4 Conclusions
  • References
  • Neuro-Fuzzy System Based Kernelfor Classification with Support Vector Machines
  • 1 Introduction
  • 2 Kernel Functions
  • 3 Construction of Kernel Function with NFS
  • 4 Experiments
  • 5 Conclusions
  • References
  • Transformation of Input Domain for SVMin Regression Task
  • 1 Introduction
  • 2 Our Approach
  • 3 Experiments
  • 3.1 Results
  • 4 Conclusions
  • References
  • Video Event Recognition with Fuzzy SemanticPetri Nets
  • 1 Introduction
  • 2 Related Works
  • 3 Fuzzy Ontology
  • 4 Fuzzy Semantic Petri Nets
  • 5 Initial Experiments
  • 6 Conclusions
  • References
  • Part IX Pattern Recognition
  • Application of Multidimensional DataVisualization in Creation of PatternRecognition Systems
  • 1 Introduction
  • 2 Observational Tunnels Method
  • 3 The Drawing Procedure
  • 4 The Source of Data
  • 5 Visualization of 5-Dimensional Space of Features
  • 6 Conclusions
  • References
  • Recognition of Emotion Intensity Basingon Neutral Speech Model
  • 1 Introduction
  • 2 Proposed Method
  • 2.1 Audio Database
  • 2.2 Feature Extraction
  • 2.3 Classification Algorithm
  • 3 Experimental Results and Discusion
  • 4 Conclusion and Future Works
  • References
  • Exploiting Co-Occurrence of Low Frequent Terms in Patents
  • 1 Introduction and Related Work
  • 2 Methodology
  • 3 Experimental Setup
  • 4 Results and Discussion
  • 5 Conclusion
  • References
  • Influence of Low-Level Features Extractedfrom Rhythmic and Harmonic Sectionson Music Genre Classific
  • 1 Introduction
  • 1.1 Motivation
  • 1.2 Drum Separation Algorithm
  • 2 Analyzed Data and Extracted Audio Features
  • 3 Experiments and Results
  • 4 Conclusion and Further Work
  • References
  • Weighting of Attributes in an Embedded RoughApproach
  • 1 Introduction
  • 2 Stylistics Attributes and Selection
  • 3 DRSA-Based Classification
  • 4 Relative Reducts and Defined Weights and Measures
  • 5 Experimental Results
  • 6 Conclusions
  • References
  • Part X Algorithms and Optimization
  • Agent-Based Approach to ContinuousOptimisation
  • 1 Introduction
  • 2 Evolutionary and Memetic Algorithms
  • 3 Agent-Based Evolutionary Computing
  • 4 Experimental Results
  • 5 Conclusions
  • References
  • Kalign-LCS —A More Accurateand Faster Variant of Kalign2 Algorithmfor the Multiple Sequence Alignm
  • 1 Introduction
  • 2 Background
  • 3 Standard Progressive Alignment Strategy
  • 4 Our Algorithm
  • 5 Experimental Results
  • 6 Conclusions
  • References
  • Subcubic Algorithms for the Sequence ExcludedLCS Problem
  • 1 Introduction
  • 2 Definitions and Background
  • 3 Our Algorithms
  • 3.1 Preliminaries
  • 3.2 A 1D Algorithm
  • 3.3 A 3D Algorithm
  • 4 Conclusions and Future Work
  • References
  • Clonal Selection Algorithm in Identificationof Boundary Condition in the Inverse StefanProblem
  • 1 Introduction
  • 2 Clonal Selection Algorithm
  • 3 Governing Equations
  • 4 Numerical Verification
  • 5 Conclusions
  • References
  • Application of General-Purpose Computingon Graphics Processing Units for Accelerationof Basic Linear
  • 1 Introduction
  • 2 GPGPU Computing
  • 3 GPU Acceleration of Principal Components Analysis
  • 3.1 The Algorithm of Use
  • 4 Multi-GPU Acceleration of Basic Linear Algebra Operations
  • 5 Summary
  • References
  • Multiobjective Differential Evolution:A Comparative Study on Benchmark Problems
  • 1 Introduction
  • 2 Algorithms of Multiobjective Differential Evolution
  • 3 Empirical Results
  • 3.1 Experimental Setup
  • 3.2 Results
  • 4 Conclusion
  • References
  • Fast and Simple Circular Pattern Matching
  • 1 Introduction and Basic Definitions
  • 2 Related Work
  • 3 Our Approach
  • 4 Experimental Results
  • 5 Conclusion and Future Work
  • References
  • Part XI Computer Networks and MobileTechnologies
  • Remote Video Verification and VideoSurveillance on Android-Based Mobile Devices
  • 1 Introduction
  • 2 System Architecture
  • 3 Video Stream Encoding
  • 4 Communication Flow
  • 5 Video Verification Server
  • 6 Client – Android Application
  • 7 Connection Quality with GSM Transmission
  • 8 Discussion and Concluding Remarks
  • References
  • Designing Frame RelayWAN Networkswith Trade-Off between Link Costand Performance
  • 1 Introduction
  • 2 Problem Formulation
  • 3 Numerical Experiments
  • 4 Conclusion
  • References
  • Review of Mobility Models for Performance Evaluation of Wireless Networks
  • 1 Introduction
  • 2 Random Models
  • 2.1 Random Walk Model
  • 2.2 Random Waypoint Model
  • 2.3 Gauss-Markov Mobility Model
  • 2.4 Truncated Levy Walk Model
  • 2.5 Human Mobility Obstacle Model – HUMO
  • 3 Social Models
  • 3.1 Sociological Interaction Mobility for Population Simulation –SIMPS
  • 3.2 Community-Based Mobility Model – CMM
  • 3.3 Home-Cell Community-Based Mobility Model – HCMM
  • 4 Hybrid Models
  • 4.1 SLAW – Self-similar Least-Action Human Walk
  • 4.2 Working Day Movement Model
  • 5 Summary
  • References
  • An Energy-Efficient Approach to the Designof Two-TierWireless Ad Hoc and SensorNetworks
  • 1 Introduction
  • 2 An Energy-Efficient Topology Design for Two-Tier WirelessAd Hoc and Sensor Networks
  • 3 Simulation Results
  • 4 Conclusion
  • References
  • Part XII Data Management Systems
  • Applying Task-Aggregating Wrapperto CUDA-Based Method of Query SelectivityCalculation Using Multidim
  • 1 Introduction
  • 2 Kernel Density Estimator and Selectivity Calculation –Preliminary Information
  • 3 Adapting the Algorithm to Use GPU
  • 3.1 Single-Query-Condition Version of Kernel Function
  • 3.2 Multiple-Query-Condition Version of Kernel Function
  • 4 Experimental Results
  • 5 Conclusions
  • References
  • The Method of Query Selectivity Estimationfor Selection Conditions Based on Sumof Sub-Independent At
  • 1 Introduction
  • 2 Motivation
  • 3 The Theoretical Background
  • 3.1 Simple Example
  • 3.2 Sub-independence of Attribute Values
  • 4 Algorithm of the Proposed Method
  • 5 Conclusions
  • References
  • The Storage Organisation Influenceon Database Operations Performance
  • 1 Introduction
  • 2 Block/File Level Storage and Their Protocols
  • 3 Test Environment Configuration
  • 4 Test Results
  • 5 Summary
  • References
  • Spatial Query Optimization Basedon Transformation of Constraints
  • 1 Introduction
  • 2 Decomposition Of Spatial Queries Based On LogicalOperators
  • 2.1 Decomposition of Disjoint Constraints
  • 2.2 Decomposition of Difference Constraints
  • 3 Conclusions
  • References
  • Database Under Pressure – Testing Performanceof Database Systems Using UniversalMulti-Agent Platfo
  • 1 Introduction
  • 2 Related Works
  • 3 Model and Architecture of the Testing System
  • 4 Sample Tests of Chosen Database Management System
  • 5 Discussion and Concluding Remarks
  • References
  • Using Graph Database in SpatialData Generation
  • 1 Introduction
  • 2 Current State of the Generator
  • 3 Graph Database in Spatial Data Generation
  • 4 Test Case: Simple Parcel Map
  • 4.1 Road Generation
  • 4.2 Road Parcels Generation
  • 4.3 Main Parcels Generation
  • 4.4 Possible Rail Track/Watercourses Placement
  • 5 Conclusion
  • References
  • A Performance Comparison of Several CommonComputation Tasks Used in Social NetworkAnalysis Performed
  • 1 Introduction
  • 2 Related Work
  • 3 Computation Tasks
  • 3.1 Domain Model and Queries
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Analysis of Results
  • 5 Conclusions and Summary
  • References
  • Erratum: Evaluation of Machine Learning Algorithmson Protein-Protein Interactions
  • Author Index