Human-Computer Interaction:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[S.l.]
PHI Learning
[ca. ] 2015
|
| Subjects: | |
| Online Access: | Klappentext Inhaltsverzeichnis |
| Physical Description: | xi, 272 S. Ill., graph. Darst. |
| ISBN: | 9788120350502 |
Staff View
MARC
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Record in the Search Index
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| adam_text |
Preface xi
1. Introduction 1
1.1 Cognitive Psychology and Computer Science 1
1.2 Capabilities of Human-Computer Interaction (HCI) 2
1.3 Goals of Human-Computer Interaction (HCI) 3
1.4 Roles of Human, Computer and Interaction in HCI 4
1.4.1 User Roles 4
1.4.2 Role of Computers 4
1.4.3 Interaction 5
1.5 Basic User Interfaces 5
1.5.1 Input Devices 5
1.5.2 Advantages and Disadvantages of Input Devices 10
133 Output Devices 10
1.6 Advanced User Interfaces 12
1.7 Justification of Interdisciplinary Nature 13
1.8 Standard Framework of HQ IS
1.9 HO Design Principles 17
1.9.1 Perception 17
1.9.2 Information Processing 18
1.10 Interface Levels in HCI 18
1.11 Steps in Designing Ha Applications 19
1.11.1 Goals of Effective Design 20
1.12 Graphical User Interface Design 21
1.12.1 Windows 21
1.12.2 Design Models of Graphical User Interface (GUI) 27
1.123 Characteristics of the Graphical User Interface 28
1.12.4 Screen Design Goals 29
1.13 Popular HO Tools 34
1.14 Architecture of HQ Systems 36
v
VI + Contents
1.15 Advances in HCl 37
1.15.1 Ubiquitous Computing and Ambient Intelligence 37
1.16 Overview 37
1.17 HCl Sample Exercises 38
El7.1 Application of Weber՜s Law in the Design of Human Interfaces
1-17.2 Goals, Operators, Methods, and Selection Rules (GOMS) 39
1.173 Colour Design for a User Form 40
Review Questions 41
Z Usability Engineering
2.1 Introduction 42
2.2 HCI and Usability Engineering 42
2.2.1 User-Centred Software Development Process 44
2.3 Usability Engineering Attributes 45
2.3.1 Role of Usability in User Interface 45
2.3.2 Software Development and Usability 46
2.4 Process of Usability 46
2.4.1 Analysis Phase of Usability 47
2.4.2 Design Phase of Usability 49
2.4.3 Evaluation Phase of Usability 51
2.5 Need for Prototyping 53
Review Questions 54
3. Modelling of Understanding Process
3.1 Introduction 55
3.2 Goals, Operators, Methods and Selection Rules (GOMS) 57
3.3 Cognitive Complexity Theory (CCT) 58
3.4 Adaptive Control of Thought-Rational (ACT-R) 59
3.4.1 Structural Organisation of ACT-R 59
3.4.2 Behavioural Description 60
3.5 State, Operator, and Result (SOAR) 62
3.5.1 Structural Organisation 62
3.5.2 Behavioural Description 63
3.5.3 Types of Impasse in SOAR 64
3.6 Belief-Desire-Intention (BDI) 67
3.6.1 Structural Organisation 67
3.6.2 Behavioural Description 69
3.7 ICARUS 71
3.7.1 Structural Organisation of ICARUS 72
3.7.2 Behavioural Description 73
3.8 Connectionist Learning with Adaptive Rule Induction On-line
(CLARION) 74
3.8.1 Structural Organisation of CLARION 75
3.8.2 Behavioural Description 76
Contents + vii
3.9 Subsumption Architecture 77
3.9.1 Structural Organisation of Subsumption Architecture 77
3.9.2 Behavioural Description 78
Review Questions 79
4. Spoken Dialogue System 80
4.1 Introduction 80
4.2 Factors Defining Dialogue System 81
4.2.1 Overview of Human-Machme Dialogue 81
4.2.2 Dialogue System Types 82
4.2.3 Application Domains for Spoken Dialogue System 83
4.2.4 Levels of Communication in Speech-based Interaction 84
4.3 General Architecture of a Spoken Dialogue System 85
4.3.1 Voice Activity Detection 86
4.3.2 Automatic Speech Recognition 86
4.3.3 Natural Language Understanding 87
4.3.4 Dialogue Management 87
4.3.5 Natural Language Generation 87
4.3.6 Text-to-Speech Synthesis 88
4.4 Dialogue Management (DM) Strategies 88
4.5 Computational Models for Dialogue Management 89
4.5.1 Finite-state-based Approach 89
4.5.2 Frame-based Approach 90
4.5.3 Agent-based Approach 90
4.6 Statistical Approaches to Dialogue Management 91
4.6.1 Reinforcement Learning in Dialogue Management 91
4.6.2 Dialogue as Markov Decision Process 93
46.3 MDP Policy Optimisation 94
4.7 Learning Automata as Reinforcement Learners 95
4.7.1 linear Reward-Penalty Scheme (L*p) 96
4.7.2 Linear Reward-Inaction Scheme (Lpj) 96
4.7.3 Linear Inaction-Penalty Scheme (L# ) 96
4.8 Case Study: Learning Dialogue Strategy Using Interconnected
Learning Automata 98
4.8.1 System Configuration and Trial Setup 99
4.8.2 Policy Optimisation and Trial Results 100
4.8.3 Comparison of Learnt Policies 102
4.9 Software and Toolkits for Spoken Dialogue Systems Development 104
Review Questions 105
5* Recommender Systems 106
51 Introduction 106
52 HCI Study Based on Personalisation 108
5.2! Application Areas of Personalisation Techniques 109
Vlii 4· Contents
5.3 Personalisation in Recommender Systems 109
5.4 Relation between Information Filtering and Recommender Systems
5.5 Application Areas of Recommender Systems 112
5.6 Recommender System Field as an Interdisciplinary Area of Research
5.7 Phases of Recommender Systems 114
f 5.8 User Profiling Approaches 115
5.9 Classification of Recommendation Techniques 116
5.9.1 Content-based Fdtering (CBF) 117
5.9.2 Collaborative Filtering Technique and its Types 119
5.9.3 Hybrid Filtering 127
5.9.4 Demographic Filtering (DF) 128
5.9.5 Knowledge-based Filtering (KBF) 129
5.9.6 Utility-based Filtering (UBF) 130
5.9.7 Community: Popularity-based Filtering 131
5.9.8 Community: Association-based Filtering 131
5.9.9 Community: Reputation-based Filtering 131
5.10 Advantages and Disadvantages of Recommender System Approaches
5.11 Need of Software Agent-based Approach in Recommender Systems
5.12 Evaluating Recommender Systems 142
5.13 Integrated Framework for Recommender Systems 143
5.14 Case Study: Music Recommender System 144
Review Questions 158
6. Advanced Visualisation Methods
6.1 Ontology Definition 159
6.1.1 Some other Definitions of Ontology 159
6.1.2 Need of Ontology Analysis 160 · - ··. . ·: L-·1
6.1.3 Types of Ontology 160 ՛ ՝ ·· i՛·
6.1.4 Components of Ontology 160
6.1.5 Ontology life Cycle 160
6.1.6 Ontology Development Process 161 -
6.1.7 Multiple Inheritance Ontology 162
6.1.8 Limiting Scope of Ontology 162
6.2 Ontology Visualisation Methods 162
6-2.1 Indented List Method 162
6.2.2 Node-Link and Tree Method 162
6.2.3 Zoomable Method 164
6.2.4 Focus + Context Method 164
6.3 Space Dimensions of Ontology Visualisation 165
6.4 Ontology Languages 165
6.4.1 Microformats 165
6.4.2 , Resource Description Framework in Attributes (RDFA) 169
6.4.3 Resource Description Framework (RDF) 169
6.4.4 RDF Schema 172
6.4.5 ՛ OIL ՝■■= *F:·
6.4.6 DAML+OIL, OWL, OWL-Lite, OWL DL and OWL Full 174
Contents 4* ІХ
6.5 Ontology Visualisation Tools 174
6.5.1 Plugins 175
6.6 Ontology Reasoning 186
6.6.1 Reasoning Techniques Used in Ontologies 186
6.7 Reasoner 188
6.8 Case Study 1: Teaching Ontology with C Programming Language 189
6.9 Case Study 2: Activity for Ontology Creation with a Case of
a Software Company Scenario 200
6.10 Case Study 3: Activity for History Ontology Creation 215
Review Questions 225
7. Ambient Intelligence: The New Dimension of
Human-Computer Interaction 226
7.1 Introduction 226
7.2 Ambient Intelligence Definition 228
7.2.1 Ami Vision 228
7.2.2 Ami Contributing Technologies 228
7.2.3 Ambient Intelligence Layered Model 228
7.3 Context-aware Systems and Human-Computer Interaction 231
7.3.1 Definition of Context 231
7.3.2 Notion of Context 231
7.3.3 History of Context-aware Systems 232
7.3 A Types of Context 233
7.3.5 Classification of Context 233
7.3.6 Context-aware Systems Architecture Classification 234
7.3.7 Life Cyde of Context-aware Systems 236
733 Use of the Layered Structure 238
7.3.9 Context-aware Applications 239
7.3.10 Recent Advances in Context-aware Systems 241
7.311 Context Model 241
7.3-12 Context Storage and Management Model 243
7.3.13 Context Modelling 243
7.3.14 Context Modelling Approach 244
7.3.15 Modelling Ontology-based Context 244
73.16 Quality of Context 246
7.3.17 Difference between Context Information and Traditional
Information Sources 246
73.18 Difficulties in Using Context 246
7.4 Middleware 247
7.4.1 Middleware Frameworks 247
7.4.2 Types of Middleware 248
7.4.3 Middleware Effidency Related Requirements 249
7.4.4 Middleware Design Considerations 250
7АБ Developing Middleware Infrastructure 251
7.4.6 Communication Platform for Ami Middleware
7.4.7 Key Technologies in Mobile Middleware 255
252
X # Contents
7.5 Modelling Data for Ami Environment 256
7.5,1 Modelling Ami Network 256
7.6 Case Study 1: Development of Context-awareness Feature in
Smart Class Room 256
7.7 Case Study 2; Context-aware Agents for Developing Ami Applications 259
Review Questions 262
References
Index
263
2 69
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| spelling | Meena, K. Verfasser aut Human-Computer Interaction K. Meena, R. Sivakumar [S.l.] PHI Learning [ca. ] 2015 xi, 272 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd rswk-swf Mensch-Maschine-Kommunikation (DE-588)4125909-9 s DE-604 Sivakumar, R. Verfasser aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028134820&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028134820&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Human-Computer Interaction |
| title_auth | Human-Computer Interaction |
| title_exact_search | Human-Computer Interaction |
| title_full | Human-Computer Interaction K. Meena, R. Sivakumar |
| title_fullStr | Human-Computer Interaction K. Meena, R. Sivakumar |
| title_full_unstemmed | Human-Computer Interaction K. Meena, R. Sivakumar |
| title_short | Human-Computer Interaction |
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| topic | Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd |
| topic_facet | Mensch-Maschine-Kommunikation |
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