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INFSCI 2955 Adaptive Web Systems Session 1-2: Adaptive E-Learning Systems

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Title: Adaptive Educational Systems on the World-Wide-Web Author: Peter Brusilovsky Last modified by: Peter Brusilovsky Created Date: 3/15/1998 12:44:54 PM – PowerPoint PPT presentation

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Title: INFSCI 2955 Adaptive Web Systems Session 1-2: Adaptive E-Learning Systems


1
INFSCI 2955Adaptive Web SystemsSession 1-2
Adaptive E-Learning Systems
  • Peter Brusilovsky
  • School of Information Sciences
  • University of Pittsburgh, USA
  • http//www.sis.pitt.edu/peterb/2955-092/

2
(No Transcript)
3
Overview
  • The Context
  • Technologies
  • Adaptive E-Learning Systems vs. Learning
    Management Systems (LMS)

4
The Context
  • Adaptive systems
  • Why adaptive?
  • Adaptive vs. intelligent

5
Adaptive systems
  • Classic loop user modeling - adaptation in
    adaptive systems

6
Adaptive Software Systems
  • Intelligent Tutoring Systems
  • Adaptive Help Systems
  • Adaptive Interfaces
  • Adaptive Hypermedia Systems
  • Adaptive Search
  • Adaptive Filtering/Recommendation
  • Adaptive

7
Major Issues
  • What is adaptive?
  • Adaptive sequencing of educational tasks
  • Adaptive content presentation
  • Adaptive ordering of search results
  • What kinds of information about user?
  • User knowledge
  • User interests
  • User individual traits

8
Why Adaptive E-Learning?
  • Adaptation was always an issue in education -
    what is special about the Web?
  • greater diversity of users
  • user centered systems may not work
  • new unprepared users
  • traditional systems are too complicated
  • users are alone
  • limited help from a peer or a teacher

9
Intelligent vs. Adaptive
  • 1. Intelligent but not adaptive (no student
    model!)
  • 2. Adaptive but not really intelligent
  • 3. Intelligent and adaptive

1
2
3
Adaptive ES
Intelligent ES
10
Technologies
  • Origins of AEL technologies
  • ITS Technologies
  • AH Technologies
  • Native Web Technologies

11
Origins of AEL Technologies
Adaptive HypermediaSystems
Intelligent TutoringSystems
Adaptive Web-basedEducational Systems
12
Origins of AEL Technologies (1)
Intelligent Tutoring Systems
Adaptive Hypermedia Systems
Adaptive Hypermedia
Intelligent Tutoring
Adaptive Presentation
Problem Solving Support
Curriculum Sequencing
Adaptive Navigation Support
Intelligent Solution Analysis
13
Technology inheritance examples
  • Intelligent Tutoring Systems (since 1970)
  • CALAT (CAIRNE, NTT)
  • PAT-ONLINE (PAT, Carnegie Mellon)
  • Adaptive Hypermedia Systems (since 1990)
  • AHA (Adaptive Hypertext Course, Eindhoven)
  • KBS-HyperBook (KB Hypertext, Hannover)
  • ITS and AHS
  • ELM-ART (ELM-PE, Trier, ISIS-Tutor, MSU)

14
Technology Fusion
Adaptive Educational Systems
Adaptive Web
Adaptive E-Learning
15
Adaptive Web
  • Adaptive information filtering
  • Adaptive search
  • Adaptive recommendation
  • Social navigation
  • Log mining
  • Machine learning
  • CSCW

Brusilovsky, P., Kobsa, A., and Neidl, W. (eds.)
(2007) The Adaptive Web Methods and Strategies
of Web Personalization. Lecture Notes in Computer
Science, Vol. 4321, Berlin Heidelberg New York
Springer-Verlag.
16
Origins of AEL Technologies (2)
Information Retrieval
CSCL
Machine Learning, Data Mining
Adaptive Hypermedia Systems
Intelligent Tutoring Systems
Intelligent Collaborative Learning
Adaptive Information Filtering
Adaptive Hypermedia
Intelligent Monitoring
Intelligent Tutoring
17
Inherited Technologies
  • Intelligent Tutoring Systems
  • course sequencing
  • intelligent analysis of problem solutions
  • interactive problem solving support
  • example-based problem solving
  • Adaptive Hypermedia Systems
  • adaptive presentation
  • adaptive navigation support

18
Inherited Technologies
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

19
How to Model User Knowledge
  • Domain model
  • The whole body of domain knowledge is decomposed
    into set of smaller knowledge units
  • A set of concepts, topics, etc
  • Student model
  • Overlay model
  • Student knowledge is measured independently for
    each knowledge unit

20
Vector vs. network models
  • Vector - no relationships
  • Precedence (prerequisite) relationship
  • is-a, part-of, analogy (Wescourt et al, 1977)
  • Genetic relationships (Goldstein, 1979)

21
Vector model
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3
22
Network model
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3
23
Simple overlay model
Concept 4
Concept 1
no
yes
Concept N
no
Concept 2
yes
no
yes
Concept 5
Concept 3
24
Simple overlay model
Concept 4
Concept 1
no
yes
Concept N
no
Concept 2
yes
no
yes
Concept 5
Concept 3
25
Weighted overlay model
Concept 4
Concept 1
3
10
Concept N
0
Concept 2
7
2
4
Concept 5
Concept 3
26
Course Sequencing
  • Oldest ITS technology
  • SCHOLAR, BIP, GCAI...
  • Goal individualized best sequence of
    educational activities
  • information to read
  • examples to explore
  • problems to solve ...
  • Curriculum sequencing, instructional planning, ...

27
Course Sequencing
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

28
Active vs. passive sequencing
  • Active sequencing
  • goal-driven expansion of knowledge/skills
  • achieve an educational goal
  • predefined (whole course)
  • flexible (set by a teacher or a student)
  • Passive sequencing (remediation)
  • sequence of actions to repair misunderstanding or
    lack of knowledge

29
Levels of sequencing
  • High level and low level sequencing

30
Sequencing options
  • On each level sequencing decisions can be made
    differently
  • Which item to choose?
  • When to stop?
  • Options
  • predefined
  • random
  • adaptive
  • student decides

31
Simple cases of sequencing
  • No topics
  • One task type
  • Problem sequencing and mastery learning
  • Question sequencing
  • Page sequencing

32
What do we need for sequencing?
  • Domain model
  • Network of concepts
  • User model
  • Overlay model of user knowledge
  • Model of Educational Material
  • Content Indexing
  • Goal model (for active sequencing)

33
Simple case one concept per ULM
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3
  • Random selection if there are no links -Scholar
  • Links can be used to restrict the order

34
Indexing ULMs with concepts
Example 1
Examples
Concepts
Concept 4
Example 2
Example M
Concept 1
Concept N
Problems
Concept 2
Problem 1
Concept 5
Problem 2
Problem K
Concept 3
35
Simple goal model
  • Learning goal as a set of topics

36
More complicated models
  • Sequence, stack, tree

37
Indexing teaching material
  • Types of indexing
  • One concept per ULM
  • Indexing of ULMs with concepts
  • How to get the ULMs indexed?
  • Manual indexing (closed corpus)
  • Computer indexing (open corpus)

38
Sequencing with models
  • Given the state of UM and the current goal pick
    up the best topic or ULM within a subset of
    relevant ones (defined by links)
  • Special cases with multi-topic indexing and
    several kinds of ULM
  • Applying explicit pedagogical strategy to
    sequencing

39
Sequencing for AES
  • Simplest technology to implement with CGI
  • Important for WBE
  • no perfect order
  • lack of guidance
  • No student modeling capability!
  • Requires external sources of knowledge about
    student
  • Problem/question sequencing is self-sufficient

40
ELM-ART question sequencing
41
SIETTE Adaptive Quizzes
Combination ofCAT and concept Based adaptation
42
Models in SIETTE
43
Beyond Sequencing Generation
44
Adaptive Problem Solving Support
  • The main duty of ITS
  • From diagnosis to problem solving support
  • Highly-interactive support
  • interactive problem solving support
  • Low-interactive support
  • intelligent analysis of problem solutions

45
Adaptive Problem Solving Support
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

46
Models for interactive problem-solving support
and diagnosis
  • Domain model
  • Concept model (same as for sequencing)
  • Bug model
  • Constraint model
  • Student model
  • Generalized overlay model (works with bug model
    and constraint model too)
  • Teaching material - feedback messages for
    bugs/constraints

47
Bug models
  • Each concept/skill has a set of associated
    bugs/misconceptions and sub-optimal skills
  • There are help/hint/remediation messages for bugs

48
Intelligent analysis of problem solutions
  • Intelligent analysis of problem solutions
  • Classic system PROUST
  • Support Identifying misconceptions (bug model)
    and broken constraints (CM)
  • Provides feedback adapted to the user model
    remediation, positive help
  • Low interactivity Works after the (partial)
    solution is completed

49
Example ELM-ART
50
Example SQL-Tutor
51
Interactive Problem Solving Support
  • Classic System Lisp-Tutor
  • The ultimate goal of many ITS developers
  • Several kinds of adaptive feedback on every step
    of problem solving
  • Coach-style intervention
  • Highlight wrong step
  • What is wrong
  • What is the correct step
  • Several levels of help by request

52
Example PAT-Online
53
Example WADEIn
http//adapt2.sis.pitt.edu/cbum/
54
Problem-solving support
  • Important for WBE
  • problem solving is a key to understanding
  • lack of problem solving help
  • Hardest technology to implement
  • research issue
  • implementation issue
  • Excellent student modeling capability!

55
Do we need bug models?
  • Lots of works on bug models in the between
    1974-1985
  • Bugs has limited applicability
  • Problem solving feedback only. Sequencing does
    not take bugs into account whatever
    misconceptions the student has - effectively we
    only can re-teach the same material
  • Short-term model once corrected should
    disappear, so not necessary to keep

56
Models for example-based problem solving support
  • Need to represent problem-solving cases
  • Episodic learner model
  • Every solution is decomposed on smaller
    components, but not concepts!
  • Keeping track what components were used and when
    - not an overlay!
  • ELM-PE and ELM-ART - only systems that use this
    model

57
Adaptive hypermedia
  • Hypermedia systems Pages Links
  • Adaptive presentation
  • content adaptation
  • Adaptive navigation support
  • link adaptation
  • Could be considered as soft sequencing
  • Helping the user to get to the right content

58
Adaptive problem solving support
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

59
Adaptive Navigation Support
  • Direct guidance
  • Hiding, restricting, disabling
  • Generation
  • Sorting
  • Annotation
  • Map adaptation

60
Adaptive Annotation Icons
  • Annotations for topic states in Manuel Excell
    not seen (white lens) partially seen (grey
    lens) and completed (black lens)

61
Adaptive Annotation Icons
4

3
2
v
1
InterBook system
  • 1. Concept role
  • 2. Current concept state

3. Current section state 4. Linked sections state
62
Adaptive Annotation Font Color
Annotations for concept states in ISIS-Tutor not
ready (neutral) ready and new (red) seen
(green) and learned (green)
63
Adaptive Hiding
Hiding links to concepts in ISIS-Tutor not ready
(neutral) links are removed. The rest of 64 links
fits one screen.
64
What Size of concept?
  • How much domain knowledge should a concept cover?
  • Two practical approaches
  • Topic-based student modeling
  • Large topics, one per ULM/page
  • Concept-based student modeling
  • Small concepts, many per ULM/page

65
Topic-based Student Modeling
  • Benefits
  • Easier for students and teachers to grasp
  • Easier for teachers to index content
  • Clear interface for presentation of progress
  • Shortcomings
  • The user model is too coarse-grained
  • Precision of user modeling is low

66
Demo QuizGuide
67
Concept-based Student Modeling
  • Benefits
  • The user model is fine-grained
  • Precision of user modeling is good
  • Shortcomings
  • Harder for students and teachers to grasp
  • Harder for teachers to index content
  • Presentation of progress is harder to integrate
    into the system interface

68
Demo NavEx
69
Indexing Examples in NavEx
  • Concepts derived from language constructs
  • C-code parser (based on UNIX lex yacc)
  • 51 concepts totally (include, void, main_func,
    decl_var, etc)
  • Ask teacher to assign examples to lectures
  • Use a subsetting approach to divide extracted
    concepts into prerequisite and outcome concepts

70
ANS Evaluation
  • ISIS-Tutor hypermedia-based ITS, adapting to
    user knowledge on the subject
  • Fixed learning goal setting
  • Learning time and number of visited nodes
    decreased
  • No effect for navigation strategies and recall

71
Adaptive Presentation
  • Conditional text filtering
  • ITEM/IP, PT, AHA!
  • Adaptive stretchtext
  • MetaDoc, KN-AHS, PUSH, ADAPTS
  • Frame-based adaptation
  • Hypadapter, EPIAIM, ARIANNA, SETA
  • Full natural language generation
  • ILEX, PEBA-II, Ecran Total

72
Adaptive Presentation
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

73
Example PUSH (stretchtext)
74
Example SASY
Scrutable adaptivepresentation
http//www.cs.usyd.edu.au/marek/sasy/
75
Adaptive presentation evaluation
  • MetaDoc On-line documentation system, adapting
    to user knowledge on the subject
  • Reading comprehension time decreased
  • Understanding increased for novices
  • No effect for navigation time, number of nodes
    visited, number of operations

76
Models for Adaptive Hypermedia
  • Domain model - same as for sequencing
  • User model - same as for sequencing
  • Goal model - same as for sequencing
  • Model of the learning material
  • For ANS - same as for sequencing
  • For AP - could use fragment or frame indexing

77
Indexing of nodes
  • Domain model

Hyperspace
Concept 4
Concept 1
Concept n
Concept 2
Concept m
Concept 3
78
Indexing of page fragments
Node
Concepts
Fragment 1
Concept 4
Concept 1
Concept N
Concept 2
Fragment 2
Concept 5
Concept 3
Fragment K
79
Adapting to User Knowledge Other Ideas
  • Adaptive interface
  • Presence of menus and widgets in an educational
    applet can be adapted to user knowledge
  • Educational animation and simulation
  • Adaptive explanations
  • Adaptive visualization

80
Demo WADEIn
81
Demo Improve
82
Adapting to Individual Traits
  • Source of knowledge
  • educational psychology research on individual
    differences
  • Known as cognitive or learning styles
  • Field dependence, wholist/serialist (Pask)
  • Kolb, VARK, Felder-Silverman classifiers

83
Style-Adaptive Hypermedia
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

84
Style-Adaptive Hypermedia
  • Different content for different style
  • Recommended/ordered links
  • Generated on a page
  • Mixed evidences in favor
  • Different navigation tools for different styles
  • Adding/removing maps, advanced organizers, etc.
  • Good review
  • Bajraktarevic, N., Hall, W., and Fullick, P.
    2003. Incorporating Learning Styles in Hypermedia
    Environment Empirical Evaluation, In Proceedings
    of Workshop on Adaptive Hypermedia and Adaptive
    Web-Based Systems, Nottingham, 41-52.
    http//wwwis.win.tue.nl/ah2003/proceedings/paper4.
    pdf

85
Example AES-CS
Interface for field-independent learners
86
Example AES-CS
Interface for field-dependent learners
87
Style-Adaptive Feedback
  • What is modeled?
  • User knowledge of the subject
  • User individual traits
  • What is adapted?
  • Order of educational activities
  • Presentation of hypertext links
  • Presented content
  • Problem solving feedback

88
Overview Classic Technologies
What? Knowledge Styles
Order of activities Sequencing ?
Feedback Adaptive diagnosis Style-adaptive feedback
Content Adaptive presentation Adaptive presentation
Links Adaptive navigation support Adaptive navigation support
89
Origins of AEL Technologies (2)
Information Retrieval
CSCL
Machine Learning, Data Mining
Adaptive Hypermedia Systems
Intelligent Tutoring Systems
Intelligent Collaborative Learning
Adaptive Information Filtering
Adaptive Hypermedia
Intelligent Monitoring
Intelligent Tutoring
90
Native Web Technologies
  • Availability of logs - helping the teacher!
  • Log-mining
  • Intelligent class monitoring - class progress is
    available!
  • One system, many users - group adaptation!
  • Adaptive collaboration support
  • Web is a large information resource - helping to
    find relevant open corpus information
  • Adaptive content recommendation
  • Possible combinations of the above
  • Collaborative recommendation
  • Social navigation

91
What You Can Get from Logs?
  • Log processing and presentation
  • Presenting student progress on topic and concept
    level making sense of class
  • Course/site improvements
  • Grouping users by learning styles
  • Intelligent class monitoring
  • Comparing progress, identifying students way
    ahead and behind

92
Adaptive Collaboration Support
  • Peer help
  • Collaborative group formation
  • Group collaboration support
  • Collaborative work support
  • Forum discussion support
  • Mutual awareness support
  • More information
  • Proceedings of the Workshop on Personalization in
    E-learning Environments at Individual and Group
    Level at the 11th International Conference on
    User Modeling, http//hermis.di.uoa.gr/PeLEIGL/PIN
    G07-proceedings.pdf

93
Personalized Access to Educational Resources
  • A lot of resources are available on the Web and
    in educational DL/Repostitories
  • A new direction of adaptation - provide
    personalized access to these resources
  • Content based recommender
  • Adding advantage of community wisdom
  • Collaborative recommender systems
  • Social navigation systems

94
Modeling User Interests
  • Concept-level modeling
  • Same domain models as in knowledge modeling, but
    the overlay models level of interests, not level
    of knowledge
  • Keyword-level modeling
  • Uses a long list of keywords (terms) in place of
    domain model
  • User interests are modeled as weigthed vector or
    terms
  • Originated from adaptive filtering/search area

95
Keyword User Profiles
96
Use of Profiles in Adaptive Web
97
Use of Profiles in AES ML Tutor
98
Social Computing
  • Web 2.0 for education
  • Collaborative filtering
  • AlteredVista
  • Collaborative resource discovery systems
  • CoFIND
  • UMtella (Demo)
  • Presence-based collaboration (Educo)
  • Social navigation support for open corpus
    resources (Knowledge Sea II)

99
Demo UMtella
100
Example EDUCO
101
Demo Knowledge Sea II
102
KSII Map Interface
Indicator of existence of annotation
Background color indicator of group traffic
Indicator of user traffic
Keywords related to documents inside the cell
Lecture Notes (landmarks)
Indicator of density of document inside the cell
103
Overview
  • The Context
  • Technologies
  • Adaptive E-Learning Systems vs. Learning
    Management Systems (LMS)

104
Learning Management Systems
  • From separate tools to Learning Management
    Systems (LMS)
  • University-level
  • Cyberprof, Mallard, CM Online...
  • Consulting
  • eCollege, Eduprise...
  • Commercial
  • TopClass, WebCT, LearningSpace, Blackboard...
  • Open Source
  • Moodle, SAKAI
  • Standardization IMS, IEEE LTCS, SCORM...

105
What LMS Can Do
  • For students
  • Course information and content delivery
  • Assessment and grades
  • Communication and collaboration
  • For teachers
  • Authoring
  • Learning control
  • Student monitoring
  • Communication

106
What AES Can Do for Students
  • Presentation
  • Adaptive presentation, adaptive navigation
    support, adaptive sequencing
  • Assessment
  • Adaptive testing
  • Communication and collaboration
  • Peer help and collaborative group formation
  • Collaboration coach
  • Learning by doing
  • Problem solving support

107
What AES Can Do for Teachers
  • Student monitoring
  • Identifying students in trouble
  • Control
  • Sequencing
  • Adaptive navigation support
  • Authoring
  • Concept-based authoring and courseware engineering

108
AES vs. LMS
  • Adaptive E-Learning systems can provide a more
    advanced support for most functions
  • Course material presentation - InterBook, AHA
  • Assessment with quizzes - SIETTE
  • Threaded discussions - collaboration agents
  • Student management - intelligent monitoring
  • Why LMS are not really adaptive?
  • Except simple control and learning design

109
Challenges
  • How to make it working in practice?
  • AES systems use advanced techniques - hard to
    develop
  • AWBES content is based on knowledge - hard to
    create
  • AES require login and user modeling - hard to
    integrate
  • Possible solutions - (watch, PhD students!)
  • Component-based architectures for AWBES
  • Authoring support
  • Open Corpus adaptive systems

110
Component-based Architectures
  • Research systems can provide a better support of
    almost each function of E-Learning process
  • Adaptive systems show how to implement nearly
    each component adaptively
  • We need the ability to assemble from components
  • Course authors can choose best components and
    best content for their needs
  • Components providers and content providers have a
    chance to compete in developing better products

111
Current State
  • Several component-based frameworks
  • ADAPT2, ActiveMath, MEDEA,
  • Attempts to develop systems with internal
    components
  • Reusable user/student model servers
  • Some matching work in the standardization movement

112
Re-use/Standards Movement
  • Learning Object Re-use supported by coming
    standards is another major research direction in
    E-Learning
  • The re-use movement joins many existing streams
    of work driven by similar ideas
  • Create content once, use many times
  • Content independent from the host system
  • Content and interfaces with the host system are
    based on standards (metadata, CMI, etc)
  • Let content providers be players in E-Learning
  • The future is components and re-use

113
What is the Future?
  • How to use good component/content if you have a
    Blackboard, Moodle or other major CMS?
  • Is the future model a Blackboard-style giant
    system where all components are advanced and
    adaptive?
  • Wait for the CMS giants to integrate better
    tools?
  • Create our own adaptive Blackboards
  • Is there any other choice?

114
ADAPT2 Architecture
Portal
ActivityServer
Value-added Service
Student Modeling Server
115
Knowledge Tree II Portal
116
Authoring Support
  • Powerful tools for authors to create intelligent
    content
  • ITS content editors
  • Algebra Tutor (Ritter)
  • Adaptive Hypermedia authoring tools
  • MetaLinks (Murray)
  • AHA! (De Bra)
  • NetCoach (Weber)

117
MetaLinks (Murray)
118
AHA! (De Bra)
119
NetCoach (Weber)
120
Open Corpus Adaptive Systems
  • Classic AES - Closed Corpus of pre-processed
    content
  • Integrate Open Corpus content
  • Bringing open corpus content in by indexing
  • KBS-HyperBook, SIGUE
  • Processing open corpus content without manual
    indexing
  • NavEx (content-based guidance)
  • Knowledge Sea (social guidance)

121
Further Reading
  • Brusilovsky, P. (2001) Adaptive hypermedia. User
    Modeling and User Adapted Interaction 11 (1/2),
    87-110
  • Brusilovsky, P. and Peylo, C. (2003) Adaptive and
    intelligent Web-based educational systems.
    International Journal of Artificial Intelligence
    in Education 13 (2-4), 159-172.
  • Brusilovsky, P. (2003) Developing Adaptive
    Educational Hypermedia Systems From Design
    Models to Authoring Tools. In T. Murray, S.
    Blessing and S. Ainsworth (eds.) Authoring Tools
    for Advanced Technology Learning Environments
    Toward cost-effective adaptive, interactive, and
    intelligent educational software. Kluwer
    Dordrecht, pp. 377-409.
  • Brusilovsky, P. and Millan, E. (2007) User models
    for adaptive hypermedia and adaptive educational
    systems. In P. Brusilovsky, A. Kobsa and W.
    Neidl (eds.) The Adaptive Web Methods and
    Strategies of Web Personalization. Lecture Notes
    in Computer Science, Vol. 4321, Berlin Heidelberg
    New York Springer-Verlag, pp. 3-53.
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