smart university - the university as a platform

Centre forIntelligent Computing

Smart UniversityThe university as a platform

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3 May 2013 ● Prof Thomas Roth-Berghofer ● Robert Gordon University, Aberdeen

Centre for Model-based

software engineering

Dr Samia Oussena

Saturday, 4 May 13

University of West LondonSlideSmart University • RGU, AberdeenCentre for Intelligent Computing

Outline of my talk

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Saturday, 4 May 13


University of West London, Ealing

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Saturday, 4 May 13


University of West London: Structure & Collaborations

School of Computing and Technology (SOCAT)

Saturday, 4 May 13

School of Psychology, Social Work and Social Sciences

Ealing Law School

London College of Music

Ealing School of Art, Design and Media

International Business School




School of Nursing, Midwifery and Healthcare

London School of Hospitality and Tourism

Saturday, 4 May 13

School of Psychology, Social Work and Social Sciences

Ealing Law School

London College of Music

Ealing School of Art, Design and Media

International Business School



E.g., course Project management

E.g., PhD “Interactive 3d portraits”

E.g., project “Audio mixing support”


School of Nursing, Midwifery and Healthcare

London School of Hospitality and Tourism

Saturday, 4 May 13

Intelligent computing Prof Thomas Roth-Berghofer

University of West LondonSlideSmart University • RGU, AberdeenCentre for Intelligent Computing 5

Research groups

Internationalisation and user experience

Dr José Abdelnour-Nocera

Networks and distributed systems

Prof Peter Komisarczuk

Information management and libraries

Dr Stephen Roberts

Model-based software engineering

Dr Samia Oussena

Civil and built environment Dr Ali Bahadori-Jahromi

& Dr Charlie Fu

Mobile ComputingDr John Moore


Saturday, 4 May 13

Intelligent computing Prof Thomas Roth-Berghofer


Thomas Roth-BerghoferHead of Research and Research Training

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Smart University

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Deliver foundational data to drive the analysis of the teaching & learning environment.

http://smartuniversity.uwl.ac.uk

Saturday, 4 May 13




Smart University

Using sensor data

Linked (open) dataexpert knowledge/experience

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Deliver foundational data to drive the analysis of the teaching & learning environment.


Saturday, 4 May 13




Daily journey of student Daily journey of lecturer

Sensor data

Humidity

Temperature

Noise

Linked (Open) DataExperience

Improving classroom experience

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Room utilisation

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Saturday, 4 May 13


Occu-Pi: First test run

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Saturday, 4 May 13


myCBR Workbench

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In co-operation with the Competence Centre Case-Based Reasoning (CCCBR) at the German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern

Saturday, 4 May 13


myCBR Workbench:Modelling perspective

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Saturday, 4 May 13



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Projects, classes and attributes

Projects & Classes

Saturday, 4 May 13



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Projects & Classes

List of similarity measures

associated with selected attribute

Similarity Measures

Saturday, 4 May 13



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Editors

Editing area for attributes, global and local similarity measures


Projects & Classes

List of similarity measures

associated with selected attribute

Similarity Measures

Saturday, 4 May 13


myCBR Workbench: Case bases perspective

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Saturday, 4 May 13



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Projects & Classes

Saturday, 4 May 13



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Projects & Classes

Lists of case bases and

available instances

Case bases and instances

Saturday, 4 May 13



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Projects & Classes

Lists of case bases and

available instances

Case bases and instances

Editors

Editing area for case bases and cases

Saturday, 4 May 13


Filling knowledge containers

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Case base

Vocabulary

Similarity measures

Adaptation knowledge Vocabulary Vo

cabu

lary

Saturday, 4 May 13


Filling vocabulary and case base

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Vocabulary

Case base

Saturday, 4 May 13


myCBR Workbench:CSV file importer

Automatic set up of initial vocabulary, i.e. attributes, data types, and rangesSetup of default similarity measures based on data typesFilling the case base

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A.Aamodt and E. Plaza. CBR: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39–59, 1994.

Saturday, 4 May 13


myCBR Workbench:CSV file importer

Automatic set up of initial vocabulary, i.e. attributes, data types, and rangesSetup of default similarity measures based on data typesFilling the case base

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A.Aamodt and E. Plaza. CBR: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39–59, 1994.

Start cycle of refine & test

Saturday, 4 May 13


...

myCBR Workbench:CSV Importer

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Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Saturday, 4 May 13


...


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R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

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...


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R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …


Set data types2

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...


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R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …


Boolean

Concept

Float

Integer

String

Symbol

+

Set data types2

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...


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R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …


...

Set data types2

Saturday, 4 May 13


...


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R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …


...

Set data types2 Generate instances3

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Improving similarity measures

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Vocabulary Case base

Saturday, 4 May 13


Improving similarity measures

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Vocabulary

Similaritymeasures

Case base

Saturday, 4 May 13


Knowledge extraction workbench (KEWo)

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Christian S. Sauer and Thomas Roth-Berghofer. Web community knowledge extraction for myCBR 3. In M. Bramer, M. Petridis, and L. Nolle, eds., Proc. of AI-2011. Springer, 2011.

Saturday, 4 May 13


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Adaptation knowledge

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Vocabulary

Similaritymeasures

Case base

Future work

Saturday, 4 May 13

http://www.jboss.org/drools/



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Adaptation knowledge

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Vocabulary

Similaritymeasures

Case base

Adaptationknowledge

For example: Integration of Drools - The Business Logic integration Platform

Future work

Saturday, 4 May 13




Audio advisor

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Saturday, 4 May 13


Saturday, 4 May 13


Mid

BassTreble

TranslationSaturday, 4 May 13


Case structure

Saturday, 4 May 13


Similarity measures

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ANNIE

Supporting case acquisition and query formulation

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Saturday, 4 May 13


Auric Advisor

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Saturday, 4 May 13


Selection of pretreatment method for refractory gold ore

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Gold extraction from its ores may require a combination of mineral processing and metallurgical processes to be performed on the ore.

There are two types of metallurgical processes:

Hydrometallurgical Pyrometallurgical - leaching - smelting - low temperature - high temperature

http://www.unige.ch/sciences/terre/mineral/fontbote/teaching/lehne_oredressing/2_callion_ore.jpg

Saturday, 4 May 13




General process chain for refractory gold ore

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Pretreatment

Crushing & Grinding

Leaching

Recovery process

Refining

Ore

Refined gold

Saturday, 4 May 13


General process chain for refractory gold ore

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Pretreatment

Crushing & Grinding

Leaching

Recovery process

Refining

Ore

Refined gold

For example:• low-pressure

oxidation• high-pressure

acidic oxidation• high-pressure non-

acidic oxidation• Nitric acid oxidation• Chlorine oxidation• Biological oxidation• Pyrometallurgical

oxidation

Saturday, 4 May 13


Basic idea: Two step recommendation process

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Identify context1

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Identify context1

2 Identify pretreatment

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Two case bases

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Problem Solution

Whole process chain of the mining operation

Mining situationDescription

[Ore/Mineral/Deposit]

Process step Process stepProcess step

Problem Solution

Oxidative Treatment [And its conditions and raw material description]

Cyanide Leaching [Next best suited process step]

Saturday, 4 May 13


Similarity measures: Example

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Saturday, 4 May 13


Auric Advisor

Saturday, 4 May 13


Auric Advisor: Goals

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Advice experts

Provide starting points to process design

Validate existing and newly designed process chains

Teach students of hydrometallurgy (or new employees) best practices or process steps in specific situations

Saturday, 4 May 13


CIC research strands: Open PhD research topics

Using sensor data to improve classroom experienceInvestigate and develop novel techniques, methodologies and support tools for using sensor data to improve the classroom experience.

Experience-based audio mastering and mixingFormalise teaching experience to improve the individual learning experience in audio engineering.

Acquisition and use of sensor data for music student teaching

Formalise teaching experience with the help of sensor technology to improve the individual learning experience.

Agent-based acquisition of explanation knowledge Acquire and use distributed explanation knowledge in the SEASALTexp environment.

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More information

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Blog: http://smartuniversity.uwl.ac.uk

Workshop at CONTEXT 2013: http://smartuni2013.workshop.hmSubmission deadline: 12 July 2013

Saturday, 4 May 13



http://smartuni2013.workshop.hm

http://smartuni2013.workshop.hm

Centre forIntelligent Computing

Smart UniversityThomas [email protected]

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Saturday, 4 May 13

mailto:[email protected]

mailto:[email protected]


Used images

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http://commons.wikimedia.org/wiki/File:Waksman_laboratory.jpg

http://commons.wikimedia.org/wiki/File:Gold-130327.jpg

http://commons.wikimedia.org/wiki/File:Blue_Drop.svg

http://commons.wikimedia.org/wiki/File:FireV2.png

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