Stipends available for  MSc Intelligent Systems
-----------------------------------------------

We are pleased to announce that for eligible EU students we have obtained
funding  to offer a bursary for our MSc Intelligent Systems October 2004
entry
worth up to 9.000 EURO as fee waiver and stipend.

***Please forward to  students who may be interested.***

The School of Computing and Technology, University of Sunderland
is delighted to announce the launch of its new MSc Intelligent Systems
programme for October 2004. Building on the School's leading edge
research in intelligent systems this masters programme will be
funded via the ESF scheme  (see below).

Intelligent Systems is an exciting field of study for science and
industry  since the currently existing computing systems have
often not yet  reached the various aspects of  human performance.
"Intelligent Systems" is a term to describe software systems and
methods, which simulate aspects of intelligent behaviour. The intention
is  to learn from nature and human performance in order to build more
powerful computing systems. The aim is to learn from cognitive science,
neuroscience, biology, engineering, and linguistics for building more
powerful computational system architectures. In this programme a
wide variety of novel and exciting techniques will be taught including
neural networks, intelligent robotics, machine learning, natural language
processing,  vision, evolutionary genetic computing, data mining,
information retrieval,  Bayesian computing, knowledge-based systems,
fuzzy methods, and  hybrid intelligent architectures.


Funding up to 6000 pounds (about 9.000 Euro) for eligible students
------------------------------

The Bursary Scheme applies to this Masters programme commencing
October 2004 and we have obtained funding through the European
Social Fund (ESF). ESF  support enables the University to waive the
normal tuition fee and provide  a bursary of £ 75 per week for 45 weeks
for eligible EU students,  together up to 6000 pounds or about 9000 Euro.

For further information in the first instance please see:
http://www.his.sunderland.ac.uk/Teaching_frame.html
http://osiris.sund.ac.uk/webedit/allweb/courses/progmode.php?prog=G550A&mode=FT&\
mode2=&dmode=C

For information on applications and start dates contact:
         gillian.potts@...  Tel: 0191 515 2758
For academic information about the programme contact:
         alfredo.moscardini@...

Please forward to interested students.

Stefan

***************************************
Stefan Wermter
Professor for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 3553
email: stefan.wermter@...
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

**** HIS'04 - Deadline Extension and Final Call for Papers ****

4th International Conference on Hybrid Intelligent Systems (HIS'04)

December 05-08, 2004
Kitakyushu, Japan

Conference URL: http://his04.hybridsystem.com
Mirror Site: http://www.cs.nmt.edu/~his04

Due to several requests we have extended the final deadline until October
15.  Please refer to the conference Web site for more details.

We look forward towards receiving your submission.

Vitorino Ramos, HIS'04 Publicity Chair

**** HIS'04 - Deadline Extension and Final Call for Papers ****

4th International Conference on Hybrid Intelligent Systems (HIS'04)

December 05-08, 2004
Kitakyushu, Japan

Conference URL: http://his04.hybridsystem.com
Mirror Site: http://www.cs.nmt.edu/~his04

Due to several requests we have extended the final deadline until October
15.  Please refer to the conference Web site for more details.

We look forward towards receiving your submission.

Vitorino Ramos, HIS'04 Publicity Chair

Stipends available for  MSc Intelligent Systems (Feb. 2005)
-----------------------------------------------------------

We are pleased to announce that for eligible EU students we have obtained
funding  to offer a bursary for our MSc Intelligent Systems February 2005
entry worth up to 9.000 EURO as fee waiver and stipend.

***Please forward to students who may be interested.***

The School of Computing and Technology, University of Sunderland
is delighted to announce the launch of its MSc Intelligent Systems
programme for February 2005 intake. Building on the School's leading edge
research in intelligent systems this masters programme will be
funded via the ESF scheme  (see below).

Intelligent Systems is an exciting field of study for science and
industry  since the currently existing computing systems have
often not yet  reached the various aspects of  human performance.
"Intelligent Systems" is a term to describe software systems and
methods, which simulate aspects of intelligent behaviour. The intention
is  to learn from nature and human performance in order to build more
powerful computing systems. The aim is to learn from cognitive science,
neuroscience, biology, engineering, and linguistics for building more
powerful computational system architectures. In this programme a
wide variety of novel and exciting techniques will be taught including
neural networks, intelligent robotics, machine learning, natural language
processing,  vision, evolutionary genetic computing, data mining,
information retrieval,  Bayesian computing, knowledge-based systems,
fuzzy methods, and  hybrid intelligent architectures.


Funding up to 6000 pounds (about 9.000 Euro) for eligible students
------------------------------------------------------------------

The Bursary Scheme applies to this Masters programme commencing
February 2005 and we have obtained funding through the European
Social Fund (ESF). ESF  support enables the University to waive the
normal tuition fee and provide  a bursary of £ 75 per week for 45 weeks
for eligible EU students,  together up to 6000 pounds or about 9000 Euro.

For further information in the first instance please see:
http://www.his.sunderland.ac.uk/Teaching_frame.html
http://osiris.sund.ac.uk/webedit/allweb/courses/progmode.php?prog=G550A&mode=FT&\
mode2=&dmode=C



For information on applications and start dates mention "MSc Intelligent
Systems" and contact:
        gillian.potts@...  Tel: 0191 515 2758
For academic information about the programme contact:
        alfredo.moscardini@...

Please forward to interested students.

Stefan Wermter

***************************************
Stefan Wermter
Professor for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 3553
email: stefan.wermter@...
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

Company: MEDai, Inc. (www.medai.com)
Position: Research Scientist
Location: Orlando, FL
Required Skills:
*PHD in statistics/applied mathematics/computer science;
*Strong analytical and statistical analysis capabilities: regression,
nearest neighbors, decision trees, classification and cluster analysis,
outliers identification, neural nets
*C++; Database capabilities;
*SAS and other math packages (e.g. MatLab or SPSS or Splus or R ...)
*Good publications;
*Experienced (Non-Manager), Team player
Duties Include: Responsibilities include programming predictive modeling
algorithms, analyses and modeling of large medical claims databases,
enhancement of currently defined models and heuristic applications,
statistical ROI.
Casual business environment, excellent benefits including fully paid health,
dental and pharmacy for individual and all dependents, matching 401K, stock
options, onsite exercise facility, and competitive salary.
Email resumes to mailto:OAsparoukhov@... or mailto:humres@...

Dear All,

below please find an announcement for an open faculty position (tenured)
in the area of CNS / AI in our department (EE and CS) at the Berlin
University of Technology.

The Berlin area has a high concentration of high quality research
institutions (three universities, Max-Planck and Fraunhofer institutes,
etc.) and a lively computational neuroscience and AI/machine learning
scene. Besides that, it is a pleasant city to live in and Germany's most
prominent place for cultural activities.

There are no German language requirements, as teaching can be done
in English at least for the first five years, but very likely also for
the period after.

Cheers

Klaus

------------------------------------------------------------------------

The Department for Electrical Engineering and Computer Science of Berlin
University of Technology (Berlin, Germany) solicits applications for a

                        Tenured Faculty Position (W2)

                      "Modeling of Cognitive Processes"

We seek a scientist with a background in computational neuroscience,
computational cognitive science, machine learning, or artificial
intelligence. The candidate should develop quantitative models of higher
brain functions as inferred, for example, from non-invasive brain signals,
and should combine this modelling work with application oriented research
in machine intelligence (e.g., autonomous agents, man-machine systems).
The faculty position is also part of the Bernstein Center for Computational
Neuroscience Berlin. A strong commitment to excellence in undergraduate and
graduate teaching at the Department of Electrical Engineering and Computer
Science and at the Bernstein Center for Computational Neuroscience is
expected.

Applications should include CV, summary of teaching and research
experience, list of publications and funding, statement of research
interests, and up to five selected publications.

All applications received before

                               14. 02. 2005

will be given full consideration. Late applications, however, may still
be considered until the position is filled.

Applications should be sent to:

Dekanat, FR 5-1, Fakultaet IV, Technische Universitaet Berlin,
Franklinstrasse 28/29, 10587 Berlin, Germany.

An electronic version should be sent to a.herz@...
(Andreas Herz) and oby@... (Klaus Obermayer) to speed up
the search process.

The Technical University of Berlin wants to increase the percentage of
women on its faculty and strongly encourages applications from
qualified individuals. Women will be preferred given equal
qualifications.

Handicapped persons will be preferred given equal qualifications.

========================================================================

Prof. Dr. Klaus Obermayer         phone:  49-30-314-73442
FR2-1, NI, Informatik                     49-30-314-73120
Technische Universitaet Berlin    fax:    49-30-314-73121
Franklinstrasse 28/29             e-mail: oby@...
10587 Berlin, Germany             http://ni.cs.tu-berlin.de/

Social Cognitive Maps, Swarm Perception and Distributed Search on Dynamic
Landscapes, CVRM-IST 127E-2005 technical report, final draft submitted to
Brains, Minds & Media,  Journal of New Media in Neural and Cognitive
Science, NRW, Germany, 2005.

http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-BMM.pdf

ABSTRACT: Swarm Intelligence (SI) is the property of a systems whereby the
collective behaviors of (unsophisticated) entities interacting locally with
their environment cause coherent functional global patterns to emerge. SI
provides a basis with which it is possible to explore collective (or
distributed) problem solving without centralized control or the provision
of a global model. To tackle the formation of a coherent social collective
intelligence from individual behaviors, we discuss several concepts related
to self-organization, stigmergy and social foraging in animals. Then, in a
more abstract level we suggest and stress the role played not only by the
environmental media as a driving force for societal learning, as well as by
positive and negative feedbacks produced by the many interactions among
agents. Finally, presenting a simple model based on the above features, we
will address the collective adaptation of a social community to a cultural
(environmental, contextual) or media informational dynamical landscape,
represented here - for the purpose of different experiments - by several
three-dimensional mathematical functions that suddenly change over time.
Results indicate that the collective intelligence is able to cope and
quickly adapt to unforeseen situations even when over the same cooperative
foraging period, the community is requested to deal with two different and
contradictory purposes.

KEYWORDS: Swarm Intelligence and Perception, Social Cognitive Maps, Social
Foraging, Self-Organization, Distributed Search and Optimization.

hope u could enjoy it. best, v.

~ v. ramos [http://alfa.ist.utl.pt/~cvrm/staff/vramos/]

Dear Colleagues:
Two of my recent works can now be found online. hope u could enjoy them.
best regards, vitorino ramos

------------------------------------------------------------------------------

Varying the Population Size of Artificial Foraging Swarms on Time Varying
Landscapes,
final draft submitted to ICCANN -05, International Conf. on Artificial
Neural Networks,
Springer-Verlag, LNCS series, Warsaw, Poland, Sep. 11-15, 2005.

link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_59.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ICANN05.pdf

ABSTRACT: Swarm Intelligence (SI) is the property of a system whereby the
collective behaviors of (unsophisticated) entities interacting locally with
their environment cause coherent functional global patterns to emerge. SI
provides a basis with wich it is possible to explore collective (or
distributed) problem solving without centralized control or the provision
of a global model. In this paper we present a Swarm Search Algorithm with
varying population of agents. The swarm is based on a previous model with
fixed population which proved its effectiveness on several computation
problems. We will show that the variation of the population size provides
the swarm with mechanisms that improves its self-adaptability and causes
the emergence of a more robust self-organized behavior, resulting in a
higher efficiency on searching peaks and valleys over dynamic search
landscapes represented here - for the purpose of different experiments - by
several three-dimensional mathematical functions that suddenly change over
time. We will also show that the present swarm, for each function,
self-adapts towards an optimal population size, thus self-regulating.

KEYWORDS: Swarm Intelligence and Perception, Dynamic Population Sizes,
Self-Regulation, Social Cognitive Maps, Social Foraging, Self-Organization
and Evolution, Distributed Search and Optimization.

------------------------------------------------------------------------------

Social Cognitive Maps, Swarm Collective Perception and Distributed Search
on Dynamic
Landscapes, CVRM-IST 127E-2005 technical report, final draft submitted to
Brains, Minds & Media,
Journal of New Media in Neural and Cognitive Science, NRW, Germany, 2005.

link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-BMM.pdf

ABSTRACT: Swarm Intelligence (SI) is the property of a system whereby the
collective behaviors of (unsophisticated) entities interacting locally with
their environment cause coherent functional global patterns to emerge. SI
provides a basis with which it is possible to explore collective (or
distributed) problem solving without centralized control or the provision
of a global model. To tackle the formation of a coherent social collective
intelligence from individual behaviors, we discuss several concepts related
to self-organization, stigmergy and social foraging in animals. Then, in a
more abstract level we suggest and stress the role played not only by the
environmental media as a driving force for societal learning, as well as by
positive and negative feedbacks produced by the many interactions among
agents. Finally, presenting a simple model based on the above features, we
will address the collective adaptation of a social community to a cultural
(environmental, contextual) or media informational dynamical landscape,
represented here - for the purpose of different experiments - by several
three-dimensional mathematical functions that suddenly change over time.
Results indicate that the collective intelligence is able to cope and
quickly adapt to unforeseen situations even when over the same cooperative
foraging period, the community is requested to deal with two different and
contradictory purposes.

KEYWORDS: Swarm Intelligence and Perception, Social Cognitive Maps, Social
Foraging, Self-Organization, Distributed Search and Optimization.

------------------------------------------------------------------------------

Stipends available for  MSc Intelligent Systems
-----------------------------------------------

We are pleased to announce that for eligible EU students we have obtained
funding  to offer a bursary for our MSc Intelligent Systems in October 2005
of about 8.000 EURO (about 5500 pounds) as fee waiver and stipend.

***Please forward to  students who may be interested.***

The School of Computing and Technology, University of Sunderland
is delighted to announce the launch of its  MSc Intelligent Systems
programme for October 2005. Building on the School's leading edge
research in intelligent systems this masters programme will be
funded via the ESF scheme  (see below).

Intelligent Systems is an exciting field of study for science and
industry  since the currently existing computing systems have
often not yet  reached the various aspects of  human performance.
"Intelligent Systems" is a term to describe software systems and
methods, which simulate aspects of intelligent behaviour. The intention
is  to learn from nature and human performance in order to build more
powerful computing systems. The aim is to learn from cognitive science,
neuroscience, biology, engineering, and linguistics for building more
powerful computational system architectures. In this programme a
wide variety of novel and exciting techniques will be taught including
neural networks, intelligent robotics, machine learning, natural language
processing,  vision, evolutionary genetic computing, data mining,
fuzzy methods, and  hybrid intelligent architectures.


Funding of about 5500 pounds (about 8.000 Euro) for eligible EU students
------------------------------

The Bursary Scheme applies to this Masters programme commencing
October 2005 and we have obtained funding through the European
Social Fund (ESF). ESF  support enables the University to waive the
normal tuition fee and provide  a bursary of £ 50 per week for 45 weeks
for eligible EU students,  together up to about 5500 pounds or about 8000 Euro.

For further information in the first instance please see:

http://www.his.sunderland.ac.uk/Teaching_frame.html

http://osiris.sund.ac.uk/webedit/allweb/courses/progmode.php?prog=G550A&mode=FT&\
mode2=&dmode=C

http://www.his.sunderland.ac.uk/teaching/sund_is_app.pdf

For information on applications and start dates contact:
         gillian.potts@...  Tel: 0191 515 2758
For academic information about the programme contact:
         alfredo.moscardini@...

Please forward to interested students.

Stefan

***************************************
Stefan Wermter
Professor for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 3553
email: stefan.wermter at sunderland.ac.uk
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

****  HIS'05 - First Call for Papers ****



5th International Conference on Hybrid Intelligent Systems (HIS'05)



November 06-09, 2005

Rio de Janeiro, Brazil



Conference URL: http://his05.hybridsystem.com

Mirror Site: http://www.ica.ele.puc-rio.br/his05




***************************************************

Deadline for Paper Submission: July 1st, 2005

***************************************************



HIS'05 is technically co-sponsored by:



- IEEE Systems Man and Cybernetics Society

- International Fuzzy Systems Association (IFSA)

- European Neural Network Society (ENNS)

- European Society for Fuzzy Logic and Technology (EUSFLAT)

- The World Federation on Soft Computing

- Brazilian Computing Society (SBC)

- Brazilian Society for Automation (SBA)



HIS'05 is organized in cooperation with:

- IEEE- Computational Intelligence Society



Hybridization of intelligent systems is a promising research field of
computational intelligence focusing on synergistic combinations of multiple
approaches to develop the next generation of intelligent systems. A fundamental
stimulus to the investigations of Hybrid Intelligent Systems (HIS) is the
awareness that combined approaches

will be necessary if the remaining tough problems in artificial intelligence are
to be solved. Neural computing, machine learning, fuzzy logic, evolutionary
algorithms, agent-based methods, among others, have been established and shown
their strength and drawbacks.

Recently, hybrid intelligent systems are getting popular due to their
capabilities in handling several real world complexities involving imprecision,
uncertainty and vagueness.



HIS'05 builds on the success of previous HIS events: HIS'04 was held in 
Kitakyushu, Japan, HIS'03 in Melbourne, Australia, HIS'02 in Santiago, Chile,
and HIS'01, the first event, in  Adelaide, Australia. All events attracted
participants from over 30 countries. HIS'05 is, therefore, the fifth
International Conference that brings together researchers, developers,
practitioners, and users of soft computing, computational intelligence,
multi-agents, and several other intelligent computing techniques.  The
objectives of this

international meeting are to increase the awareness of the research community of
the broad spectrum of hybrid techniques, to bring together AI researchers from
around the world to present their cutting-edge results, to discuss the current
trends in HIS research, to develop a collective vision of future opportunities,
to establish international collaborative opportunities, and as a result to
advance the state of the art of the field.



HIS'05 invites authors to submit original and unpublished work that demonstrates
current research in hybrid intelligent systems research and their applications
in science, technology, business and commerce. Submitted papers have to be
original, containing new and original

results. The assessment criteria will be heavily weighted towards originality,
potential impact and relevance to HIS'05 themes. All papers will be peer
reviewed by three independent referees of the international program committee of
HIS'05.



HIS'05 will focus on the following topics:



** Theoretical Advances in Hybrid Intelligent System Architectures



     *   Interactions between neural networks and fuzzy inference systems

     *   Hybrid learning techniques (supervised/unsupervised/reinforcement
learning)

     *   Artificial neural network optimization using global optimization
techniques

     *   Fuzzy inference system optimization using global optimization algorithms

     *   Hybrid systems involving support vector machines, rough sets, Bayesian
networks, probabilistic reasoning, minimum message length, etc.

     *   Hybrid computing using neural networks - fuzzy systems - evolutionary
algorithms

     *   Hybrid optimization techniques (evolutionary algorithms, simulated
annealing, tabu search, GRASP etc.)

     *   Hybrid of soft computing and statistical learning techniques

     *   Integration with Intelligent agents (architectures, environments,
adaptation/learning and knowledge management)

     *   Hybrid models using inductive logic programming, logic synthesis,
grammatical inference, case-based reasoning etc.

  *   System-of-Systems Engineering.



** Hybrid Approaches and Applications



     * Robotics and automation

     * Biomimetic applications

     * Bioinformatics

     * Web intelligence

     * Image and signal processing

     * Adaptive systems

     * Data mining

     * Behavioral simulations

     * Affective computing

     * Soft computing for control and automation

     * Multi-agent systems

     * Knowledge management

     * Communication and networking

     * Business systems and financial engineering.

     * Power engineering



We invite you to submit a:

- full paper of 6 pages, for oral presentation, A4 size, IEEE 2 columns format,
using MS Word/LaTeX



- proposal to organize a technical session

(see the Call for Events Proposals in the conference Web page for more
information).



Submitted papers have to be original, containing new results.

The proceedings of the Conference will be published by IEEE Computer Society and
will be available during the conference. It is assumed that all accepted
manuscripts will be presented at the conference. All accepted papers must be
accompanied by a full paid registration in order to appear in the proceedings.

All full papers are to be submitted in PDF electronically via the web site.

Hard copies should be sent only if electronic submission is not possible.



* Journal Publication Opportunities



a) International Journal on Hybrid Intelligent Systems (IJHIS)

Papers addressing strong HIS theoretical developments

(based on the referee recommendations) after substantial revision may be

considered for publication in the IJHIS  (http://ijhis.hybridsystem.com).



b) Applied Soft Computing

Extended versions of 10 application papers of the conference will also be

invited for a "fast track" submission for the Elsevier Science Applied Soft

Computing Journal (http://www.elsevier.com/locate/inca/621920).



c) International Journal of Knowledge Management (IJKM), World Scientific,
Singapore

Extended versions of 6 to 8 selected papers of the conference will also

be invited for submission to the IJKM
(http://www.worldscinet.com/jikm/jikm.shtml).



d) Neural Computing and Applications, Springer-Verlag London

Extended version of selected papers on Hybrid aspects of neurocomputing will
also

be invited for submission to the Neural Computing and Applications journal.



*****************************************************************

Important Dates



* Deadline for sessions/tutorial proposals: June 12th, 2005

* Deadline for Paper Submission (full paper): July 1st, 2005

* Notification of Acceptance: August 1st, 2005

* Deadline for Authors' Registration: August 19th, 2005

* Deadline for Camera-Ready Papers: August 19th, 2005

*****************************************************************





HIS'05 Conference Organization



Honorary Chair

Fernando Gomide, Universidade Estadual de Campinas, Brazil



General Chairs

Marley Vellasco, Pontifícia Universidade Católica do Rio de Janeiro, Brazil

Ajith Abraham, Chung-Ang University, Korea

Mario Köppen, Fraunhofer IPK, Berlin, Germany



Program Chairs

Nadia Nedjah, Universidade do Estado do Rio de Janeiro, Brazil

Luiza de Macedo Mourelle, Universidade do Estado do Rio de Janeiro, Brazil



Local Organizing Chairs

Alexandre Evsukoff, Universidade Federal do Rio de Janeiro, Brazil

Flavio Joaquim de Souza, Universidade do Estado do Rio de Janeiro, Brazil

Gerson Zaverucha, Universidade Federal do Rio de Janeiro, Brazil

Karla Figueiredo, Universidade do Estado do Rio de Janeiro, Brazil

Marco Aurélio Pacheco, Pontifícia Universidade Católica do Rio de Janeiro,
Brazil

Maria Luiza Velloso, Universidade do Estado do Rio de Janeiro, Brazil

Ricardo Tanscheit, Pontifícia Universidade Católica do Rio de Janeiro, Brazil



International Steering Committee



Andre Carlos Ponce de Leon Ferreira de Carvalho, Universidade de São Paulo,
Brazil

Antony Satyadas, IBM, USA

David Corne, University of Exeter, UK

David Fogel, Natural Selection Inc., USA

Janusz Kacprzyk, Polish Academy of Sciences, Poland

Jae Oh, Syracuse University, USA

Javier Ruiz-del-Solar, Universidad de Chile, Chile

Oscar Castillo, Tijuana Institute of Technology, Mexico

Takeshi Yamakawa, Kyushu Institute of Thecnology, Japan

Vasile Palade, University of Oxford, UK



Executive Chairs

Maria do Carmo Nicoletti, Universidade Federal de São Carlos (UFSCar), Brazil

Estevam Rafael Hruschka Junior, Universidade Federal de São Carlos (UFSCar),
Brazil



Special Event Chairs

Karla Figueiredo, Universidade do Estado do Rio de Janeiro, Brazil

Marco Aurélio Pacheco, Pontifícia Universidade Católica do Rio de Janeiro,
Brazil



Publicity Chair
Crina Grosan, University of Babes Boylai at Cluj-Napoca, Romania
(cgrosan@...)



International Program Committee



Akira Asano, Hiroshima University, Japan

Aladdin Ayesh, De Montfort University, UK

Alberto Prieto, Univ. Granada, Spain

Andre Carlos Ponce de Leon Ferreira de Carvalho, Universidade de São Paulo,
Brazil Andreas Koenig, University of Kaiserslautern, Germany

Andrew Sung,  New Mexico Tech, USA

Antonio de Pádua Braga, UFMG, Brazil

Aureli Soria-Frisch, Fraunhofer IPK-Berlin, Germany

Berend Jan van der Zwaag, University of Twente, The Netherlands

Bernard de Baets, Ghent University, Belgium

Bernard Grabot, LGP/ENIT, France

Bernardete Ribeiro, University of Coimbra, Portugal

Bob Mckay, University of New South Wales, USA

Bruno Apolloni, Universita degli Studi di Milano, Italy

Carlos Coello, CINVESTAV-IPN, Mexico

Carlos Henrique Costa Ribeiro, ITA, Brazil

Christian Veenhuis, Fraunhofer IPK Berlin, Germany

Costa-Branco P J, Instituto Superior Tecnico Lisboa, Portugal

Crina Grosan, University of Babes Boylai at Cluj Napoca, Romania

David Corne, University of Exeter, UK

Dong Hwa Kim, Hanbat National University, South Korea

Eiji Uchino, University of Yamaguchi, Japan

Elisa Bertino, Purdue University, USA

Emilia Barakova, Brain Science Institute RIKEN, Japan

Erkki Oja, Helsinki University of Technology, Finland

Estevam Rafael Hruschka Junior, Universidade Federal de São Carlos (UFSCar),
Brazil

Etienne Kerre, Ghent University, Belgium

Fabio Abbattista, Universita di Bari, Italy

Felipe Maia Galvão França, UFRJ, Brazil

Francisco Pereira, Universidade de Coimbra, Portugal

Frank Hoffmann, University of Dortmund, Germany

Frank Klawonn, TFH Braunschweig/Wolfenbuettel, Germany

Gary Fogel, Natural Selection, Inc., USA

Gheorghe Tecuci, George Mason University, USA

Giovanni Semeraro, Universita di Bari, Italy

Graham Williams, ANU, Canberra, Australia

Guenther Raidl, Vienna University of Technology, Austria

Hajime Nobuhara, Tokyo Institute of Technology, Japan

Henry Tirri, NRC Nokia Research Center, Finland

Hirofumi Nagashino, University of Tokushima, Japan

Hisao Ishibuchi, Osaka Prefecture University, Japan

Jacek Wachowicz, Technical University of Gdansk, Poland

Janina Jakubczyc, Wroclaw University of Economics, Poland

Janos Abonyi, University of Veszprem, Hungary

Janusz Kacprzyk, Polish Academy of Sciences, Poland

Javier Ruiz-del-Solar, Universidad de Chile, Chile

Jerzy Grzymala-Busse, University of Kansas, USA

John MacIntyre, University of Sunderland, UK

Jun Munemori, Wakayama University, Japan

Kaori Yoshida, Kyushu Institute of Technology, Japan

Katrin Franke, Fraunhofer IPK Berlin, Germany

Kazumi Nakamatsu, University of Hyogo, Japan

Krzysztof Wecel, The Poznan University of Economics, Poland

Leandro dos Santos Coelho, Pontifical Catholic University of Paraná,  Brazil

Louis Vuurpijl, NICI Nijmegen, The Netherlands

Luis Magdalena, Universidad Politecnica de Madrid, Spain

Marcin Paprzycki, Oklahoma State University, USA

Maria do Carmo Nicoletti, Universidade Federal de São Carlos (UFSCar), Brazil

Maria Ganzha, Gizycko Private Higher Educational Institute, Poland

Matjaz Gams, Jozef Stefan Institute Ljubljana, Slovenia

Mieczyslaw L. Owoc, Wroclaw University of Economics, Poland

Mika Sato-Ilic, University of Tsukuba, Japan

Miroslav Karny, Academy of Sciences of Czech Republic, Czech Republic

Nik Kasabov, Auckland University of Technology, New Zealand

Paramasivan Saratchandran, Nanyang Technological University, Singapore

Rafael Marti, Universitat de València, Spain

Rajkumar Roy, Cranfield University, UK

Rene Thomsen, Aarhus University, Denmark

Richard Weber, Universidad de Chile, Chile

Ronald R. Yager, Iona College, USA

Saman Halgamuge, The University of Melbourne, Australia

Sandra Sandri, INPE, Brazil

Sankar K. Pal, Indian Statistical Institute, India

Sebastian Lozano, Escuela Superior de Ingenieros, Spain

Seppo Ovaska, Helsinki University of Technology, Finland

Shuji Hashimoto, Waseda University, Japan

Shusaku Tsumoto, Shimane Medical University, Japan

Stefan Wermter, University of Sunderland, UK

Suliman M. Hawamdeh, University of Oklahoma, USA

Sung-Bae Cho, Yonsei University, Korea

Teresa Bernarda Ludermir, UFPE, Brazil

Tim Hendtlass, Swinburne University of Technology, Australia

Udo Seiffert, IPK Gatersleben, Germany

Uwe Zimmer, Australian National University, Australia

Vasile Palade, Oxford University, UK

Wiliam Browne, University of Reading, UK

William Langdon, University of Essex, UK

Witold Pedrycz, University of Alberta, Canada

Xiao-Zhi Gao, Helsinki University of Technology, Finland

Valmir Barbosa, UFRJ, Brazil

Yanqing Zhang, Georgia State University, USA

Yaochu Jin, Honda Research Institute Europe, Germany

Yasuhiko Dote, Muroran Institute of Technology, Japan

Yoshiteru Ishida, Toyohashi Univ. of Tech., Japan

Yuehui Chen, Jinan University, China

Yuzo Hirai, University of Tsukuba, Japan

Zensho Nakao, University of the Ryukyus, Japan


[Non-text portions of this message have been removed]

Dear all

I am looking for distributed or parallel algorithms for Machine Learning. I
am specially interested on surveys (apart from that by Peter Stone), and on
parallel / distributed versions of stardard algorithms (from decision
trees, bayesian approaches or rulte learners to neural networks, SVMs, etc).

Thank you very much

          Jose Maria


Jose Maria Gomez Hidalgo
Departamento de Sistemas Informáticos
Universidad Europea de Madrid
28670 - Villaviciosa de Odon - MADRID
(+34) 912115670
jmgomez@...
http://www.esp.uem.es/~jmgomez/
http://www.esp.uem.es

La legislación española ampara el secreto de las comunicaciones. Este
correo electrónico es estrictamente confidencial y va dirigido
exclusivamente a su destinatario/a. Si no es Ud., le rogamos que no difunda
ni copie la transmisión y nos lo notifique cuanto antes.

Spanish law guarantees privacy in electronic communications. This
electronic transmission is strictly confidential and intended solely for
the addressee. If you are not the intended addressee, you are kindly
requested not to disclose nor to copy this transmission and to notify us as
soon as possible.

Dear all

Two days ago I posted the following question:

"I am looking for distributed or parallel algorithms for Machine Learning.
I am specially interested on surveys (apart from that by Peter Stone), and
on parallel / distributed versions of stardard algorithms (from decision
trees, bayesian approaches or rulte learners to neural networks, SVMs, etc)."

Thank you to all who replied to my message, in particular: Hillol Kargupta,
Alex Freitas, Rui Camacho, Balazs Kegl, Claudia Antunes, Jiri Ocenasek and
Jose Carlos Cortizo. They have suggested me the following references:

BOOKS -----------------------------------------------------

Yike Guo and Robert Grossman, editors, High Performance Data Mining:
Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers, 1999.

A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel
Processing. Kluwer, 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/book-springer-ukc.html

Mohammed J. Zaki, Ching-Tien Ho (Eds). Large-Scale Parallel Data Mining.
Springer-Verlag GmbH, Lecture Notes in Computer Science, 1759 / 2000.

PAPERS ----------------------------------------------------

A.A. Freitas. A Survey of Parallel Data Mining. Proc. 2nd Int. Conf. on the
Practical Applications of Knowledge Discovery and Data Mining, 287-300.
London: The Practical Application Company, Mar. 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html

Nuno Fonseca, Fernando Silva, Rui Camacho. Strategies to parallelize ILP
systems. In Proceedings of ILC 2005
http://ilp2005.in.tum.de/accepted-papers.html
* Thank you for sending me the draft

S. Gambs, B. Kégl, and E. Aïmeur. "Privacy-preserving boosting" Data Mining
and Knowledge Discovery, 2005 (submitted).
http://www.iro.umontreal.ca/~kegl/research/publications/
* This is for adaboost, and the authors suggest looking at the references:
[Lazarevic and Obradovic, 2002] and [Fan et al., 1999] for adaboost,
[Lindell and Pinkas, 2002] for trees.

Mohammed J. Zaki, "Parallel and Distributed Association Mining: A Survey",
IEEE Concurrency, special issue on Parallel Mechanisms for Data Mining,
Vol. 7, No. 4, pp14-25, December, 1999
* Good intro in terms of pattern mining / association rules

Ocenasek, J., Schwarz, J., Pelikan, M.: Design of Multithreaded Estimation
of Distribution Algorithms. In: Cantú-Paz et al. (Eds.): Genetic and
Evolutionary Computation Conference - GECCO 2003. Springer Verlag: Berlin,
2003, pp. 1247-1258.
* More papers, thesis and code by Jiri Ocenasek (http://jiri.ocenasek.com/).

BIBLIOGRAPHIES -----------------------------------------

Online distributed data mining bibliography:
http://www.cs.umbc.edu/~hillol/DDMBIB/

Again, thank you all. I will imr`pove the summary if I get more replies.

Best regards


Jose Maria Gomez Hidalgo
Departamento de Sistemas Informáticos
Universidad Europea de Madrid
28670 - Villaviciosa de Odon - MADRID
(+34) 912115670
jmgomez@...
http://www.esp.uem.es/~jmgomez/
http://www.esp.uem.es

La legislación española ampara el secreto de las comunicaciones. Este
correo electrónico es estrictamente confidencial y va dirigido
exclusivamente a su destinatario/a. Si no es Ud., le rogamos que no difunda
ni copie la transmisión y nos lo notifique cuanto antes.

Spanish law guarantees privacy in electronic communications. This
electronic transmission is strictly confidential and intended solely for
the addressee. If you are not the intended addressee, you are kindly
requested not to disclose nor to copy this transmission and to notify us as
soon as possible.

Concurrency and Computation: Practice and Experience
====================================================
http://research.bioinformatics.ulster.ac.uk/article.php3?id_article=81


Call for Papers

Special issue on Computational analysis and exploration of distributed data

DEADLINE FOR SUBMISSION: January 15, 2006

Summary

We invite submissions for a special issue of the journal Concurrency
And Computation: Practice And Experience to address the exploration and
analysis of distributed data. Papers should describe practical
experience relating to the mining and visualization of distributed data
in eScience or analogous industrial and commercial contexts.

Motivation and Themes
---------------------

One the principal drivers behind the development of grid computing and
eScience is the "data avalanche" being experienced across a wide range
of scientific domains.

From astronomy to zoology, researchers are facing a rapid expansion in
the number and size of data sources available to them over the
Internet.

Data mining and visualization techniques can offer invaluable help in
the extraction of scientific knowledge from these large volumes of
data. Those trying to apply such data exploration and analysis
techniques in eScience, or in similar commercial contexts, face many
common challenges, independent of their application domain. These
include a range of the generic problems which characterise eScience and
grid computing, e.g. data integration, trust and security, data
provenance, workflow, service composition and interoperability.

Additional issues relate to the application of existing data mining and
visualization techniques to the distributed, and often very large,
datasets found in eScience. Solving these problems requires the
coordinated efforts by researchers from various disciplines.

From computer scientists designing the data exploration algorithms and
developing the computational infrastructure within which they will run,
to the researchers in diverse scientific fields who will use them to do
their science.

For this special issue we invite members of this multi-disciplinary
community to report on their progress to date.

Papers for this special issue could cover, but are not necessarily
limited to, the following topics:

   o Example science and commercial drivers for the exploration of
     distributed data and the requirements they place on computational
     infrastructure;
   o The applicability of current generic middleware to the exploration
     of distributed data;
   o The suitability of existing data mining and visualization techniques
     for exploring inherently distributed and large data sources in e-Science
     and commercial settings with similar data requirements;
   o Collaborative data exploration (interactive data mining)

Timetable and Submission Process
--------------------------------

   Submission deadline: January 15, 2006
   Review results: March 15, 2006
   Updated versions: April 15, 2006
   Comments on updated versions: May 15, 2006
   Second update: June 1, 2006
   Papers to publisher: June 15, 2006

All submissions must be in electronic format, and should be sent to:
Omer Rana (o.f.rana@...) no later than January 15, 2006.

Format and Selection Criteria
-----------------------------

Please submit your electronic manuscript printable on A4 paper, double
spaced, using 10-12pt font size. There is no page limit, although
authors are encouraged to keep their contribution below 50 pages.
Papers will be reviewed and selected by the Special Issue Editors and
designated reviewers on the basis of technical quality, relevance to
the Special Issues focus, originality, significance, and clarity.

Please format your paper using the guidelines provided at
http://www3.interscience.wiley.com/cgi-bin/jabout/77004395/LaTexClassFile.html

Special Issue Editors:
---------------------

Werner Dubitzky,
University of Ulster, School of Biomedical Sciences,
Cromore Road, Coleraine BT52 1SA, Northern Ireland.
w.dubitzky@...

Robert Mann,
Institute for Astronomy, University of Edinburgh,
Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ.
rgm@...

Omer Rana,
School of Computer Science and Welsh eScience Centre,
Cardiff University, 5 The Parade, Cardiff CF24 3AA, UK.
o.f.rana@...


--
http://www.cs.cf.ac.uk/User/O.F.Rana/index.html / work-fax:+44(0)29-2087-4598
work:+44(0)29-2087-5542 / other:+44(0)7956-299981 / distributed collaborative
computing / room n2.14 / school of computer science / cardiff university
   queen's buildings / newport road / cardiff cf24 3aa / wales / uk

Dear all

After getting more answers to the following question, I repost an improved
summary.

"I am looking for distributed or parallel algorithms for Machine Learning.
I am specially interested on surveys (apart from that by Peter Stone), and
on parallel / distributed versions of stardard algorithms (from decision
trees, bayesian approaches or rulte learners to neural networks, SVMs, etc)."

Thank you to all who replied to my messages, in particular: Hillol
Kargupta, Alex Freitas, Rui Camacho, Balazs Kegl, Claudia Antunes, Jiri
Ocenasek, Jose Carlos Cortizo, Nitesh Chawla and Hillol Kargupta. They have
suggested me the following references:

BOOKS ----------------------------------------------------- *** ADDITIONS

Yike Guo and Robert Grossman, editors, High Performance Data Mining:
Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers, 1999.

A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel
Processing. Kluwer, 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/book-springer-ukc.html

Mohammed J. Zaki, Ching-Tien Ho (Eds). Large-Scale Parallel Data Mining.
Springer-Verlag GmbH, Lecture Notes in Computer Science, 1759 / 2000.

Advances in Distributed and Parallel Knowledge Discovery. Edited by Hillol
Kargupta and Philip Chan
MIT/AAAI Press
http://www.aaai.org/Press/Books/Kargupta1/kargupta1.html

Data Mining: Next Generation Challenges and Future Directions. Edited by H.
Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha
MIT/AAAI Press
http://www.cs.umbc.edu/~hillol/Kargupta/ngdmbook.html
( *This book has several chapters on DDM* )


PAPERS ---------------------------------------------------- *** ADDITIONS

A.A. Freitas. A Survey of Parallel Data Mining. Proc. 2nd Int. Conf. on the
Practical Applications of Knowledge Discovery and Data Mining, 287-300.
London: The Practical Application Company, Mar. 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html

Nuno Fonseca, Fernando Silva, Rui Camacho. Strategies to parallelize ILP
systems. In Proceedings of ILC 2005
http://ilp2005.in.tum.de/accepted-papers.html
* Thank you for sending me the draft

S. Gambs, B. Kégl, and E. Aïmeur. "Privacy-preserving boosting" Data Mining
and Knowledge Discovery, 2005 (submitted).
http://www.iro.umontreal.ca/~kegl/research/publications/
* This is for adaboost, and the authors suggest looking at the references:
[Lazarevic and Obradovic, 2002] and [Fan et al., 1999] for adaboost,
[Lindell and Pinkas, 2002] for trees.

Mohammed J. Zaki, "Parallel and Distributed Association Mining: A Survey",
IEEE Concurrency, special issue on Parallel Mechanisms for Data Mining,
Vol. 7, No. 4, pp14-25, December, 1999
* Good intro in terms of pattern mining / association rules

Ocenasek, J., Schwarz, J., Pelikan, M.: Design of Multithreaded Estimation
of Distribution Algorithms. In: Cantú-Paz et al. (Eds.): Genetic and
Evolutionary Computation Conference - GECCO 2003. Springer Verlag: Berlin,
2003, pp. 1247-1258.
* More papers, thesis and code by Jiri Ocenasek (http://jiri.ocenasek.com/).

"Learning Ensembles from Bites: A Scalable and Accurate Approach," Nitesh
V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Journal
of Machine Learning Research (JMLR), 5(Apr):421--451, 2004.

“Distributed Learning with Bagging like Performance,”  Chawla, N.V., Moore,
T.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P., Springer C. Pattern
Recognition Letters, 24 (1-3) (2003), 455 -- 471.


BIBLIOGRAPHIES -----------------------------------------

Online distributed data mining bibliography:
http://www.cs.umbc.edu/~hillol/DDMBIB/

Again, thank you all. I will imr`pove the summary if I get more replies.

Best regards


Jose Maria Gomez Hidalgo
Departamento de Sistemas Informáticos
Universidad Europea de Madrid
28670 - Villaviciosa de Odon - MADRID
(+34) 912115670
jmgomez@...
http://www.esp.uem.es/~jmgomez/
http://www.esp.uem.es

La legislación española ampara el secreto de las comunicaciones. Este
correo electrónico es estrictamente confidencial y va dirigido
exclusivamente a su destinatario/a. Si no es Ud., le rogamos que no difunda
ni copie la transmisión y nos lo notifique cuanto antes.

Spanish law guarantees privacy in electronic communications. This
electronic transmission is strictly confidential and intended solely for
the addressee. If you are not the intended addressee, you are kindly
requested not to disclose nor to copy this transmission and to notify us as
soon as possible.

<<< We apologize for multiple postings >>>


--------------------------------------------------------------------------
--------------------------------------------------------------------------
         SWARM INTELLIGENCE and DATA MINING
--------------------------------------------------------------------------
       http://alfa.ist.utl.pt/~cvrm/staff/vramos/SIDM.html

     Call for BOOK chapters at SPRINGER SCI Series
--------------------------------------------------------------------------

Swarm Intelligence (SI) indicates a recent computational and
behavioral metaphor for solving distributed problems that originally
took its inspiration from the biological examples provided by social
insects (ants,termites, bees, wasps) and by swarming, flocking,
herding behaviors in vertebrates.
We seek to explore the applicability of these bio-inspired approaches
to the development of self-organizing, evolving, adaptive and
autonomous information technologies, which will meet the requirements
of next-generation information systems, such as diversity,
scalability, robustness, and resilience. This edited volume is
targeted to present the latest state-of-the-art methodologies in data
mining using SI techniques. Both theoretical papers (preferably
including simulations) and application papers related to different
data mining methodologies are welcome.

Topic includes but is not limited to:

- New SI techniques for clustering, data analyzing, Classification,
Sorting, Data Retrieval
- Particle Swarm / Cultural Algorithms.
- Complex Adaptive Systems
- Artificial Life as well as other Animal Societies bio-inspired algorithms.
- Flocks, Herds and Schools
- Swarms and Cooperative Robotics.
- Distributed algorithms, self-regulation, self-repair and
self-maintenance ontologies.
- Biomedical, multimedia and e-commerce applications.
- Hybridization with other methods (e.g. Evolutionary Computation and
Neural Networks)

The book is intended to be published in the Springer Verlag series
'Studies in Computational Intelligence'
http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-40521-69-49211831-0,00\
.html

Please prepare the manuscript using TeX format and following the
author guidelines given in the above link. For initial reviews, please
submit the PDF and source files of the manuscript to
<ajith.abraham@...> with a copy to
<vitorino.ramos@...> and <cgrosan@...>.
Please restrict the technical contents of the manuscript not
to exceed 40 pages.

The time schedule for this publication is as follows:

Author's intentions to contribute a book chapter: July 31, 2005
Manuscript submission: August 31, 2005
Authors Notification:  October 03, 2005
Camera-ready submission: November 01, 2006
Publication: March 2006

We look forward to receiving your contribution. If you need any
further information, please feel free to discuss with one of the
editors.

Editors:

Vitorino Ramos
CVRM-IST, Technical University of Lisbon, Portugal
http://alfa.ist.utl.pt/~cvrm/staff/vramos

Ajith Abraham
Chung-Ang Univerity, Seoul, Korea
http://falklands.globat.com/~softcomputing.net/

Crina Grosan
Babes-Bolyai University, Cluj-Napoca, Romania
http://www.cs.ubbcluj.ro/~cgrosan/
--------------------------------------------------------------------------------\
-----------


~ v. ramos, http://alfa.ist.utl.pt/~cvrm/staff/vramos, "Interactions among
many sporuliferous and ubiquitous abstractions may lead to increasing
reality", V. Ramos, 2001.

about me


[Non-text portions of this message have been removed]

I will be out of the office starting  08/23/2005 and will not return until
10/10/2005.

I will be on an extended business trip and vacation from Aug. 23 through
Oct. 10. Will be attending conferences during the first and last weeks and
will be on vacation in Europe from Aug. 29 to Sept. 30.

I'll have my PC with me, checking mail several times per week, and I'll be
working part-time while on vacation to keep important projects moving
forward. My secretary, Marguerite Lambert (695-4479) has my complete travel
itinerary. In an emergency, you can contact me by cell phone 302-562-5793.


This communication is for use by the intended recipient and contains information
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<< PLEASE ACCEPT OUR APOLOGIES IF YOU RECEIVE MULTIPLE COPIES >>


SWARM INTELLIGENCE AND PATTERNS 2006 (SIP´06)

http://alfa.ist.utl.pt/~cvrm/staff/vramos/SIP06.html

3rd Int. Workshop Session at ISDA 2006 - 6th IEEE International Conference
on Intelligent
System Design and Applications, Jinan, Shandong, CHINA, October 16-18, 2006


Session Chairs:

Vitorino Ramos (CVRM-IST, Technical Univ. of Lisbon, Lisbon, PORTUGAL) and
Ajith Abraham  (Chung Ang Univ., Seoul, South Korea).

SCOPE AND CALL FOR PAPERS:

   Self-organizing intelligent complex systems typically are comprised of a
large number of frequently similar components or events. Through their
process, a pattern at the global-level of a system emerges solely from
numerous interactions among the lower-level components of the system.
Moreover, the rules specifying interactions among the system’s components
are executed using only local information, without reference to the global
pattern, which, as in many real-world problems is not easily accessible or
possible to be found. Stigmergy, a kind of indirect communication and
learning by the environment found in social insects is a well know example
of self-organization, providing not only vital clues in order to understand
how the components can interact to produce a complex pattern and engineer
applications, as can pinpoint simple biological non-linear rules and means
to achieve an improved design of artificial intelligent systems.
   Swarm Intelligence is precisely a relatively novel discipline devoted to
the study of self-organizing collective processes in Nature and Human
artefacts as well as on their applications. An example of particularly
successful research direction in swarm intelligence is ant colony
optimization (ACO), which focuses on discrete optimization problems, and
has been applied successfully to a large number of hard discrete
optimization problems including the travelling salesman, the quadratic
assignment, scheduling, vehicle routing, etc., as well as to routing in
telecommunication networks. However, apart from the remarkable successful
applications in optimization as well as on their critical features as a
bio-inspired computational paradigm, a small number of works have still
been devoted to Data Classification and Retrieval Systems, Clustering,
Pattern Recognition, Distributed Data-Mining, Web Mining and GRIDS,
Collaborative Filtering, Image Analysis and Signal Processing, Pattern
Formation, Perception, Memory and Generalization.
   At the present section we seek to explore the applicability of these
bio-inspired approaches to the development of self-organizing, evolving,
adaptive and autonomous information technologies, which will meet the
requirements of next-generation information systems, such as diversity,
scalability, robustness, and resilience.

   SIP 2006 constitutes the 3rd edition of this International Workshop
series. The first ones were held within ISDA'04, Budapest, Hungary, and
WSTST '05, Muroran, Japan.

TOPICS OF INTEREST include, but are not limited to, applications and theory
dealing with any aspect of Swarm Intelligence, and Pattern Recognition,
Data and Image Processing, Artificial Habitats and New Media as:

- Intelligent Systems Design.
- Advanced Signal and Image processing algorithms.
- Pattern Recognition and Emergent Behaviour.
- Data Categorization, Visualization. Data and Knowledge Extraction /
Representation.
- Feature Extraction and Selection. Unsupervised Learning.
- Information Systems and Knowledge Management.
- Collective Intelligence, Behaviour and Search. Exploring versus Exploiting.
- Artificial Habitats and Information.
- Exploratory Data Analysis. Data-Mining.
- Cognition, Interactivity, Signals and Communication.
- Bottom-up Strategies and Non-Hierarchical Systems.
- Adpative Systems and Self-Configuration.
- Mapping Concepts, Cognitive Maps and Self-Organizing Maps.
- Particle Swarm / Cultural Algorithms.
- Complex Adaptive Systems.
- Stigmergy, Self-Organization, Metamorphosis, Emergence and Co-Evolution.
- Artificial Life as well as other Animal Societies bio-inspired algorithms.
- Flocks, Herds and Schools.
- Artificial Societies and Web-based Communities.
- Wireless Communication, Cellular Systems, Indirect Communication through
artefacts.
- Social Networks and New Media.
- Artificial Immune Systems and Self-Organization.
- Classification, Sorting, Data Retrieval, Clustering.
- Web Mining, Semantic Web, Collaborative Mining, GRIDS, Network security.
- Auto-Catalysis, Positive and Negative Feedbacks, Cybernetics.
- Swarms and Cooperative Robotics.
- Distributed algorithms, self-regulation, self-repair and self-maintenance
ontologies.
- Biomedical, multimedia and e-commerce applications.
- Collective on-line Games. iDesign, Active aLif(v)e Art and e-Artefacts.
- Generative and Computational Art.
- Hybridization with other methods (e.g. Evolutionary Computation and
Neural Networks).

PAPER SUBMISSION:

All accepted papers should follow the IEEE CS style format. Authors can use
the style IEEE_CS_Latex.zip and the ISDA2006_word_format.doc included in
8.5x11-Formatting files.zip  file together with macros and author
guidelines. Prospective authors are invited to submit full paper of 6 pages
(Letter or A4 format) by the submission deadline.
You can find additional information using
ftp://pubftp.computer.org/Press/Outgoing/proceedings/.
Please send the full paper (PDF) as an email attachment to Vitorino Ramos
with a cc to Ajith Abraham no longer than May 15, 2006.
Accepted papers will be published by IEEE Computer Science.


IMPORTANT DATES:

Paper submission due (full paper) / Deadline:May 15, 2006.
Notification of acceptance: June 15, 2006.
Deadline for camera ready papers and authors' registration: July 10, 2006.
Conference: Jinan, Shandong, CHINA, October 16-18, 2006.


CONTACTS:

Vitorino Ramos: vitorino.ramos@...
Ajith Abraham: ajith.abraham@...

~ v. ramos, http://alfa.ist.utl.pt/~cvrm/staff/vramos, "Interactions among
many sporuliferous and ubiquitous abstractions may lead to increasing
reality", V. Ramos, 2001.

We are pleased to announce the new book

“Biomimetic Neural Learning for Intelligent Robots”

Stefan Wermter, Günther Palm, Mark Elshaw (Eds) 2005, Springer


This book presents research performed as part of the EU project on
biomimetic multimodal learning in a mirror neuron-based robot
(MirrorBot) and contributions presented at the International AI-Workshop
in NeuroBotics. The overall aim of the book is to present a broad
spectrum of current research into biomimetic neural learning for
intelligent autonomous robots. There seems to be a need for a new type
of robots which is inspired by nature and so performs in a more flexible
learned manner than current robots. This new type of robots is driven by
recent new theories and experiments in neuroscience indicating that a
biological and neuroscience-oriented approach could lead to new
life-like robotic systems.


The book focuses on some of the research progress made in the MirrorBot
project which uses concepts from mirror neurons as a basis for the
integration of vision, language and action. In this book we show the
development of new techniques using cell assemblies, associative neural
networks, and Hebbian-type learning in order to associate vision,
language and motor concepts. We have developed biomimetic multimodal
learning and language instruction in a robot to investigate the task of
searching for objects. As well as the research performed in this area
for the MirrorBot project, the second part of this book incorporates
significant contributes from essential research in the field of
biomimetic robotics. This second part of the book concentrates on the
progress made in neuroscience-inspired robotic learning approaches (in
short: Neuro-Botics).

We hope that this book stimulates and encourages new research in this area.

Further details can be found at

http://www.his.sunderland.ac.uk/mirrorbot/mirrorbook.html and

http://www.springeronline.com/sgw/cda/frontpage/0,11855,3-40109-22-55007983-0,00\
.html



Chapters


Towards Biomimetic Neural Learning for Intelligent Robots
Stefan Wermter, Günther Palm, Cornelius Weber and Mark Elshaw

The Intentional Attunement Hypothesis. The Mirror Neuron System and its
Role in Interpersonal Relations
Vittorio Gallese

Sequence Detector Networks and Associative Learning of Grammatical
Categories
Andreas Knoblauch and Friedemann Pulvermüller

A Distributed Model of Spatial Visual Attention
Julien Vitay, Nicolas Rougier and Frédéric Alexandre

A Hybrid Architecture using Cross-Correlation and Recurrent Neural
Networks for Acoustic Tracking in Robots
John Murray, Harry Erwin and Stefan Wermter

Image Invariant Robot Navigation Based on Self Organising Neural Place Codes
Kaustubh Chokshi, Stefan Wermter, Christo Panchev and Kevin Burn

Detecting Sequences and Understanding Language with Neural Associative
Memories and Cell Assemblies
Heiner Markert, Andreas Knoblauch and Günther Palm

Combining Visual Attention, Object Recognition and Associative
Information Processing in a NeuroBotic System
Rebecca Fay, Ulrich Kaufmann, Andreas Knoblauch, Heiner Markert and
Günther Palm

Towards Word Semantics from Multi-modal Acoustico-Motor Integration:
Application of the Bijama Model to the Setting of Action-Dependant
Phonetic Representations
Olivier Ménard, Frédéric Alexandre and Hervé Frezza-Buet

Grounding Neural Robot Language in Action
Stefan Wermter, Cornelius Weber, Mark Elshaw, Vittorio Gallese and
Friedemann Pulvermüller

A Spiking Neural Network Model of Multi-Modal Language Processing of
Robot Instructions
Christo Panchev

A Virtual Reality Platform for Modeling Cognitive Development
Hector Jasso and Jochen Triesch

Learning to Interpret Pointing Gestures: Experiments with Four-Legged
Autonomous Robots
Verena Hafner and Frédéric Kaplan

Reinforcement Learning Using a Grid Based Function Approximator
Alexander Sung, Artur Merke and Martin Riedmiller

Spatial Representation and Navigation in a Bio-inspired Robot
Denis Sheynikhovich, Ricardo Chavarriaga, Thomas Strosslin and Wulfram
Gerstner

Representations for a Complex World: Combining Distributed and Localist
Representations for Learning and Planning
Joscha Bach

MaximumOne: an Anthropomorphic Arm with Bio-Inspired Control System
Michele Folgheraiter and Giuseppina Gini

LARP, Biped Robotics Conceived as Human Modelling
Umberto Scarfogliero, Michele Folgheraiter and Giuseppina Gini

Novelty and Habituation: The Driving Force in Early Stage Learning for
Developmental Robotics
Qinggang Meng and Mark Lee

Modular Learning Schemes for Visual Robot Control
Gilles Hermann, Patrice Wira and Jean-Philippe Urban

Neural Robot Detection in RoboCup
Gerd Mayer, Ulrich Kaufmann, Gerhard Kraetzschmar and Günther Palm

A Scale Invariant Local Image Descriptor for Visual Homing
Andrew Vardy and Franz Oppacher


***************************************
Professor Stefan Wermter
Chair for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

email: stefan.wermter **AT** sunderland.ac.uk
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

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Two Open Positions in Machine Learning
--------------------------------------

The IDIAP Research Institute seeks two qualified candidates for PhD positions
in machine learning.

The objective of the first project is to develop novel kernel based algorithms
for the analysis of sequences of high level events, such as automatic speech
recognition (ASR). State-of-the-art ASR systems are based on generative Hidden
Markov Models (HMMs). On the other hand, recent machine learning research have
shown promising results in kernel based large margin discriminant models such
as Support Vector Machines (SVMs) which maximize the margin between positive
and negative examples. More recently, new kernels were proposed for various
time-series tasks. The objective of this project is to study how these
kernels could be modified in the context of more complex temporal tasks such
as speech and video understanding.

The objective of the second project is to develop novel machine learning
algorithms for multi-channel sequence processing. Modeling jointly several
sources of information (recorded from several cameras, microphones, etc) has
several practical applications, including audio-visual speech recognition,
multimodal person tracking, or complex scene analysis. Several machine learning
models have already been proposed for such task, mainly for the case of
two channels.  The goal of this project is to propose theoretical and applied
solutions for the case of more than two (potentially asynchronous) channels.
Generative (Markovian based) models as well as margin-based models will be
considered for the task.

The ideal candidates will hold a degree in computer science, statistics, or
related fields. She or he should have strong background in statistics, linear
algebra, signal processing, C++, Perl and/or Python scripting languages, and
the Linux environment. Knowledge in statistical machine learning and speech
processing is an asset.

Appointment for a PhD position is for a maximum of 4 years, provided
successful progress, and should lead to a dissertation. Annual gross salary
ranges from 36,000 Swiss Francs (first year) to 40,000 Swiss Francs (last
year). Starting date is immediate.

IDIAP is an equal opportunity employer and is actively involved in the
European initiative involving the Advancement of Women in Science. IDIAP seeks
to maintain a principle of open competition (on the basis of merit) to appoint
the best candidate, provides equal opportunity for all candidates, and equally
encourages both females and males to consider employment with IDIAP.

Although IDIAP is located in the French part of Switzerland, English is the
main working language. Free English and French lessons are provided.

IDIAP is located in the town of Martigny in Valais, a scenic region in the
south of Switzerland, surrounded by the highest mountains of Europe, and
offering exciting recreational activities, including hiking, climbing and
skiing, as well as varied cultural activities. It is within close proximity
to Montreux (Jazz Festival) and Lausanne.

Interested candidates should send a letter of motivation, along with their
detailed CV and names of 3 references to jobs@.... More information can
also be obtained by contacting Samy Bengio.

----
Samy Bengio
Senior Researcher in Machine Learning.
IDIAP, CP 592, rue du Simplon 4, 1920 Martigny, Switzerland.
tel: +41 27 721 77 39, fax: +41 27 721 77 12.
mailto:bengio@..., http://www.idiap.ch/~bengio

Dear Colleagues:

Seven recent works hybridizing novel algorithms in the Swarm Intelligence
area with applications in Pattern Recognition, Perception, Image
Processing, Image Analysis, Mathematical Morphology, Image Classification
and retrieval are now online. Keywords subject areas are:  Swarm
Intelligence, Perception and Image Processing, Pattern Recognition,
Mathematical Morphology, Social Cognitive Maps, Social Foraging,
Self-Organization, Distributed Search, among others. For those following
research in similar areas or are somehow interest, here follows specific
links, as well as direct PDF links:

# Carlos Fernandes, Vitorino Ramos, Agostinho C. Rosa, Self-Regulated
Artificial Ant Colonies on Digital Image Habitats, accepted in WCLC-05, 2nd
World Congress on Lateral Computing, Springer-Verlag, LNCS Series,
Bangalore, India, 16-18 Dec., 2005.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_65.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-WCLC05b.pdf

# Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F.
Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images,
accepted in WCLC-05, 2nd World Congress on Lateral Computing,
Springer-Verlag, LNCS Series, Bangalore, India, 16-18 Dec., 2005.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_53.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-WCLC05a.pdf

# Vitorino Ramos, Fernando Muge, Pedro Pina, Self-Organized Data and Image
Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in
Artificial Ant Colonies, in Javier Ruiz-del-Solar, Ajith Abraham and Mario
Köppen (Eds.), Frontiers in Artificial Intelligence and Applications, Soft
Computing Systems - Design, Management and Applications, 2nd Int. Conf.
on  Hybrid Intelligent Systems, IOS Press, Vol. 87, ISBN 1 5860 32976, pp.
500-509, Santiago, Chile, Dec. 2002.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_39.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-HIS02.pdf

# Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive
Maps, Swarm Collective Perception and Distributed Search on Dynamic
Landscapes, submitted to Brains, Minds & Media, Journal of New Media in
Neural and Cognitive Science, NRW, Germany (2006).
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-BMM.pdf

# Vitorino Ramos, Self-Organizing the Abstract: Canvas as a Swarm Habitat
for Collective Memory, Perception and Cooperative Distributed Creativity,
in 1st Art & Science Symposium - Models to Know Reality, J. Rekalde, R.
Ibáñez and Á. Simó (Eds.), pp. 59, Facultad de Bellas Artes EHU/UPV,
Universidad del País Vasco, 11-12 Dec., Bilbao, Spain, 2003.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_47.html
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/aswarm.html

# Vitorino Ramos, On the Implicit and on the Artificial - Morphogenesis and
Emergent Aesthetics in Autonomous Collective Systems, in ARCHITOPIA Book,
Art, Architecture and Science, INSTITUT D'ART CONTEMPORAIN, J.L. Maubant et
al. (Eds.), pp. 25-57, Chapter 2, ISBN 2905985631 - EAN 9782905985637,
France, Feb. 2002.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_37.html
PDF direct link:
http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ARCHITOPIA.pdf

# Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image
Habitats - A Mass Behaviour Effect Study on Pattern Recognition,
Proceedings of ANTS´2000 - 2nd International Workshop on Ant Algorithms
(From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf &
Thomas Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.
Link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_29.html
PDF direct link: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-ANTS00.pdf

Dear All,

below please find the advertisement for the tenured faculty position

                  Modeling of Cognitive Processes

within the Department of Computer Science and Electrical Engineering
of the Berlin University of Technology and the recently established
Bernstein Center for Computational Neuroscience Berlin.

The Berlin Bernstein Center, which is funded by the German federal
government, integrates interdisciplinary research initiatives in the
brain sciences and in AI across the city's three major universities
and across several research institutes in Berlin, covering
neuroscience, medicine, physics, mathematics, computer science
and engineering. The Center plans to launch an international
Master/PhD program in Computational Neuroscience by fall 2006.

The Department of Computer Science and Engineering is currently
creating a focus area in AI and machine learning with at least
four core faculty positions (Artificial Intelligence, Machine Learning,
Modelling of Cognitive Processes, Neural Information Processing) and
several other labs with AI related research. The Department will
introduce "intelligent systems" as an area of specialization in
its new Master program in Computer Science by fall 2006.

More information about the Center and TU Berlin can be found via
http://www.tu-berlin.de/eng/index.html and http://www.bccn-berlin.de/,
but I am also happy to answer any questions related to the position
and our Berlin research environment.

Note, that proficiency in German is *not* a requirement, as all the
relevant courses - at least during the first years - will be taught
in English.

All the best

Klaus

-------------------------------------------------------------------------

The Department of Electrical Engineering and Computer Science at the
Technische Universität Berlin invites applications for a tenured faculty
position

 		  Modeling of Cognitive Processes (W2)

The position is associated with the recently established Bernstein Center
for Computational Neuroscience Berlin (http://www.bccn-berlin.de).

The Professorship is devoted to the development of quantitative models of
higher brain functions (as inferred, for example, from non-invasive methods
like EEG or fMRI) in order to better understand the neural basis of cognitive
processes. Modeling work should be complemented by application oriented
research in machine intelligence and artificial cognitive systems (e.g.
autonomous intelligent agents, man-machine systems, etc.). The successful
candidate is expected to establish a cooperative, innovative research program
and have a strong committment to excellence in undergraduate and graduate
teaching at the TU department as well as within the Bernstein Center.

The Technische Universität Berlin is an equal opportunity employer, committed
to the advancement of individuals without regard to ethnicity, religion, sex,
age, disability, or any other protected status.

Applications should include CV, summary of teaching and research experience,
list of publications and funding, statement of research interests, and up to
five selected publications. For legal details also see (BerlHG, Par. 100)
http://www.bccn-berlin.de/positions/berlhg-p-100.

Applications should be sent by Nov. 21st, 2005, to the Dekanat, Fakultät IV,
TU Berlin, Franklinstrasse 28/29, 10587 Berlin, Germany and per email to Prof.
Dr. Klaus Obermayer (oby@...) to speed up the search process.

=============================================================================

Prof. Dr. Klaus Obermayer         phone:  49-30-314-73442
FR2-1, NI, Fakultaet IV                   49-30-314-73120
Technische Universitaet Berlin    fax:    49-30-314-73121
Franklinstrasse 28/29             e-mail: oby@...
10587 Berlin, Germany             http://ni.cs.tu-berlin.de/


[Non-text portions of this message have been removed]

Societal Implicit Memory and his Speed on Tracking Extrema in Dynamic
Environments using Self-Regulatory Swarms,
final draft submitted to Journal of Systems Architecture, Special issue on
Nature Inspired Applied Systems, Elsevier, 2006.
Authors: Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa.

http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-NIAS06.pdf

Abstract: In order to overcome difficult dynamic optimization and
environment extrema tracking problems, we propose a Self-Regulated Swarm
(SRS) algorithm which hybridizes the advantageous characteristics of Swarm
Intelligence as the emergence of a societal environmental memory or
cognitive map via collective pheromone laying in the landscape (properly
balancing the exploration/exploitation nature of the search strategy), with
a simple Evolutionary mechanism that through a direct reproduction
procedure linked to local environmental features is able to self-regulate
the above exploratory swarm population, speeding it up globally. In order
to test his adaptive response and robustness, we have recurred to different
dynamic multimodal complex functions as well as to Dynamic Optimization
Control (DOC) problems. Measures were made for different dynamic settings
and parameters such as, environmental upgrade frequencies, landscape
changing speed severity, type of dynamic (linear or circular), and to
dramatic changes on the algorithmic search purpose over each test
environment (e.g. shifting the extrema). Finally, comparisons were made
with traditional Genetic Algorithms (GA), Bacterial foraging algorithms
(BFOA), as well as with more recent Co-Evolutionary approaches. SRS, were
able to demonstrate quick adaptive responses, while outperforming the
results obtained by the other approaches. Additionally, some successful
behaviors were found: SRS was able not only to achieve quick adaptive
responses, as to maintaining a number of different solutions, while
adapting to new unforeseen extrema; the possibility to spontaneously create
and maintain different subpopulations on different peaks, emerging
different exploratory corridors with intelligent path planning
capabilities; the ability to request for new agents over dramatic changing
periods, and economizing those foraging resources over periods of
stabilization. Finally, results prove that the present SRS collective swarm
of bio-inspired agents is able to track about 65% of moving peaks traveling
up to ten times faster than the velocity of a single ant composing that
precise swarm tracking system. This emerged behavior is probably one of the
most interesting ones achieved by the present work.

~ v. ramos [http://alfa.ist.utl.pt/~cvrm/staff/vramos/]

The 3rd IEEE International Conference on Autonomic
Computing (ICAC-06)
Call for Papers
University College Dublin, Dublin, Ireland
13 - 16 June, 2006
http://www.autonomic-conference.org

	 **********************************************************
		 SUBMISSION DEADLINE EXTENDED TO
			 JANUARY 29, 2006
	 **********************************************************

To deal with the increasing complexity of large-scale computer
systems,
computers must learn to manage themselves, in accordance with
high-level guidance from humans -- a vision that has been referred
to as
autonomic computing. Meeting the grand challenges of autonomic
computing requires scientific and technological advances in a wide
variety of fields, as well as new software and system architectures
that support the effective integration of the constituent
technologies.

The purpose of the 3rd International Conference on Autonomic
Computing
is to bring together researchers and practitioners addressing aspects
of self-management in computing systems. In doing so, we hope to
develop and nurture a community that can work together to realize the
vision of large-scale self-managing systems. Papers are solicited on
a
broad array of topics of relevance to autonomic computing;
particularly
those that bear on connections and relationships among different
areas
of research or report on prototype systems or experiences. Topics of
interest include, but are not limited to:

- Autonomic computing systems or prototype systems that exhibit
self-monitoring, self- configuration, self-optimization, self-
healing,
and/or self-protection.

- Software architectures for self-managing systems, based on
interoperable Grid Services, agent- based systems, Web Services, or
novel paradigms such as biological, economic or social.

- Specific self-managing components, such as server, client,
database,
storage, or network elements. Emphasis should be placed on
interactions
with other components, or techniques or lessons that may generalize
to
other components.

- Toolkits, environments, models, languages, runtime and compiler
technologies for building self-managing components, systems or
applications.

- New technologies supporting system management, such as those based
on
service-level agreements, negotiation or conversation support, and
behavior enforcement.

- System-level technologies, middleware or services that entail
interactions among two or more components of self-managing systems
(e.g., health monitoring, dependency analysis, problem localization
or
remediation, workload management, and provisioning).

- Interfaces to autonomic systems, including user interfaces,
interfaces for monitoring and controlling behavior, techniques for
defining, distributing, and understanding policies.

- Fundamental science of self-managing systems: understanding,
controlling, or exploiting emergent behavior, theoretical
investigations of coupled feedback loops, predictive methods,
robustness, and related topics.

- Experiences with autonomic system or component prototypes:
measurements, evaluations, or analyses of system behavior, user
studies, or experiences with large-scale deployments of self-
managing
systems or applications.


PAPER AND POSTER SUBMISSIONS
============================

Full papers (a maximum of 10 pages in length) and posters (2 pages)
are
invited on a wide variety of topics relating to autonomic computing
as
indicated above. All manuscripts will be reviewed and judged on
merits
including correctness, originality, technical strength, quality of
presentation, and relevance to the conference themes. Submitted
papers
must include original work, and may not be under consideration for
another conference or a journal. They should also not be under review
or be submitted to another forum during the ICAC-06 review process.
Posters are not subject to any of these restrictions. Authors should
submit full papers or posters electronically (PDF or postscript) via
the ICAC-06 conference web site at http://www.autonomic-
conference.org,
and should follow IEEE CS format - style files can be found at
ftp://pubftp.computer.org/Press/Outgoing/proceedings/.

DEMO/EXHIBIT SESSION

ICAC 2006 will feature a demo and exhibit session consisting of
prototypes and technology artifacts such as demonstrating autonomic
software or autonomic computing principles. A separate call for
demonstrations and exhibits will be issued. Entries will be judged
by a
separate subcommittee led by the demo/exhibit chair.  Please see the
conference web site for more information.

STUDENT AWARDS

A student best paper award will be presented. It will consist of a
plaque, complementary student registration to the conference and an
honorarium that will partially cover travel & hotel costs. A student
paper is defined as one in which the principal (not sole) author is a
student. The student will be required attend the conference to
present
the paper and receive the award.

PUBLICATION

Accepted papers and posters will appear in proceedings published by
IEEE Computer Society Press, which will be distributed at the
conference.

IMPORTANT DATES
Full paper submissions: 10:00 PM PST, Jan 29, 2006
		 ***** EXTENDED from Jan 22 ******
Author notification: February 27, 2006
Demo/Exhibit submission: March 03, 2006
Tutorial/Workshop submissions: March 03, 2006
Final manuscripts due: April 03, 2006
Conference: June 13-16, 2006

INFORMATION
WWW: www.autonomic-conference.org

GENERAL CO-CHAIRS
Jeffrey Kephart, IBM Research, USA
Manish Parashar, Rutgers Univ., USA

STEERING COMMITTEE
Salim Hariri, Univ. of Arizona, USA (Chair)
Jeffrey Kephart, IBM, USA
Karsten Schwan, Georgia Tech, USA
Manish Parashar, Rutgers Univ., USA
Rajarshi Das, IBM, USA
Yi-Min Wang, Microsoft Research, USA

PROGRAM CO-CHAIRS
Mazin Yousif, Intel Corporation, USA
Omer F. Rana, Cardiff University, UK

PROGRAM COMMITTEE
Albert Zomaya, Univ. of Sydney, Australia
Alexander Wolf, Univ. of Colorado, USA
Alva Couch, Tufts Univ., USA
Boualem Benatallah, Univ. of New South Wales, Australia
Bruce Childers, Univ. of Pittsburgh, USA
Bruno Schulze, LNCC, Brazil
Craig Lee, Aerospace Corporation, USA
D. K. Arvind, Univ. of Edinburgh, UK
Daniel Menasce, George Mason Univ., USA
Dave Chess, IBM Research, USA
David Kaminsky, IBM Corporation, USA
Dongyan Xu, Purdue Univ., USA
Duncan Johnston-Watt, Enigmatec, UK
Emmett Witchel, Univ. of Texas, Austin, USA
Emre Kiciman, Microsoft Research, USA
Fabian E. Bustamante, Northwestern Univ., USA
Frances Brazier, Vrije Univ., the Netherlands
Franco Zambonelli, Univ. Modena & Regio Emilia, Italy
Gail Kaiser, Columbia Univ., USA
Guofei Jiang, NEC Laboratories, USA
Giovanna Di Marzo, Univ. of Geneva, Switzerland
Ian Marshall, University of Kent, UK
Jeff Bradshaw, Institute Human & Machine Cognition, USA
Jeff Chase, Duke Univ., USA
Joerg Mueller, Technische Universität Clausthal, Germany
John Vicente, Intel Corporation, USA
Jose Fortes, Univ. of Florida, USA
Julian Padget, Bath University, UK
Julie McCann, Imperial College, UK
Ken Birman, Cornell Univ., USA
Laurence T. Yang, St. Francis Xavier Univ., Canada
Leonard Barolli, Fukuoka Institute of Technology, Japan
Martin Purvis, Univ. of Otago, New Zealand
Naveen Sharma, Xerox Lab, USA
Ozalp Baboglu, Univ. of Bologna, Italy
Paul Maglio, IBM Research, USA
Peter Steenkiste, Carnegie Mellon Univ., USA
Rami Melhem, Univ. Of Pittsburgh, USA
Roy Sterritt, Univ. of Ulster, UK
Santosh Shrivastava, Univ. of Newcastle, UK
Sven Graupner, HP Labs, USA
Tarek Abdelzaher, Univ. of Virginia, USA
Torsten Eymann, Univ. of Freiburg, Germany

DEMO/EXHIBIT CHAIR
Dean Yao, Intel Corporation, USA

TUTORIAL/WORKSHOP CHAIR
Milan Milenkovic, Intel Corporation, USA

PUBLICIY CO-CHAIRS
Monique Calisti, Whitestein Technologies, Switzerland
Peinan Zhang, Intel Corporation, China
Tisson Mathew, Intel Corporation, USA

LOCAL ARRANGEMENTS CO-CHAIRS
Fabrice Saffre, British Telecom, UK
Simon Dobson, University College, Dublin, Ireland

FINANCE CHAIR & Industry Liaison
Patricia Rago, IBM Corporation, USA

Stipends available for  MSc Intelligent Systems for EU students
---------------------------------------------------------------

We are pleased to announce that for eligible selected EU
students we have just obtained notice of funding  to offer
places with free fees and a bursary for our MSc Intelligent
Systems. This stipend of free fees and bursary was about 8000 EURO
last term. This scheme applies to our Feb/March entry 2006
and also our entry in October 2006 for selected EU students.

***Please forward to  students who may be interested.***

The School of Computing and Technology, University of Sunderland
is delighted to announce the launch of its  MSc Intelligent Systems
programme for 2006. Building on the School's leading edge
research in intelligent systems this masters programme will be
funded partially via the ESF scheme.

Intelligent Systems is an exciting field of study for science and
industry  since the currently existing computing systems have
often not yet  reached the various aspects of  human performance.
"Intelligent Systems" is a term to describe software systems and
methods, which simulate aspects of intelligent behaviour. The intention
is  to learn from nature and human performance in order to build more
powerful computing systems. The aim is to learn from cognitive science,
neuroscience, biology, engineering, and linguistics for building more
powerful computational system architectures. In this programme a
wide variety of novel and exciting techniques will be taught including
neural networks, intelligent robotics, machine learning, natural
language processing,  vision, evolutionary genetic computing, data
mining, fuzzy methods, and  hybrid intelligent architectures.

The Bursary Scheme applies to this Masters programme commencing
February/March 2006 and October 2006 and we have obtained funding
through the European Social Fund (ESF). ESF  support enables the
University to waive the normal tuition fee and provide  a bursary
of £ 50 per week for 45 weeks for eligible selected EU students,
together up to about 5500 pounds or about 8000 Euro. We also
have support for fee-only stipends and further support under the
women into science programme for UK and EU students.

For further information in the first instance please see:

http://www.his.sunderland.ac.uk/Teaching_frame.html

http://osiris.sund.ac.uk/webedit/allweb/courses/progmode.php?prog=G550A&mode=FT&\
mode2=&dmode=C

http://www.his.sunderland.ac.uk/teaching/sund_is_app.pdf

For information and applications contact:
alfredo.moscardini@...

Please forward to interested students.

Stefan

***************************************
Stefan Wermter
Professor for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 3553
email: stefan.wermter at sunderland.ac.uk
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

Dear worldwide ML colleagues:
I have recently moved to another Lab. (LaSEEB - Evolutionary Systems
and Biomedical Engineering Lab., IST), and a new webpage is now available.
My research interests involve Artificial Life, Bio-Inspired
Computation and Complex Systems. Many of my works on these areas as
well as broad applications in Data Mining, Image Processing, Pattern
Recognition, Learning, Classification, Optimization, etc, are now
available through PDF files.
Some examples follow bellow. Hope they could of interest to some ML
list members.
Kind regards, Vitorino

[] Less is More - Genetic Optimisation of Nearest Neighbour
Classifiers, http://www.laseeb.org/vramos/ref13.html .
[] From Feature Extraction to Classification: A multidisciplinary
Approach applied to Portuguese Granites,
http://www.laseeb.org/vramos/ref21.html .
[] Image Colour Segmentation by Genetic Algorithms,
http://www.laseeb.org/vramos/ref26.html .
[] Artificial Ant Colonies in Digital Image Habitats - A Mass
Behaviour Effect Study on Pattern Recognition,
http://www.laseeb.org/vramos/ref29.html .
[] Map Segmentation by Colour Cube Genetic K-Mean Clustering,
http://www.laseeb.org/vramos/ref31.html .
[] The Biological Concept of Neoteny in Evolutionary Colour Image
Segmentation -
Simple Experiments in Simple Non-Memetic Genetic Algorithms,
http://www.laseeb.org/vramos/ref35.html .
[] Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic
Synergistic Relationships in Artificial Ant Colonies,
http://www.laseeb.org/vramos/ref39.html .
[] Self-Organized Stigmergic Document Maps: Environment as a
Mechanism for Context Learning, http://www.laseeb.org/vramos/ref42.html .
[] Swarms on Continuous Data, http://www.laseeb.org/vramos/ref45.html .
[] Web Usage Mining Using Artificial Ant Colony Clustering and
Genetic Programming, http://www.laseeb.org/vramos/ref48.html .
[] Evolving a Stigmergic Self-Organized Data-Mining,
http://www.laseeb.org/vramos/ref51.html .
[] Swarming around Shellfish Larvae Images,
http://www.laseeb.org/vramos/ref53.html .
[] ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion
Detection Systems, http://www.laseeb.org/vramos/ref54.html .
[] Varying the Population Size of Artificial Foraging Swarms on Time
Varying Landscapes, http://www.laseeb.org/vramos/ref56.html .
[] Self-Regulated Artificial Ant Colonies on Digital Image Habitats,
http://www.laseeb.org/vramos/ref59.html .
[] On Self-Regulated Swarms, Societal Memory, Speed and Dynamics,
http://www.laseeb.org/vramos/ref61.html .
[] Social Cognitive Maps, Swarm Collective Perception and Distributed
Search on Dynamic Landscapes, http://www.laseeb.org/vramos/ref63.html .
[] Societal Implicit Memory and his Speed on Tracking Extrema over
Dynamic Environments
using Self-Regulatory Swarms, http://www.laseeb.org/vramos/ref64.html .
[] Stigmergic Optimization in Dynamic Binary Landscapes,
http://www.laseeb.org/vramos/ref65.html .
[] Binary Ant Algorithm, http://www.laseeb.org/vramos/ref69.html .
[] Computational Chemotaxis in Ants and Bacteria over Dynamic
Environments, http://www.laseeb.org/vramos/ref70.html .



~ v.ramos, LaSEEB, IST, http://www.laseeb.org/vramos/pub.html

[...] Interactions among many sporuliferous and ubiquitous abstractions
may lead to increasing reality [...] Vitorino Ramos, 2001.

Machine Learning in Space: Extending Our Reach
Special Issue of the Machine Learning Journal

Amy McGovern and Kiri L. Wagstaff, guest editors

URL:  http://www.wkiri.com/ml4space
Submission deadline:  July 1, 2007

Remote space environments simultaneously present significant challenges to the
machine learning community and enormous opportunities for advancement.
Enhancing spacecraft autonomy with machine learning has the potential to permit
new discoveries that pre-scripted activities would preclude. On-board machine
learning could enable intelligent filtering or prioritizing of data as it is
collected to make the best use of the available bandwidth.  Rovers with
learning capabilities could more thoroughly and more quickly explore new
environments, relating them to previously observed areas and highlighting novel
or unexpected observations.  While some initial tests have been made in this
direction, the increasing computational power now available on spacecraft has
broadened the field of what could feasibly be done on-board.  Ultimately,
machine learning can help these spacecraft graduate from their current status
as "science prosthetics" into "science assistants".

The purpose of this special issue is to collect recent advances in machine
learning for remote space or planetary environments and to identify novel space
applications where machine learning could significantly increase capabilities,
robustness, and/or efficiency.

Key topics of interest include:
- How to perform machine learning in a high-risk, remote environment
- Learning with resource constraints (memory, computation, etc.)
- Multi-instrument machine learning
- Multi-mission machine learning
- Novel applications and uses of machine learning in space
- How to evaluate and validate machine learning methods prior to
    deployment on-board a spacecraft
- Methods for safe real-time learning
- Methods that trade off exploration and exploitation, given mission
    science goals and safety/reliability requirements
- Methods for reducing risk and increasing acceptance of machine
    learning in space flight missions
- A survey of space-borne machine learning accomplishments

We encourage all prospective authors to email us with a brief summary of the
paper concept for feedback, especially for survey papers or papers focused on
applications.

Submissions are expected to represent high-quality, significant contributions
in the area of machine learning algorithms and/or applications.  Authors should
follow standard formatting guidelines for Machine Learning manuscripts.

Administrative notes:
* Authors retain the copyrights to their papers. (See publication
        agreement on the MLJ website:
        http://pages.stern.nyu.edu/~fprovost/MLJ/.)
* Submissions and reviewing will be handled electronically using
    standard procedures for Machine Learning (http://mach.edmgr.com).
* Authors must register with the system before they can submit their
    manuscripts.
* Authors must select the appropriate Article Type -- Machine
    Learning in Space -- when submitting their manuscripts.
* Accepted papers will be published electronically and citable
        immediately (before the print version appears).

Schedule
        Submission Deadline:         July 1, 2007
        Send Papers to Reviewers:    July 15, 2007
        Reviews Due Back to Editors: September 1, 2007
        Decisions Announced:         September 15, 2007
        Camera-Ready Due:            October 31, 2007
        Print Publication:           Early 2008

----------------------------------------------------------------------

The deadline for this special issue is one month away.  We encourage you
to contact us now with your paper ideas, and we look forward to receiving
your full submissions.  Cheers!

------------------------------------------------------------
Machine Learning in Space: Extending Our Reach
Special Issue of the Machine Learning Journal

Amy McGovern and Kiri L. Wagstaff, guest editors

URL:  http://www.wkiri.com/ml4space
Submission deadline:  July 1, 2007

Remote space environments simultaneously present significant challenges to
the machine learning community and enormous opportunities for advancement.
Enhancing spacecraft autonomy with machine learning has the potential to
permit new discoveries that pre-scripted activities would preclude.
On-board machine learning could enable intelligent filtering or
prioritizing of data as it is collected to make the best use of the
available bandwidth.  Rovers with learning capabilities could more
thoroughly and more quickly explore new environments, relating them to
previously observed areas and highlighting novel or unexpected
observations.  While some initial tests have been made in this direction,
the increasing computational power now available on spacecraft has
broadened the field of what could feasibly be done on-board.  Ultimately,
machine learning can help these spacecraft graduate from their current
status as "science prosthetics" into "science assistants".

The purpose of this special issue is to collect recent advances in machine
learning for remote space or planetary environments and to identify novel
space applications where machine learning could significantly increase
capabilities, robustness, and/or efficiency.

Key topics of interest include:
- How to perform machine learning in a high-risk, remote environment
- Learning with resource constraints (memory, computation, etc.)
- Multi-instrument machine learning
- Multi-mission machine learning
- Novel applications and uses of machine learning in space
- How to evaluate and validate machine learning methods prior to
    deployment on-board a spacecraft
- Methods for safe real-time learning
- Methods that trade off exploration and exploitation, given mission
    science goals and safety/reliability requirements
- Methods for reducing risk and increasing acceptance of machine
    learning in space flight missions
- A survey of space-borne machine learning accomplishments

We encourage all prospective authors to email us with a brief summary of
the paper concept for feedback, especially for survey papers or papers
focused on applications.

Submissions are expected to represent high-quality, significant
contributions in the area of machine learning algorithms and/or
applications.  Authors should follow standard formatting guidelines for
Machine Learning manuscripts.

Administrative notes:
* Authors retain the copyrights to their papers. (See publication
          agreement on the MLJ website:
          http://pages.stern.nyu.edu/~fprovost/MLJ/.)
* Submissions and reviewing will be handled electronically using
    standard procedures for Machine Learning (http://mach.edmgr.com).
* Authors must register with the system before they can submit their
    manuscripts.
* Authors must select the appropriate Article Type -- Machine
    Learning in Space -- when submitting their manuscripts.
* Accepted papers will be published electronically and citable
          immediately (before the print version appears).

Schedule
          Submission Deadline:         July 1, 2007
          Send Papers to Reviewers:    July 15, 2007
          Reviews Due Back to Editors: September 1, 2007
          Decisions Announced:         September 15, 2007
          Camera-Ready Due:            October 31, 2007
          Print Publication:           Early 2008

----------------------------------------------------------------------

--
------ Kiri Wagstaff, Ph.D. ------ kiri.wagstaff@... ------
            Senior Researcher at the Jet Propulsion Laboratory
   Machine Learning and Instrument Autonomy:  http://ml.jpl.nasa.gov/
--------------------------------------------------------------------

Note: forwarded message attached.


[Non-text portions of this message have been removed]