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Stephen Hawking thinks computers may surpass human intelligence and take over the world. This view is based on the ideology that all aspects of human mentality will eventually be realised by a program running on a suitable compu...


Connection Science

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Al-Rifaie on BBC

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Rose wins the Loebne...

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AISB Convention 2015

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Yasemin Erden on BBC

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Mark Bishop on BBC ...

Mark Bishop, Chair of the Study of Artificial Intelligence and the Simulation of Behaviour, appeared on Newsnight to discuss the ethics of ‘killer robots’. He was approached to give his view on a report raising questions on the et...


AISB YouTube Channel

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AISB event Bulletin Item

CFP: ACM Symposium on Applied Computing SAC 2009 - extended deadline


ACM Symposium on Applied Computing
The 24nd Annual ACM Symposium on Applied Computing
Hilton Hawaiian Village Beach Resort & Spa
Waikiki Beach, Honolulu, Hawaii, USA
March 8 - 12, 2009

Data Streams Track

Aug   23, 2008:  Submission of papers ** EXTENDED **
Oct   11, 2008:  Notification of acceptance/rejection
Oct   25, 2008:  Camera-ready copies of accepted papers


The rapid development in information science and technology in general
and in growth complexity and volume of data in particular have
introduced new challenges for the research community. Many sources
produce data continuously. Examples include sensor networks, wireless
networks, radio frequency identification (RFID), customer click streams,
telephone records, multimedia data, scientific data, sets of retail
chain transactions, etc. These sources are called data streams.  A data
stream is an ordered sequence of instances that can be read only once or
a small number of times using limited computing and storage
capabilities. These sources of data are characterized by being
open-ended, flowing at high-speed, and generated by non stationary


We are looking for all possible contributions related to algorithms on
data streams. Topics include (but are not restricted) to:

Data Stream Models
Data Stream Management Systems
Data Stream Query Languages
Continuous queries and Summarization from Data Streams
Sampling Data Streams
Single-Pass Algorithms
Scalable Algorithms
Change Detection Algorithms
Clustering on Data Streams
Classification and Regression on Data Streams
Association Rules on Data Streams
Feature Selection on Data Streams
Visualization Techniques for Data Streams
Evaluation of Data Streams Models
Data Stream applications
Sensor Networks
Real-Time Applications


Papers should be submitted in PDF using the SAC 2009 conference
management system:

The author(s) name(s) and address(es) must NOT appear in the body of
the paper, and self-reference should be in the third person. This is
to facilitate blind review.  Only the title should be shown at the
first page without the author's information.

The conference proceedings will be published by ACM. Hence, all accepted
papers should be submitted in ACM 2-column camera ready format for
publication in the symposium proceedings. The maximum number of pages
allowed for the final papers is 5 pages (about 4000 words), with the
option (at additional expense) to add up to three (3) more pages. There
is a set of templates to support the required paper format for a number
of document preparation systems at

Each submitted paper will be fully refereed and undergo a blind review
process by at least three referees.


Jose Avila, University Malaga, Spain
Andre Carvalho, University S. Paulo, Brazil
Antoine Cornujols, Institut National Paris, France
Alfredo Cuzzocrea, University of Calabria, Italy
Mohamed Gaber, Monash University, Australia
Joo Gama, University Porto, Portugal
Ricard Gavald, Polytecnic Cataluna, Spain
Georges Hbrail, Telecom Paris, France
Geoff Holmes, University Waikato, New Zealand
Eamonn Keogh, University California, United States
Ralf Klinkenberg, Rapid-I GmbH, Germany
Miroslav Kubat, University Miami, United States
Mark Last, University Ben Gorion, Israel
Rosa Meo, University of Torino, Italy
Pedro Rodrigues, University Porto, Portugal
Josep Roure, Polytechnic Cataluna, Spain
Elaine Sousa, University S. Paulo, Brazil
Eduardo Spinosa, University S. Paulo, Brazil
Min Wang, IBM, United States
Sean Wang, University Vermon, United States
Jiong Yang, Case Western Reserve University, United States
Ying Yang, Australian Taxation Office, Australia
Philip S. Yu, IBM, United States