Photo Rendering with Swarms: From Figurative to Abstract Pherogenic Imaging

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Paper presented at the IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC 2013), held as a part of the IEEE Symposium Series on Computational Intelligence (SSCI 2013), Singapore, 15-19 April 2013

Carlos M. Fernandes, Antonio M. Mora, J.J. Merelo
(University of Granada)
C. Cotta
(University of Malaga)
A.C. Rosa
(Technical University of Lisbon)
2013 IEEE Symposium Series on Computational Intelligence

}  KANTS is swarm
intelligence clustering
algorithm.
}  Uses stigmergy –
communication with and
via the environment – as
the basic rule.
}  Data samples are the
swarm. They communicate
and self-organize into
clusters of similar
samples.
2013 IEEE Symposium Series on Computational Intelligence

}  Swarm: data samples
}  Habitat: grid of cells
◦  Each cell has a vector – randomly initialized - with same
cardinality as the data samples vectors.
}  Three rules:
◦  R1: move towards regions with more similar vectors.
◦  R2: update cell vector
◦  R3: evaporation - in each time-step, every vector of the
grid is again “pulled” towards its initial value.
2013 IEEE Symposium Series on Computational Intelligence

}  Result: ants/data samples tend to cluster.
2013 IEEE Symposium Series on Computational Intelligence
Example: Iris data set (quantifies the morphologic variation of Iris
flowers of three related species)
Three classes: red: Setos, green: Versicolor, blue: Virginica
t=0 t=50 t=100 t=150

2013 IEEE Symposium Series on Computational Intelligence
Rule 2: update vectors
Rule 3: Evaporation
Rule 1: Move
Please note parameters
β and δ. They define
how the ants move.
Pi!j =
w( j)r( j)
w(k)r(k)
k"M
#

2013 IEEE Symposium Series on Computational Intelligence
The swarm after 1000 iterations with different values for β and δ
There is a region of the parameter space in which the
system self-organizes.

}  Remember Rule #2!
◦  Ants change the values of the habitat vectors.
}  Visualize the habitat grid.
◦  One variable – grey-level image
◦  Three variables – RGB or Lab.
◦  Four variables – 3-dimensional coloured image?
◦  More than four variables...
2013 IEEE Symposium Series on Computational Intelligence
data
samples
KANTS Grid
Translate
to RGB

2013 IEEE Symposium Series on Computational Intelligence
Pherographia (drawing with pheromones) is based on an algorithm
with same basic principles as KANTS. The algorithm detects the edges
of grayscale images.

2013 IEEE Symposium Series on Computational Intelligence
Carlos M. Fernandes, The Horse and the Ants, 2008
C.M. Fernandes, Pherographia: Drawing by Ants, Leonardo 43(2), pp. 107-112, April 2010

}  Sleep data is used as input of the system.
}  Hjorth parameters describe sleep
Electroencephalogram (EEG) with three-
variable vectors.
}  Translation to RGB is trivial and direct.
}  In a way, the images are representations of a
person’s sleep.
}  Each person and each person’s night sleep
generates a different image: fingerprints of
sleep.
2013 IEEE Symposium Series on Computational Intelligence

2013 IEEE Symposium Series on Computational Intelligence

2013 IEEE Symposium Series on Computational Intelligence
Data
samples=
list of RGB
vectors
KANTS
Grid
Extract the data samples (three-variable RGB vectors) directly from a
coloured image and then use these samples as KANTS output.

}  Winner of the Evolutionary, Design and Competition Art
Competition (GECCO’12)
2013 IEEE Symposium Series on Computational Intelligence
Carlos M. Fernandes, Abstracting the Abstract #4 (after Kandinsky), 2012

2013 IEEE Symposium Series on Computational Intelligence
Carlos M. Fernandes, Abstracting the Abstract #5 (after Miró), 2012

2013 IEEE Symposium Series on Computational Intelligence
t=1 t=10 t=25 t=50

2013 IEEE Symposium Series on Computational Intelligence
photo β=8 β=16 β=32

2013 IEEE Symposium Series on Computational Intelligence
r = 10 r = 25 r = 50 r = 100

}  Devise other forms of representation when
the cardinality of the vectors is >3
}  4-variables: maybe 3-dimensional
representations.
}  Many variables. How to represent them?
2013 IEEE Symposium Series on Computational Intelligence

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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI. UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities. Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes. What will you get from this session? 1. Insights into integrating generative AI. 2. Understanding how this integration enhances test automation within the UiPath platform 3. Practical demonstrations 4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath Topics covered: What is generative AI Test Automation with generative AI and Open AI. UiPath integration with generative AI Speaker: Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack. While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack. I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:

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Photo Rendering with Swarms: From Figurative to Abstract Pherogenic Imaging

1. Carlos M. Fernandes, Antonio M. Mora, J.J. Merelo (University of Granada) C. Cotta (University of Malaga) A.C. Rosa (Technical University of Lisbon) 2013 IEEE Symposium Series on Computational Intelligence

2. }  KANTS is swarm intelligence clustering algorithm. }  Uses stigmergy – communication with and via the environment – as the basic rule. }  Data samples are the swarm. They communicate and self-organize into clusters of similar samples. 2013 IEEE Symposium Series on Computational Intelligence

3. }  Swarm: data samples }  Habitat: grid of cells ◦  Each cell has a vector – randomly initialized - with same cardinality as the data samples vectors. }  Three rules: ◦  R1: move towards regions with more similar vectors. ◦  R2: update cell vector ◦  R3: evaporation - in each time-step, every vector of the grid is again “pulled” towards its initial value. 2013 IEEE Symposium Series on Computational Intelligence

4. }  Result: ants/data samples tend to cluster. 2013 IEEE Symposium Series on Computational Intelligence Example: Iris data set (quantifies the morphologic variation of Iris flowers of three related species) Three classes: red: Setos, green: Versicolor, blue: Virginica t=0 t=50 t=100 t=150

5. 2013 IEEE Symposium Series on Computational Intelligence Rule 2: update vectors Rule 3: Evaporation Rule 1: Move Please note parameters β and δ. They define how the ants move. Pi!j = w( j)r( j) w(k)r(k) k"M #

6. 2013 IEEE Symposium Series on Computational Intelligence The swarm after 1000 iterations with different values for β and δ There is a region of the parameter space in which the system self-organizes.

7. }  Remember Rule #2! ◦  Ants change the values of the habitat vectors. }  Visualize the habitat grid. ◦  One variable – grey-level image ◦  Three variables – RGB or Lab. ◦  Four variables – 3-dimensional coloured image? ◦  More than four variables... 2013 IEEE Symposium Series on Computational Intelligence data samples KANTS Grid Translate to RGB

8. 2013 IEEE Symposium Series on Computational Intelligence Pherographia (drawing with pheromones) is based on an algorithm with same basic principles as KANTS. The algorithm detects the edges of grayscale images.

9. 2013 IEEE Symposium Series on Computational Intelligence Carlos M. Fernandes, The Horse and the Ants, 2008 C.M. Fernandes, Pherographia: Drawing by Ants, Leonardo 43(2), pp. 107-112, April 2010

10. }  Sleep data is used as input of the system. }  Hjorth parameters describe sleep Electroencephalogram (EEG) with three- variable vectors. }  Translation to RGB is trivial and direct. }  In a way, the images are representations of a person’s sleep. }  Each person and each person’s night sleep generates a different image: fingerprints of sleep. 2013 IEEE Symposium Series on Computational Intelligence

11. 2013 IEEE Symposium Series on Computational Intelligence

12. 2013 IEEE Symposium Series on Computational Intelligence Data samples= list of RGB vectors KANTS Grid Extract the data samples (three-variable RGB vectors) directly from a coloured image and then use these samples as KANTS output.

13. }  Winner of the Evolutionary, Design and Competition Art Competition (GECCO’12) 2013 IEEE Symposium Series on Computational Intelligence Carlos M. Fernandes, Abstracting the Abstract #4 (after Kandinsky), 2012

14. 2013 IEEE Symposium Series on Computational Intelligence Carlos M. Fernandes, Abstracting the Abstract #5 (after Miró), 2012

15. 2013 IEEE Symposium Series on Computational Intelligence

16. 2013 IEEE Symposium Series on Computational Intelligence

17. 2013 IEEE Symposium Series on Computational Intelligence t=1 t=10 t=25 t=50

18. 2013 IEEE Symposium Series on Computational Intelligence photo β=8 β=16 β=32

19. 2013 IEEE Symposium Series on Computational Intelligence r = 10 r = 25 r = 50 r = 100

20. }  Devise other forms of representation when the cardinality of the vectors is >3 }  4-variables: maybe 3-dimensional representations. }  Many variables. How to represent them? 2013 IEEE Symposium Series on Computational Intelligence

21. ANYSELF Please, find us in Facebook and in Twitter AnyselfProject @AnyselfProject

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