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A new Portugal – I had a quite weird dream last night. Probably due to the recent super full moon, during my sleep, I imagined that all the sea coast waters suddenly retreated around 1000 meters down or so, creating a whole new country, which in part I only thought possible. So, as a first thing this morning I decided to use Mathematical Morphology (MM) in order to segment the image of an old bathymetry map from a paper 24 years old: Dias, J.A.(1990) “A Evolução Actual do Litoral Português“, Geonovas, 11:15-29. The result is here. Using MM and partially the MM watershed algorithm, I tried to simultaneously obtain the actual Portugal (right, black) as well as the new country (in white), with the ocean sea waters retreating from 700m-1000m. To my surprise, it was much bigger than in my dream. And beautiful!
First, it will had a huge bay, probably the size in area of the actual Portugal (A). It was not there on my dream. Second, it will had several big islands (B), probably not far from the actual Azores archipelago. The now famous Nazaré canyon would still be there (C), and Nazaré would be the only Portuguese ‘old’ town still connected to the sea. The Douro river natural watercourse would probably end on a stream mouth near (D). And the actual ‘Galiza’ Finisterra would be huge and end on (E). My birthplace (Algarve) would end on (F) with several bays, On my dream I was imaging a huge plateu there, but not as long as this, ending as big as this at the bottom left of this image. Would love to know where it ends. The Tagus and Sado rivers would probably connect together as one and would create a giant mouth on (G) and Lisbon would instead be there. It echoes on my head still. As more or less on my dream!
Four different snapshots (click to enlarge) from one of my latest books, recently published in Japan: Ajith Abraham, Crina Grosan, Vitorino Ramos (Eds.), “Swarm Intelligence in Data Mining” (群知能と データマイニング), Tokyo Denki University press [TDU], Tokyo, Japan, July 2012.
Image – Reese Inman, DIVERGENCE II (2008), acrylic on panel 30 x 30 in Remix (Boston, 2008), a solo exhibition of handmade computer art works by Reese Inman, Gallery NAGA in Boston.
Apophenia is the experience of seeing meaningful patterns or connections in random or meaningless data. The term was coined in 1958 by Klaus Conrad, who defined it as the “unmotivated seeing of connections” accompanied by a “specific experience of an abnormal meaningfulness”, but it has come to represent the human tendency to seek patterns in random information in general (such as with gambling). In statistics, apophenia is known as a Type I error – the identification of false patterns in data. It may be compared with a so called false positive in other test situations. Two correlated terms are synchronicity and pareidolia (from Wikipedia):
Synchronicity: Carl Jung coined the term synchronicity for the “simultaneous occurrence of two meaningful but not causally connected events” creating a significant realm of philosophical exploration. This attempt at finding patterns within a world where coincidence does not exist possibly involves apophenia if a person’s perspective attributes their own causation to a series of events. “Synchronicity therefore means the simultaneous occurrence of a certain psychic state with one or more external events which appear as meaningful parallels to a momentary subjective state”. (C. Jung, 1960).
Pareidolia: Pareidolia is a type of apophenia involving the perception of images or sounds in random stimuli, for example, hearing a ringing phone while taking a shower. The noise produced by the running water gives a random background from which the patterned sound of a ringing phone might be “produced”. A more common human experience is perceiving faces in inanimate objects; this phenomenon is not surprising in light of how much processing the brain does in order to memorize and recall the faces of hundreds or thousands of different individuals. In one respect, the brain is a facial recognition, storage, and recall machine – and it is very good at it. A by-product of this acumen at recognizing faces is that people see faces even where there is no face: the headlights & grill of an auto-mobile can appear to be “grinning”, individuals around the world can see the “Man in the Moon”, and a drawing consisting of only three circles and a line which even children will identify as a face are everyday examples of this..
Figure (click to enlarge) – Cover from one of my books published last month (10 July 2012) “Swarm Intelligence in Data Mining” recently translated and edited in Japan (by Tokyo Denki University press [TDU]). Cover image from Amazon.co.jp (url). Title was translated into 群知能と データマイニング. Funny also, to see my own name for the first time translated into Japanese – wonder if it’s Kanji. A brief synopsis follow:
(…) Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviours observed in flocks of birds, schools of fish, or swarms of bees, and even human social behaviour, from which the idea is emerged. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behaviour of real ants, which are used to solve discrete optimization problems. Historically the notion of finding useful patterns in data has been given a variety of names including data mining, knowledge discovery, information extraction, etc. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. In order to achieve this, data mining uses computational techniques from statistics, machine learning and pattern recognition. Data mining and Swarm intelligence may seem that they do not have many properties in common. However, recent studies suggests that they can be used together for several real world data mining problems especially when other methods would be too expensive or difficult to implement. This book deals with the application of swarm intelligence methodologies in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapters giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. (…) (more)
Video – Water has Memory (from Oasis HD, Canada; link): just a liquid or much more? Many researchers are convinced that water is capable of “memory” by storing information and retrieving it. The possible applications are innumerable: limitless retention and storage capacity and the key to discovering the origins of life on our planet. Research into water is just beginning.
Water capable of processing information as well as a huge possible “container” for data media, that is something remarkable. This theory was first proposed by the late French immunologist Jacques Benveniste, in a controversial article published in 1988 in Nature, as a way of explaining how homeopathy works (link). Benveniste’s theory has continued to be championed by some and disputed by others. The video clip above, from the Oasis HD Channel, shows some fascinating recent experiments with water “memory” from the Aerospace Institute of the University of Stuttgart in Germany. The results with the different types of flowers immersed in water are particularly evocative.
This line of research also remembers me back of an old and quite interesting paper by a colleague, Chrisantha Fernando. Together with Sampsa Sojakka, both have proved that waves produced on the surface of water can be used as the medium for a Wolfgang Maass’ “Liquid State Machine” (link) that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Amazingly, Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. What follows is the abstract of their paper entitled “Pattern Recognition in a Bucket“, as well a PDF link onto it:
Figure – Typical wave patterns for the XOR task. Top-Left: [0 1] (right motor on), Top-Right: [1 0] (left motor on), Bottom-Left: [1 1] (both motors on), Bottom-Right: [0 0] (still water). Sobel filtered and thresholded images on right. (from Fig. 3. in in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003.
[...] Abstract. This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network. [...] in Chrisantha Fernando and Sampsa Sojakka, “Pattern Recognition in a Bucket“, ECAL proc., European Conference on Artificial Life, 2003. [PDF link]
[...] Swing music, also known as swing jazz or simply swing, is a form of jazz music that developed in the early 1930s and became a distinctive style by 1935 in the United States. Swing uses a strong rhythm section of double bass and drums as the anchor for a lead section of brass instruments such as trumpets and trombones, woodwinds including saxophones and clarinets, and sometimes stringed instruments such as violin and guitar, medium to fast tempos, and a “lilting” swing time rhythm. The name swing came from the phrase ‘swing feel’ where the emphasis is on the off-beat or weaker pulse in the music (unlike classic music). Swing bands usually featured soloists who would improvise on the melody over the arrangement. The danceable swing style of bandleaders such as Benny Goodman and Count Basie was the dominant form of American popular music from 1935 to 1945. The verb “to swing” is also used as a term of praise for playing that has a strong rhythmic “groove” or drive. [...] from Wikipedia (link).
nota bene – tomorrow is Jazz day, isn’t it?!
No, not a CA (Cellular Automata). Instead, this is actually the Hadamard code matrix which was embedded on the NASA Mariner 9 spacecraft, launch in 1971. Mariner 9 was a NASA space orbiter that helped in the exploration of planet Mars. It was launched toward planet Mars on May 30, 1971 from Cape Canaveral Air Force Station and reached the planet on November 13 of the same year, becoming the first spacecraft to orbit another planet — only narrowly beating Soviet Mars 2 and Mars 3, which both arrived within a month. After months of dust-storms it managed to send back the first clear pictures of the surface (for more do check on the Mariner 9 Wikipedia entry).
Hadamard codes (named after Jacques Hadamard) are algorithmic systems used for signal error detection, fault detection and correction, probably one of the first of their kind to follow the idea of an machine Artificial Intelligent self-regulation. Hadamard codes are in fact, just a special case of the more universal Reed–Muller codes (here for an intro). In particular, the first order Reed–Muller codes are equivalent to Hadamard codes. Positively, without them, it would be impossible back then for Mariner 9 to arrive Mars taking stunning images like the one below (one of the first, Human eyes ever seen). Beyond the geniality of the Hadamard code as a primeval approximation for machine self-regulation (details onto which are important for my own work), along with it’s practical utility on different fields of our current daily life, among so many other things, what strikes me, is that the Hadamard code picture above – in some instances – resembles itself one of the first photos from the red planet ever taken…
Image – Mariner 9 view of the Noctis Labyrinthus “labyrinth” at the western end of Valles Marineris on Mars. Linear graben, grooves, and crater chains dominate this region, along with a number of flat-topped mesas. The image is roughly 400 km across, centered at 6 S, 105 W, at the edge of the Tharsis bulge. North is up. (from Wikipedia)
p.s. – (disclaimer) I did play a lot over the title on this present blog post. From Hadamard, to Hada-mars, into Ada, you know, Ada Lovelace, Augusta, that terrific lovely English girl born in the 1800’s. Not my fault. In fact, they were all there onto the same space-time voyage.
… fortunately for all of us.
“The three stages of response to a new idea: 1. Ridicule 2. Outrage 3. Declaration that it’s obvious” ~ Arthur Schopenhauer.
[...] However, Cage himself never softened. The culture might have moved on, but he kept on his radical edge, continuing his revolution in a quiet way for those who cared not only to listen, but to act on and live by his words. Through the 1980’s, Cage’s influence was felt in the underground, influencing many of the more interesting cultural movements of that decade–the birth of indy rock, the renewal of Conceptual Art, and the rise of Language Poetry. Many of these artists studied Cage in the ’60s and ’70s and went on to synthesize newer aesthetic/cultural concerns with older Cageian ideals. While the 80’s played out in the media with Wall Street Yuppies and decadent consumerists grabbing the spotlight, many of us spent time on the edge of the culture, which in turn planted the seeds for the more politically charged times in which we now live. [...] The final essay here is “Poethics of a Complex Realism” by Joan Retallack and note the word realism in the title. Retallack begins her essay with an invocation of American Pragmatist John Dewey’s “Art As Experience” and launches into a long discussion of the idea of weather as it relates to the ideas of John Cage. Cage said that he wanted his music to be like the weather–unpredictable, omnidirectional, impermanent, and always changing–complex systems that parallel the conditions of our daily life. He did several works involving the weather, modeling his ideas after nature (again, a tip of the hat to American Transcendentalist Henry David Thoreau), which are described here. Retallack takes the word play of weather/whether and sets up a correspondence between the physical (realized) and the theoretical (unrealized). She then posits an ethic based on the principle of weather/whether. Imagine, she says, a culture sophisticated and open enough to be able to accept difference and otherness, a culture that rejects the oversimplified media response of black/white, yes/no, a culture that embraces complexity and contradiction–a “breathable” culture. And it is here where the book brilliantly dovetails with the multicultural attitudes sweeping the country today. Cage stands in opposition to the reductive and closed ideas that multiculturalism have come to stand for. While multiculturalism plays by the media-supplied dualistic rules, Cage seems to dump the idea of rules altogether and instead celebrates the idea of difference and unpredictability as a prerequisite to understanding and accepting the difficulties inherent in a pluralistic culture. It appeals to this reader as the path of least resistance and being based in action, seems entirely workable. The multicultural debate has made many people aware of the issues, but it stands in theory only and lacks the kind of pragmatism and functionality that could lead to real change as prescribed here. [...], in Kenneth Goldsmith, University of Buffalo, 1995, reviewing and revisiting “John Cage Composed In America“, Essays edited by Marjorie Perloff & Charles Junkerman 1994, 286 pages, paperback, The University of Chicago Press, USA.
Video – John Cage, appearing on a 1960 CBS gameshow called I’ve Got A Secret (from Ian Leslie + Alex Ross). Cage’s ‘secret’ is that he is an avant-garde composer. After being introduced by the presenter he performs a piece called Water Walk (… more).
“Work in the invisible world at least as hard as you do in the visible one” ~ Mawlana Jalaladdin Rumi
What if the “invisible” were around you, and you could not see it, … unless you worked hard, really hard. And even if you worked really hard, the only thing you could saw was his shadow. The invisible’s shadow visible. No, by all means, my post is not about religion, believe me. Instead, valid science. For instance, if I gave you 6 matchsticks, and ask you to draw 4 triangles without crossing any two matchsticks, could you do it? The answer is positive. If you really think out of the box, indeed you can.
Carl Sagan (below) starts with a famous passage from Edwin Abbott Abbott‘s “Flatland – A Romance of many dimensions” (which I do vividly recommend – book cover above). A spheric creature from the 3th dimension visits Flatland, where only 2th dimension creatures live. And while a 2-D (a square) creature keeps worrying about his own sanity, the 3rd dimension creature feels highly frustrated with the outcome from their Spielberg-like “Close Encounters of the Third Kind“. In fact, the sphere his unhappy for being considered an psychological aberration. At his own risk, and without worrying about his hypothetical unfriendly gesture from dimension to dimension, the sphere then, decides to start some ‘bizarre‘ experiences. The story goes…, but suddenly, Carl do moves on, … on what really matters:
[...] Getting into another dimension, provides an instantial benefit, a kind of X-ray vision [...] Well, (says the square), … I was on another mystical dimension, called ‘Up‘ [...] Now, if you look at the shadow, what you see is that not all lines appear equal, not all the angles are right angles [...] The 3-D object has not been perfectly represented in his projection in 2 dimensions, but that is part of the cost of loosing a dimension in the projection [...] Now, I can not show you a tesseract , because I and you are trapped in 3 dimensions, but what I can show you is the shadow into 3 dimensions [...] The 4-D hypercube, the real tesseract would have all right angles. That’s not what we see here, but that’s the penalty of projection [...]
[...] So you see. While we cannot imagine the world of four dimensions, we can certainly think about it perfectly well [...]
From the author of “Rock, Paper, Scissors – Game Theory in everyday life” dedicated to evolution of cooperation in nature (published last year – Basic Books), a new book on related areas is now fresh on the stands (released Dec. 7, 2009): “The Perfect Swarm – The Science of Complexity in everyday life“. This time Len Fischer takes us into the realm of our interlinked modern lives, where complexity rules. But complexity also has rules. Understand these, and we are better placed to make sense of the mountain of data that confronts us every day. Fischer ranges far and wide to discover what tips the science of complexity has for us. Studies of human (one good example is Gum voting) and animal behaviour, management science, statistics and network theory all enter the mix.
One of the greatest discoveries of recent times is that the complex patterns we find in life are often produced when all of the individuals in a group follow similar simple rules. Even if the final pattern is complex, rules are not. This process of “Self-Organization” reveals itself in the inanimate worlds of crystals and seashells, but as Len Fisher shows, it is also evident in living organisms, from fish to ants to human beings, being Stigmergy one among many cases of this type of Self-Organized behaviour, encompassing applications in several Engineering fields like Computer science and Artificial Intelligence, Data-Mining, Pattern Recognition, Image Analysis and Perception, Robotics, Optimization, Learning, Forecasting, etc. Since I do work on these precise areas, you may find several of my previous posts dedicated to these issues, such as Self-Organized Data and Image Retrieval systems, Stigmergic Optimization, Computer-based Adaptive Dynamic Perception, Swarm-based Data Mining, Self-regulated Swarms and Memory, Ant based Data Clustering, Generative computer-based photography and painting, Classification, Extreme Dynamic Optimization, Self-Organized Pattern Recognition, among other applications.
For instance, the coordinated movements of fish in schools, arise from the simple rule: “Follow the fish in front.” Traffic flow arises from simple rules: “Keep your distance” and “Keep to the right.” Now, in his new book, Fisher shows how we can manage our complex social lives in an ever more chaotic world. His investigation encompasses topics ranging from “swarm intelligence” (check links above) to the science of parties (a beautiful example by ICOSYSTEM inc.) and the best ways to start a fad. Finally, Fisher sheds light on the beauty and utility of complexity theory. For those willing to understand a miriad of some basic examples (Fischer gaves us 33 nice food-for-thought examples in total) and to have a well writen introduction into this thrilling new branch of science, referred by Stephen Hawking as the science for the current century (“I think complexity is the science for the 21st century”), Perfect Swarm will be indeed an excelent companion.
The Austrian composer Peter Ablinger transferred the frequency spectrum of one child’s voice to his computer controlled mechanical piano – A “speaking piano” reciting the Proclamation of the European Environmental Criminal Court at World Venice Forum 2009. It’s all in German, but what the piano says is all English, and it’s really neat to watch. All of a sudden the words of the Declaration become understandable to a European Environmental Criminal Court. Wien Modern was one out of ten cultural institutions asked for an artistic contribution to the event in Palazzo Ducale in Venice. The ambitious goal was to make this message audible with musical means, without falling back to a simple setting. [link]
[...] We hear sounds that obviously aren’t normal Music, but neither they are language, and one could say that sometimes, a bridging happens. Personally, I think you can understand individual words even without knowing the text, and the Eureka moment happens when you see the text, and suddenly, the language is there. [...]
Figure – My first Swarm Painting SP0016 (Jan. 2002). This was done attaching the following algorithm into a robotic drawing arm. In order to do it however, pheromone distribution by the overall ant colony were carefully coded into different kinds of colors and several robotic pencils (check “The MC2 Project [Machines of Collective Conscience]“, 2001, and “On the Implicit and on the Artificial“, 2002). On the same year when the computational model appeared (2000) the concept was already extended into photography (check original paper) – using the pheromone distribution as photograms (“Einstein to Map” in the original article along with works like “Kafka to Red Ants” as well as subsequent newspaper articles). Meanwhile, in 2003, I was invited to give an invited talk over these at the 1st Art & Science Symposium in Bilbao (below). Even if I was already aware of Jeffrey Ventrella outstanding work as well as Ezequiel Di Paolo, it was there where we first met physically.
 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.
Many animals can produce very complex intricate architectures that fulfil numerous functional and adaptive requirements (protection from predators, thermal regulation, substrate of social life and reproductive activities, etc). Among them, social insects are capable of generating amazingly complex functional patterns in space and time, although they have limited individual abilities and their behaviour exhibits some degree of randomness. Among all activities by social insects, nest building, cemetery organization and collective sorting, is undoubtedly the most spectacular, as it demonstrates the greatest difference between individual and collective levels. Trying to answer how insects in a colony coordinate their behaviour in order to build these highly complex architectures, scientists assumed a first hypothesis, anthropomorphism, i.e., individual insects were assumed to possess a representation of the global structure to be produced and to make decisions on the basis of that representation. Nest complexity would then result from the complexity of the insect’s behaviour. Insect societies, however, are organized in a way that departs radically from the anthropomorphic model in which there is a direct causal relationship between nest complexity and behavioural complexity. Recent works suggests that a social insect colony is a decentralized system composed of cooperative, autonomous units that are distributed in the environment, exhibit simple probabilistic stimulus-response behaviour, and have only access to local information. According to these studies at least two low-level mechanisms play a role in the building activities of social insects: Self-organization and discrete Stigmergy, being the latter a kind of indirect and environmental synergy. Based on past and present stigmergic models, and on the underlying scientific research on Artificial Ant Systems and Swarm Intelligence, while being systems capable of emerging a form of collective intelligence, perception and Artificial Life, done by Vitorino Ramos, and on further experiences in collaboration with the plastic artist Leonel Moura, we will show results facing the possibility of considering as “art”, as well, the resulting visual expression of these systems. Past experiences under the designation of “Swarm Paintings” conducted in 2001, not only confirmed the possibility of realizing an artificial art (thus non-human), as introduced into the process the questioning of creative migration, specifically from the computer monitors to the canvas via a robotic harm. In more recent self-organized based research we seek to develop and profound the initial ideas by using a swarm of autonomous robots (ARTsBOT project 2002-03), that “live” avoiding the purpose of being merely a simple perpetrator of order streams coming from an external computer, but instead, that actually co-evolve within the canvas space, acting (that is, laying ink) according to simple inner threshold stimulus response functions, reacting simultaneously to the chromatic stimulus present in the canvas environment done by the passage of their team-mates, as well as by the distributed feedback, affecting their future collective behaviour. In parallel, and in what respects to certain types of collective systems, we seek to confirm, in a physically embedded way, that the emergence of order (even as a concept) seems to be found at a lower level of complexity, based on simple and basic interchange of information, and on the local dynamic of parts, who, by self-organizing mechanisms tend to form an lived whole, innovative and adapting, allowing for emergent open-ended creative and distributed production.
Fig. – Illusion created by Prof. Akiyoshi Kitaoka (Dep. of Psychology, Ritsumeikan Univ., Kyoto, Japan). If you don’t see any illusion at all, don’t worry. That’s exactly why this optical illusion is so great. The illusion is not there, or is it?! Meanwhile over his page, Akiyoshi warns: This page contains some works of “anomalous motion illusion”, which might make sensitive observers dizzy or sick. Should you feel dizzy, you had better leave this page immediately (more).
Where’s the illusion, right? Well,… what if I just tell you that no blue at all is used over this picture! No matter how strongly you want to believe you are seeing blue and green spirals here, there is no blue color in this image. There is only green, red and orange. What you think is blue is actually green. Don’t worry, … you are not daltonic. I mean, I’m a little bit but, you could check this out through Paint Shop Pro or Photoshop, if you need an affirmation. Indeed, these are just “Vain speculation undeceived by the senses” (1670’s Scilla’s treatise) .
In fact, Relations here, between different colors (green, red and orange), are more important than each color by itself. Relations plus context are the key (more here over Generative Art, and here over Swarm Intelligence based Pattern Recognition). Through these relations, much probably using Gestalt‘s principles (the German word Gestalt could be translated into “configuration or pattern”), here Akiyoshi manages to emerge us the blue color over our perception. This does not cheat a computer of course, however could cheat our own eyes. In other areas the opposite could also be found. For instance, Humans can easily recognize a car over background trees (segment it, in just tiny lapses of a second), while this natural task could be extremely painful for computers over some cases (here is one example).
Born in Prague (inspired by 1890’s works of Christian von Ehrenfels, Austrian philosopher), then later absorbed by a great and tremendous intellectual period occurred from Germany back to Austria (Bauhaus), the Gestalt Laws of Organization have guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. I would say that most certainly some Wertheimer’s gestaltic principles were used in here: Figure and Ground, Similarity, Proximity or Contiguity, Continuity, Closure, Area, and Symmetry (check Gestalt Theory of Visual Perception). We could see this happening also in other areas, … in Music for instance:
[...] Gestalt theory first arose in 1890 as a reaction to the prevalent psychological theory of the time – atomism. Atomism examined parts of things with the idea that these parts could then be put back together to make wholes. Atomists believed the nature of things to be absolute and not dependent on context. Gestalt theorists, on the other hand, were intrigued by the way our mind perceives wholes out of incomplete elements [1, 2]. “To the Gestaltists, things are affected by where they are and by what surrounds them…so that things are better described as “more than the sum of their parts.” [1, p. 49]. Gestaltists believed that context was very important in perception. An essay by Christian von Ehrenfels discussed this belief using a musical example. Take a 12 note melody. Play it in one key, say the key of C. Now change to another key, say the key of A flat. There might not be any notes the same in the two songs, yet a person listening to it knows that it is the same tune. It is the relationships between the notes that give us the tune, the whole, not which notes make up the tune. [...], from “Gestalt Principles of Perception“, Bonnie Skaalid, Univ. of Saskatchewan, Canada, 1999.
Care for an contemporary example? Well, … the first thing that comes to my mind is DUB music genre. In fact, I do have several albums from different musicians over my house. Dub music evolved in Jamaica (1968) from early rastafarian instrumental reggae music and versions that incorporated fairly primitive reverbs and echo sound effects, being found by accident (engineer Byron Smith left the vocal track out by accident). Over decades, it inspired immense groups of musicians from well-known bands such as The Police, The Clash, UB40 up to reputed musicians such as Bill Laswell. Of course !, it was not far from what John Cage have made for the solo piano Music of Changes, to determine which notes should be used and when they should sound. In the fifty’s, Cage start it to use the mechanism of the I Ching (Chinese “Book of Changes”) in the composition of his music in order to provide a framework for his uses of chance.
Other most recent bands include, Leftfield, Massive Attack, Bauhaus, The Beastie Boys, Asian Dub Foundation, Underworld, Thievery Corporation, Gorillaz, Kruder & Dorfmeister, and DJ Spooky. But what is then so special about Dub? Well, one of this genre’s most striking features is the fact that some if not all musical sentences are incomplete. Those special sentences (Gestaltic, let me add), are normally followed by a pause. The most amazing thing however, is that us, Humans could perceive the entire sentence being formed on the back of our minds! So the music is not there, and at the same time, we are listening to two adjacent simultaneous melodies, as we were a composer. By just using relations among a few notes, we soon start to emerge a perception for the whole sentence, as if they were self-organizing! Being it extremely rhythmic, this often could lead us to a sweet soft state of overwhelming emotion, or exalted organic feel to the music .
As you will probably know by now, the same could happen over misplaced letters over an entire phrase. Even if some letters are not at their right proper place, at each word, we could still perceive the whole sentence meaning. Up to your gestaltic neurons to decipher.
Next time you go to a rave party (I never did, neither pretend to), do think about the title of this post, the figure above, as well as on all those great past musicians, along with – unfortunately – awkward current DJ’s, who pass on for hours strident music mixes without knowing at all what Gestalt is all about! Oh, … by the way, should you feel extremely dizzy, do follow Akiyoshi’s advice: If you start feeling unwell when using this website (rave party), immediately cover one eye with your hand and then leave the page (leave the party). Do not close your both eyes because that can make the attack worse!
Transition behavior of one Artificial Ant Colony in presence of a sudden change in his artificial digital image Habitat, between two different Digital Grey Images (face of Einstein and a Map). Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels [in, V. Ramos, F. Almeida, "Artificial Ant Colonies in Digital Image Habitats - a mass behavior effect study on Pattern Recognition", ANTS'00 Conf., Brussels, Belgium, 2000].
After “Einstein face” is injected as a substrate at t=0, 100 iterations occur. At this point you could recognize the face. Then, a new substrate (a new “environmental condition”) is imposed (Map image). The colony then adapts quickly to this new situation, losing their collective memory of past contours.
In white, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trough out the trails). It’s exactly this artificial evaporation and the computational ant collective group synergy reallocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and “perception” of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the successive alteration that they found on the Habitat. If you however, inject Einstein image again as a substrate, the whole ant society will converge again to it, but much faster than the first time, due to the residual memory distributed in the environment.
As a whole, the system is constantly trying to establish a proper compromise between memory (past solutions – via pheromone reinforcement) and novel ones in order to adapt (new conditions on the habitat, through pheromone evaporation). The right compromise, ables the system to tackle two contradictory situations: keeping some memory while learning something radically new. Antagonist features such as exploration and exploitation are tackled this way.
Springer book “Swarm Intelligence in Data Mining” (Studies in Computational Intelligence Series, Vol. 34) published in late 2006, is receiving a fair amount of attention, so much so, that early this year, Tokyo Denki University press (TDU) decided to negotiate with Springer the translation rights and copyrights in order to released it over their country in Japanese language. The Japanese version will now become shortly available, and I do hope – being one of the scientific editors – it will receive increasing attention as well in Japan, being it one of the most difficult and extraordinary real-world areas we could work nowadays among computer science. Multiple Sequence Alignment (MSA) within Bio-informatics is just one recent example, Financial Markets another. The amount of data – 100000 DVD’s every year -, CERN’s Large Hadron Collider (LHC) will collect is yet another. In order to transform data into information, and information into useful and critical knowledge, reliable and robust Data Mining is more than ever needed, on our daily life.
Meanwhile, I wonder how the Japanese cover design will be?! Starting with it’s own title, which appears to be pretty hard to translate. According to Yahoo BabelFish the Japanese characters (群れの知性) – derived among other language scripts from Kanji – correspond to the English sentence “Swarm Intelligence“. I wonder if this translation is correct or not, since “swarm” in itself, is kind of difficult to translate. Some meanings of it point out to a spaghetti dish, as well, which kind of makes some logic too. Moreover, the technical translation of it is also difficult. I guess the best person to handle the translation (at least from the list of colleagues around the world I know) is Claus Aranha. (IBA Lab., University of Tokyo). Not only he works in Japan for several years now, as well as some of his works focus this precise area.
SIDM book (Swarm Int. in Data Mining) focus on the hybridization of these two areas. As you may probably now, Data Mining (see also; Knowledge Extraction) refers to a collection of techniques – many of them classical – that envisions to tackle large amounts of data, in order to perform classification, clustering, sorting, feature selection, search, forecasting, decision, meaningful extraction, association rule discovery, sequential pattern discovery, etc. In recent years however (1985-2000), state of the art Artificial Intelligence such as Evolutionary Computation was also used, since some of his problems could be seen as – or properly translated to – optimization problems (namely, combinatorial). The same now happens with Swarm Intelligence, since some of it’s unique self-organizing distributed features (allowing direct applications over Grid Computing) seems ideal to tackle some of the most complex data mining problems we may face today.
For those willing for more, I will leave you with it’s contents (chapters), a foreword to this book by James Kennedy (one of the founding fathers of PSO - Particle Swarm Optimization, along with Russell C. Eberhart, and Yuhui Shi) which I vividly recommend (starting with the sentence “Science is a Swarm“!), as well as a more detailed description to it:
Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behavior of real ants, which are used to solve discrete optimization problems. Historically the notion of finding useful patterns in data has been given a variety of names including data mining, knowledge discovery, information extraction, etc. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. In order to achieve this, data mining uses computational techniques from statistics, machine learning and pattern recognition. Data mining and Swarm intelligence may seem that they do not have many properties in common. However, recent studies suggests that they can be used together for several real world data mining problems especially when other methods would be too expensive or difficult to implement. This book deals with the application of swarm intelligence methodologies in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapters giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
The eleven chapters are organized as follows. In Chapter 1, Grosan et al. present the biological motivation and some of the theoretical concepts of swarm intelligence with an emphasis on particle swarm optimization and ant colony optimization algorithms. The basic data mining terminologies are explained and linked with some of the past and ongoing works using swarm intelligence techniques. Martens et al. in Chapter 2 introduce a new algorithm for classification, named AntMiner+, based on an artificial ant system with inherent selforganizing capabilities. AntMiner+ differs from the previously proposed AntMiner classification technique in three aspects. Firstly, AntMiner+ uses a MAX-MIN ant system which is an improved version of the originally proposed ant system, yielding better performing classifiers. Secondly, the complexity of the environment in which the ants operate has substantially decreased. Finally, AntMiner+ leads to fewer and better performing rules. In Chapter 3, Jensen presents a feature selection mechanism based on ant colony optimization algorithm to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. The proposed method is applied to two very different challenging tasks, namely web classification and complex systems monitoring. Galea and Shen in the fourth chapter present an ant colony optimization approach for the induction of fuzzy rules. Several ant colony optimization algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific class. The final outcome is a fuzzy rulebase that has been evolved so that individual rules complement each other during the classification process. In the fifth chapter Tsang and Kwong present an ant colony based clustering model for intrusion detection. The proposed model improves existing ant-based clustering algorithms by incorporating some meta-heuristic principles. To further improve the clustering solution and alleviate the curse of dimensionality in network connection data, four unsupervised feature extraction algorithms are also studied and evaluated. Omran et al. in the sixth chapter present particle swarm optimization algorithms for pattern recognition and image processing problems. First a clustering method that is based on PSO is discussed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. Then PSO-based approaches that tackle the color image quantization and spectral unmixing problems are discussed.
In the seventh chapter Azzag et al. present a new model for data clustering, which is inspired from the self-assembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Authors have also introduced an incremental version of the artificial ants algorithm. Kazemian et al. in the eighth chapter presents a new swarm data clustering method based on Flowers Pollination by Artificial Bees (FPAB). FPAB does not require any parameter settings and any initial information such as the number of classes and the number of partitions on input data. Initially, in FPAB, bees move the pollens and pollinate them. Each pollen will grow in proportion to its garden flowers. Better growing will occur in better conditions. After some iterations, natural selection reduces the pollens and flowers and the gardens of the same type of flowers will be formed. The prototypes of each gardens are taken as the initial cluster centers for Fuzzy C Means algorithm which is used to reduce obvious misclassification errors. In the next stage, the prototypes of gardens are assumed as a single flower and FPAB is applied to them again. Palotai et al. in the ninth chapter propose an Alife architecture for news foraging. News foragers in the Internet were evolved by a simple internal selective algorithm: selection concerned the memory components, being finite in size and containing the list of most promising supplies. Foragers received reward for locating not yet found news and crawled by using value estimation. Foragers were allowed to multiply if they passed a given productivity threshold. A particular property of this community is that there is no direct interaction (here, communication) amongst foragers that allowed us to study compartmentalization, assumed to be important for scalability, in a very clear form. Veenhuis and Koppen in the tenth chapter introduce a data clustering algorithm based on species clustering. It combines methods of particle swarm optimization and flock algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, i.e., clusters. The data to be clustered are assigned to datoids which form a swarm on a two-dimensional plane. A datoid can be imagined as a bird carrying a piece of data on its back. While swarming, this swarm divides into sub-swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters. In the last chapter Yang et al. present a clustering ensemble model using ant colony algorithm with validity index and ART neural network. Clusterings are visually formed on the plane by ants walking, picking up or dropping down projected data objects with different probabilities. Adaptive Resonance Theory (ART) is employed to combine the clusterings produced by ant colonies with different moving speeds. We are very much grateful to the authors of this volume and to the reviewers for their tremendous service by critically reviewing the chapters. The editors would like to thank Dr. Thomas Ditzinger (Springer Engineering Inhouse Editor, Studies in Computational Intelligence Series), Professor Janusz Kacprzyk (Editor-in-Chief, Springer Studies in Computational Intelligence Series) and Ms. Heather King (Editorial Assistant, Springer Verlag, Heidelberg) for the editorial assistance and excellent cooperative collaboration to produce this important scientific work. We hope that the reader will share our excitement to present this volume on ‘Swarm Intelligence in Data Mining’ and will find it useful.
Ajith Abraham, Chung-Ang University, Seoul, Korea
Crina Grosan, Cluj-Napoca, Babes-Bolyai University, Romania
Vitorino Ramos, IST Technical University of Lisbon, Portugal