Working notes from the Scandinavian Institute for Computational Vandalism

Database populism

Although early users of database technology were predominantly large institutions, the database was also a key technology in the populist vision of personal computing generated by microcomputer fans, researchers, hobbyists, and entrepreneurs in the 1970s and 1980s. Informed by science fiction sensitive to the authoritarian use of database technology, these personal computing advocates hoped that experience with small database systems might sharpen popular critique of mass-scale information processing efforts. As database design receded from the desktop in the 1990s, however, the populist promises were largely forgotten and the database became an exclusively institutional technology once again.
A social history of database technology situates the web’s massive databases among more than a century of mass-scale information processing systems. Evidence of popular anxiety recurs throughout this history and indicates that non-specialists often struggle to apprehend the limits of database technology and may alternately over- and under-estimate the extent of mass data collection and the types of analytic outcomes that are possible. Meanwhile, the cautious optimism of the microcomputer era points to a latent database populism that may yet be revived should users grow sufficiently frustrated by the lack of transparency among large, data-driven institutions.

From Punched Cards to “Big Data”: A Social History of Database Populism, Kevin Driscoll, 2012

A system for detecting an accessible bug

We have described each of the operations on the retinal image in terms of what common factors in a
large variety of stimuli cause response and what common factors have no effect. What, then, does a particular fiber in the optic nerve measure? We have considered it to be how much there is in a stimulus of that quality which excites the fiber maximally, naming that quality.

The operations thus have much more the flavor of perception than of sensation if that distinction has any meaning now. That is to say that the language in which they are best described is the language of complex abstractions from the visual image. We have been tempted,for example, to call the convexity detectors “bug perceivers.” Such a fiber [operation 2] responds best when a dark object, smaller than a receptive field, enters that field, stops, and moves about intermittently thereafter. The response is not affected if the lighting changes or if the background (say a picture of grass and flowers) is
moving, and is not there if only the background, moving or still, is in the field. Could one better describe a system for detecting an accessible bug?

What the Frog’s Eye Tells the Frog’s Brain? Lettvin et al, 1940.

You are green

“As I recovered at home, colour started slowly creeping back into my life, whispering around corners. This was a very perplexing time, for often I only felt I was seeing a colour but was unable to identify it. I would stare endlessly at trees and lamp-posts, desperate to match the colour I believed was there with the strange sensory experience I was having. Bright primary colours were the first I could identify with any conviction. Red led the way, followed by blues and yellows – but cloudy and faded. I struggled enormously with greens, greys and any pale or muted colours. This was not the vibrant rainbow world I was used to.
I still had some channels of information transmitting the colours around me to my brain, but I was receiving only part of the message. My lifelong emotional associations with colours were still intact, even if my sight was not. I tried to use language to help myself recover. “You are green,” I would tell the grass. I believed the more I stimulated my brain by observing the world around me and reminding myself what colour was, the more the damaged circuitry in my brain would reconnect and bring my normal vision back online. I found the more I did this, the more it worked.”

Read the story of Vanessa Potter recovering sight.

The screen is looking at you in the eyes

Computer vision syndrome (CVS) is a condition resulting from focusing the eyes on a computer or other display device for protracted, uninterrupted periods of time. Some symptoms of CVS include headaches, blurred vision, neck pain, fatigue, eye strain, dry eyes, irritated eyes, double vision, vertigo/dizziness, polyopia, and difficulty refocusing the eyes.

In CVS, the screen is not considered as a passive object where information is merely displayed. It is an active device that emits radiations towards the user of the computer. For CVS researchers, the screen is emitting phototoxic light whose damaging effect can be reduced by wearing blue light-filtering lenses. An alternative remedy to shielding the retina with specialized spectacles is to follow the 20 20 20 rule. 20 20 20 is an algorithmic formulation of the sequences of action one should follow to avoid CVS: “Every 20 minutes, focus the eyes on an object 20 feet (6 meters) away for 20 seconds”.

From Wikipedia

Detection of Face Morphing Attacks by Deep Learning

Identification by biometric features has become more popular in the last decade. High quality video and fingerprint sensors have become less expensive and are nowadays standard components in many mobile devices. Thus, many devices can be unlocked via fingerprint or face verification. The state of the art accuracy of biometric facial recognition systems prompted even systems that need high security standards like border control at airports to rely on biometric systems. While most biometric facial recognition systems perform quite accurate under a controlled environment, they can easily be tricked by morphing attacks. The concept of a morphing attack is to create one synthetic face image that contains characteristics of two different individuals and to use this image on a document or as reference image in a database. Using this image for authentication, a biometric facial recognition system accepts both individuals. In this paper, we propose a morphing attack detection approach based on convolutional neural networks. We present an automatic morphing pipeline to generate morphing attacks, train neural networks based on this data and analyze their accuracy. The accuracy of different well-known network architectures are compared and the advantage of using pretrained networks compared to networks learned from scratch is studied.

Detection of Face Morphing Attacks by Deep Learning, Clemens Seibold, Wojciech Samek, Anna Hilsmann and Peter Eisert

Escape from the monochrome room

“Imagine a girl called Mary. She is a brilliant neuroscientist and a world expert on colour vision. But because she grew up entirely in a black and white room, she has never actually seen any colours. Many black and white books and TV programmes have taught her all there is to know about colour vision. Mary knows facts like the structure of our eyes and the exact wavelengths of light that stimulate our retinas when we look at a light blue sky.
One day, Mary escapes her monochrome room, and as she walks through the grey city streets, she sees a red apple for the first time.
What changes upon Mary’s encounter with the red apple? Has Mary learnt anything new about the colour red upon seeing the colour for the first time?”

What Did Mary Know? by Marina Gerner.

Read Gerner’s article

Dead lends a hand, murder lends glasses

In Dead Lends a Hand, the investigator Brimmer kills a woman he attempts to blackmail. The camera shows Brimmer’s face just after the murder and zooms in. After a few seconds, the image of the face becomes still as if Brimmer was paralyzed in shock. At the same time, while his face stays motionless, the narrative continues within the frame of his glasses. We see him cleaning up the room, removing his fingerprints from the objects and the victim’s body. The glasses function as a split screen showing the same action asynchronously while the face’s image stays frozen at the time of the murder, becoming a static backdrop. The glasses are the device through which he can see himself removing (every trace of) himself from the murder he just committed.

Machines to write with the hereafter

Machine à écrire avec l’au-delà, Jean Perdrizet, 1971.

Oui-Ja électrique, Jean Perdrizet, 1971.

Perdrizet drew the designs for several devices meant to communicate with the beyond. Ghosts and the dead would trigger electrical signals translated in the letters of the alphabet by Perdrizet’s inventions. As Perdrizet didn’t believe ghosts would speak a human language, he invented the T language, the sidereal Esperanto.

More about Perdrizet

When I interpret the removal of a hat

all sieves are shadows of the substances they sort

[…] we apperceive through our sieves as much as we sieve through our apperception. We appersieve, if you will. Or, if you go back to Kant ([1781] 1965), who defined the ego as the transcendental unity of apperception (whatever that means), we are our sieves.
Indeed, crucially, sieves have to take on (and not just take in ) features of the substances they sieve, if only as “inverses” of them. A hole in the ground, for example, constitutes a simple sieve: anything with a diameter less than the hole will fall through; anything with a diameter larger than the hole will stay on top. In this way, to sieve a substance, the sieve must often have an (elective) affinity with the substance to be sieved and, in particular, the qualities sieved for—in this case size. In some sense, all sieves are inverses or even shadows of the substances they sort. By necessity, they exhibit a radical kind of intimacy

Note, then, that sieves — such as spam filters—have desires built into them (inso-far as they selectively permit certain things and prohibit others); and they have beliefs built into them (insofar as they exhibit ontological assumptions). And not only do sieves have beliefs and desires built into them (and thus, in some sense, embody values that are relatively derivative of their makers and users); they may also be said to have emergent beliefs and desires (and thus embody their own relatively originary values, however unconscious they and their makers and users are of them). In particular, the values of the variables are usually steps ahead of the consciousness of the programmers (and certainly of users)—and thus constitute a kind of prosthetic unconsciousness with incredibly rich and wily temporal dynamics. Note, then, that when we make algorithms and then set those algorithms loose, there is often no way to know what’s going to happen next (Bill Maurer, personal communication).

Paul Kockelman in The anthropology of an equation, Sieves, spam filters, agentive algorithms, and ontologies of transformation