Friday, April 10, 2020

Portable Pravetz-8VC, ICL CLAN 3 Unix Server & Terminal at a Bulgarian Computer Museum



Rare "portable" Pravetz-8VC computer in action(an Apple IIc clone), playing Karateka. ICL CLAN 3 Unix Server - circuit boards exploration and attempts to run it, data tapes. Running a Unix terminal.

Filmed at Compu Plovdiv, video produced by Todor.

Thursday, January 30, 2020

Z***** 0.0001's Point of View to the Match



Z***** 0.0001, watching Djokovic vs Federer match at Australlian Open 2020 on Eurosport.
Art from my AGI Prototype, in this application it's in the form of computer vision.

Current name is still tentative since it's in "stealth", the old name of the system was "SuperCogAlg" - it was just a cheesy first pick due to "Super Contra" and "CogAlg" etc.

The latter name is also funny sounding abbreviation, again coined by me for BK's lengthy "Cognitive Algorithm" somewhere in 2015? during its pre-code era.

Sunday, January 5, 2020

CogAlg News - January 2020

Several new developers are trying to join lately, but the code progress seems cosmetic. The project has finally accepted my mid 2019 notice that the low level Frame_blobs of CogAlg in fact is a form of breadh-first-search traversal vertical flood-filll. It seems I've been cited, but without being referenced:
https://github.com/boris-kz/CogAlg/commit/33db6b0cf171591e0da52f6a267908ccd58bc263#diff-9b63966e9c5ddf883920a1c522318f75

I challenge the final claims in Readme:

https://github.com/boris-kz/CogAlg/commit/b0a88d9c54156983bc43cf2c7e2504427751c534

B.K.: "...In 2D image processing, basic comparison is done by edge detectors, which form gradient and its angle. They are used as first layer in the proposed model, same as in CNN. It then segments image into blobs (2D patterns) by the sign of gradient deviation, which is also pretty conventional. But these blobs are parameterized with summed pixel-level intensity, derivatives (initially gradient and angle) and dimensions. I don’t know of any model that performs such parameterization, so the algorithm seems to be novel from this point on...."
Exactly the same - I don't know, - but similar ideas of incremental evaluation of whatever measurements (derivatives) - I don't think so. For instance the pre-CNN methods for Object recognition in Computer vision and Shape analysis in topology (3D-reconstruction, fixing broken laser scans, medical imaging) use parameters - "derivatives" - which are coordinates/paths, "dimensions"(boxes), curves (contours), curvatures, lengths, distances, angles, ratios, areas, normals. The "pixel-level intensities" of a topology map could be vector fields with curvature, "heat maps"etc., set of derivatives and operations on them.

RAZDEL 2004

In published works of mine, see e.g. that one from my fresh months at the University, working in sound domain, done in late 2003 - early 2004: http://eim.twenkid.com/old/_5/31/analiz_na_zvuk.htm

Notice the data structure which encompassed the basic patterns (almost-periodic functions):

typedef struct DATA
       {
         int begin;
         int end;
         char type;
         unsigned char cycles;
         Zvukk max;
         Zvukk min;
         Zvukk absmax;
         Zvukk absmaxcycle;
         int period;
         char position;
         int changes;
         int accumulated;
         int average;
         Zvukk primer[MAXPRIMER];
       };

BK's CogAlg, which started to have any code just a few years ago, and its actual function started to become intelligible just about an year ago, has similar basic parameters like "ave" (average, filters); it has summation of the "pixel-level intensities" (lowest level input) as one of the basic operators (accumulated here), it records explicit coordinates/"dimension"(begin, end - location in the record), starts with basic types of classification - 2 in CogAlg, 3 here - silence, noise, tone; corresponding to the binary positive and negative patterns and their sign.

RAZDEL is also a bit more bottom-up at that lowest level, because it searches for correlated sequences and adjusts the first filter (ave) correspondingly to the actual sampled input. Also it doesn't do a comparison of the whole record ("frame" in current CogAlg) with one fixed ave, which CogAlg does, i.e. it is more granular. The usage of fixed filter in the latter is a binary thresholding, which you could see illustrated in the pictures from SuperCogAlg. Razdel has a separate "same-filter-span" in per pattern, in CogAlg terms.

The article introduces incremental derivative ...
"Sacred Computer" #31, published 4.2005. The article is written and first published in 3.2004 internally to the Plovdiv University Research Institute in Computational Linguistics:

(...)

The correlations which we could use are for example:

(...)

Etc. other later works to be reviewed later (...)

* Thanks to C. for a recent discussion and comments

Friday, January 3, 2020

The Wild Plovdiv Video Series | Дивия Пловдив - видеопоредица

 

      Watch the Wild Plovdiv  in the "Sacred Computer" e-zine

The Wild Life in the city of Plovdiv, depicted in poetic, calming, majestic and comic music videos with animals, birds, views and sounds. Wonderful pictures, ironic cats' yawns and fights in the fig forest, laughing sparrows, walking in forests' labyrinths; giggling crows, diving in the evening skies; restless squirrels with their nuts; mother collared dove, teaching her children to fly; easygoing cats and fearful kittens; butterflies, surviving a storm; curious little owls with their fluffy outfit and other beauties, invisible for naked eye.  
Created by (C) Todor Arnaudov 2019, Music by (CC) Kevin MacLeod.

 Гледай Дивия Пловдив  в "Свещения сметач" 

Дивото в Пловдив в поетични, успокояващи, величествени и комични музикални видеоклипове с животни, птици, гледки и звуци. Приказни картини, иронични котешки прозявки, смеещи се врабчета в горски лабиринти и кикотещи се гарвани, гмуркащи се в гаснещата синева; трескави катерички с орехите им; майки гугутки и техните деца, учещи се да летят; безгрижни котараци и плашливи котенца; пеперуди, оцеляващи в буря; любопитни чухалчета с пухени премени и други красоти, неуловими с просто око. Автор и продуцент: (C) Тодор Арнаудов 2019. Музика: (CC) Kevin MacLeod.





12. Wonderful sparrows -Приказни врабчета

6. The Mother and Her Children - Майката гугутка и нейните деца

7. Demons in the fig forest -Демони в смокиновата гора 

9. Morning at the Cat's nest - Утро в котешкото гнездо 

10. The Tree of Devil -Дървото на дявола 

11. Dream in a Summer's Rain - Сън в летен дъжд 

5. Fluffy Squirrel - Катеричка рунтавелка 

3. Crows are Diving in the Dying Day - Гарвани се гмуркат в гаснещия ден 

2. The Crow Cook and the Woodpecker - Гарванът готвач и един кълвач 

4. Chicks and Cats - Мацки и котки 

1. Owl -Чухал 

8. Butterfly in the Rain - Пеперуда в дъждаСл

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