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1/12/18

Power Shell: Advice & Critic Welcome.

PowerShell is a task automation and configuration management framework from Microsoft, consisting of a command-line shell and associated scripting language.

Shell is a tool for human interacting with computer via scripts & commands.

Internet acquaintance (Darkpowder) who worked as administrator professionally, advised me this tool as popular among professional admins.

i am preparing to work as network admin in 2-4 years, to understand networking, configuration management & administration - as a next step for developing in
IT Security/Hacking for Professional Work. My focus is Quantum Hacking as well as Classic Hacking, both ethical.

Should i learn Power Shell, or there are better tools for Network Administration - not only from practical, professional perspective?

i am not ashamed that i join Esoterics, Spirituality with Sciences, so should i consider something that can be read 'Powers Hell'?

Hells' symbolics & lore is important in Esoterics, as Ethics & Wisdom are.

i was born 13 Sep 1977, numerologically 13 & two 7's, both luck & disluck together.

i was born on Programmer's Day (256th, 28th day of the year) under the TAROT Mage Card Sign.

Names, Dates & Symbols affect greatly our lives, have power.

Spiritual Minorities, both good, neutral or evil are important target groups & markets for my technologies, tools & services.

The Clock Subsystem & Events.

Clocks play important part in computing.

Every computer has clock built-in, synchronizing clocks is imprecise & difficult - even with Internet, as there are delays in transmission & software running delays.

i think that in many Distributed Systems it's worth to invest in dedicated computer for running Clock Subsystem, that serves events - either periodical and/or preordered.

Events are passed either directly, or to event bus - which might be hierarchical, distributed or simplified to a single machine/object.

1/11/18

Memory Banking.

(article in making, to be edited & continued).

Memory Banking is an idea for managing computer memory with Operating Systems uses in mind, both distributed or not.

For now i'll write about in-single-machine memory banking, starting simple.

Processess lease memory cells for a time at a price.

There are memory chunks leased together, with given size - there's option to change size of chunks dynamically at runtime.

There's invariant held in observable moments that largest chunks of memory are kept at beginning of data structure, it's sorted from largest to smallest as memory cell addressess advance. In case of same sizes, earlier-created chunk preceeds newer chunk(s).

Method of ensuring this invariant is sorting defragmentation, as well as process/thread atomicity & concurrency.

For prototype in Java considerations, data structures used are:
- TreeMap, with keys being memory cell addresses, with values being chunk unique identifiers & a position in it's chunk,
- HashMap, with keys being chunk identifiers, with values being chunk sizes as well as timestamp of creation.

There's possibility of copying a lot of data into memory, more than one chunk possibly, as well as merging chunks, at beginning or end of other chunk.

TreeMaps & Comparators have use in sorting, reconstructing TreeMap probably is neccessary to enforce sorting.

1/4/18

Different Technologies, Different Ways.

When i was at a beginning of my programming career, i imagined that projects are done using a single language, a single, internally-coherent technology.

Unfortunately, reality is far worse.

Let's look at web applications, for example.

There's presentation layer that uses html (basic page layout & content), javascript
(in-browser scripts), css (looks & style), perhaps also different frameworks / libraries for javascript, perhaps flash or other plugin-solutions.

These technologies complement each other, are used together.

This is very cumbersome to use so many tools for a single product, to think using many ways, many paradigms at once.

But reality is such, for now at least.

With web applications not only in-browser presentation matters, there's also server side & client-server communication protocol.

For server side there are different solutions - Java, C#, PHP, Python, Perl - to name a few of the more popular languages.

Server side solutions can cooperate, can be integrated - while it's not neccessary to learn a few, it makes sense for some to integrate web applications, to make them communicate & cooperate.

Again there are many surrounding technologies around these languages - for database interaction, for distributed communication, for handling typical technical problems.

But is it worthwhile to learn everything?

There's so much of work & solutions done that no single man or woman can learn it all in a single lifetime - Computer Sciences are vast, professionals do specialize.

More than that - there are technologies that are not used together, that are used in an either this or that way.

For example: GWT (Google Web Toolkit) & JSF (Java Server Faces).

These are frameworks for presentation layer, it makes no sense to use them both at once. Doing this could be compared to using both fork, knife, spoon & rice chopsticks at the same time.

One certainly should not combine these in a single project.

GWT & JSF are different paradigms, once one learns one way, it sets one's thinking certain way - making it difficult to change habits.

More than that, both projects are developed - there's a lot of work in keeping up with changes & current versions of chosen technology.

Pursuing both at once would be confusing & time-wasting, as well - there's so much to learn to keep up with one's career already, anyway.

Finally - while learning both GWT & JSF has it's advantages, as it makes one more versatile in what contracts one can fulfill, there are much better ways of using one's time to make himself or herself more attractive on work market.

Let's be wise, let's choose our tools wisely, let's learn what's beneficial & meaningful, let's not waste our time on stupid ideas.

Personally i chose GWT, and am fine with it.

Despite many offers, i prefer to not touch JSF, to not waste my precious time.

Yes, i tried to use JSF Professionally - it's certainly not my way.

10/10/17

Gratitude & Thanks.

i wish to thank people who enabled me my education & advancement in Sciences.

Mostly Jakub Kruszona-Zawadzki, of Warsaw University (MIM) who taught me Assembler (Motorola 68000 on Atari ST) & his wife, Agata Godlewska-Kruszona.

When i was studying Low Level Programming (Assembler & C mostly) at MIM UW, i was best on the year - because of my interrest at youth.

Even if i didn't understand at first, at youth - it still opened my mind & i was ready for that very difficult lesson later - so i excelled on time.

They played with me AD&D as well, suffered my foolishness, unattractiveness & other faults.

Rest of the AD&D team as well, including Michał Bobran, Paweł Suchocki, Jakub Wysoczański & others.

Most if not all of them studied on MIM UW as well.

Warsaw University (mostly MIM) i thank as well.

Special Gratitude to Teachers as well: mostly for Marcin Engel, Piotr Krzyżanowski (Przykry), Krzysztof Stencel, Mirosława Miłkowska, Leszek Plaskota & Zbigniew Jurkiewicz.

i think they should have more than words, my opinion is that they should have benefits from Dragonfly Algorithm & it's technologies.

i wish to return to MIM UW later in life, to study Mathematics this time.

Piotr Krzyżanowski opened my eyes on how bad i am with Numeric Methods.

i think Numeric Methods is not enough for me, i wish for full Masters Degree in Mathematics - to turn my Weakness into Strength.

i failed to get graduating grade at Numeric Methods, it was defeat. i still passed Conditionally to 2nd year of Study, didn't complete that lesson yet. But i think that one learns best from defeats, from victories - much less. As it was with at least one of my Martial Arts Lessons.

Mathematics has uses in Cryptography, Computer Graphics, Proofs of Correctness, Artificial Intelligence, Fractals & other Sciences as well.

i think i'll benefit myself & others more with Studying Mathemathics, than by Studying Computer Sciences formally. Computer Sciences i can learn easily enough from books at my stage of Scientific Development anyway.

10/8/17

NAITR.

NAITR is an acronym for 'Nano-AI-Tractor-Repulsor'.

NAITR is idea for weapon for neutralizing Air Vehicles & Missles.

NAITR attacks it's targets by sending NEMS nanites into fuel tanks, that release chemicals that interact with fuel & cause explosion ...

NEMS nanites are small enough to not be seen by naked eye, to be able to pass through matter as well.

NEMS nanites are in electromagnetic flight - propelled by Repulsor beams & Tractor beams from Ground, Sea and/or Space (from Satellites for example), are coordinated by Artificial Intelligence to handle the complexity of the maneuvers.

NEMS nanites might also be equipped with Repulsor beams & Tractor beams, as well as with Communication Directional Rays to hold moblie real-time formation.

As a far more Advanced Version, NEMS nanites might use Repulsor beams & Tractor Beams contained within them, as a way for Vector-Thrust movement, making them independent of Ground/Sea/Space infrastructure.

i see NAITR as an use case for distributed machine, with enough of time probably can be used with 'Ola AH' Programming Language.

'Distributed Machine' is a way of seeing the 'Internet of Things', graph of objects, graph of machines talking with each other, doing tasks together.

Electromagnetic Flight & NEMS nanites.

Introduction.

i am considering technology that will enable flying of NEMS nanites, the flight in the Electromagnetic Field.


Antennas & Electromagnetic Radiation.

So far my most developed idea is to use ground & space antennas (mounted on satellites, for example) to emit electromagnetic radiation in a precise direction.

Waves from multiple sources (directions) can push objects in different directions. Gravity & wind count as well, but NEMS nanites are very small objects, with very small surface & mass.

Both Tractor beams & Repulsor beams can be used.

Electromagnetic waves propagate very fast, especially in vacuum.


Background Theory.

Electromagnetic waves are waves which can travel through the vacuum of outer space. Mechanical waves, unlike electromagnetic waves, require the presence of a material medium in order to transport their energy from one location to another. Sound waves are examples of mechanical waves while light waves are examples of electromagnetic waves.

Electromagnetic waves are created by the vibration of an electric charge. This vibration creates a wave which has both an electric and a magnetic component. An electromagnetic wave transports its energy through a vacuum at a speed of 3.00 x 108 m/s (a speed value commonly represented by the symbol c). The propagation of an electromagnetic wave through a material medium occurs at a net speed which is less than 3.00 x 108 m/s.

Source: Propagation of an Electromagnetic Wave.

See also, if You wish: Dangerous Trade, Tractor beam, Light’s Pull and Push.

10/6/17

Dangerous Trade.

Will i trade Dragonfly Algorithm technologies?

No, i do not write it for myself.

But perhaps my voice still counts in this respect.

i write this Technology for a Buddhist woman i Love, and i know which - and for Lama Ole Nydahl.

My opinion & voice is that Lama Ole Nydahl & HH Buddha Karmapa 17th Trinley Thaye Dorje should decide about potential sales, including prices & other benefits.

Dragonfly Algorithm technologies are potentially dangerous & powerful.

NEMS nanites controlled by Artificial Intelligence can pass through matter, potentially can fly in the electromagnetic field, enter fighter plane's fuel tank and release chemicals causing explosion - without giving much chance. At least in theory. There's much more, but this is one of the eye-openers. ;)

i am not ready to decide if and whom to sell, and when, and for what price.

if i had to decide whom to sell, i would be very cautious and not sell to anyone but offer for a Buddhist Woman i Love & for Lama Ole Nydahl, rewarding those who helped me as well - without precising details.

But probably this is too cautious & unwise.

i still try to be responsible, and not too bold.

Better to be cautious than sorry, even better to trust Wiser.


See also, if You wish: NAITR, Gratitude & Thanks.

9/18/17

Qabbalistic Golem, Israeli & India's Markets & My Technology.

What is Golem?

Is it fantasy-games-idea only?

i think it comes from Israeli Kabbalah, from Ancient Judaism Lore.

Golem is about animated machine, about 'living machine'.

While robots are not living as of yet, there are works on 'artificial life' as well.

i think robots & drone armies can still be seen as Golem Project Manifestation, from a certain perspective at least - even if it's not the whole Truth.

This blog's author wishes to study Qabbalah, Hermetic version of Kabbalah - more of Magick than Judaism, but still close & related, i think.

i plan to participate in the Golem Project realization with Artificial-Intelligence-controlled NEMS nanite swarms.

i wish to include Qabbalistic ideas so Israelites & rest of the World can understand, believe & absorb these lessons easier.

Not only Buddhist Ideas, but also Israeli, Hindu, Christian, Wiccan, Qabbalistic & other should inspire Dragonfly Algorithm technologies.

It's not only an attempt to enter the Rich, Influential & Resillent Israeli Markets - it's also fight for ideas i support, including Protection from Terror, including fight for Israeli-Palestinian Peace.

i wish to give Israeli People significiant discount on my technologies anyway, as a gesture of goodwill & support.

India's Markets & people are also important for us.

Even if it's not me who decides - i am creating these technologies for others - perhaps my vote still counts.


See also, if You wish: Practical Kabbalah: The Golem.

7/22/17

Abstracted Neural Networks & Token Game.

Neural Networks.

In neural networks we consider error between result that we consider 'correct & desired', and the result we get as an output vector of neural net.

We wish to minimize the error, to teach our neural net to provide accurate results that we can apply to other vectors than the training vectors.

We provide training data, a set of training vectors to the neural net and it adjusts weights of neuron signals accordingly.

After enough of iterations we have 'calibrated' neural net - with appropriate weight values at each of neurons - so it provides fairly accurate results at output neurons for non-training data vectors.

The output neurons answer us abstract questions about data we wish to classify, categorizing input fairly accurately.


Token Game's Abstracted Neural Networks.

In this case we do something similar, except in a simpler way.

We provide input data, we adjust weights at neurons, we pass tokens with payload information.


Example 1: Oval Recognition, with an image without the crossing lines & without 'noise' data.

First we need to 'describe' the image by a serie of 8 marker points at a circle bordering image's edge.

We place one marker point at top, one at bottom, one at left, one at right, and four remaining in between - but not on top of each other.

We assume initial weight of each of neurons as of 3.

For each of marker points we create input neuron. For each of neurons we calculate an error - a distance between that marker point and a point closest on the image.

Depending on calculated error value we modify weight accordingly - the more error, the more of adjustment.

First we move marker point left, by a value of sigmoidal function's for calculated weight.

Then we calculate error again, modify weight and move marker point right.

Then top & bottom similar way - repeating the process until we are close enough to the point on the image.

We repeat the process once for each of input neurons, once for each of marker points.

We have described the image with a possible small error.


Then we compare our image with marker points, counting distance from each of marker points to closest point on an image. Then we sum distances to calculate error.

If error is beyond certain threshold, image is not recognized.


We can recognize any of simple images that way.


Example 2: Face recognition, without crossing lines & without 'noise' data.

We recognize face's oval, eye shapes, nose shape & lips shape - passing tokens with marker points to initial neurons of second neural net, along with combined errors that determine initial weight of input neurons in second neural net. We'll call these shapes marker shapes since now, and compare these with face's template on an image.

If first neural net's error was too much, we pass nothing and input neuron isn't activated.

We move marker points as in example 1 until we get close enough to image's templates to 'describe' template shapes that way.

We move centers of marker shapes to align with centers of template shapes.

For each of input neurons, we count combined error between marker shape's marker points and template shape's closest points. If error is not beyond certain threshold, we recognized face. This is weight we might pass on to next layer of neural net if we need.


Example 3: Recognizing flat data.

It's similar as 'describing image' in an example 1, except we move only up & down in case of numbers, and we use sigmoidal function after we count error & weights.

Enumerations can be represented as numbers as well.

Mindful Imaging & Editor.

Mindful Imaging Module can read from Stitie Space to visualize it, with machines, states, strategies & links.

As a complement, planning to add Editor that will allow to insert Light Point Objects into machines, to move machines and their links, or transform Stitie Space in an any way.

Light Point Objects' code & state can be inserted into the Stitie Space's 'machine' during the runtime, to be interpreted there - either via network or from the Mindul Imaging-based 4GL interface. A live system can be modelled as with Smalltalk programming language, considering security & permission of course.

See also: Agile Transformation of Information State.

7/21/17

AI Exercise.

(article to be edited once i understand AI Machine Learning theories).


i've played with Java's Weka library for machine learning, for artificial intelligence.

While i didn't understand classification algorithms used, i was able to produce a code that learned how to classify & somehow classified 'plants' using the MultilayerPerceptron classifier. i read it's basicly the neural network, but considering my current knowledge - can't confirm.

i don't know if classification was succesful, as i don't know much about plants. but judging from data-nearness, it looked good.

i believe that experiments with code are very important parts of learning computer sciences, so i did this exercise despite my lacks in knowledge. hopefully it'll be useful for others as well.

i swallowed my own shame of not knowing theories, and posted this article for benefit of others.

There were other tools found such as assessing errors or adding weights to attributes consiering algorithms used, that i didn't understand. i think professional AI programmer should be fluent with all of these ideas & their uses.

Files: WekaTest.java, Datasets.

Library used: Weka.