Friday, 9 October 2015

Against the idea of a computable universe

The computable universe hypothesis, states that every operation and phenomenon in the universe is computable. So if you can describe all the phenomena in state s1 of some part of the universe (or of the whole universe), then you can compute the physical operations in any state si that is a successor of s1.
An equivalent hypothesis is that every set of physical operations can be simulated by a Turing machine or an automaton. This means that there is a Turing or some other computational machine that will accept as input the data that describe these operations and will output the result of them and then it will halt. The two hypotheses are equivalent as the simulation of every operation by a Turing machine implies the computability of the transition from state s1 to any state si.
These hypotheses also support the idea that the universe is deterministic. The transition from s1 to si is computable and as a result predictable which makes it deterministic.
There are many papers and textbooks that propose theories that are based on these hypotheses, there are also many that oppose such ideas. My opinion is against the idea of a computable universe, the reasons for this are: randomness, incompleteness and uncomputability.

Randomness


The existence of randomness in physical processes remains a debate among scientists. Einstein rejected it, as he believed that universe is deterministic and stating that "God does not play dishes"; a phrase that marked the debate. The truth is that there are many indications that some physical operations must be random.
Steven Hawking provides some very convincing arguments in one of his lectures. According to Hawking the way the universe evolves seems to be not deterministic, moreover the existence of extreme objects in it like the black holes and the Uncertainty Principle makes accurate computations and predictions impossible. Although in computer science we can only use pseudo-random operations in order to approach true randomness, in other fields true randomness seems to be present. Quantum mechanics build probabilistic models in order to study quantum phenomena that are believed to evolve truly randomly.
 

Incompleteness


Any not trivial system of logic is incomplete. This is a very simplified description of the incompleteness theorem stated and proven by Kurt Godel which is one the most important theorems in science.
Any system physical or not that operates under certain laws may be efficiently described by mathematics and more specifically by some system of mathematical logic using propositions and axioms. If the system of logic is not trivial (is not very simple) then there are true statements in it that may not be proven as true; the system is incomplete. This results that there are phenomena that may not be computed by mathematics.
This lecture of Hawking approaches incompleteness theorem from a physicist perspective reaching the same conclusions.
The issue that we face is that in order to compute some mathematical object O we need to use or to build some proper mathematical structure. But no matter how this structure will be it will always be incomplete, so a part of O will not be able to be proven using our mathematics.
 

Uncomputability


The theoretical background of uncomputability in Computer Science is related to Godel’s incompleteness theorem and states that there are instances of computational problems that may not be computed by a machine or a human. Computational procedures may be simulated by Turing Machines that perform them and then halt, so we usually state that a problem P is uncomputable if there is not a Turing Machine that may compute all the instances of P and then halt. Equivalently, if you input some data in a Turing Machine you may not know if it will halt or not.
The foundation for this was set by Alan Turing before the first general purpose computer was ever built.
Its affect in programming is something that we experience every day. No matter how hard have you worked on a piece of software you can never be sure that it will work as expected.
A parallelism in physics that I have thought is included in the question 
“How can you be sure that for operation H there is a Turing Machine that will halt on input of the description of H?”
The above arguments may seem to be weak but there is a stronger one for which I talked about in a previous post.
Causation (or else causality) refers to the cause-effect relation that may exist between two events or phenomena. The causal inference problem regards the determination of causation between events.
In my previous post I present a proof on which Causal Inference is logically and computationally undecidable. Relations among phenomena and physical processes are important factors in the structure of the universe. The Causal Inference undecidability results that there are physical phenomena for which we may not decide or compute if they are related to each other or not. This makes a part of the universe uncomputable and unexplained using logical arguments.

Thursday, 17 September 2015

How to make a hosted web app listed in app stores and installable for the end users

Building a hosted web app has many advantages. One advantage is that it is not necessary to create big software packages containing the whole app where the users will have to download and install. It is only necessary for them to open the main page of the app and work with it. Also the app may be updated whenever it is necessary and the end users receive the update in real time.
Hosted apps do not require to be placed in an app store and follow certain specifications on the way they are built. Just find a host which may be free, paid or the server of your company and put your creation in there.
There is a small down side in this practice. Apps that require installation create a stronger bind to the end users while hosted apps have a more loose attachment to their users. Also, apps that are listed in an app store receive some promotion just by being there; they may be searched and found easier.
The good news are that there are three app stores that list hosted web apps and two of them provide some methods of installing a link or a small app pointing to your hosted app in the end user's computer.
I had to discover all these things I write about in this post after I finished developing WMS Map Viewer on line. It is a web app and it was really easier to develop it than to find out what to do with it afterwards.
An easy choice for listing your app is the Opera Mobile Store. The store lists apps mainly for mobile devices (new, old and very old) but also accepts submissions for HTML5 web apps for desktop computers. The submission procedure is similar to any other app store. The submitted apps are being reviewed before publication (which I find a good idea) and usually it doesn’t take more than one or two days. When you click on a web app you view its store listing and there is a download button which simply opens the homepage of your app. See my listing for example.
The other two app stores are the Chrome Web Store and the Firefox Marketplace. Both stores address to certain browsers and they both share an interesting feature. When a user downloads an app from these stores the app (or at least a link) gets installed on the user’s computer.
In Chrome Web Store you have to pay a onetime fee (like in Play Store) this fee is currently 5$ and you may place up to 20 apps. Then you have to confirm that the web page you submit is yours by logging in to Google web masters and put a metadata file in the root directory of your app. You can do the submission procedure with any browser but the apps are available for download only in Chrome.
During submission it is only necessary for you to provide a .json type manifest file, a description of the app and some screenshots. But as the store accepts submissions of apps, themes and extensions for Chrome with the same procedure, you have to be very careful and submit the correct manifest. Otherwise the submission procedure might assume that you submit an extension and then your store listing will simply not work. The only way to solve this if it happens to you, is to make a new submission. The previous one is characterized as an extension and this cannot be changed and the submission cannot be deleted. Use this tutorial for instructions don’t forget the part

  "app": {
    "urls": [
      "http://mysubdomain.example.com/"
    ],
    "launch": {
      "web_url": "http://mysubdomain.example.com/"
    }


this makes all the difference between an app and an extension.
See the listing of my app as an example.
After listing your app, any Chrome user may download it. Chrome places downloaded apps in the app menu regardless if they are packed or hosted. So the apps are functional in the Chrome environment like any other app or extension.
In Firefox Marketplace you are also required to place a .json type manifest, but as it has to have the .webapp extension you have to declare in the metadata of your server that the manifest is of type
<mime-type>application/x-web-app-manifest+json</mime-type>
this is a platform dependent procedure and there is a lot of documentation about it in the help files of the Marketplace. The syntax of the manifest is quite trivial, unless you wish to put special installation parameters.
The interesting thing is that when a Firefox user downloads an app in Windows, the app is installed like a regular windows app that may be removed only from the Control Panel. In fact, Firefox installs a small app which is basically a browsers window named after the downloaded app with some cache memory. When a user runs this installation the hosted app opens in this window which has different functionality than Firefox normal windows. This feature is useful as your hosted app runs like a local app improving user experience.
The downside of Firefox Marketplace is the review process. It takes a lot of days, a week or more. And some of the reviewers must not be very experienced. The reviewer of my app didn’t know how to run it; despite the help files in it. So I had to send him detailed instructions and wait even more days for the approval of my submission.

Tuesday, 25 August 2015

An online map viewing application

WMS Map Viewer on line is a light-weighted client for web map services that use the WMS protocol. It is a web application compatible with all modern versions of WMS and also supports the projection of kml files as overlays in the chosen WMS connections.
It is a follow up of my Android application and I like especially the fact that you can simply open it in a browser and just work with it. Most of the WMS viewers are heavy applications that require to be downloaded installed etc.
Here are a few of the many sites that you  can find and provide WMS maps.

Europe
http://eusoils.jrc.ec.europa.eu/wms/wms.htm
http://ows.terrestris.de/dienste.html
CUZK Geoportal - Orthophoto

America
http://viewer.nationalmap.gov/exa…/services/serviceList.html
http://www.data.gov.bc.ca/dbc/geographic/connect/index.page?

Sunday, 19 July 2015

Proving uncountability without diagonalization



In this post, I propose a proof of the uncountability of the set of real numbers without using the famous diagonalization method of Georg Cantor. My proof is based on the Church-Turing thesis which states that any computable operation may be computed or simulated by a Turing Machine. So, if a set of mathematical objects is countable a counting operation may be performed and there is a Turing Machine that may compute it.

Definition. Let S be an ordered set of discrete objects. If S is countable then there is Turing Machine M which on input of the descriptions of objects a and b it outputs c which is a representation of the number of elements in S between objects a and b.

If we may count the elements between a and b (or else the distance of a and b in S) then we may count the whole set, as a and b may be any objects. This definition avoids a direct connection of the set N of natural numbers to S, so it differs from diagonalization, yet as I show below it reaches the same conclusions on natural, rational and real numbers. Output c has to be a number and not the symbol of infinity or anything similar, otherwise we cannot consider it as a counting result. First let’s test this definition on natural and rational numbers.

Theorem 1. Set N of natural numbers and set Q or rational numbers are countable.

Proof for N. Set N may be defined as an ordered set of linear structure. For every i we place in the i-th place of N number i. Let a and b be the representations of two natural numbers where a represents a smaller value than b and M a TM that computes their distance. In order to make the presentation more simple and intuitive and without loss of generality, let M be a 2-tape machine and a, b and c be represented in unary numeral system. Then we print a in tape-1 and b in tape-2. For any “1” printed in tape-1 M erases one “1” that is printed in tape-2. Finally a representation of c is left in tape-2 and this is the output.

For instance, on a = 8 and b = 13 we print
tape-1 : #11111111######
tape-2 : #1111111111111#
After running the configuration of M we get
tape-1 : #11111111######
tape-2 : #11111#########

Proof for Q. Let’s first define Q as an ordered set. It is known that Q may be defined as the Cartesian product of sets X and Y, where X and Y are equal to N / {0}. For every ordered pair (x, y) of the product there is corresponding number q which is in Q and stands that q = y / x. Using this definition we may create an ordered set of linear structure equivalent to Q.
Set Q may be structured as an ordered set of subsets. Initially we place numbers “0” and “1 /1”, then for every i > 2 we place in the i-th place of the set the subset of Q that for all its elements stands x+y=i. By placing in ascending order the elements within each subset we create an ordered set of linear structure with all the elements of Q. On this procedure we end up with a representation of Q that is commonly used in literature.
On input of the descriptions of a and b, TM M using the above procedure creates the ordered subset of Q that contains a and b and then computes their distance by simply counting the elements between them one by one.
The ordered set of linear structure that is subset of Q and contains a and b is computable as it may be constructed algorithmically as shown above and is finite as I will show next.
Let a=y1/x1 and b=y2/x2, then between a and b there is a finite number of subsets d= (x2+y2)-(x1+y1). For each subset stands that i=x+y and is finite as there is a finite number of combinations that may generate i given that i, x, y belong in N.

Cartesian product

1/1   1/2   1/3   1/4 ……
2/1   2/2   2/3   2/4 ……
3/1   3/2   3/3   3/4 ……
4/1   4/2   4/3   4/4 ……
………………………..

Order of elements

 1       2       4       7  ……
 3       5       8      11 ……
 6       9      12     14 ……
10     13     15     16 ……
………………………...


Q as set of subsets

(0), (1/1), (1/2, 2/1), (1/3, 2/2, 3/1), (1/4, 2/3, 3/2, 4/1), (… 2/4, 3/3, 4/2 …), (… 3/4, 4/3 …), (… 4/4 …)


Q as an ordered set of linear structure

0, 1/1, 1/2, 2/1, 1/3, 2/2, 3/1, 1/4, 2/3, 3/2, 4/1, …, 2/4, 3/3, 4/2, …, 3/4, 4/3, …, 4/4 …


Theorem 2. The set R of real numbers is uncountable.

Proof. It is known that there is no algorithmic procedure which may produce the numbers in R. That is because most of numbers in R are uncomputable. 
Let's consider a description d for the numbers in R so that they may be represented in TM M. If there is procedure implemented in M that structures R as an ordered set then M computes uncomputable numbers which is a contradiction. As a result R may not be structured as an ordered set and is not countable by M.

Friday, 26 June 2015

System security as a computational problem

In this post I talk about system security and I examine it as a computational problem. The motivation of thinking about this came from a very interesting post by Dick Lipton and Ken Regan that was published on their blog a few days ago.
The problem of developing a secure system regards the development of a system that will maintain a set of information F which will only be accessible by authorised users. As a result, any unauthorised user will be excluded from accessing F. A good way to study the system is to model it using Turing thesis.
Any user  has to provide some input like username and password in order to get clearance and usually F is represented by some text or binary information, so let's define F as a set of strings and w as a string which is the user input. Also let A be a Turing Machine that simulates the system we have developed and we consider it secure.
Some systems in order to identify authorised users demand extensive interaction which results that users have to fill questionnaires and forms. Without loss of generality, we may enclose all the user input in w also the acceptance or rejection of it by the system may be modeled efficiently by the operation of A. On this assumption, A will accept a valid w and output F or reject input from not authorized users and output "NO".
Let S be a set of strings so that if A decides that input w belongs to S then the user is considered as authorized and F is outputted. There are two cases where this model does not work as expected.
Case1. There is string e that does not belong to S and on input in A, it outputs F. So an intruder may use e in order to gain access to F without knowing w.
Case2. String w may be discovered algorithmically by unauthorized users.

Some explanation on these cases.
In case1 I model the cases of the real world where A is hacked. The intruder does not input the identification of some authorized user in order to access F, so user input does not belong to S. The intruder inputs a string that will cause A to get undesired behavior and this will result the output of F. In real world, sometimes the intruders exploit security holes and send data to the systems (like macros, telnet commands or Trojans) that cause such undesired behavior.

Case2 refers to the complexity of w and how easily it can be guessed.

Intentionally I leave out the cases where w has been stolen from its owner with techniques likes phishing or similar; this seems to be more of a social than a computational problem.

The computability of security and why hacking is easier that defending


The following language describes Case1 as a computational problem.
SECURITY1 = {A, S, F, e | There is string e that does not belong to set S, TM A outputs F and halts on input e.}

Theorem. Problem SECURITY1 is recognizable and undecidable.

Proof. SECURITY1 is recognizable. We input in A string e that does not belong to S and we get F. If SECURITY1 is also decidable then for any input s that does not belong to S, we may decide whether machine A halts and outputs F or “NO”. As A can be any TM and s any string, these assumptions stand only if the halting problem is decidable and this results a contradiction.

Although both the developer of A and an intruder face the same problem, as they both have to solve one instance of SECURITY1 in order to build or hack A. Yet, the job of the intruder is easier than the job of the developer. The intruder has to solve the recognizable side of the problem while the defender has to approach the undecidable side of it.
In other words, if you try to intrude A you build a process described by string e and once you input it to A you can find out if it works. If you are a defender you can never be sure that the system that you build will be strong enough to resist attacks. Building A is a far more complex task than discovering e and that is why you see systems that are developed by experienced developing teams to have been hacked by some technology enthusiast teenager.

 

About Case 2


Case2 refers to the complexity of w or else to the complexity of the procedure of entering user identification data. There is no point on examining the computability of such processes as most of them are in theory computable. For instance, when a user has to enter a 4 digit PIN in a system the number of possible PINs that may be entered is bounded and in theory it is computable. If we enter the restriction that the user has only three attempts to enter the correct PIN then it gets difficult for an intruder to guess the PIN.
The proposed approach by Lipton and Regan actually increases the complexity of the procedure of identification. It gets harder for an intruder to guess w which may be subject to frequent changes if the questions asked by the system change frequently.



The image on this post is freely available under Creative Commons License. Check the source for the original image and many more.