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fili el sayed

Object Oriented Programming - Abstraction | My Mind Leaks... - 6 views

mymindleaks.com/...iented-programming-abstraction

shared by fili el sayed on 24 Jun 09 - Cached
  • Ahmed One on 24 Jun 09
     
    7elw awi el Article dah.....GO On.
  • Islam TeCNo on 24 Jun 09
     
    Great ya Fili
Ahmed Mansour

JAVA Developer's Guide Book - 2 views

www.webbasedprogramming.com/JAVA-Developers-Guide

Java E-Books Guide

shared by Ahmed Mansour on 04 Jul 09 - Cached
Ahmed Attyah liked it
  • JAVA Developer's Guide
  • Ahmed Mansour on 04 Jul 09
     
    really great reference for who want to learn Java ... (Y)
fili el sayed

Introduction to Model View Control (MVC) Pattern using C# - 3 views

www.c-sharpcorner.com/...MVC_intro.aspx

shared by fili el sayed on 07 Jul 09 - Cached
  • we need to figure out what the heck it is.
    • fili el sayed
      fili el sayed on 07 Jul 09
       
      LOOOOOOOL, this is a good aproach to start analysis
Abdelrahman Ogail

Defensive programming - Wikipedia, the free encyclopedia - 3 views

en.wikipedia.org/...Defensive_programming

Software-Engineering Programming Q&A

shared by Abdelrahman Ogail on 06 Jan 10 - Cached
  • Defensive programming is a form of defensive design intended to ensure the continuing function of a piece of software in spite of unforeseeable usage of said software. The idea can be viewed as reducing or eliminating the prospect of Murphy's Law having effect. Defensive programming techniques are used especially when a piece of software could be misused mischievously or inadvertently to catastrophic effect.
Abdelrahman Ogail

Belief-Desire-Intention model - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...Belief-Desire-Intention_model

BDI-Architecture Artificial-Intelligence Agent

shared by Abdelrahman Ogail on 06 Aug 09 - Cached
  • The Belief-Desire-Intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories.
Abdelrahman Ogail

Mutation testing - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...Mutation_testing

Mutation Testing Testing-Techniques

shared by Abdelrahman Ogail on 01 Aug 09 - Cached
  • Mutation testing (or Mutation analysis) is a method of software testing, which involves modifying program's source code in small ways.[1] These, so-called mutations, are based on well-defined mutation operators that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero). The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.
  • For example, consider the following C++ code fragment: if (a && b) c = 1; else c = 0; The condition mutation operator would replace '&&' with '||' and produce the following mutant: if (a || b) c = 1; else c = 0;
  • Many mutation operators can produce equivalent mutants. For example, consider the following code fragment: int index=0; while (...) { . . .; index++; if (index==10) break; } Boolean relation mutation operator will replace "==" with ">=" and produce the following mutant: int index=0; while (...) { . . .; index++; if (index>=10) break; }
Abdelrahman Ogail

Artificial life - Wikipedia, the free encyclopedia - 2 views

en.wikipedia.org/...Artificial_life

Artificial-Intelligence alife AI-In-Games

shared by Abdelrahman Ogail on 07 Aug 09 - Cached
  • Artificial life (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry.[1] There are three main kinds of alife[2], named for their approaches: soft[3], from software; hard[4], from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate biological phenomena.[5] The term "artificial life" is often used to specifically refer to soft alife
  • The modeling philosophy of alife strongly differs from traditional modeling, by studying not only “life-as-we-know-it”, but also “life-as-it-might-be” [7].
Abdelrahman Ogail

Simulated annealing - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...Simulated_annealing

AI AI-In-Games Adaptation

shared by Abdelrahman Ogail on 18 Aug 09 - Cached
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • ...1 more annotation...
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Abdelrahman Ogail on 18 Aug 09
     
    Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration - provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima-which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Abdelrahman Ogail on 18 Aug 09
     
    A natural AI approach
Abdelrahman Ogail

CodeProject: C# vs C/C++ Performance. Free source code and programming help - 0 views

www.codeproject.com/...CSharpVsCPP.aspx

C# c++

shared by Abdelrahman Ogail on 17 Jun 09 - Cached
  • is compiled twice. Once while the program is written and second when the program is executed at the user's site. The first compilation is done by your C# builder and the second by the .NET Framework on the user's machine. The reason why C# compiled applications could be faster is that, during the second compilation, the compiler knows the actual run-time environment and processor type and could generate instructions that targets a specific processor.
  • A well designed C# program is more than 90% as fast as an equivalent "well-designed" C++ program
  • The problem with "not-freeing" the memory at the right time is that the working set of the application increases which increases the number of "page faults"
  • ...1 more annotation...
  • That's a nice question. Except for writing time-critical blocks of code, prefer C#. Write all your algorithmic code in C++ (not VC++ .NET), compile it into a dll and call that using a Dll Interop through C#. This should balance the performance. This technique is not new or not invented by me or anyone. It's similar the old age C programming vs Assembly, where people on one camp fight assembly programming is faster and the other camp stating C is easier to develop and then people started using assembly embedded within a C program for time-critical applications using an asm block.
  • Abdelrahman Ogail on 17 Jun 09
     
    C# is compiled twice. Once while the program is written and second when the program is executed at the user's site. The first compilation is done by your C# builder and the second by the .NET Framework on the user's machine. The reason why C# compiled applications could be faster is that, during the second compilation, the compiler knows the actual run-time environment and processor type and could generate instructions that targets a specific processor
Abdelrahman Ogail

Flocking (behavior) - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/...Flocking_(behavior)

ALife Artificial-Intelligence

shared by Abdelrahman Ogail on 07 Aug 09 - Cached
  • Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.
  • Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.
Islam TeCNo

Assembly in .NET - 0 views

www.c-sharpcorner.com/...1DotnetAssemblies.aspx

shared by Islam TeCNo on 24 Jun 09 - Cached
  • An assembly can be a single file or it may consist of the multiple files. In case of multi-file, there is one master module containing the manifest while other assemblies exist as non-manifest modules. A module in .NET is a sub part of a multi-file .NET assembly
  • The .NET assembly is the standard for components developed with the Microsoft.NET. Dot NET assemblies may or may not be executable, i.e., they might exist as the executable (.exe) file or dynamic link library (DLL) file. All the .NET assemblies contain the definition of types, versioning information for the type, meta-data, and manifest. The designers of .NET have worked a lot on the component (assembly) resolution.
    • Rock Fcis
      Rock Fcis on 24 Jun 09
       
      @ ISlam ma fel wiki katb aho kda The .NET assembly is the standard for components developed with the Microsoft.NET. Dot NET assemblies may or may not be executable, i.e., they might exist as the executable (.exe) file or dynamic link library (DLL) file.
  • Rock Fcis on 24 Jun 09
     
    tb ana delwa2ty fhmt 2n el assembly 7agteen ya 2emma .Exe file 2w el Dll ft7t brdi fel wiki la2et 2n hoa by2ol 2n feh fel assembly ya 2emma single file or multi file 7d yfhmny eah asdo b single 2w multi hal ya3ni asdo 2n el dll feh mmkn kaza class w kda walla a
  • Islam TeCNo on 24 Jun 09
     
    ma3takdsh ya Rock eno y2sod bel DLL Multifile
Islam TeCNo

3APL - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/3APL

shared by Islam TeCNo on 24 Jun 09 - Cached
Ahmed One liked it
  • An Abstract Agent Programming Language or Artificial Autonomous Agents Programming Language or 3APL (pronounced triple-A-P-L) is an experimental tool and programming language for the development, implementation and testing of multiple cognitive agents using the Belief-Desire-Intention (BDI) approach. The newest incarnation of 3APL is 2APL (A Practical Agent Programming Language).
    • Abdelrahman Ogail
      Abdelrahman Ogail on 24 Jun 09
       
      Anyone get anything about 2APL kindly tell me :D
    • Islam TeCNo
      Islam TeCNo on 24 Jun 09
       
      wad7 enha 7aga 7elwa awi !
Islam TeCNo

Widget or gadget: Which is it? - Event Tech Blog - Blog on Tradeshow Week - 0 views

www.tradeshowweek.com/...80029608.html

shared by Islam TeCNo on 24 Jun 09 - Cached
  • Islam TeCNo on 24 Jun 09
     
    dah Link Abu-Bakr edaholi
Islam TeCNo

LOL - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/Lol

shared by Islam TeCNo on 25 Jun 09 - Cached
  • OL (also written with some or all letters lowercase) is an abbreviation for laughing out loud[1][2] or laugh out loud.[3] LOL is a common element of Internet slang used historically on Usenet, but now widespread in other forms of computer-mediated communication, and even face-to-face communication. It is one of many initialisms for expressing bodily reactions, in particular laughter, as text, including initialisms such as ROTFL[4][5][6][7] or ROFL [8] ("roll(ing) on the floor laughing"), a more emphatic expression of laughter, and BWL ("bursting with laughter"), above which there is "no greater compliment" according to technology columnist Larry Magid.[9] Other unrelated expansions include the now mostly historical "lots of luck" or "lots of love" used in letter-writing.[10
    • Abdelrahman Ogail
      Abdelrahman Ogail on 25 Jun 09
       
      Source of the LOL
    • Islam TeCNo
      Islam TeCNo on 25 Jun 09
       
      hehe LOL :D
  • Corruptions of "LOL"
    • Abdelrahman Ogail
      Abdelrahman Ogail on 25 Jun 09
       
      This is a big LOL
Hassan Ibraheem

Making the most of your first job - Brief Article | Careers and Colleges | Find Article... - 0 views

findarticles.com/...ai_70739437

career

shared by Hassan Ibraheem on 25 Jun 09 - Cached
Ahmed One liked it
  • Hassan Ibraheem on 25 Jun 09
  • Ahmed One on 26 Jun 09
     
    Useful Article....Thanks
Islam TeCNo

What is Tunneling? - 0 views

www.tech-faq.com/tunneling.shtml

shared by Islam TeCNo on 17 Jul 09 - Cached
Ahmed Mansour liked it
  • This process is different from a normal data transfer between nodes. Every frame passing through the tunnel will be encrypted with an additional layer of tunneling encryption and encapsulation which is also used for routing the packets to the right direction
  • This process is different from a normal data transfer between nodes. Every frame passing through the tunnel will be encrypted with an additional layer of tunneling encryption and encapsulation which is also used for routing the packets to the right direction
  • This process is different from a normal data transfer between nodes. Every frame passing through the tunnel will be encrypted with an additional layer of tunneling encryption and encapsulation which is also used for routing the packets to the right direction
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