Object Oriented Programming - Abstraction | My Mind Leaks... - 6 views
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Ahmed One on 24 Jun 097elw awi el Article dah.....GO On. -
Islam TeCNo on 24 Jun 09Great ya Fili
JAVA Developer's Guide
JAVA Developer's Guide Book - 2 views
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really great reference for who want to learn Java ... (Y)
milw0rm - exploits : vulnerabilities : videos : papers : shellcode - 1 views
Defensive programming - Wikipedia, the free encyclopedia - 3 views
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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.
Simulated annealing - Wikipedia, the free encyclopedia - 1 views
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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].
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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].
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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].
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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].
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A natural AI approach
Production system - Wikipedia, the free encyclopedia - 0 views
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A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.
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A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.
Flocking (behavior) - Wikipedia, the free encyclopedia - 0 views
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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.
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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.
Clockwork universe theory - Wikipedia, the free encyclopedia - 1 views
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The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.
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The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.
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"God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time" <-- ???
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Artificial life - Wikipedia, the free encyclopedia - 2 views
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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
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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].
Steady state - Wikipedia, the free encyclopedia - 0 views
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A system in a steady state has numerous properties that are unchanging in time. The concept of steady state has relevance in many fields, in particular thermodynamics. Steady state is a more general situation than dynamic equilibrium. If a system is in steady state, then the recently observed behavior of the system will continue into the future. In stochastic systems, the probabilities that various different states will be repeated will remain constant.
Belief-Desire-Intention model - Wikipedia, the free encyclopedia - 1 views
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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.
Belief-Desire-Intention software model - Wikipedia, the free encyclopedia - 0 views
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The Belief-Desire-Intention (BDI) software model (usually referred to simply, but ambiguously, as BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.
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Stress on the point "BDI Agents spent time about choosing what to do more that how to execute them"
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Google C++ Style Guide - 5 views
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Google C++ Style Guide
Genetic programming - Wikipedia, the free encyclopedia - 0 views
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In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
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In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
Mutation testing - Wikipedia, the free encyclopedia - 1 views
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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.
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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;
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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; }
Quis custodiet ipsos custodes? - Wikipedia, the free encyclopedia - 0 views
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The question is put to Socrates, "Who will guard the guardians?" or, "Who will protect us against the protectors?" Plato's answer to this is that they will guard themselves against themselves. We must tell the guardians a "noble lie."[1] The noble lie will inform them that they are better than those they serve and it is therefore their responsibility to guard and protect those lesser than themselves. We will instill in them a distaste for power or privilege; they will rule because they believe it right, not because they desire it.
Hill climbing - Wikipedia, the free encyclopedia - 0 views
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In computer science, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is relatively simple to implement, making it a popular first choice. Although more advanced algorithms may give better results, in some situations hill climbing works just as well. Hill climbing can be used to solve problems that have many solutions, some of which are better than others. It starts with a random (potentially poor) solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal, but it is not guaranteed that hill climbing will ever come close to the optimal solution. For example, hill climbing can be applied to the traveling salesman problem. It is easy to find a solution that visits all the cities but will be very poor compared to the optimal solution. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. Eventually, a much better route is obtained. Hill climbing is used widely in artificial intelligence, for reaching a goal state from a starting node. Choice of next node and starting node can be varied to give a list of related algorithms.
Stochastic optimization - Wikipedia, the free encyclopedia - 0 views
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Stochastic optimization (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices, etc.), or in both [1]. The concept contrasts with the deterministic optimization methods, where the values of the objective function are assumed to be exact, and the computation is completely determined by the values sampled so far.
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In Artificial Intelligence, Genetic Algorithms belongs to class Stochastic search that is explained below
Genetic algorithm - Wikipedia, the free encyclopedia - 0 views
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A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).
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A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain.
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GE are primary used in Learning in AI
What is Tunneling? - 0 views
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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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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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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
