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So bone up on JavaScript, CSS, and HTML to succeed over the next five years

Every top-flight developer I’ve met recommends learning at least one dynamic or functional programming language to learn new ways of thinking, and from personal experience, I can tell you that it works.

These headers tell us which web server you were trying to contact.  If that seems odd, bear in mind that many web sites can be "hosted" on a single server, so when the request is received it needs to know which web site you were attempting to access

The request method is usually either "GET" or "POST".  Basically if you fill in and submit a form on a web page it might generate a POST request (or it might be "GET"), whereas if you just click on a link, or activate one of your browser's "bookmarks" or "favourites", then the request method will always be "GET". Therefore, if it's "POST", we can tell that a form was definitely submitted.  The contents of the form would appear here, and there would also be some "Content-" headers describing the data. Web browsers generate two kinds of "POST" data: either "multipart/form-data", which is used when uploading files to a web server, or the more common "application/x-www-form-urlencoded".

The "referer" header tells us which document referred you to us - in essence, if you followed a link to get to this page, it is the URL of the page you came from to get here.

A pattern must explain why a particular situation causes problems, and why the proposed solution is considered a good one. Christopher Alexander describes common design problems as arising from "conflicting forces" -- such as the conflict between wanting a room to be sunny and wanting it not to overheat on summer afternoons. A pattern would not tell the designer how many windows to put in the room; instead, it would propose a set of values to guide the designer toward a decision that is best for their particular application. Alexander, for example, suggests that enough windows should be included to direct light all around the room. He considers this a good solution because he believes it increases the enjoyment of the room by its occupants. Other authors might come to different conclusions, if they place higher value on heating costs, or material costs. These values, used by the pattern's author to determine which solution is "best", must also be documented within the pattern. A pattern must also explain when it is applicable. Since two houses may be very different from one another, a design pattern for houses must be broad enough to apply to both of them, but not so vague that it doesn't help the designer make decisions. The range of situations in which a pattern can be used is called its context. Some examples might be "all houses", "all two-story houses", or "all places where people spend time." The context must be documented within the pattern. For instance, in Christopher Alexander's work, bus stops and waiting rooms in a surgery are both part of the context for the pattern "A PLACE TO WAIT."

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.

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.

COUNT(*) returns the number of items in a group. This includes NULL values and duplicates. COUNT(ALL expression) evaluates expression for each row in a group and returns the number of nonnull values. COUNT(DISTINCT expression) evaluates expression for each row in a group and returns the number of unique, nonnull values.

design pattern is a widely accepted solution to a recurring design problem in OOP a design pattern describes how to structure classes to meet a given requirement provides a general blueprint to follow when implementing part of a program does not describe how to structure the entire application does not describe specific algorithms focuses on relationships between classes

design patterns: make you more productive help you write cleaner code Observer and Singleton are just two of the many available if you like design patterns, try these resources: GoF book -- Design Patterns: Elements of Reusable Object-oriented Software design pattern examples in Java, see Design Patterns in Java Reference and Example Site

learn what a design pattern is

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.

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.

Voice over Internet Protocol (VoIP) is a general term for a family of transmission technologies for delivery of voice communications over IP networks such as the Internet or other packet-switched network

Internet telephony refers to communications services—voice, facsimile, and/or voice-messaging applications—that are transported via the Internet, rather than the public switched telephone network (PSTN). The basic steps involved in originating an Internet telephone call are conversion of the analog voice signal to digital format and compression/translation of the signal into Internet protocol (IP) packets for transmission over the Internet; the process is reversed at the receiving end.[1]

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.

IPv4 Internet Protocol version 4

IPv6 Internet Protocol version 6

TCP Transmission Control Protocol UDP User Datagram Protocol

In telecommunications and software engineering, scalability is a desirable property of a system, a network, or a process, which indicates its ability to either handle growing amounts of work in a graceful manner, or to be readily enlarged.[1] For example, it can refer to the capability of a system to increase total throughput under an increased load when resources (typically hardware) are added. An analogous meaning is implied when the word is used in a commercial context, where scalability of a company implies that the underlying business model offers the potential for economic growth within the company.

Neuroplasticity (also referred to as brain plasticity, cortical plasticity or cortical re-mapping) is the changing of neurons and the organization of their networks and so their function by experience. This idea was first proposed in 1892 by Santiago Ramón y Cajal the proposer of the neuron doctrine though the idea was largely neglected for the next fifty years.[1] The first person to use the term neural plasticity appears to have been the Polish neuroscientist Jerzy Konorski.[2]

Rich Internet applications (RIAs) are web applications that have some of the characteristics of desktop applications, typically delivered by way of a proprietary web browser plug-ins or independently via sandboxes or virtual machines[1]. Examples of RIA frameworks include Curl (programming language), Adobe Flash/Adobe Flex/AIR, Java/JavaFX[2], uniPaaS[3] and Microsoft Silverlight[4].

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.