Simulation

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning.
There are two types of simulation:-
Physical simulation refers to simulation in which physical objects are substituted for the real thing (some circles use the term for computer simulations modelling selected laws of physics, but this article doesn't). These physical objects are often chosen because they are smaller or cheaper than the actual object or system.
Interactive simulation is a special kind of physical simulation, often referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.
Human in the loop simulations can include a computer simulation as a so-called synthetic environment.

Wireless communication

The term wireless is normally used to refer to any type of electrical or electronic operation which is accomplished without the use of a "hard wired" connection. Wireless communication is the transfer of information over a distance without the use of electrical conductors or "wires ".The distances involved may be short (a few meters as in television remote control) or very long (thousands or even millions of kilometers for radio communications). When the context is clear the term is often simply shortened to "wireless". Wireless communications is generally considered to be a branch of telecommunication .
Wireless operations permits services, such as long range communications, that are impossible or impractical to implement with the use of wires. The term is commonly used in the telecommunications industry to refer to telecommunications systems (e.g., radio transmitters and receivers, remote controls, computer networks, network terminals, etc.) which use some form of energy to transfer information without the use of wires. Information is transferred in this manner over both short and long distances.
The term "wireless" has become a generic and all-encompassing word used to describe communications in which electromagnetic waves or RF (rather than some form of wire) carry a signal over part or the entire communication path.
Wireless networking (i.e. the various flavors of unlicensed 2.4 GHz WiFi devices) is used to meet a variety of needs. Perhaps the most common use is to connect laptop users who travel from location to location. Another common use is for mobile networks that connect via satellite. A wireless transmission method is a logical choice to network a LAN segment that must frequently change locations.

Neural Networks

In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses , are usually formed from axons to dendrite , though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex. Whilst a detailed description of neural systems is nebulous, progress is being charted towards a better understanding of basic mechanisms.

Simplified view of an artificial neural network

Artificial intelligence and cognitive modelling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems.

In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition,image analysis and adaptive control , in order to construct software agents(in computer and video games) or autonomous robots . Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimization and control theory.

The cognitive modelling field involves the physical or mathematical modeling of the behaviour of neural systems; ranging from the individual neural level (e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism (e.g. behavioural modelling of the organism's response to stimuli).

Telecommunication

Telecommunication is the assisted transmission of signals a distance for the purpose of communication. In earlier times, this may have involved the use of smoke signals, drums, semaphores,flags, or heliograph . In modern times, telecommunication typically involves the use of electronic transmitters such as the telephone, television, radio,computer. Early inventors in the field of telecommunication include antonio meucci ,Alexander Graham Bell,Guglielmo marconi and john logie baird . Telecommunication is an important part of the world economy and the telecommunication industry's revenue has been placed at just under 3 percent of the gross world product .

Artificial Learning

Much effort has been devoted to understanding learning and reasoning in artificial intelligence, giving rise to a wide collection of models. For the most part, these models focus on some observed characteristic of human learning, such as induction or analogy, in an effort to emulate (and possibly exceed) human abilities. We propose seven desirable properties for artificial learning systems: incrementality, non-monotonicity, inconsistency and conflicting defaults handling, abstraction, self-organization, generalization, and computational tractability.We examine each of these properties in turn and show how their (combined) use can improve learning and reasoning, as well as potentially widen the range of applications of artificial learning systems.
Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.

If features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoreical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.

All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology , a new mathematical theory of information,an understanding of control and stability called cybernetics , and above all, by the invention of the digital computer , a machine based on the abstract essence of mathematical reasoning

Sylvan learning

Sylvan Learning is a chain of franchised tutoring centers which provide personalized tutoring in readnig,writing, mathematics, skills and test-prep for college entrance and state exams. Since June of 2007, Sylvan has been owned by Edge Acquisition, LLC, which operates a variety of for-profit educational businesses. Sylvan Learning is headquartered in Baltimore,Maryland.

By 1987 Sylvan had slightly over 500 franchises nationwide and went public on the NASDAQ exchange. By the summer of 1987 most of the stock was acquired by KinderCare, Inc. (Montgomery, AL). The company moved its headquarters from suburban Seattle to Alabama at that time.

KinderCare executives by late 1988 had replaced the original Sylvan founding staff.

Sylvan Learning began in Portland, OR in the early 1980's and was founded by former school teacher, W. Berry Fowler. By late 1983, Sylvan was successfully managing multiple franchises in the western USA and moved to suburban Seattle. Julie Davis was responsible for the educational programs started by Fowler. She added more reading programs and math, college prep plus pre-K programs. Claude Rorabaugh (who later was responsible for the development and growth of LaserGrade Computer Testing in the 2000's) was responsible for marketing and franchise center development. Besides profitable center development, he and his team offered tuition financing and actual guarantees of student growth. ("In 3 and a half months your child can gain a full grade level...")Those marketing tools plus endorsements from former Reagan cabinet member, Education Secretary William Bennett, fueled the growth of company in the mid '80's.

Its Services:-

Sylvan Learning offers instruction in the form of remedial help or enrichment for high-achieving students. For students who are experiencing difficulty in school, Sylvan's programs include Beginning Reading, Academic Reading, Academic Writing, Math Essentials, Advanced Math, Study Skills, and Homework Support. Students who are on track in school may enroll in Sylvan's SAT and ACT prep courses, as well as Advanced Reading, which is a form of speed reading . Starting in 2008, Sylvan Learning Center now offers a College Writing Prep class, matching that with rival Huntington Learning Center.

learning by analogy

Reasoning by analogy generally involves abstracting details from a a particular set of problems and resolving structural similarities between previously distinct problems. Analogical reasoning refers to this process of recognition and then applying the solution from the known problem to the new problem. Such a technique is often identified as case-based reasoning. Analogical learning generally involves developing a set of mappings between features of two instances. Paul Thagard and Keith Holyoak have developed a computational theory of analogical reasoning that is consistent with the outline above, provided that abstraction rules are provided to the model.
Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. The history of work on analogy in modern cognitive science is sketched, focusing on contributions from cognitive psychology, artificial intelligence, and philosophy of science. This review sets the stage for the 3 articles that follow in this Science Watch section.
Many professors rely on analogy, metaphor and over-generalization to maximize complex learning, and it's a skill that is taught in graduate schools across the world. I've taught over 80 classes in graduate school, and the only way to teach complex topics like statistics and database are by using analogies, building on the prerequisite conceptual framework classes such as "Algorithms" and "Data Structures" courses.
Teaching by analogy and over-generalization is an integral part of the learning process, especially for complex concepts, and academic research confirms the centuries-old belief that children learn complex concepts best by analogy and over-simplification.
Ontology Recapitulates Phylogny is the analogy (untrue, BTW), that a developing zygote/fetus reproduces it's own evolutionary stages

a quotes given by william wordsworth

Science appears as what in truth she is,
Not as our glory and our absolute boast,
But as a succedaneum, and a prop
To our infirmity.

learning by induction

Inductive learning is essentially learning by example. The process itself ideally implies some method for drawing conclusions about previously unseen examples once learning is complete. More formally, one might state: Given a set of training examples, develop a hypothesis that is as consistent as possible with the provided data. It is worthy of note that this is an imperfect technique. As Chalmers points out, "an inductive inference with true premises [can] lead to false conclusions". The example set may be an incomplete representation of the true population, or correct but inappropriate rules may be derived which apply only to the example set.

A simple demonstration of this type of learning is to consider the following set of bit-strings (each digit can only take on the value 0 or 1), each noted as either a positive or negative example of some concept. The task is to infer from this data (or "induce") a rule to account for the given classification:

A rule one could induce from this data is that strings with an even number of 1's are "+", those with an odd number of 1's are "-". Note that this rule would indeed allow us to classify previously unseen strings (i.e. 1001 is "+").

Techniques for modeling the inductive learning process include: Quinlan's decision trees (results from information theory are used to partition data based on maximizing "information content" of a given sub-classification) , connection decision list techniques , among others. (most neural network models rely on training techniques that seek to infer a relationship from examples) and

This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods both connectionist and symbolic

The intrinsic accuracy of an inductive problem is the accuracy achieved by exhaustive table look-up. Intrinsic accuracy is the upper bound for any inductive method. Hard concepts are concepts that have high intrinsic accuracy, but which cannot be learned effectively with traditional inductive methods. To learn hard concepts, we must use constructive induction - methods that create new features. We use measures of concept dispersion to explore (conceptually and empirically) the inherent weaknesses of traditional inductive approaches. These structural defects are buried in the design of the algorithms and prevent the learning of hard concepts. After studying some examples of successful and unsuccessful feature construction ("success" being defined here in terms of accuracy), we introduce a single measure of inductive difficulty that we call variation. We argue for a specific approach to constructive induction that reduces variation by incorporating various kinds of domain knowledge. All of these kinds of domain knowledge boil down to utility invariants, i.e., transformations that group together non-contiguous portions of feature space having similar class-membership values. Utility invariants manifest themselves in various ways: in some cases they exist in the user's stock of domain knowledge, in other cases they may be discovered via methods we describe

LEARNING CENTERS

Online Schools ensures that all schools have effective access to the communications and information resources of the Internet. Online Schools has developed guidelines for cost-effective Internet Learning Centers with reliable access to the Internet. In creating these guidelines, Online Schools considered various elements such as Internet connectivity, computer availability, acquisition costs, scalability, technical support, and educational tools.

An internet learning centers are made up of a network of ten high-speed, Pentium-class computers with color monitors, two printers, a scanner and a digital still camera. Software included word processing, presentation, spreadsheet, antivirus, web publishing, and image editing applications.

Online Schools suggests a variety of ways to access the Internet, including dial-up, leased-line, and wireless.
The Project Coordinator should create partnerships with a local Internet Service Provider (ISP) and local
technology distributors to support reliable, and sustainable Internet access.

In locations where high bandwidth is available, leased-line access would be appropriate. Regions that are more
rural, but maintain telephone lines should use dial-up access. Remote regions and areas without reliable
telephone access should investigate the feasibility of wireless Internet access. Internet speed should be in the
range from 28K to 128K. The Internet should be available for use during the school day for a minimum of eight
hours.