Saturday, July 31, 2010

What is the difference between final, finally and finalize?

Short answer:
final - declares constant
finally - relates with exception handling
finalize - helps in garbage collection
If asked to give details, explain:
final field, final method, final class
try/finally, try/catch/finally
protected void finalize() in Object class

What are use cases?

A use case describes a situation that a program might encounter and what behavior the program should exhibit in that circumstance. It is part of the analysis of a program. The collection of use cases should, ideally, anticipate all the standard circumstances and many of the extraordinary circumstances possible so that the program will be robust.

What are use cases?

What is the benefit of subclass?

Generally: The sub class inherits all the public methods and the implementation.
The sub class inherits all the protected methods and their implementation.
The sub class inherits all the default(non-access modifier) methods and their implementation.
The sub class also inherits all the public, protected and default member variables from the super class.
The constructors are not part of this inheritance model.

What is polymorphism?

Polymorphism means "having many forms". It allows methods (may be variables) to be written that needn't be concerned about the specifics of the objects they will be applied to. That is, the method can be specified at a higher level of abstraction and can be counted on to work even on objects of un-conceived classes.

How the object oriented approach helps us keep complexity of software development under control?

We can discuss such issue from the following aspects:
Objects allow procedures to be encapsulated with their data to reduce potential interference.
Inheritance allows well-tested procedures to be reused and enables changes to make once and have effect in all relevant places.
The well-defined separations of interface and implementation allow constraints to be imposed on inheriting classes while still allowing the flexibility of overriding and overloading.

What kind of security tools are available in J2SE 5.0?

There are three tools that can be used to protect application working within the scope of security policies set at remote sites.
keytool -- used to manage keystores and certificates.
jarsigner -- used to generate and verify JAR signatures.
policytool -- used for managing policy files.
There are three tools that help obtain, list and manage Kerberos tickets.
kinit -- used to obtain Kerberos V5 tickets.
tklist -- used to list entries in credential cache and key tab.
ktab -- used to help manage entries in the key table.

What is weak reference in Java

A weak reference is one that does not prevent the referenced object from being garbage collected. You might use them to manage a HashMap to look up a cache of objects. A weak reference is a reference that does not keep the object it refers to alive. A weak reference is not counted as a reference in garbage collection. If the object is not referred to elsewhere as well, it will be garbage collected

In which case would you choose a static inner class?

Interesting one, static inner classes can access the outer class's protected and private fields. This is both a positive and a negitive point for us since we can, in essence, violate the encapsulation of the outer class by mucking up the outer class's protected and private fields. The only proper use of that capability is to write white-box tests of the class -- since we can induce cases that might be very hard to induce via normal black-box tests (which don't have access to the internal state of the object). Second advantage,if I can say, is that, we can this static concept to impose restriction on the inner class. Again as discussed in earlier point, an Inner class has access to all the public, private and protected members of the parent class. Suppose you want to restrict the access even to inner class, how would you go ahead? Making the inner class static enforces it to access only the public static members of the outer class( Since, protected and private members are not supposed to be static and that static members can access only other static members). If it has to access any non-static member, it has to create an instance of the outer class which leads to accessing only public members.

What are the different types of inner classes?

There are four different types of inner classes in Java. They are: a)Static member classes , a static member class has access to all static methods of the parent, or top-level, class b) Member classes, the member class is instance specific and has access to any and all methods and members, even the parent's this reference c) Local classes, are declared within a block of code and are visible only within that block, just as any other method variable. d) Anonymous classes, is a local class that has no name

What kind of security tools are available in J2SE 5.0?

What is the difference between shallow copy and deep copy?

Shallow copy shares the same reference with the original object like cloning, whereas the deep copy get a duplicate instance of the original object. If the shallow copy has been changed, the original object will be reflected and vice versa.

Friday, July 30, 2010

What are use cases?

What is design by contract?

The design by contract specifies the obligations of a method to any other methods that may use its services and also theirs to it. For example, the preconditions specify what the method required to be true when the method is called. Hence making sure that preconditions are. Similarly, postconditions specify what must be true when the method is finished, thus the called method has the responsibility of satisfying the post conditions.
In Java, the exception handling facilities support the use of design by contract, especially in the case of checked exceptions. The assert keyword can be used to make such contracts.

Does the code in finally block get executed if there is an exception and a return statement in a catch block?

If an exception occurs and there is a return statement in catch block, the finally block is still executed. The finally block will not be executed when the System.exit(1) statement is executed earlier or the system shut down earlier or the memory is used up earlier before the thread goes to finally block.

What is a Java package and how is it used?

A Java package is a naming context for classes and interfaces. A package is used to create a separate name space for groups of classes and interfaces. Packages are also used to organize related classes and interfaces into a single API unit and to control accessibility to these classes and interfaces

What is casting?

There are two types of casting, casting between primitive numeric types and casting between object references. Casting between numeric types is used to convert larger values, such as double values, to smaller values, such as byte values. Casting between object references is used to refer to an object by a compatible class, interface, or array type reference.

What is the difference between a Window and a Frame?

Heavy weight components like Abstract Window Toolkit (AWT), depend on the local windowing toolkit. For example, java.awt.Button is a heavy weight component, when it is running on the Java platform for Unix platform, it maps to a real Motif button. In this relationship, the Motif button is called the peer to the java.awt.Button. If you create two Buttons, two peers and hence two Motif Buttons are also created. The Java platform communicates with the Motif Buttons using the Java Native Interface. For each and every component added to the application, there is an additional overhead tied to the local windowing system, which is why these components are called heavy weight.

What is the difference between interface and abstract class?

interface contains methods that must be abstract; abstract class may contain concrete methods. interface contains variables that must be static and final; abstract class may contain non-final and final variables. members in an interface are public by default, abstract class may contain non-public members. interface is used to "implements"; whereas abstract class is used to "extends". interface can be used to achieve multiple inheritance; abstract class can be used as a single inheritance. interface can "extends" another interface, abstract class can "extends" another class and "implements" multiple interfaces. interface is absolutely abstract; abstract class can be invoked if a main() exists. interface is more flexible than abstract class because one class can only "extends" one super class, but "implements" multiple interfaces. If given a choice, use interface instead of abstract class.

What is the difference between the Boolean & operator and the && operator?

If an expression involving the Boolean & operator is evaluated, both operands are evaluated. Then the & operator is applied to the operand. When an expression involving the && operator is evaluated, the first operand is evaluated. If the first operand returns a value of true then the second operand is evaluated. The && operator is then applied to the first and second operands. If the first operand evaluates to false, the evaluation of the second operand is skipped.
Operator & has no chance to skip both sides evaluation and && operator does. If asked why, give details as above.

What is the numeric promotion?

Numeric promotion is used with both unary and binary bitwise operators. This means that byte, char, and short values are converted to int values before a bitwise operator is applied.
If a binary bitwise operator has one long operand, the other operand is converted to a long value.
The type of the result of a bitwise operation is the type to which the operands have been promoted. For example:
short a = 5;
byte b = 10;
long c = 15;
The type of the result of (a+b) is int, not short or byte. The type of the result of (a+c) or (b+c) is long.

What is the difference between preemptive scheduling and time slicing?

Under preemptive scheduling, the highest priority task executes until it enters the waiting or dead states or a higher priority task comes into existence. Under time slicing, a task executes for a predefined slice of time and then reenters the pool of ready tasks. The scheduler then determines which task should execute next, based on priority and other factors.

What is the difference between Process and Thread?

A process can contain multiple threads. In most multithreading operating systems, a process gets its own memory address space; a thread doesn't. Threads typically share the heap belonging to their parent process. For instance, a JVM runs in a single process in the host O/S. Threads in the JVM share the heap belonging to that process; that's why several threads may access the same object. Typically, even though they share a common heap, threads have their own stack space. This is how one thread's invocation of a method is kept separate from another's. This is all a gross oversimplification, but it's accurate enough at a high level. Lots of details differ between operating systems. Process vs. Thread A program vs. similar to a sequential program an run on its own vs. Cannot run on its own Unit of allocation vs. Unit of execution Have its own memory space vs. Share with others Each process has one or more threads vs. Each thread belongs to one process Expensive, need to context switch vs. Cheap, can use process memory and may not need to context switch More secure. One process cannot corrupt another process vs. Less secure. A thread can write the memory used by another thread.

What are three ways in which a thread can enter the waiting state?

A thread can enter the waiting state by invoking its sleep() method, by blocking on IO, by unsuccessfully attempting to acquire an object's lock, or by invoking an object's wait() method. It can also enter the waiting state by invoking its (deprecated) suspend() method

What are synchronized methods and synchronized statements?

Synchronized methods are methods that are used to control access to a method or an object. A thread only executes a synchronized method after it has acquired the lock for the method's object or class. Synchronized statements are similar to synchronized methods. A synchronized statement can only be executed after a thread has acquired the lock for the object or class referenced in the synchronized statement

What is synchronization and why is it important?

With respect to multithreading, synchronization is the capability to control the access of multiple threads to shared resources. Without synchronization, it is possible for one thread to modify a shared object while another thread is in the process of using or updating that object's value. This often causes dirty data and leads to significant errors.

What do you understand by Synchronization?

Synchronization is a process of controlling the access of shared resources by the multiple threads in such a manner that only one thread can access one resource at a time. In non synchronized multithreaded application, it is possible for one thread to modify a shared object while another thread is in the process of using or updating the object's value.
Synchronization prevents such type of data corruption.
E.g. Synchronizing a function:
public synchronized void Method1 () {
// Appropriate method-related code.
}
E.g. Synchronizing a block of code inside a function:
public myFunction (){
synchronized (this) {
// Synchronized code here.
}
}

NullPointerException in java

When an object is not initialized, the default value is null. When the following things happen, the NullPointerException is thrown:
--Calling the instance method of a null object.
--Accessing or modifying the field of a null object.
--Taking the length of a null as if it were an array.
--Accessing or modifying the slots of null as if it were an array.
--Throwing null as if it were a Throwable value.
The NullPointerException is a runtime exception. The best practice is to catch such exception even if it is not required by language design.

Construction of digital circuits

A digital circuit is often constructed from small electronic circuits called logic gates. Each logic gate represents a function of boolean logic. A logic gate is an arrangement of electrically controlled switches.

Each logic symbol is represented by a different shape. The actual set of shapes was introduce in 1984 under IEEE\ANSI standart 91-1984. "The logic symbol given under this standard are being increasingly used now and have even started appearing in the literature published by manufactors of digital integrated circuits."^[3]

The output of a logic gate is an electrical flow or voltage, that can, in turn, control more logic gates.

Logic gates often use the fewest number of transistors in order to reduce their size, power consumption and cost, and increase their reliability.

Integrated circuits are the least expensive way to make logic gates in large volumes. Integrated circuits are usually designed by engineers using electronic design automation software (See below for more information).

Another form of digital circuit is constructed from lookup tables, (many sold as "programmable logic devices", though other kinds of PLDs exist). Lookup tables can perform the same functions as machines based on logic gates, but can be easily reprogrammed without changing the wiring. This means that a designer can often repair design errors without changing the arrangement of wires. Therefore, in small volume products, programmable logic devices are often the preferred solution. They are usually designed by engineers using electronic design automation software (See below for more information).

When the volumes are medium to large, and the logic can be slow, or involves complex algorithms or sequences, often a small microcontroller is programmed to make an embedded system. These are usually programmed by software engineers.

When only one digital circuit is needed, and its design is totally customized, as for a factory production line controller, the conventional solution is a programmable logic controller, or PLC. These are usually programmed by electricians, using ladder logic

Advantages of digital electronics

One advantage of digital circuits when compared to analog circuits is ^[2] that signals represented digitally can be transmitted without degradation due to noise. For example, a continuous audio signal, transmitted as a sequence of 1s and 0s, can be reconstructed without error provided the noise picked up in transmission is not enough to prevent identification of the 1s and 0s. An hour of music can be stored on a compact disc as about 6 billion binary digits.

In a digital system, a more precise representation of a signal can be obtained by using more binary digits to represent it. While this requires more digital circuits to process the signals, each digit is handled by the same kind of hardware. In an analog system, additional resolution requires fundamental improvements in the linearity and noise charactersitics of each step of the signal chain.

Computer-controlled digital systems can be controlled by software, allowing new functions to be added without changing hardware. Often this can be done outside of the factory by updating the product's software. So, the product's design errors can be corrected after the product is in a customer's hands.

Information storage can be easier in digital systems than in analog ones. The noise-immunity of digital systems permits data to be stored and retrieved without degradation. In an analog system, noise from aging and wear degrade the information stored. In a digital system, as long as the total noise is below a certain level, the information can be recovered perfectly

Digital electronics represent signals by discrete levels, rather than by a continuous range. In most cases these states are represented by two voltage levels: one near to zero volts and a higher level near the supply voltage.

Hitachi J100

Intel 80486DX2

Digital techniques are useful because it is easier to get an electronic device to switch into one of a number of known states than to accurately reproduce a continuous range of values.

Digital electronics are usually made from large assemblies of logic gates, simple electronic representations of Boolean logic functions.^[1]

Thursday, July 29, 2010

A common way to define entropy for text is based on the Markov model of text. For an order-0 source (each character is selected independent of the last characters), the binary entropy is:

$H(\mathcal{S}) = - \sum p_i \log_2 p_i, \,\!$

where p_i is the probability of i. For a first-order Markov source (one in which the probability of selecting a character is dependent only on the immediately preceding character), the entropy rate is:

$H(\mathcal{S}) = - \sum_i p_i \sum_j \ p_i (j) \log_2 p_i (j), \,\!$

where i is a state (certain preceding characters) and

p i (j)

is the probability of

j

given

i

as the previous character.

For a second order Markov source, the entropy rate is

$H(\mathcal{S}) = -\sum_i p_i \sum_j p_i(j) \sum_k p_{i,j}(k)\ \log_2 \ p_{i,j}(k). \,\!$

[edit]b-ary entropy

In general the b-ary entropy of a source $\mathcal{S}$ = (S,P) with source alphabet S = {a₁, ..., a_n} and discrete probability distribution P = {p₁, ..., p_n} where p_i is the probability of a_i (say p_i = p(a_i)) is defined by:

$H_b(\mathcal{S}) = - \sum_{i=1}^n p_i \log_b p_i, \,\!$

Note: the b in "b-ary entropy" is the number of different symbols of the "ideal alphabet" which is being used as the standard yardstick to measure source alphabets. In information theory, two symbols are necessary and sufficient for an alphabet to be able to encode information, therefore the default is to let b = 2 ("binary entropy"). Thus, the entropy of the source alphabet, with its given empiric probability distribution, is a number equal to the number (possibly fractional) of symbols of the "ideal alphabet", with an optimal probability distribution, necessary to encode for each symbol of the source alphabet. Also note that "optimal probability distribution" here means a uniform distribution: a source alphabet with n symbols has the highest possible entropy (for an alphabet with n symbols) when the probability distribution of the alphabet is uniform. This optimal entropy turns out to be $\log_b \, n$

From Wikipedia, the free encyclopedia

Entropy of a Bernoulli trial as a function of success probability, called the binary entropy function.

In information theory, the binary entropy function, denoted $H(p) \,$ or $H_{\mathrm b}(p) \,$ , is defined as the entropy of a Bernoulli trial with probability of success p. Mathematically, the Bernoulli trial is modelled as a random variable X that can take on only two values: 0 and 1. The event

X = 1

is considered a success and the event

X = 0

is considered a failure. (These two events are mutually exclusive and exhaustive.)

Pr(X = 1) = p,

then

Pr(X = 0) = 1 - p

and the entropy of X is given by

$H(X) = H_{\mathrm b}(p) = -p \log p - (1 - p) \log (1 - p). \,$

where

0 l o g 0

is taken to be 0. The logarithms in this formula are usually taken (as shown in the graph) to the base 2. See binary logarithm.

When $p=\frac 1 2 ,$ the binary entropy function attains its maximum value. This is the case of the unbiased bit, the most common unit of information entropy.

H (p)

is distinguished from the entropy function by its taking a single scalar constant parameter. For tutorial purposes, in which the reader may not distinguish the appropriate function by its argument,

H 2 (p)

is often used; however, this could confuse this function with the analogous function related to Rényi entropy, so

H b (p)

(with "b" not in italics) should be used to dispel ambiguity.

The entropy H of a discrete random variable X with possible values {x₁, ..., x_n} is

$H(X) = \operatorname{E}(I(X)).$

Here E is the expected value function, and I(X) is the information content or self-information of X.

I(X) is itself a random variable. If p denotes the probability mass function of X then the entropy can explicitly be written as

$H(X) = \sum_{i=1}^n {p(x_i)\,I(x_i)} = -\sum_{i=1}^n {p(x_i) \log_b p(x_i)},$

where b is the base of the logarithm used. Common values of b are 2, Euler's number e, and 10, and the unit of entropy is bit for b = 2, nat for b = e, and dit (or digit) for b = 10.^[3]

In the case of p_i = 0 for some i, the value of the corresponding summand 0 log_b 0 is taken to be 0, which is consistent with the limit

$\lim_{p\to0+}p\log p = 0.$

Entropy (information theory)

From Wikipedia, the free encyclopedia

(Redirected from Information entropy)

This article is about the Shannon entropy in information theory. For other uses, see Entropy (disambiguation).

In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. Equivalently, the Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable. The concept was introduced by Claude E. Shannon in his 1948 paper "A Mathematical Theory of Communication".

Shannon's entropy represents an absolute limit on the best possible lossless compression of any communication, under certain constraints: treating messages to be encoded as a sequence ofindependent and identically-distributed random variables, Shannon's source coding theorem shows that, in the limit, the average length of the shortest possible representation to encode the messages in a given alphabet is their entropy divided by the logarithm of the number of symbols in the target alphabet.

A fair coin has an entropy of one bit. However, if the coin is not fair, then the uncertainty is lower (if asked to bet on the next outcome, we would bet preferentially on the most frequent result), and thus the Shannon entropy is lower. Mathematically, a coin flip is an example of a Bernoulli trial, and its entropy is given by the binary entropy function. A long string of repeating characters has an entropy rate of 0, since every character is predictable. The entropy rate of English text is between 1.0 and 1.5 bits per letter,^[1] or as low as 0.6 to 1.3 bits per letter, according to estimates by Shannon based on human experiments

Overview

The main concepts of information theory can be grasped by considering the most widespread means of human communication: language. Two important aspects of a concise language are as follows: First, the most common words (e.g., "a", "the", "I") should be shorter than less common words (e.g., "benefit", "generation", "mediocre"), so that sentences will not be too long. Such a tradeoff in word length is analogous to data compression and is the essential aspect of source coding. Second, if part of a sentence is unheard or misheard due to noise — e.g., a passing car — the listener should still be able to glean the meaning of the underlying message. Such robustness is as essential for an electronic communication system as it is for a language; properly building such robustness into communications is done by channel coding. Source coding and channel coding are the fundamental concerns of information theory.

Note that these concerns have nothing to do with the importance of messages. For example, a platitude such as "Thank you; come again" takes about as long to say or write as the urgent plea, "Call an ambulance!" while the latter may be more important and more meaningful in many contexts. Information theory, however, does not consider message importance or meaning, as these are matters of the quality of data rather than the quantity and readability of data, the latter of which is determined solely by probabilities.

Information theory is generally considered to have been founded in 1948 by Claude Shannon in his seminal work, "A Mathematical Theory of Communication". The central paradigm of classical information theory is the engineering problem of the transmission of information over a noisy channel. The most fundamental results of this theory are Shannon's source coding theorem, which establishes that, on average, the number of bits needed to represent the result of an uncertain event is given by its entropy; and Shannon's noisy-channel coding theorem, which states that reliablecommunication is possible over noisy channels provided that the rate of communication is below a certain threshold called the channel capacity. The channel capacity can be approached in practice by using appropriate encoding and decoding systems.

Information theory is closely associated with a collection of pure and applied disciplines that have been investigated and reduced to engineering practice under a variety of rubrics throughout the world over the past half century or more: adaptive systems, anticipatory systems, artificial intelligence, complex systems, complexity science, cybernetics, informatics, machine learning, along with systems sciences of many descriptions. Information theory is a broad and deep mathematical theory, with equally broad and deep applications, amongst which is the vital field of coding theory.

Coding theory is concerned with finding explicit methods, called codes, of increasing the efficiency and reducing the net error rate of data communication over a noisy channel to near the limit that Shannon proved is the maximum possible for that channel. These codes can be roughly subdivided into data compression (source coding) and error-correction (channel coding) techniques. In the latter case, it took many years to find the methods Shannon's work proved were possible. A third class of information theory codes are cryptographic algorithms (both codes and ciphers). Concepts, methods and results from coding theory and information theory are widely used in cryptography and cryptanalysis. See the article ban (information) for a historical application.

Information theory is also used in information retrieval, intelligence gathering, gambling, statistics, and even in musical composition.

Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Since its inception it has broadened to find applications in many other areas, includingstatistical inference, natural language processing, cryptography generally, networks other than communication networks — as in neurobiology,^[1] the evolution^[2] and function^[3] of molecular codes, model selection^[4] in ecology, thermal physics,^[5] quantum computing, plagiarism detection^[6] and other forms of data analysis.^[7]

A key measure of information in the theory is known as entropy, which is usually expressed by the average number of bits needed for storage or communication. Intuitively, entropy quantifies the uncertainty involved when encountering a random variable. For example, a fair coin flip (2 equally likely outcomes) will have less entropy than a roll of a die (6 equally likely outcomes).

Applications of fundamental topics of information theory include lossless data compression (e.g. ZIP files), lossy data compression (e.g. MP3s), and channel coding (e.g. for DSL lines). The field is at the intersection of mathematics, statistics, computer science, physics, neurobiology, and electrical engineering. Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, and numerous other fields^{[citation needed]}. Important sub-fields of information theory are source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information-theoretic security, and measures of information.

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