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ziro4ka [17]
1 year ago
15

What does the binary odometer show about representing large numbers​

Computers and Technology
1 answer:
Gelneren [198K]1 year ago
7 0

Answer and Explanation:

The binary odometer represents the large number to judge that what happened when there is a large number that gets too large

Here we visit the level 2 of the binary odometer widget in the chapter of Code studio

This represents a widget that reproduced an odometer of a car in which the tracking of a device could be known that how much far the car is driven with respect to the miles or kilometers

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1. Discuss why it is so important for all application builders to always check data received from unknown sources, such as Web a
Ivan

Answer:

1. It is so important for all application builders to always check data received from unknown sources before using that data. This is because of the Security related reasons and vulnerabilities .For example the data received might contain harmful hidden viruses.  Web applications are accessed by internet and these are the most vulnerable to attacks by hacker or intruders using harmful data containing malware. This can cause security breaches due to the security flaws or bugs in Web applications. So to overcome such security risks which can cause damage in the Web applications, data from unknown sources should be checked.

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Problem 2 - K-Best Values - 30 points Find the k-best (i.e. largest) values in a set of data. Assume you are given a sequence of
masya89 [10]

Answer:

See explaination

Explanation:

/**KBestCounter.java**/

import java.util.ArrayList;

import java.util.List;

import java.util.PriorityQueue;

public class KBestCounter<T extends Comparable<? super T>>

{

PriorityQueue heap;

int k;

public KBestCounter(int k)

{

heap = new PriorityQueue < Integer > ();

this.k=k;

}

//Inserts an element into heap.

//also takes O(log k) worst time to insert an element

//into a heap of size k.

public void count(T x)

{

//Always the heap has not more than k elements

if(heap.size()<k)

{

heap.add(x);

}

//if already has k elements, then compare the new element

//with the minimum element. if the new element is greater than the

//Minimum element, remove the minimum element and insert the new element

//otherwise, don't insert the new element.

else if ( (x.compareTo((T) heap.peek()) > 0 ) && heap.size()==k)

{

heap.remove();

heap.add(x);

}

}

//Returns a list of the k largest elements( in descending order)

public List kbest()

{

List al = new ArrayList();

int heapSize=heap.size();

//runs O(k)

for(int i=0;i<heapSize;i++)

{

al.add(0,heap.poll());

}

//Restoring the the priority queue.

//runs in O(k log k) time

for(int j=0;j<al.size();j++) //repeats k times

{

heap.add(al.get(j)); //takes O(log k) in worst case

}

return al;

}

}

public class TestKBest

{

public static void main(String[] args)

{

int k = 5;

KBestCounter<Integer> counter = new KBestCounter<>(k);

System.out.println("Inserting 1,2,3.");

for(int i = 1; i<=3; i++)

counter.count(i);

System.out.println("5-best should be [3,2,1]: "+counter.kbest());

counter.count(2);

System.out.println("Inserting another 2.");

System.out.println("5-best should be [3,2,2,1]: "+counter.kbest());

System.out.println("Inserting 4..99.");

for (int i = 4; i < 100; i++)

counter.count(i);

System.out.println("5-best should be [99,98,97,96,95]: " + counter.kbest());

System.out.println("Inserting 100, 20, 101.");

counter.count(100);

counter.count(20);

counter.count(101);

System.out.println("5-best should be [101,100,99,98,97]: " + counter.kbest());

}

}

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A.  

Picture

B.  

Shapes

C.  

Clip Art

D.  

SmartArt

E.  

Charts

Explanation:

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