Answer:
The code to this question can be given as:
Code:
int lastVector = newScores.size() -1; //define variable lastVector that holds updated size of newScores.
newScores = oldScores; //holds value.
for (i = 0; i < SCORES_SIZE - 1; i++) //define loop.
{
newScores.at(i) = newScores.at(i+1); //holds value in newScores.
}
newScores.at(lastVector) = oldScores.at(0); //moving first element in last.
Explanation:
- In the given C++ program there are two vector array is defined that are "oldScores and newScores". The oldScores array holds elements that are "10, 20, 30, 40".
- In the above code, we remove the array element at first position and add it to the last position. To this process, an integer variable "lastVector" is defined.
- This variable holds the size of the newScores variable and uses and assigns all vector array elements from oldScores to newScores. In the loop, we use the at function the removes element form first position and add in the last position.
- Then we use another for loop for print newScores array elements.
Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
Many admins set their firewalls to drop echo-request packets to prevent their networks from being mapped via "Ping Sweeps".
A remote possibility is that there's too many hops between the source and target and the packet's TTL expires.
Answer:
highlighted items in webpage are called links.
<span>The answer to your question is D. both A and C</span>
