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aksik [14]
2 years ago
15

PLSS HELP ASAP ILL GIVE BRAINLIEST THANKS

Computers and Technology
2 answers:
olasank [31]2 years ago
5 0

Answer:

I think division I am not sure

Explanation:

but hope that helps

Leona [35]2 years ago
3 0

Box. Atleast thats what my class calls it.

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The answer is A.؜؜؜؜؜؜؜؜؜؜؜؜؜؜؜؜؜؜

7 0
3 years ago
When are numbered lists generally used?
laiz [17]
<span>C. when listing items that have an order of priority </span>
4 0
3 years ago
Read 2 more answers
ossless compression tools generally use either Huffman coding or Lempel-Ziv-Welch (LZW) coding. Discuss the advantages and disad
anygoal [31]

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.

3 0
3 years ago
? An attempt to harm damage or cause threat to a system or network is broadly termed as
Radda [10]

Answer:

A cyber attack or hacking

Explanation:

6 0
2 years ago
If you posted an advertisement for your research study on marriage on the Internet and the couples who responded (i.e., the coup
WITCHER [35]

Answer:

B. volunteer bias.

Explanation:

-Experimenter bias is when the expectations of the experimenter in regards to the outcome are communicated to the participants in any form.

-Volunteer bias refers to a situation in which the people that volunteer to take place in a study doesn't represent the general public.

-Research bias is when the experimenter influence the result to get a specific outcome.

-Social desirability bias is when the people taking part in a study give their responses in a way that is viewed as favorable.

According to this, the answer is that the situation would be an example of volunteer bias.

3 0
2 years ago
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