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Showing posts with label file. Show all posts
Showing posts with label file. Show all posts

Tuesday, 21 January 2014

How To Prevent cut, paste, copy, delete, re-naming of files & folders.

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We are pleased to release Prevent v 1.0, a freeware app which runs on all Windows. If you don’t want anyone deleting or renaming or messing around with your data, maybe your younger sibling, then Prevent may be able to help you.
The downloaded zip file consists of:
1. Prevent.exe
2. Pre_1
3. Pre_2
4. Read Me file.
5. Uninstall
Run the Prevent installer setup. The installer only places the Prevent folder in the system Program Files folder. A desktop shortcut will also be created. To run the program, click on Prevent. Set your Hot key to stop Prevent. You may set it asCtrl+P if you wish. Hotkeys Win+F8 kills Pre_1 and Win+F9 kills Pre_2, too. But the single hotkey set by you will kill all Prevent processes at the same time.
Prevent :1. Stops Cut
2. Stops Paste
3. Stops Copy
4. Stops Delete
5. Stops Copy To
6. Stops Move to
7. Stops Send To
8. Prevents renaming
9. Disables Task Manager’s End Process button. Alsoit doesn’t allow you to right click on process name and click on end process. It also grays out the context menu items, disable Ctrl+C, Ctrl+X and Ctrl+V and/or stops the process.
To uninstall or remove Prevent, use the Uninstaller situated in the Prevent folder, or uninstall it via the Control Panel or simply delete its Program folder.
download1 Prevent cut, paste, copy, delete, re naming of files & folders.
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Friday, 27 December 2013

7 Tips to Revamp Your Job Search for 2014

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Here are seven tips to help you refresh and refocus your job search in 2014:
1. Don't be a copycat candidate. Job searches are a very personal experience and one-size-fits-all strategies will not help you stand out among the competition. Even though a certain interview tactic or style was successful for one candidate doesn't mean it is the best strategy for you. Take into consideration your personal experiences, preferences and career goals and use them to position yourself as a unique candidate.
2. Learn to look at job titles differently. Be open-minded about your preconceived notions of job titles. Roles in compliance, human resource, or administration, for example, are often perceived as being boring, career-limiting or otherwise undesirable. Such preconceptions, however, about the scope, strategic importance and long-term potential of these positions are not always true in today’s market. In many cases I’ve seen, these jobs offer exceptional opportunity for influential and attractive long-term careers. 
3. First impressions are everywhere. With 92% of employers using social media in the hiring process, the content of your social profile forms an employer’s first impression before you even sit down with for an interview.
You should take special consideration to job-proof your social media profiles. For example, use a picture that represents you as a professional. Don’t rely on privacy settings to keep your personal information safe. Your best bet is to assume everything will be seen by a potential employer, so clean up your content and edit your pictures accordingly. Ask yourself the age old question, "What would my grandmother think of this?"
4. Be prepared to land the job. Here’s one job seeker mistake that’s definitely worth kicking to the curb this year — the idea of an interview being just informational. You should go into every interview prepared to land the job, not just learn more about the organization. You only get one shot at your first interview, and this mistake will get you caught looking unprepared and unprofessional. Instead, always come ready for a formal interview; you will never regret being prepared.
5. Be strategic with social media. Social media is a vast resource for job seekers. The amount of content and connections thrown at you every day can become overwhelming and a time-suck if you’re not careful.
Be strategic with how you use social media to seek out job opportunities. First, know where recruiters and hiring managers for your desired industry spend most of their time. For example, in you’re looking for a job in finance, LinkedIn might be a more valuable social media site to keep updated as opposed to, say, Twitter. If you are looking to land a job at a media company, on the other hand, Twitter is a great resource. Be sure to also connect with the profiles or pages of companies you want to work for to stay up to date on job openings and announcements.
6. Network with your peers. You should aim to network with your peers, in addition to your more senior team members. Find ways to be the person that comes to mind when recruiters ask them who they’d recommend for the job. Your peers can also offer off-the-cuff, honest appraisals about your performance and work reputation when referring you for a job. These connections have the power either to open or close doors and your relationships with them will have a direct impact on which way the opportunity swings.

7. Be realistic. Be honest about what you can realistically offer to a new employer. It's tempting to apply for a more challenging and prestigious role, but make sure you have both the skills and the commitment to be successful. Don’t get me wrong, sometimes shooting for the stars really does pay off! However, this becomes a risky strategy when you promise more than you can reasonably deliver.
Take a candid look at your current lifestyle and think about how it would be impacted by the position you’re considering. Just because the perfect opportunity has come along doesn’t mean it is the perfect time to accept. Holding back from applying for this job right now will prevent you from burning bridges for a role that may be a better fit later in your career.
Looking for a job can often be challenging and frustrating. Taking into account these seven tips can best position you for success in a competitive job market. Good luck and here's to a successful 2014!
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Monday, 5 August 2013

How File Compression Works

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Introduction to How File Compression Works

Most types of computer files are fairly redundant --
they have the same information listed over and over again.
If you download many programs and files off the Internet, you've probably encountered ZIP files before. This compression system is a very handy invention, especially for Web users, because it lets you reduce the overall number of bits and bytes in a file so it can be transmitted faster over slower Internet connections, or take up less space on a disk. Once you download the file, your computer uses a program such as WinZip or Stuffit to expand the file back to its original size. If everything works correctly, the expanded file is identical to the original file before it was compressed.
At first glance, this seems very mysterious. How can you reduce the number of bits and bytes and then add those exact bits and bytes back later? As it turns out, the basic idea behind the process is fairly straightforward. In this article, we'll examine this simple method as we take a very small file through the basic process of compression.
Most types of computer files are fairly redundant -- they have the same information listed over and over again. File-compression programs simply get rid of the redundancy. Instead of listing a piece of information over and over again, a file-compression program lists that information once and then refers back to it whenever it appears in the original program.
As an example, let's look at a type of information we're all familiar with: words.
In John F. Kennedy's 1961 inaugural address, he delivered this famous line:
"Ask not what your country can do for you -- ask what you can do for your country."
The quote has 17 words, made up of 61 letters, 16 spaces, one dash and one period. If each letter, space or punctuation mark takes up one unit of memory
, we get a total file size of 79 units. To get the file size down, we need to look for redundancies.
Immediately, we notice that:
  • "ask" appears two times
  • "what" appears two times
  • "your" appears two times
  • "country" appears two times
  • "can" appears two times
  • "do" appears two times
  • "for" appears two times
  • "you" appears two times
Ignoring the difference between capital and lower-case letters, roughly half of the phrase is redundant. Nine words -- ask, not, what, your, country, can, do, for, you -- give us almost everything we need for the entire quote. To construct the second half of the phrase, we just point to the words in the first half and fill in the spaces and punctuation.

Redundancy and Algorithms

Most compression programs use a variation of the LZ adaptive dictionary-based algorithm to shrink files. "LZ" refers to Lempel and Ziv, the algorithm's creators, and "dictionary" refers to the method of cataloging pieces of data.
The system for arranging dictionaries varies, but it could be as simple as a numbered list. When we go through Kennedy's famous words, we pick out the words that are repeated and put them into the numbered index. Then, we simply write the number instead of writing out the whole word.
So, if this is our dictionary:
  1. ask
  2. what
  3. your
  4. country
  5. can
  6. for
  7. you
Our sentence now reads:  "1 not 2 3 4 5 6 7 8 -- 1 2 8 5 6 7 3 4"
If you knew the system, you could easily reconstruct the original phrase using only this dictionary and number pattern. This is what the expansion program on your computer does when it expands a downloaded file. You might also have encountered compressed files that open themselves up. To create this sort of file, the programmer includes a simple expansion program with the compressed file. It automatically reconstructs the original file once it's downloaded.
But how much space have we actually saved with this system? "1 not 2 3 4 5 6 7 8 -- 1 2 8 5 6 7 3 4" is certainly shorter than "Ask not what your country can do for you; ask what you can do for your country;" but keep in mind that we need to save the dictionary itself along with the file.
In an actual compression scheme, figuring out the various file requirements would be fairly complicated; but for our purposes, let's go back to the idea that every character and every space takes up one unit of memory. We already saw that the full phrase takes up 79 units. Our compressed sentence (including spaces) takes up 37 units, and the dictionary (words and numbers) also takes up 37 units. This gives us a file size of 74, so we haven't reduced the file size by very much.
But this is only one sentence! You can imagine that if the compression program worked through the rest of Kennedy's speech, it would find these words and others repeated many more times. And, as we'll see in the next section, it would also be rewriting the dictionary to get the most efficient organization possible.

Searching for Patterns

In our previous example, we picked out all the repeated words and put those in a dictionary. To us, this is the most obvious way to write a dictionary. But a compression program sees it quite differently: It doesn't have any concept of separate words -- it only looks for patterns. And in order to reduce the file size as much as possible, it carefully selects which patterns to include in the dictionary.
If we approach the phrase from this perspective, we end up with a completely different dictionary.
If the compression program scanned Kennedy's phrase, the first redundancy it would come across would be only a couple of letters long. In "ask not what your," there is a repeated pattern of the letter "t" followed by a space -- in "not" and "what." If the compression program wrote this to the dictionary, it could write a "1" every time a "t" were followed by a space. But in this short phrase, this pattern doesn't occur enough to make it a worthwhile entry, so the program would eventually overwrite it.
The next thing the program might notice is "ou," which appears in both "your" and "country." If this were a longer document, writing this pattern to the dictionary could save a lot of space -- "ou" is a fairly common combination in the English language. But as the compression program worked through this sentence, it would quickly discover a better choice for a dictionary entry: Not only is "ou" repeated, but the entire words "your" and "country" are both repeated, and they are actually repeated together, as the phrase "your country." In this case, the program would overwrite the dictionary entry for "ou" with the entry for "your country."
The phrase "can do for" is also repeated, one time followed by "your" and one time followed by "you," giving us a repeated pattern of "can do for you." This lets us write 15 characters (including spaces) with one number value, while "your country" only lets us write 13 characters (with spaces) with one number value, so the program would overwrite the "your country" entry as just "r country," and then write a separate entry for "can do for you." The program proceeds in this way, picking up all repeated bits of information and then calculating which patterns it should write to the dictionary. This ability to rewrite the dictionary is the "adaptive" part of LZ adaptive dictionary-based algorithm. The way a program actually does this is fairly complicated, as you can see by the discussions on Data-Compression.com.
No matter what specific method you use, this in-depth searching system lets you compress the file much more efficiently than you could by just picking out words. Using the patterns we picked out above, and adding "__" for spaces, we come up with this larger dictionary:
  1. ask__
  2. what__
  3. you
  4. r__country
  5. __can__do__for__you 
And this smaller sentence: "1not__2345__--__12354"
The sentence now takes up 18 units of memory, and our dictionary takes up 41 units. So we've compressed the total file size from 79 units to 59 units! This is just one way of compressing the phrase, and not necessarily the most efficient one. (See if you can find a better way!)
So how good is this system? The file-reduction ratio depends on a number of factors, including file type, file size and compression scheme.
In most languages of the world, certain letters and words often appear together in the same pattern. Because of this high rate of redundancy, text files compress very well. A reduction of 50 percent or more is typical for a good-sized text file. Most programming languages are also very redundant because they use a relatively small collection of commands, which frequently go together in a set pattern. Files that include a lot of unique information, such as graphics or MP3 files, cannot be compressed much with this system because they don't repeat many patterns (more on this in the next section).
If a file has a lot of repeated patterns, the rate of reduction typically increases with file size. You can see this just by looking at our example -- if we had more of Kennedy's speech, we would be able to refer to the patterns in our dictionary more often, and so get more out of each entry's file space. Also, more pervasive patterns might emerge in the longer work, allowing us to create a more efficient dictionary.

Lossy and Lossless Compression

The type of compression we've been discussing here is called lossless compression, because it lets you recreate the original file exactly. All lossless compression is based on the idea of breaking a file into a "smaller" form for transmission or storage and then putting it back together on the other end so it can be used again.
Lossy compression works very differently. These programs simply eliminate "unnecessary" bits of information, tailoring the file so that it is smaller. This type of compression is used a lot for reducing the file size of bitmap pictures, which tend to be fairly bulky. To see how this works, let's consider how your computer might compress a scanned photograph.
A lossless compression program can't do much with this type of file. While large parts of the picture may look the same -- the whole sky is blue, for example -- most of the individual pixels are a little bit different. To make this picture smaller without compromising the resolution, you have to change the color value for certain pixels. If the picture had a lot of blue sky, the program would pick one color of blue that could be used for every pixel. Then, the program rewrites the file so that the value for every sky pixel refers back to this information. If the compression scheme works well, you won't notice the change, but the file size will be significantly reduced.

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