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Search engines are internet encyclopedias that allow us to find and filter out relevant information. With any given search engine, it takes some skill to find exactly what you are looking for. You must understand how the search engine works and how your search queries are interpreted.

More advanced search engines will meet you halfway, by providing forms for advanced searches, better interpreting your queries, suggesting keywords, or finding unusual context.

In this article I introduce five search engines with such advanced features.

General Search

Whenever you are looking for written information, the general search engines will do the trick. The advanced search gives access to additional features that easily let you refine your search query.

Google

advanced search engines

Why?

  • keeps setting new standards.
  • comprehensive, yet easy to use interface.
  • excellent search term suggestions.

Reverse Image Search

While most general search engines can search for images based on file names or tags, more advanced search engines can read the image and make its content searchable.

TinEye

advanced search engines

Why?

  • creates an image fingerprint.
  • does reverse image search based on the fingerprint.
  • reveals where and how images are used.

Similar Image Search

Similar image search doesn’t recognize exact copies of a given image, but similar features, such as color, texture, or structures within the image.

[NO LONGER WORKS] GazoPa

all advanced search engines

Why?

  • extracts general image characteristics, such as color and shape.
  • searches similar images based on their general characteristics.
  • works with uploaded images and image URLs.

Invisible Search

Information that is stored in databases is largely invisible to standard search engines because they merely index the contents of websites, following one link after the next. Invisible search engines specialize in hidden data in the so-called Deep Web.

CompletePlanet

search invisible data

Why?

  • access dynamic databases.
  • search within data range.
  • well-documented help section.

Semantic Search

Semantic search is concerned with the exact meaning of a search term, its definition and the search context. Search engines based on semantic search algorithms are thus better at eliminating irrelevant results.

all advanced search engines

Why?

  • choose intended meaning for ambiguous terms.
  • save a myriad of personal settings.
  • search other search engines from DuckDuckGo using its !bang feature.

Source : http://www.makeuseof.com/

Auhtor : Tina Sieber

Categorized in Search Engine

Results of the “Web IQ” Quiz

American internet users’ knowledge of the modern technology landscape varies widely across a range of topics, according to a new knowledge quiz conducted by the Pew Research Center as part of its ongoing series commemorating the 25th anniversary of the World Wide Web. To take the quiz for yourself before reading the full report, click here.

The survey—which was conducted among a nationally representative sample of 1,066 internet users—includes 17 questions on a range of issues related to technology, including: the meaning and usage of common online terms; recognition of famous tech figures; the history of some major technological advances; and the underlying structure of the internet and other technologies.

The “Web IQ” of American Internet Users

Substantial majorities of internet users are able to correctly answer questions about some common technology platforms and everyday internet usage terms. Around three-quarters know that a megabyte is bigger than a kilobyte, roughly seven in ten are able to identify pictures corresponding to terms like “captcha” and “advanced search,” and 66% know that a “wiki” is a tool that allows people to modify online content in collaboration with others. A substantial majority of online adults do not use Twitter, but knowledge of Twitter conventions is fairly widespread nonetheless: 82% of online Americans are aware that hashtags are most commonly used on the social networking platform, and 60% correctly answer that the service limits tweets to 140 characters.

On the other hand, relatively few internet users are familiar with certain concepts that underpin the internet and other modern technological advances. Only one third (34%) know that Moore’s Law relates to how many transistors can be put on a microchip, and just 23% are aware that “the Internet” and “the World Wide Web” do not, in fact, refer to the same thing.

Many online Americans also struggle with key facts relating to early—and in some cases, more recent—technological history. Despite an Oscar-winning movie (The Social Network) about the story of Facebook’s founding, fewer than half of internet users (42%) are able to identify Harvard as the first university to be on the site; and only 36% correctly selected 2007 as the year the first iPhone was released. The Mosaic web browser is an especially poorly-remembered pioneer of the early Web, as just 9% of online Americans are able to correctly identify Mosaic as the first widely popular graphical web browser.

When tested on their recognition of some individual technology leaders, a substantial 83% of online Americans are able to identify a picture of Bill Gates (although 10% incorrectly identified him as his long-time rival, former Apple CEO Steve Jobs). But just 21% are able to identify a picture of Sheryl Sandberg, a Facebook executive and author of the recent best-selling book Lean In.

Americans also have challenges accurately describing certain concepts relating to internet policy. Six in ten internet users (61%) are able to correctly identify the phrase “Net Neutrality” as referring to equal treatment of digital content by internet service providers. On the other hand, fewer than half (44%) are aware that when a company posts a privacy statement, it does not necessarily mean that they are actually keeping the information they collect on users confidential.

Age differences in web knowledge

Younger internet users are more knowledgeable about common usage terms, social media conventions

Younger internet users are more knowledgeable than their elders on some—but by no means all—of the questions on the survey. These differences are most pronounced on the questions dealing with social media, as well as common internet usage conventions. Compared with older Americans, younger internet users are especially likely to know that Facebook originated at Harvard University and that hashtags are commonly used on Twitter, to correctly identify pictures representing phrases like “captcha” and “advanced search,” and to understand the definition of a “wiki.”

At the same time, internet users of all ages are equally likely to believe—incorrectly—that the internet and the World Wide Web are the same thing. There are also no major age differences when it comes to the meaning of phrases like “Net Neutrality” or “privacy policy,” and older and younger internet users correctly identify pictures of Bill Gates and Sheryl Sandberg at comparable rates.

Educational differences in web knowledge

College grads more familiar with common tech terms

College graduates tend to score relatively highly on most Pew Research Center knowledge quizzes, and also tend to have high rates of usage for most consumer technologies. As such, it is perhaps not surprising that this group tends to do relatively well when it comes to knowledge of the internet and technology.

Compared with internet users who have not attended college, college graduates have much greater awareness of facts such as Twitter’s character limit, or the meaning of terms such as “URL” and “Net Neutrality.” Still, there are some elements of the technology world on which even this highly educated group rates poorly. For instance, just one in five correctly answered that the internet and World Wide Web are not the same thing, and only 12% know that Mosaic was the first widely available graphical web browser.

Author:  AARON SMITH

Source:  http://www.pewinternet.org/

Categorized in Science & Tech

Law enforcement agencies have been scouring the dark web in search of digital breadcrumbs to curb criminal activity. Terbium Labs, a company focusing on dark web data intelligence, has been working on a project called Matchlight. This dark web data intelligence tool is now globally available, and there is a high chance it will affect data theft as well as the sale of hacked credentials.

MATCHLIGHT IS NOT YOUR AVERAGE DATA INTELLIGENCE TOOLKIT

The name Matchlight may ring a bell for some people, as a beta version of this toolkit has been available since June of 2015. It did not take long for security firms to show an interest in this project, as Matchlight is highly efficient. The primary objective of this tool is to keep information safe at any given time, and Terbium Labs have developed in-house solutions to do so.

To put things into perspective, detecting an information breach on the dark web takes an average of 200 days. In most cases a data theft goes by unnoticed for a year or longer. Even then it still takes a lot of money, human resources, and research to not only track down stolen information but improve overall company security at the same time.

Matchlight is capable of doing all of this at a fraction of the cost, which is part of the reason why the project proved to be such a success from day one of the beta. But there is more, as this toolkit detects information breaches within minutes and is very accurate while doing so. Up until this point no major false positives have been recorded using the toolkit, which is a positive sign.

Now that Matchlight has been made available to the general public, data breaches will hopefully become a thing of the past. Enterprises can even customize this toolkit to suit their individual needs as well as ensure compatibility with other security solutions they may have in place already.

Under the hood the Matchlight toolkit scans the dark web for any information it has been programmed to detect. This can range from specific documents to digital signatures attached to data. In fact, this program can scan every nook and cranny of the entire deep web for vital information rather than just going through the top marketplaces where information may be sold.

While it remains to be seen how successful this tool can be, the prospect of monitoring thedeep web around the clock at a small cost should excite a lot of companies. Given the number of data breaches recently, solutions like these can make a significant impact. Mainly companies with limited resources may want to check out this project and see what it can do for their business.

Source : themerkle

Categorized in Deep Web

YESTERDAY, THE 46-YEAR-OLD Google veteran who oversees the company’s search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal’s rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google—and across the tech world as a whole.

Giannandrea, you see, oversees Google’s work in artificial intelligence. This includes deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale.

If AI is the future of Google Search, it’s the future of so much more.

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This approach, called deep learning, is rapidly reinventing so many of the Internet’s most popular services, from Facebook to Twitter to Skype. Over the past year, it has also reinvented Google Search, where the company generates most of its revenue. Early in 2015, as Bloomberg recently reported, Google began rolling out a deep learning system called RankBrain that helps generate responses to search queries. As of October, RankBrain played a role in “a very large fraction” of the millions of queries that go through the search engine with each passing second.

As Bloomberg says, it was Singhal who approved the roll-out of RankBrain. And before that, he and his team may have explored other, simpler forms of machine learning. But for a time, some say, he represented a steadfast resistance to the use of machine learning inside Google Search. In the past, Google relied mostly on algorithms that followed a strict set of rules set by humans. The concern—as described by some former Google employees—was that it was more difficult to understand why neural nets behaved the way it did, and more difficult to tweak their behavior.

These concerns still hover over the world of machine learning. The truth is that even the experts don’t completely understand how neural nets work. But they do work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language—words or phrases that people might type into a search engine—it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers. Artificial intelligence is the future of Google Search, and if it’s the future of Google Search, it’s the future of so much more.

Sticking to the Rules

This past fall, I sat down with a former Googler who asked that I withhold his name because he wasn’t authorized to talk about the company’s inner workings, and we discussed the role of neural networks inside the company’s search engine. At one point, he said, the Google ads team had adopted neural nets to help target ads, but the “organic search” team was reluctant to use this technology. Indeed, over the years, discussions of this dynamic have popped up every now and again on Quora, the popular question-and-answer site.

These technologies may sacrifice some control. But the benefits outweigh the sacrifice.

Edmond Lau, who worked on Google’s search team and is the author of the book The Effective Engineer, wrote in a Quora post that Singhal carried a philosophical bias against machine learning. With machine learning, he wrote, the trouble was that “it’s hard to explain and ascertain why a particular search result ranks more highly than another result for a given query.” And, he added: “It’s difficult to directly tweak a machine learning-based system to boost the importance of certain signals over others.” Other ex-Googlers agreed with this characterization.

Yes, Google’s search engine was always driven by algorithms that automatically generate a response to each query. But these algorithms amounted to a set of definite rules. Google engineers could readily change and refine these rules. And unlike neural nets, these algorithms didn’t learn on their own. As Lau put it: “Rule-based scoring metrics, while still complex, provide a greater opportunity for engineers to directly tweak weights in specific situations.”

But now, Google has incorporated deep learning into its search engine. And with its head of AI taking over search, the company seems to believe this is the way forward.

Losing Control

It’s true that with neural nets, you lose some control. But you don’t lose all of it, says Chris Nicholson, the founder of the deep learning startup Skymind. Neural networks are really just math—linear algebra—and engineers can certainly trace how the numbers behave inside these multi-layered creations. The trouble is that it’s hard to understand why a neural net classifies a photo or spoken word or snippet of natural language in a certain way.

“People understand the linear algebra behind deep learning. But the models it produces are less human-readable. They’re machine-readable,” Nicholson says. “They can retrieve very accurate results, but we can’t always explain, on an individual basis, what led them to those accurate results.”

Ways do exist to trace what is happening inside these multi-layered creations.

What this means is that, in order to tweak the behavior of these neural nets, you must adjust the math through intuition, trial, and error. You must retrain them on new data, with still more trial and error. That’s doable, but complicated. And as Google moves search to this AI model, it’s unclear how the move will affect its ability to defend its search results against claims of unfairness or change the results in the face of complaints.

These concerns aren’t trivial. Today, Google is facing an European anti-trust investigation into whether it unfairly demoted the pages of certain competitors. What happens when it’s really the machines making these decisions, and their rationale is indecipherable? Humans will still guide these machines, but not in the same way they were guided in the past.

In any event, deep learning has arrived on Google Search. And the company may have used other forms of machine learning in recent years, as well. Though these technologies sacrifice some control, Google believes, the benefits outweigh that sacrifice.

Deep Learnings

To be sure, deep learning is still just a part of how Google Search works. According to Bloomberg, RankBrain helps Google deal with about 15 percent of its daily queries—the queries the system hasn’t seen in the past. Basically, this machine learning engine is adept at analyzing the words and phrases that make up a search query and deciding what other words and phrases carry much the same meaning. As a result, it’s better than the old rules-based system when handling brand new queries—queries Google Search has never seen before.

But over time, systems like this will play an even greater role inside Internet services like Google Search. At one point, Google ran a test that pitted its search engineers against RankBrain. Both were asked to look at various web pages and predict which would rank highest on a Google search results page. RankBrain was right 80 percent of the time. The engineers were right 70 percent of the time.

This doesn’t detract from Singhal’s work. He joined Google in 2000, and a year later was named a Google Fellow, the highest honor Google bestows on its engineers. For most of Google’s history, he has ruled the company’s search engine, and that search engine pretty much ruled the Internet.

But machine learning is rapidly changing that landscape. “By building learning systems, we don’t have to write these rules anymore,” John Giannandrea told a room full of reporters inside Google headquarters this fall. “Increasingly, we’re discovering that if we can learn things rather than writing code, we can scale these things much better.”

Source : wired

Categorized in Search Engine

From video glitches to memory leaks, today’s browser bugs are harder to pin down, even as they slow the web to a crawl

Web browsers are amazing. If it weren’t for browsers, we wouldn’t be able to connect nearly as well with users and customers by pouring our data and documents into their desktops, tablets, and phones. Alas, all of the wonderful content delivered by the web browser makes us that much more frustrated when the rendering isn’t as elegant or bug-free as we would like.

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When it comes to developing websites, we’re as much at the mercy of browsers as we are in debt to them. Any glitch on any platform jumps out, especially when it crashes our users’ machines. And with design as such a premium for standing out or fitting in, any fat line or misapplied touch of color destroys the aesthetic experience we’ve labored to create. Even the tiniest mistake, like adding an extra pixel to the width of a line or misaligning a table by a bit, can result in a frustrating user experience, not to mention the cost of discovering, vetting, and working around it.

Of course, it used to be worse. The vast differences between browsers have been largely erased by allegiance to W3C web standards. And the differences that remain can be generally ignored, thanks to the proliferation of libraries like jQuery, which not only make JavaScript hacking easier but also paper over the ways that browsers aren’t the same.

These libraries have a habit of freezing browser bugs in place. If browser companies fix some of their worst bugs, the new “fixes” can disrupt old patches and work-arounds. Suddenly the “fix” becomes the problem that’s disrupting the old stability we’ve jerry-rigged around the bug. Programmers can’t win.

The stability brought by libraries like jQuery has also encouraged browser builders to speed up and automate their browser updating processes. Mozilla is committedto pushing out a new version of Firefox every few months. In the past, each version would be a stable target for web developers, and we could put a little GIF on our sites claiming that they work best in, say, IE5. Now the odometer turns so quickly that a new version of Firefox will be released in the time it takes the HTML to travel from the server to the client.

Meanwhile, we ask the browsers to do so much more. My local newspaper’s website brings my machine to its knees -- expanding popover ads, video snippets that autoplay, code to customize ads to my recent browsing history. If my daughter looks at a doll website, the JavaScript is frantically trying to find a doll ad to show me. All this magic gums up the CPU.

All of this means that today’s browser bugs are rarer but harder to pin down. Here’s a look at the latest genres of browser bugs plaguing -- or in many cases, simply nagging -- web designers and developers.

Layout

The most visible browser bugs are layout glitches. Mozilla’s Bugzilla database of bugs has 10 sections for layout problems, and that doesn’t include layout issues categorized as being related to the DOM, CSS, or Canvas. The browser’s most important job is to arrange the text and images, and getting it right is often hard. 

Many layout bugs can seem small to the point of being almost esoteric. Bugzilla bug 1303580, for instance, calls out Firefox for using the italic version of a font when CSS tags call for oblique. Perhaps only a font addict would notice that. Meanwhile Bugzilla bug 1296269 reports that parts of the letters in Comic Sans are chopped off, at least on Windows. Font designers make a distinction, and it matters to them. When they can’t get the exact right look and feel across all browsers, web designers can become perhaps a bit overly frustrated.

There are hundreds, thousands, perhaps even millions of these bugs. At InfoWorld, we’ve encountered issues with images disappearing in our CMS editor and span tags that appear in only the DOM.

Memory leaks

It’s often hard to notice the memory leaks. By definition, they don’t change any visible properties. The website is rendered correctly, but the browser doesn’t clean up after the fact. A few too many trips to websites that trigger the leak and your machine slows to a crawl because all the RAM is locked up holding a data structure that will never be repurposed. Thus, the OS frantically swaps blocks of virtual memory to disk and you spend your time waiting. The best choice is to reboot your machine. 

The details of memory leak bugs can be maddeningly arcane, and we’re lucky that some programmers take the time to fix them. Consider issue 640578 from the Chronium browser stack. Changing a part of the DOM by fiddling with the innerHTMLproperty leaks memory. A sample piece of code with a tight repeated loop calling requestAnimationFrame will duplicate the problem. There are dozens of issues like this.

Of course, it’s not always the browser’s fault. Chromium issue 640922, for instance, also details a memory leak and provides an example. Further analysis, though, shows that the example code was creating Date() objects along the way to test the time, and they were probably the source of the problem.

Flash

It’s pretty much official. Everyone has forgotten about the wonderful anti-aliased artwork and web videos that Adobe Flash brought to the web. We instead blame it for all of the crashes that may or may not have been its fault. Now it’s officially being retired, but it’s not going quickly. Even some of the most forward-thinking companies pushing web standards still seem to have Flash code in their pages. I’m surprised how often I find Flash code outside of MySpace and GeoCities websites.

Touches and clicks

It’s not easy to juggle the various types of input, especially now that tablets and phones generate touches that may or may not act like a mouse click. It shouldn’t be surprising then to find there are plenty of bugs in this area. The Bootstrap JavaScript framework keeps a hit list of its most infuriating bugs, and some of the worst fall in this category.

Safari, for instance, will sometimes miss finger taps on the text in the <body> tag (151933). Sometimes the <select> menus don’t work on the iPad because the browser has shifted the rectangle for looking for input (150079). Sometimes the clicks trigger a weird wiggle in the item -- which might even look like it was done on purpose by an edgy designer (158276). All of these lead to confusion when the text or images on the screen don’t react the way we expect.

Video

The plan has always been to simplify the delivery of audio and video by moving the responsibility inside the browser and out of the world of plugins. This has eliminated interface issues, but it hasn’t removed all the problems. The list of video bugs is long, and many of them are all too visible. Bugzilla entry 754753 describes “mostly red and green splotches that contain various ghost images,” and Bugzilla entry 1302991 “’stutters’ for lack of a better word.” 

Some of the most complex issues are emerging as the browsers integrate the various encryption mechanisms designed to prevent piracy. Bug 1304899 suggests that Firefox isn’t automatically downloading the right encryption mechanism (EME) from Adobe. Is it Firefox’s fault? Adobe’s? Or maybe a weird proxy?

Video bugs are going to continue to dominate. Integrating web video with other forms of content by adding video tags to HTML5 has opened up many new possibilities for designers, but each new possibility means new opportunities for bugs and inconsistencies to appear.

Hovering

The ability for the web page to follow the mouse moving across the page helps web designers give users hints about what features might be hidden behind an image or word. Alas, hovering events don’t always make their way up the chain as quickly as they could.

The new Microsoft Edge browser, for instance, doesn’t hide the cursor when the mouse is hovering over some <select> input items (817822). Sometimes the hovering doesn’t end (5381673). Sometimes the hover event is linked to the wrong item (7787318). All of this leads to confusion and discourages the use of a pretty neat effect.

Malware

While it’s tempting to lay all of the blame for browser bugs on browser developers, it’s often unfair. Many of the problems are caused by malware designed to pose as useful extensions or plugins. In many cases, the malware does something truly useful while secretly stealing clicks or commerce in the background.

The problem is that the extension interface is pretty powerful. An extension can insert arbitrary tags and code into all websites. In the right hands, this is very cool, but it’s easy to see how the new code from the extension can bump into the code from the website. What? You didn’t want to redefine the behavior of the $ function?

This isn’t so much a bug as a deep, philosophical problem with a very cool feature. But with great power comes great responsibility -- perhaps greater than any extension programmer can muster. The best way to look at this issue is to realize it’s the one area where we, the users, have control. We can turn off extensions and limit them to only a few websites where there are no issues. The API is a bit too powerful for everyday use -- so powerful that it’s tempting to call extensions APIs the biggest bugs of all. But that would deny everything it does for us.

Source : infoworld

Categorized in Search Engine

 

Deep Web Search Engine:

 Our Today’s article is about which most of the people reading this might never have been heard before. We are going to through light on Deep Web Search. We are going to clear many of the basic concepts, logics and ideas related to Deep Web Search and Deep Web Search engines from what, how to why?

Deep Web Simply Refers to: 

The content available on the Internet or the World Wide Web which is not usually indexed by the traditional search engines. This sometimes also refers to as Dark Web. But Dark Web is a completely different chapter. There may be many reasons why traditional search engines might not prefer to index such type of content.

One more thing about the Deep Web Search is that it also means to browse the web anonymously.

What is Deep Web Search?

When we search about something on any search engine, it simply displays up a few results consisting of about 10 links and we found at least one link to satisfy our searched term most of the times. This is call as simple searching or we may web surfing. This way we are simply surfing over the web pages using a traditional search engine. But what is exactly meant by Deep Web Search? To explain this, we are going to take help of illustrative examples. We use the Internet, means the Web to explore, learn and find a lot of things. These things include the information gathering, photos and videos gathering, documents gathering etc.

Way to Explore More than Usual

When one make use of Internet to find anything, there are two type of methods that may be used under our today’s scenario. The very first method is to find the relevant information by searching through the Search Engine like Google and then afterwards surfing the web in simple way. The next method is the Deep Web Search which is not known to most of us. Deep Web Search is to browse the web in an advance way to find a kind of hidden information or any other kind of data which we cannot found by simply browsing the web using the search engines. I may also be said that Deep Web means to explore the hidden Internet.

Most of the people think that using a search engine like Google, they may find some relevant information and hence also got satisfied but actually they don’t know Internet is not just limited to it. Sometimes, we come across some websites which are itself a search engine such as the collection database sites.

Deep Web Search Verbal Meaning

Now coming to understand the meaning of Deep Web from the words itself. The word “Deep” clears and exclaims as the web which is deeply hidden in the depths of web. There may be a lot of reasons why search engines do not index these kind of information in the search engine. There might be the factors like the owners of the deep web content may not like to display up their content publically via the search engines and so on more. Anyhow, we are going to learn now about Deep Web Search Engine in Detail.

What are Deep Web Search Engines?

If you have read the above given explanation carefully, then you will be able to answer to his question yourself. Am I right? There are also different types of Deep Web Search Engines to research or simply search about different type of contents. Some Deep Web Search engines are meant to simply found the deep web textual content and some to find deep web media content. According to some source, the size and the volume of content of the Deep Web is much more than the normal web which browse in general from day to day. But why we are unable to find this kind of web information? The simple answer it is deeply packed, protected or even hacked.

Why Deep Web is Not Indexed by Traditional Search Engines?

Some of the more reasons why Traditional Search Engines do not index these type of Deep Web Search content are described below, hence we have described some of the properties of the Deep Web Search Engines. These Deep Web resources may contain complex databases which are not easily understood by the Search Engine bots and thus not indexed also. Such Non-Indexed Content may include Contextual Web, Dynamic Content, Limited Access Content, Non-HTML Content, Private Web Content, Software, Archives etc. Coming a little out from the world of Deep Web Search Engine, and reviewing the Deep Web Search. There is no any such thing that a Deep Web Search Engine is necessary to browse the Deep Web but it the only Best option to browse the Deep Web. We can also browse the Deep Web using a lot of other ways or methods. In other words, we may say that Deep Web Search Engines are one of the Best options to choose to search the Web Deeply.

Source : http://www.deepwebsiteslinks.com/

 

Categorized in Deep Web
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