Learning words from pictures

Learning words from pictures

Software, The Web
MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings.

Image: MIT News

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.

But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

At the Neural Information Processing Systems conference this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. The system then learns which acoustic features of the recordings correlate with which image characteristics.

“The goal of this work is to try to get the machine to learn language more like the way humans do,” says Jim Glass, a senior research scientist at CSAIL and a co-author on the paper describing the new system. “The current methods that people use to train up speech recognizers are very supervised. You get an utterance, and you’re told what’s said. And you do this for a large body of data.

“Big advances have been made — Siri, Google — but it’s expensive to get those annotations, and people have thus focused on, really, the major languages of the world. There are 7,000 languages, and I think less than 2 percent have ASR [automatic speech recognition] capability, and probably nothing is going to be done to address the others. So if you’re trying to think about how technology can be beneficial for society at large, it’s interesting to think about what we need to do to change the current situation. And the approach we’ve been taking through the years is looking at what we can learn with less supervision.”

Joining Glass on the paper are first author David Harwath, a graduate student in electrical engineering and computer science (EECS) at MIT; and Antonio Torralba, an EECS professor.

Visual semantics

The version of the system reported in the new paper doesn’t correlate recorded speech with written text; instead, it correlates speech with groups of thematically related images. But that correlation could serve as the basis for others.

If, for instance, an utterance is associated with a particular class of images, and the images have text terms associated with them, it should be possible to find a likely transcription of the utterance, all without human intervention. Similarly, a class of images with associated text terms in different languages could provide a way to do automatic translation.

Conversely, text terms associated with similar clusters of images, such as, say, “storm” and “clouds,”  could be inferred to have related meanings. Because the system in some sense learns words’ meanings — the images associated with them — and not just their sounds, it has a wider range of potential applications than a standard speech recognition system.

To test their system, the researchers used a database of 1,000 images, each of which had a recording of a free-form verbal description associated with it. They would feed their system one of the recordings and ask it to retrieve the 10 images that best matched it. That set of 10 images would contain the correct one 31 percent of the time.

“I always emphasize that we’re just taking baby steps here and have a long way to go,” Glass says. “But it’s an encouraging start.”

The researchers trained their system on images from a huge database built by Torralba; Aude Oliva, a principal research scientist at CSAIL; and their students. Through Amazon’s Mechanical Turk crowdsourcing site, they hired people to describe the images verbally, using whatever phrasing came to mind, for about 10 to 20 seconds.

For an initial demonstration of the researchers’ approach, that kind of tailored data was necessary to ensure good results. But the ultimate aim is to train the system using digital video, with minimal human involvement. “I think this will extrapolate naturally to video,” Glass says.

Merging modalities

To build their system, the researchers used neural networks, machine-learning systems that approximately mimic the structure of the brain. Neural networks are composed of processing nodes that, like individual neurons, are capable of only very simple computations but are connected to each other in dense networks. Data is fed to a network’s input nodes, which modify it and feed it to other nodes, which modify it and feed it to still other nodes, and so on. When a neural network is being trained, it constantly modifies the operations executed by its nodes in order to improve its performance on a specified task.

The researchers’ network is, in effect, two separate networks: one that takes images as input and one that takes spectrograms, which represent audio signals as changes of amplitude, over time, in their component frequencies. The output of the top layer of each network is a 1,024-dimensional vector — a sequence of 1,024 numbers.

The final node in the network takes the dot product of the two vectors. That is, it multiplies the corresponding terms in the vectors together and adds them all up to produce a single number. During training, the networks had to try to maximize the dot product when the audio signal corresponded to an image and minimize it when it didn’t.

For every spectrogram that the researchers’ system analyzes, it can identify the points at which the dot-product peaks. In experiments, those peaks reliably picked out words that provided accurate image labels — “baseball,” for instance, in a photo of a baseball pitcher in action, or “grassy” and “field” for an image of a grassy field.

In ongoing work, the researchers have refined the system so that it can pick out spectrograms of individual words and identify just those regions of an image that correspond to them.

“Possibly, a baby learns to speak from its perception of the environment, a large part of which may be visual,” says Lin-shan Lee, a professor of electrical engineering and computer science at National Taiwan University. “Today, machines have started to mimic such a learning process. This work is one of the earliest efforts in this direction, and I was really impressed when I first learned of it.”

“Perhaps even more exciting is just the question of how much we can learn with deep neural networks,” adds Karen Livescu, an assistant professor at the Toyota Technological Institute at the University of Chicago. “The more the research community does with them, the more we realize that they can learn a lot from big piles of data. But it is hard to label big piles of data, so it’s really exciting that in this work, Harwath et al. are able to learn from unlabeled data. I am really curious to see how far they can take that.”

Originally published @ MIT NEWS

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Helping policy and technology work together

Helping policy and technology work together

Software, The Web, Tips & Tricks

Engineering grad student Keertan Kini is working to strengthen the intersection of policy and technology.


“When you’re part of a community, you want to leave it better than you found it,” says Keertan Kini, an MEng student in the Department of Electrical Engineering, or Course 6. That philosophy has guided Kini throughout his years at MIT, as he works to improve policy both inside and out of MIT.

As a member of the Undergraduate Student Advisory Group, former chair of the Course 6 Underground Guide Committee, member of the Internet Policy Research Initiative (IPRI), and of the Advanced Network Architecture group, Kini’s research focus has been in finding ways that technology and policy can work together. As Kini puts it, “there can be unintended consequences when you don’t have technology makers who are talking to policymakers and you don’t have policymakers talking to technologists.” His goal is to allow them to talk to each other.

At 14, Kini first started to get interested in politics. He volunteered for President Obama’s 2008 campaign, making calls and putting up posters. “That was the point I became civically engaged,” says Kini. After that, he was campaigning for a ballot initiative to raise more funding for his high school, and he hasn’t stopped being interested in public policy since.

High school was also where Kini became interested in computer science. He took a computer science class in high school on the recommendation of his sister, and in his senior year, he started watching computer science lectures on MIT OpenCourseWare (OCW) by Hal Abelson, a professor in MIT’s Department of Electrical Engineering and Computer Science.

“That lecture reframed what computer science was. I loved it,” Kini recalls. “The professor said ‘it’s not about computers, and it’s not about science’. It might be an art or engineering, but it’s not science, because what we’re working with are idealized components, and ultimately the power of what we can actually achieve with them is not based so much on physical limitations so much as the limitations of the mind.”

In part thanks to Abelson’s OCW lectures, Kini came to MIT to study electrical engineering and computer science. Kini is currently pursuing an MEng in electrical engineering and computer science, a fifth-year master’s program following his undergraduate studies in electrical engineering and computer science.

Combining two disciplines

Kini set his policy interest to the side his freshman year, until he took 6.805J (Foundations of Information Policy), with Abelson, the same professor who inspired Kini to study computer science. After taking Abelson’s course, Kini joined him and Daniel Weitzner, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory, in putting together a big data and privacy workshop for the White House in the wake of the Edward Snowden leak of classified information from the National Security Agency. Four years later, Kini is now a teaching assistant for 6.805J.

With Weitzner as his advisor, Kini went on to work on a SuperUROP, an advanced version of the Undergraduate Research Opportunities Program in which students take on their own research project for a full year. Kini’s project focused on making it easier for organizations that had experienced a cybersecurity breach to share how the breach happened with other organizations, without accidentally sharing private or confidential information as well.

Typically, when a security breach happens, there is a “human bottleneck,” as Kini puts it. Humans have to manually check all information they share with other organizations to ensure they don’t share private information or get themselves into legal hot water. The process is time-consuming, slowing down the improvement of cybersecurity for all organizations involved. Kini created a prototype of a system that could automatically screen information about cybersecurity breaches, determining what data had to be checked by a human, and what was safe to send along.

Once finished with his SuperUROP, Kini became involved in the development of Votemate, a web app designed to simplify the voter registration process in all 50 states.

Kini’s interest in Votemate wasn’t only about increasing voter registration. “I think most people in this nation are centrist, and one of the reasons our political system gets polarized is because people who are polarized primarily turn out to vote,” he says. “I think the only reliable way to fix that is to get more people to turn out to vote.”

Shaping policy on campus

Kini is also involved in making changes within the Institute. “I feel like the same interest that’s gotten me interested in policy is the same thing that’s gotten me interested in working with the Department of Electrical Engineering and Computer Science,” Kini admits.

As a member of the Undergraduate Student Advisory Group (USAGE), Kini has been involved in exploring ways to revitalize the electrical engineering curriculum, redesigning the undergraduate lounge, and compiling a list of the resources available to Course 6 students. On the reason for the list of resources, Kini recalls, “When I was a senior, I realized there were some resources that I had no idea about. And this was after I had been involved in the department and USAGE for 5 years! I should have known.”

Kini is especially interested in making sure students know about the MIT resources for prospective entrepreneurs, such as StartMIT, in which he enrolled last year.

StartMIT is an Independent Activities Period course designed to help students learn about what it takes to create a startup from the ground up. With the advice of over 60 speakers involved in the startup space, StartMIT offers practical advice on how to actually get a startup off the ground.

On the usefulness of StartMIT, Kini says, “at MIT, we try to solve very difficult challenges, we try to solve very meaningful technical problems, but what gets lost in the shuffle, is after you come up with a great idea, how do you get it out of your head and into the world?” He adds, “There’s a saying: ‘If you build it they will come.’ I disagree heartily. But StartMIT helps bridge that divide.”

Thanks to his experience at StartMIT, Kini knows that he wants to start his own company one day. “I see starting a company not only as an option, but the option. It’s a way to make sustainable change in the world.”

Looking back at his experience with USAGE, curriculum development, and policy making, Kini observes, “it’s not just about the nitty gritty of education, its about community.”

Originally published @ MIT NEWS

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Making big data manageable

Making big data manageable


One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.

The methods for creating such “coresets” vary according to application, however. Last week, at the Annual Conference on Neural Information Processing Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and the University of Haifa in Israel presented a new coreset-generation technique that’s tailored to a whole family of data analysis tools with applications in natural-language processing, computer vision, signal processing, recommendation systems, weather prediction, finance, and neuroscience, among many others.

“These are all very general algorithms that are used in so many applications,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper. “They’re fundamental to so many problems. By figuring out the coreset for a huge matrix for one of these tools, you can enable computations that at the moment are simply not possible.”

As an example, in their paper the researchers apply their technique to a matrix — that is, a table — that maps every article on the English version of Wikipedia against every word that appears on the site. That’s 1.4 million articles, or matrix rows, and 4.4 million words, or matrix columns.

That matrix would be much too large to analyze using low-rank approximation, an algorithm that can deduce the topics of free-form texts. But with their coreset, the researchers were able to use low-rank approximation to extract clusters of words that denote the 100 most common topics on Wikipedia. The cluster that contains “dress,” “brides,” “bridesmaids,” and “wedding,” for instance, appears to denote the topic of weddings; the cluster that contains “gun,” “fired,” “jammed,” “pistol,” and “shootings” appears to designate the topic of shootings.

Joining Rus on the paper are Mikhail Volkov, an MIT postdoc in electrical engineering and computer science, and Dan Feldman, director of the University of Haifa’s Robotics and Big Data Lab and a former postdoc in Rus’s group.

The researchers’ new coreset technique is useful for a range of tools with names like singular-value decomposition, principal-component analysis, and latent semantic analysis. But what they all have in common is dimension reduction: They take data sets with large numbers of variables and find approximations of them with far fewer variables.

In this, these tools are similar to coresets. But coresets are application-specific, while dimension-reduction tools are general-purpose. That generality makes them much more computationally intensive than coreset generation — too computationally intensive for practical application to large data sets.

The researchers believe that their technique could be used to winnow a data set with, say, millions of variables — such as descriptions of Wikipedia pages in terms of the words they use — to merely thousands. At that point, a widely used technique like principal-component analysis could reduce the number of variables to mere hundreds, or even lower.

The researchers’ technique works with what is called sparse data. Consider, for instance, the Wikipedia matrix, with its 4.4 million columns, each representing a different word. Any given article on Wikipedia will use only a few thousand distinct words. So in any given row — representing one article — only a few thousand matrix slots out of 4.4 million will have any values in them. In a sparse matrix, most of the values are zero.

Crucially, the new technique preserves that sparsity, which makes its coresets much easier to deal with computationally. Calculations become lot easier if they involve a lot of multiplication by and addition of zero.

The new coreset technique uses what’s called a merge-and-reduce procedure. It starts by taking, say, 20 data points in the data set and selecting 10 of them as most representative of the full 20. Then it performs the same procedure with another 20 data points, giving it two reduced sets of 10, which it merges to form a new set of 20. Then it does another reduction, from 20 down to 10.

Even though the procedure examines every data point in a huge data set, because it deals with only small collections of points at a time, it remains computationally efficient. And in their paper, the researchers prove that, for applications involving an array of common dimension-reduction tools, their reduction method provides a very good approximation of the full data set.

That method depends on a geometric interpretation of the data, involving something called a hypersphere, which is the multidimensional analogue of a circle. Any piece of multivariable data can be thought of as a point in a multidimensional space. In the same way that the pair of numbers (1, 1) defines a point in a two-dimensional space — the point one step over on the X-axis and one step up on the Y-axis — a row of the Wikipedia table, with its 4.4 million numbers, defines a point in a 4.4-million-dimensional space.

The researchers’ reduction algorithm begins by finding the average value of the subset of data points — let’s say 20 of them — that it’s going to reduce. This, too, defines a point in a high-dimensional space; call it the origin. Each of the 20 data points is then “projected” onto a hypersphere centered at the origin. That is, the algorithm finds the unique point on the hypersphere that’s in the direction of the data point.

The algorithm selects one of the 20 data projections on the hypersphere. It then selects the projection on the hypersphere farthest away from the first. It finds the point midway between the two and then selects the data projection farthest away from the midpoint; then it finds the point midway between those two points and selects the data projection farthest away from it; and so on.

The researchers were able to prove that the midpoints selected through this method will converge very quickly on the center of the hypersphere. The method will quickly select a subset of points whose average value closely approximates that of the 20 initial points. That makes them particularly good candidates for inclusion in the coreset.

Originally published @ MIT NEWS

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How to Stop Microsoft from Collecting Your Data – Disable Keylogger

Software, Tips & Tricks

Microsoft openly puts a keylogger in its Windows 10 operating system to give users a personalized experience. Windows 10’s FAQs mention, “When you interact with your Windows device by speaking, writing (handwriting), or typing, Microsoft collects speech, inking, and typing information—including information about your Calendar and People (also known as contacts).” As long debated by our readers, this is not something that other tech companies haven’t engaged in previously.

This post, however, is not about the debate around this topic or about how wrong or right (seriously, folks?) Microsoft is with its data collection attempts through Windows 10. In this guide, we help you turn off Windows 10 keylogger. Here is how:

Disable Windows 10 keylogger:

1. Go to Start Menu > click on the Settings menu.

2. Click on Privacy. 

windows 10 settings

3. Now under the General section, tweak different privacy settings. 

windows 10 privacy settings

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4. For instance, you can toggle the following setting off: Send Microsoft info about how I write to help us improve typing and writing in the future

5. Also, click go to Speech, inking and typing section to switch off the Stop getting to know me feature.

disable windows 10 keylogger

6. Apart from these, you can also turn off different other privacy invading features here. Go to Microphone or Camera to turn these off for the device or for specific apps.

turn off windows 10 keylogger

You can also explore several more settings in the Privacy section that you may want to check out to see what else you want to switch off. Remember, we have seen reports that claim that even after switching these settings off, Microsoft doesn’t stop sending your personal data to its own servers. But, there’s no harm in turning these settings off to be sure that you aren’t explicitly allowing Microsoft to engage in data collection.

originally posted here

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Cheat sheet! Microsoft releases printable Windows 10 key shortcut list

Software, Tips & Tricks
How well do you know your Windows key shortcuts in Windows 10? If you need a cheat sheet, Microsoft has just published one that you can download and print.While Microsoft already offers online documentation on keyboard shortcuts, the format of the page can be difficult to sift through. Fortunately, Microsoft now offers an offline version in Word .DOCX format. (Hat tip to ZDNet’s Mary Jo Foley for spotting it.)There are 42 shortcuts in total, mostly dealing with window management, the Start menu, the Task view, and Cortana. Keep in mind the list only includes Windows key shortcuts, not shortcuts involving Ctrl or Alt.
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But Microsoft didn’t do a great job formatting its new document. The gigantic header takes up half of the first page, splitting the document across three pages as a result. That’s hardly ideal if you want to print out the list or view them on a single screen.

With a simple edit, however, you can delete the header, and everything will fit on two pages. When viewed in “Multiple Pages” mode, you can view the full list of shortcuts on a single screen. We’ve posted our modified version on Dropbox. Otherwise you can grab the official document straight from Microsoft.

Why this matters: Microsoft has added several new Windows key shortcuts in Windows 10, and they’re especially important if you want to snap programs side-by-side on a single display, manage multiple monitors, or juggle several Virtual Desktops. Taking a moment to print or save these shortcuts could save you lots of time in the long run.

Originally posted here on PCWORLD



Windows 10 Privacy Settings Worth Checking

Windows 10 Privacy Settings Worth Checking


This screen shot provided by Microsoft shows Cortana, Microsoft’s voice-activated digital assistant, left, in Windows 10. Microsoft’s new Windows 10 system offers more personalization than before, but it also collects more data than people might be used to on PCs, from contacts and appointments to their physical location and even Wi-Fi passwords. (Microsoft via AP)

Microsoft’s new Windows 10 system offers more personalization than before, but it also collects more data than people might be used to on PCs, from contacts and appointments to their physical location and even Wi-Fi passwords.

The information is used by Cortana, Microsoft’s voice-activated digital assistant, and other new features that try to be helpful by remembering a user’s likes and habits. Apple and Google have developed similar services for smartphones in recent years. Microsoft’s new features are a big part of its strategy to make Windows more relevant in a world where people use multiple devices throughout the day.

Most of these features get turned on when you set up Windows 10 with the “Get going fast” option. But you can take back control and disable features in the settings. Here are some examples:



A feature called Wi-Fi Sense promises to make it easy for users and their friends to connect with new Wi-Fi networks. It lets Windows 10 computers log in automatically to known networks, so your friends don’t have to ask for the password when they visit.

Despite some initial reports, Wi-Fi Sense doesn’t hand over your password to all your friends. Instead it stores your password online in an encrypted form. It then provides that encrypted code to your friend’s Windows 10 device so it can automatically log into your network. Your friends never actually see the password, and Microsoft says your friends won’t get access to other computers or files on the network.

Even so, critics say the feature shares too freely, as you can’t choose which friends to share with — only with your full list of friends or contacts on Facebook, Outlook.com or Skype. To disable this, open the “Settings” menu in Windows 10, select “Network & Internet” and click on “Manage Wi-Fi Settings.” You can uncheck groups you don’t want to share with. You can also choose not to share access to a particular network when you log in for the first time; just uncheck the box next to “Share network with my contacts.”

But if you let friends manually log into your network by giving them your password, be aware they might be able to share the password via Wi-Fi Sense with their friends. You can ask them not to, or completely block Wi-Fi Sense by changing your Wi-Fi network’s name to include the underscore followed by these characters.



Many people are used to voice-activated services like Apple’s Siri or “OK Google” on smartphones and tablets. Windows 10 brings Microsoft’s digital assistant, Cortana, to desktops and laptops. Cortana can answer questions, remind you of appointments and even recommend nearby restaurants. But to do that, Cortana uploads and saves information about your Web browsing, search queries and location, as well as some details from your messages, contacts and calendar.

Microsoft says it doesn’t use the Cortana personalization to target ads. Nor will it use your emails, chats or personal files for advertising. But it does tailor ads to websites visited with its Edge browser and queries made on its Bing search engine, including queries through Cortana. (Google’s browser and search engine do this, too.)

You can review what Cortana knows about you: Click on the search field in the lower left of your screen, then click the “Notebook” icon and select “About Me” to edit or delete individual items. If you want to turn Cortana off, open “Notebook,” click on “Settings” and toggle Cortana to “Off.” That clears information stored on the device, but not the data uploaded to Microsoft’s servers. To get to that, open “Notebook,” choose “Settings” and click “Manage what Cortana knows about me in the cloud.”

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Anyone concerned about privacy should take a run through the “Privacy” section of the Windows 10 “Settings” menu. This is different from the “Settings” menu for Cortana. You find it by clicking on the Windows icon in the lower left of your screen.

Windows 10 assigns each user on each device a unique “advertising ID,” which lets app developers track how each person uses the device and apps. If that bothers you, you’ll find the button to turn it off by going to “Settings” and opening the “Privacy” section. You might have to hit the back arrow at the top left if you’re already in another section. Click on “General” in the left-hand column to turn off advertising ID. You might still get ads, but they won’t be tailored to you.

Similarly, open “Privacy” and click on “Location” to turn off location-tracking or clear the history of where you’ve traveled with your laptop, tablet or Windows phone.

Another heading under “Privacy” has the innocuous title of “Other devices.” That’s where you can turn off the ability to “Sync with devices.” That feature lets apps on your device share information with things like store-tracking beacons, which send you ads as you walk nearby. If that sounds creepy, turn it off.

Some critics complain that Microsoft hasn’t been more up front about all the ways Windows 10 collects user information. But you can find most of them by scrolling through the nooks and crannies of the “Settings” menu. That’s a good thing to do with any new software program or Internet service. It’s also good to go back there from time to time to make sure the settings match your comfort level.


Originally posted here

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Make your Computer Welcome You

Make your Computer Welcome You

Software, Tips & Tricks

Do you watch movies? Have you always loved the way how Computers in movies welcome their users by calling out their names? I bet that you too would want to know how you can achieve similar results on your PC and have a computer said welcome.

Then you are at the right place, this article describes exactly how you can make your computer welcome you like this.

With this trick, you can make your Computer welcome you in its computerized voice. You can make your Windows based computer say “Welcome to your PC, Username.”

Make Windows Greet you with a Custom Voice Message at Startup

To use this trick, follow the instructions given below:-

  1. Click on Start. Navigate to All Programs, Accessories and Notepad.
  2. Copy and paste the exact code given below.

Dim speaks, speech
speaks=”Welcome to your PC, Username
Set speech=CreateObject(“sapi.spvoice”)
speech.Speak speaks

3.  Replace Username with your own name.
4.  Click on File Menu, Save As, select All Types in Save as Type option, and save the file as Welcome.vbs or “*.vbs”.
5.  Copy the saved file.
6.  Navigate to C:\Documents and Settings\All Users\Start Menu\Programs\Startup (in Windows XP) and toC:\Users\ {User-Name}\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup (in Windows 8, Windows 7 and Windows Vista) if C: is your System drive. AppData is a hidden folder. So, you will need to select showing hidden folders in Folder options to locate it.
7.  Paste the file.

 Make your Computer Welcome you at startup

Now when the next time you start your computer, Windows will welcome you in its own computerized voice.

Note: For best results, it is recommended to change sound scheme to No Sounds.
You can change the sound scheme to No Sounds by following the steps given below:-

  1. Go to Control Panel.
  2. Then click on Switch to Classic View.
  3. Then Click on Sounds and Audio Devices.
  4. Then Click on the Sounds Tab.
  5. Select No Sounds from the Sound Scheme option.
  6. If you wish to save your Previous Sound Scheme, you can save it by clicking Yes in the popup menu.
  7. Click on OK.Change Sound Scheme to No Sounds

Try it yourself to see how it works. In my personal opinion, this is an excellent trick. Whenever I start my PC in front of anybody and the PC welcomes me, the fellow is left wondering how brilliant a computer do I have.

Originally published here


Make a YouTube video into an animated Gif

Graphics, Software, Tips & Tricks

YouTube is a great site for watching videos and using the site gifyoutube you can make any section of any video into an animated Gif. To create an animated Gif using this site follow the steps below.

Note: To make this page easier the YouTube and Gif YouTube links open in a new tabs.

1. Open YouTube and find the video you want to make a clip. Copy the URL of the video you want to use. In this example, we are using the below URL.

2. Open GifYouTube and paste the URL into the text box and click the Create GIF button.

3. In the Configure Your GIF window select the Start Time, Gif length, and Title of your animated Gif and then click Create GIF.

4. Once the image has been crated you can share on any of the major social networking sites or click the chain icon to get the path to the image file or path to GifYt. Below is an example of an animation of the URL used earlier.

Animated gif of YouTube video

Tip: An easy method of creating any animated Gif images in the future is to remember that you can add “gif” in front of any YouTube URL to automatically be forward to the site. For example, https://www.youtube.com/watch?v=MKVWBUwoV4o would become https://www.gifyoutube.com/watch?v=MKVWBUwoV4o.

Originally published here

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Top 10 computer mistakes beginners make

Hardware, Software, Tips & Tricks

Below we’ve listed the top 10 mistakes we find beginner computer users making and how you can avoid falling into the same mistakes.
Not backing up important files

One of the biggest mistakes anyone can make is not backing up important information. Today, there are so many different methods of backing up your information that there is no longer any excuse for not backing up your information. Make sure to backup all important information before it is too late.

Clicking Next or Ok without reading

Everyone has become more impatient thanks to the instant gratification we all enjoy every day on the Internet. However, because of this impatience it is not uncommon for new users to click Ok or Next without reading what they are agreeing to and not making sure there are no check boxes still checked. Make sure you read every prompt before agreeing, or you may be agreeing to install new browser toolbars, a program you didn’t intend to install, or other crapware.

Not saving work

While working on a document either offline or online make sure that the program is automatically saving your work. If a program does not automatically save your work, you need to make sure you are saving your work every 10-15 minutes. If the computer loses power, Internet connection, or the program crashes everything is lost that hasn’t been saved.

Turning off the computer improperly

With more users learning on Smartphones and Tablets before learning the computer, not all new users are familiar with the proper method to shut down (turn off) a computer. When you are done with a computer and want to turn it off make sure to save any work, close open programs, and shut down the computer properly.

Opening e-mail attachments

E-mail attachmentA common method of getting infected with a computer virus or malware is from opening e-mail attachments. Be extremely cautious and doubtful on all e-mail attachments you receive including any e-mail attachments you receive from friends, family, and co-workers. One of the most common tactics malicious users use to send viruses is from people you know to gain a false sense of trust.
Falling for phishing, spam, or chain mail

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As computers become more secure, and users get more tech savvy, many malicious individuals have moved to attacking people using phishing tactics. Make sure you are aware of how phishing works and how you can make sure you do not become a victim of identity theft.


Almost all spam today is distributed by infected computers or malicious users. Replying to these spam messages will not unsubscribe you from any list and usually is never looked at or received. In some cases, a spammer may even use your reply as a verification that an e-mail works and send you more spam or share your e-mail address with other spammers. If you get spam, just delete it from your inbox

Chain mail

You should also never forward your friends and family chain mail. If you find an e-mail hard to believe, make sure it is true before you forward the myth or rumor to anyone else.


Downloading and installing bad software

Today, the most common ways a computer gets infected with viruses, malware, and other crapware is from downloading and installing bad software on the computer. Always be cautious of free software and who is providing you with the free software. To subsidize costs many developers include other bundled programs or toolbars, and if you are not careful, you may install them during the install. As mentioned earlier, always be sure to read what the program is doing during the install.

Unfortunately, reading is also not always enough and sites offering free things like cursors, fonts, wallpaper, emotions, and other small downloads may be bundled with other bad software. When downloading anything, keep the below suggestions in mind.

Downloads -Where are you getting the download?

There are malicious people who download valid copies of a popular download, modify the file with malicious software, and then upload the file with the same name. Make sure you are downloading from the developer’s web page or a reputable company.


Don’t install download manager

Many sites suggest or require you to install an installer or a download manager before allowing you to download a program you may be interested in downloading. These tools almost always cause your computer more problems and may even have malware or other spyware. Avoid any site claiming anything must be installed first before you can continue with your download.

Avoid advertisements on download pages

To help make money and pay for the bandwidth costs of supplying free the software, the final download page may have ads. Watch out for anything that looks like advertisements on the download page. Many advertisers try to trick viewers into clicking an ad with phrases like “Download Now”, “Start Download”, or “Continue” and that ad may open a separate download.

Cancel or deny any automatic download

Some sites may automatically try start a download or give the appearance that something needs to be installed or updated before the site or video can be seen. Never accept or install anything from any site unless you know what is downloading.

Not keeping operating system and software up-to-date

The evolution of computers and the software that computer’s use is always evolving. After a program is released bugs and security threats are almost always discovered by other users. Installing the latest updates for a program makes sure everything runs smooth and if security fixes are found fix those problems, so your data is kept secure.

Keep a computer on a surge protector or UPS

If you plug your desktop computer, laptop, tablet, or smartphone into a wall outlet consider using a surge protector instead. A surge protector can help keep your computer protected during an electrical storm and make sure that nothing is damaged if a surge travels over your power lines.

APC Battery BackupAlso, if you are using a desktop computer we highly recommend also using a UPS on your computer. Although these can be more expensive, a UPS protects your computer from a surge, brown out, and keeps the computer running if the power goes out for a minute or two.
Buying incompatible hardware or peripherals

Computers are becoming more diversified with Chrome books, hybrid computers, laptops, smartphones, and tablets. Although all of these devices are considered computers, not all hardware is compatible with every type of computer. Also, this is true with Apple computers vs. PC computers, and PC computers running Windows or Linux, which are all running different operating systems.

Before purchasing hardware or upgrading older hardware make sure its compatible with your computer, operating system, and meets the system requirements.

Originally Published here


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