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This blog is being developed so as to help the students to know about various things regarding their particular course of study. We felt that there is no such site which does the thing, that we are doing now..!!!

Like, there is no such site, which provides the Engineering students, the basic things and tricks that are needed in their College life (regarding Education).

Finally, this blog is very easy to navigate and as you see, there are labels above this post and below the header. You can easily go to the particular branch and see the posts related to that particular course.
You can provide us your feedback by Commenting below the posts, so that we can know better what to improve to serve you better.

Keep Visiting us..... and give your knowledge a boost..!!!!!

One of the Business Strategies...!!!!

Exit Strategy:

How do you know when you’ve achieved success, and what do you do when you get there? These are questions we often forget, and sometimes we think there is a right answer.

The reality is that every person and business can want different things. Some want to sell their businesses and make a bazillion dollars; others want to provide for their families; and some just want to do what they love. Once you remind yourself of your goals, it will help you shape many of your business decisions. Realize too, that your objectives may have shifted over time, resulting in the need for some changes to your business strategies.

In fact, with the economy threatening to turn around sometime soon, maybe it’s a good time to think again about your business holistically to confirm if you are in a good place. Here are some typical elements to define and integrate:
• Elevator Pitch
• Branding
• Business Strategy
• Target Audience/Competitors/Market
• Infrastructure
• Financial

Survivors must adapt if they expect to be successful. If it’s been too long since you’ve done your strategic business planning, or if you need some support on this, please reach out to us.

Collaborative learning -- for robots: New algorithm

Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It's also the technique that autonomous robots typically use to build models of their environments.

That type of model-building gets complicated, however, in cases in which clusters of robots work as teams. The robots may have gathered information that, collectively, would produce a good model but which, individually, is almost useless. If constraints on power, communication, or computation mean that the robots can't pool their data at one location, how can they collectively build a model?

At the Uncertainty in Artificial Intelligence conference in July, researchers from MIT's Laboratory for Information and Decision Systems will answer that question. They present an algorithm in which distributed agents -- such as robots exploring a building -- collect data and analyze it independently. Pairs of agents, such as robots passing each other in the hall, then exchange analyses.

In experiments involving several different data sets, the researchers' distributed algorithm actually outperformed a standard algorithm that works on data aggregated at a single location.

"A single computer has a very difficult optimization problem to solve in order to learn a model from a single giant batch of data, and it can get stuck at bad solutions," says Trevor Campbell, a graduate student in aeronautics and astronautics at MIT, who wrote the new paper with his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics. "If smaller chunks of data are first processed by individual robots and then combined, the final model is less likely to get stuck at a bad solution."

Campbell says that the work was motivated by questions about robot collaboration. But it could also have implications for big data, since it would allow distributed servers to combine the results of their data analyses without aggregating the data at a central location.

"This procedure is completely robust to pretty much any network you can think of," Campbell says. "It's very much a flexible learning algorithm for decentralized networks."

Matching problem

To get a sense of the problem Campbell and How solved, imagine a team of robots exploring an unfamiliar office building. If their learning algorithm is general enough, they won't have any prior notion of what a chair is, or a table, let alone a conference room or an office. But they could determine, for instance, that some rooms contain a small number of chair-shaped objects together with roughly the same number of table-shaped objects, while other rooms contain a large number of chair-shaped objects together with a single table-shaped object.

Over time, each robot will build up its own catalogue of types of rooms and their contents. But inaccuracies are likely to creep in: One robot, for instance, might happen to encounter a conference room in which some traveler has left a suitcase and conclude that suitcases are regular features of conference rooms. Another might enter a kitchen while the coffeemaker is obscured by the open refrigerator door and leave coffeemakers off its inventory of kitchen items.

Ideally, when two robots encountered each other, they would compare their catalogues, reinforcing mutual observations and correcting omissions or overgeneralizations. The problem is that they don't know how to match categories. Neither knows the label "kitchen" or "conference room"; they just have labels like "room 1" and "room 3," each associated with different lists of distinguishing features. But one robot's room 1 could be another robot's room 3.

With Campbell and How's algorithm, the robots try to match categories on the basis of shared list items. This is bound to lead to errors: One robot, for instance, may have inferred that sinks and pedal-operated trashcans are distinguishing features of bathrooms, another that they're distinguishing features of kitchens. But they do their best, combining the lists that they think correspond.

When either of those robots meets another robot, it performs the same procedure, matching lists as best it can. But here's the crucial step: It then pulls out each of the source lists independently and rematches it to the others, repeating this process until no reordering results. It does this again with every new robot it encounters, gradually building more and more accurate models.

Imposing order

This relatively straightforward procedure results from some pretty sophisticated mathematical analysis, which the researchers present in their paper. "The way that computer systems learn these complex models these days is that you postulate a simpler model and then use it to approximate what you would get if you were able to deal with all the crazy nuances and complexities," Campbell says. "What our algorithm does is sort of artificially reintroduce structure, after you've solved that easier problem, and then use that artificial structure to combine the models properly."

In a real application, the robots probably wouldn't just be classifying rooms according to the objects they contain: They'd also be classifying the objects themselves, and probably their uses. But Campbell and How's procedure generalizes to other learning problems just as well.

The example of classifying rooms according to content, moreover, is similar in structure to a classic problem in natural language processing called topic modeling, in which a computer attempts to use the relative frequency of words to classify documents according to topic. It would be wildly impractical to store all the documents on the Web in a single location, so that a traditional machine-learning algorithm could provide a consistent classification scheme for all of them. But Campbell and How's algorithm means that scattered servers could churn away on the documents in their own corners of the Web and still produce a collective topic model.

"Distributed computing will play a critical role in the deployment of multiple autonomous agents, such as multiple autonomous land and airborne vehicles," says Lawrence Carin, a professor of electrical and computer engineering and vice provost for research at Duke University. "The distributed variational method proposed in this paper is computationally efficient and practical. One of the keys to it is a technique for handling the breaking of symmetries manifested in Bayesian inference. The solution to this problem is very novel and is likely to be leveraged in the future by other researchers."

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The above story is based on materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty. Note: Materials may be edited for content and length.

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Source: ScienceDaily

Verification protocol for data storage or transmission: Ghost writing the whip

Ghost imaging" sounds like the spooky stuff of frivolous fiction, but it's an established technique for reconstructing hi-res images of objects partly obscured by clouds or smoke. Now a group of researchers at the National University of Singapore (NUS) is applying the same idea in reverse to securing stored or shared electronic data.

Described in the journal Applied Physics Letters, from AIP Publishing, the work establishes "marked ghost imaging" technology as a new type of multi-layer verification protocol for data storage or transmission.

By "ghosting up" data, the scientists can hide the contents of electronic communications from hackers, deconstructing it into multiple foggy files that make no sense on their own and can only be reconstructed by someone who has the right decoder key (technically called a "reference intensity sequence").

"The sender can send out a huge number of different reference intensity sequences -- only one is authentic, and others are counterfeit -- for confusing the attackers," said Wen Chen, an NUS professor who led the work with co-author Xudong Chen.

"This novel method based on ghost imaging can dramatically enhance system security, and it may be straightforward to apply it to other optical security systems," Chen added.

How the Technology Works

Information security has become one of the most important social and academic topics in recent years as massive increases in data storage have coincided with rapidly developing modern technologies for accessing that data virtually anywhere. Imaging technology has attracted more and more attention in computer security circles because of its promise to enhance the security of data storage or transmission, which is what led Chen and colleagues to develop their marked ghost imaging technology based on traditional optical ghost imaging.

Traditional ghost imaging uses digital cameras to detect light bouncing directly off of an object as well as light that does not directly bounce from the object to the detector. It allows solid images of objects to be reconstructed by shining light into a beamsplitter and separating it into two correlated beams -- one directed at the object and the other, reference arm directed at the camera lens. When these two beams are correlated, they create a silhouette image of the object.

Chen and colleagues report that they can do the same thing either virtually, using software, or physically, by altering the optics of the data transmission. Their technology allows them to create highly-sparse reference intensity patterns that act as security keys and lowly-sparse intensity patterns of the object as ciphertexts, the information being decoded. To decode object data, the reference-arm patterns are then processed to 'rebuild' one new reference intensity sequence. This is crucial because requiring only one rebuilt intensity sequence doesn't increase the system's complexity, while allowing multiple marks (the keys) to be hidden.

Future research includes analysis of the upper limit of keys that can be embedded without increasing the system's complexity and developing greater robustness of the system against attacks.

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The above story is based on materials provided by American Institute of Physics (AIP). Note: Materials may be edited for content and length.

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Journal Reference:

1. Wen Chen and Xudong Chen. Marked ghost imaging. Appl. Phys. Lett., 2014 DOI: 10.1063/1.4879843

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Source: Science Daily 

Android security weaknesses caused by performance design identified.

Georgia Tech researchers have identified a weakness in one of Android's security features and will present their work at Black Hat USA 2014, which will be held August 6-7 in Los Vegas.

The research, titled Abusing Performance Optimization Weaknesses to Bypass ASLR, identifies an Android performance feature that weakens a software protection called Address Space Layout Randomization (ASLR), leaving software components vulnerable to attacks that bypass the protection. The work is aimed at helping security practitioners identify and understand the future direction of such attacks.

The work was conducted at the Georgia Tech Information Security Center (GTISC) by Ph.D. students Byoungyoung Lee and Yeongjin Jang and research scientist Tielei Wang, and reveals that the introduction of performance optimization features can inadvertently harm the security guarantees of an otherwise vetted system. In addition to describing how vulnerabilities originate from such designs, they demonstrate real attacks that exploit them.

"To optimize object tracking for some programming languages, interpreters for the languages may leak address information," said Lee, lead researcher for the effort. "As a concrete example, we'll demonstrate how address information can be leaked in the Safari web browser by simply running some JavaScript."

Bypassing ASLR using hash table leaks was previously believed to be obsolete due to its complexity. By exhaustively investigating various language implementations and presenting concrete attacks, the research aims to show that the concern is still valid.

"As part of our talk, we'll present an analysis of the Android Zygote process creation model," Lee said. "The results show that Zygote weakens ASLR as all applications are created with largely identical memory layouts. To highlight the issue, we'll show two different ASLR bypass attacks using real applications -- Google Chrome and VLC Media Player."

The Black Hat Briefings were created approximately 16 years ago to provide computer security professionals a place to learn the very latest in information security risks, research and trends. Presented by the brightest in the industry, the briefings cover everything from critical information infrastructure to widely used enterprise computer systems to the latest InfoSec research and development. These briefings are vendor-neutral, allowing the presenters to speak candidly about the real problems and potential solutions across both the public and private sectors.

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The above story is based on materials provided by Georgia Institute of Technology.Note: Materials may be edited for content and length.

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Can we see the arrow of time? Algorithm can determine, with 80 percent accuracy, whether video is running forward or backward.

Einstein's theory of relativity envisions time as a spatial dimension, like height, width, and depth. But unlike those other dimensions, time seems to permit motion in only one direction: forward. This directional asymmetry -- the "arrow of time" -- is something of a conundrum for theoretical physics.

But is it something we can see?

An international group of computer scientists believes that the answer is yes. At the IEEE Conference on Computer Vision and Pattern Recognition this month, they'll present a new algorithm that can, with roughly 80 percent accuracy, determine whether a given snippet of video is playing backward or forward.

"If you see that a clock in a movie is going backward, that requires a high-level understanding of how clocks normally move," says William Freeman, a professor of computer science and engineering at MIT and one of the paper's authors. "But we were interested in whether we could tell the direction of time from low-level cues, just watching the way the world behaves."

By identifying subtle but intrinsic characteristics of visual experience, the research could lead to more realistic graphics in gaming and film. But Freeman says that that wasn't the researchers' primary motivation.

"It's kind of like learning what the structure of the visual world is," he says. "To study shape perception, you might invert a photograph to make everything that's black white, and white black, and then check what you can still see and what you can't. Here we're doing a similar thing, by reversing time, then seeing what it takes to detect that change. We're trying to understand the nature of the temporal signal."

Word perfect

Freeman and his collaborators -- his students Donglai Wei and YiChang Shih; Lyndsey Pickup and Andrew Zisserman from Oxford University; Changshui Zhang and Zheng Pan of Tsinghua University; and Bernhard Schölkopf of the Max Planck Institute for Intelligent Systems in Tübingen, Germany -- designed candidate algorithms that approached the problem in three different ways. All three algorithms were trained on a set of short videos that had been identified in advance as running either forward or backward.

The algorithm that performed best begins by dividing a frame of video into a grid of hundreds of thousands of squares; then it divides each of those squares into a smaller, four-by-four grid. For each square in the smaller grid, it determines the direction and distance that clusters of pixels move from one frame to the next.

The algorithm then generates a "dictionary" of roughly 4,000 four-by-four grids, where each square in a grid represents particular directions and degrees of motion. The 4,000-odd "words" in the dictionary are chosen to offer a good approximation of all the grids in the training data. Finally, the algorithm combs through the labeled examples to determine whether particular combinations of "words" tend to indicate forward or backward motion.

Following standard practice in the field, the researchers divided their training data into three sets, sequentially training the algorithm on two of the sets and testing its performance against the third. The algorithm's success rates were 74 percent, 77 percent, and 90 percent.

One vital aspect of the algorithm is that it can identify the specific regions of a frame that it is using to make its judgments. Examining the words that characterize those regions could reveal the types of visual cues that the algorithm is using -- and perhaps the types of cues that the human visual system uses as well.

The next-best-performing algorithm was about 70 percent accurate. It was based on the assumption that, in forward-moving video, motion tends to propagate outward rather than contracting inward. In video of a break in pool, for instance, the cue ball is, initially, the only moving object. After it strikes the racked balls, motion begins to appear in a wider and wider radius from the point of contact.

Probable cause

The third algorithm was the least accurate, but it may be the most philosophically interesting. It attempts to offer a statistical definition of the direction of causation.

"There's a research area on causality," Freeman says. "And that's actually really quite important, medically even, because in epidemiology, you can't afford to run the experiment twice, to have people experience this problem and see if they get it and have people do that and see if they don't. But you see things that happen together and you want to figure out: 'Did one cause the other?' There's this whole area of study within statistics on, 'How can you figure out when something did cause something else?' And that relates in an indirect way to this study as well."

Suppose that, in a video, a ball is rolling down a ramp and strikes a bump that briefly launches it into the air. When the video is playing in the forward direction, the sudden change in the ball's trajectory coincides with a visual artifact: the bump. When it's playing in reverse, the ball suddenly leaps for no reason. The researchers were able to model that intuitive distinction as a statistical relationship between a mathematical model of an object's motion and the "noise," or error, in the visual signal.

Unfortunately, the approach works only if the object's motion can be described by a linear equation, and that's rarely the case with motions involving human agency. The algorithm can determine, however, whether the video it's being applied to meets that criterion. And in those cases, its performance is much better.

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The above story is based on materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty. Note: Materials may be edited for content and length.

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Massachusetts Institute of Technology. "Can we see the arrow of time? Algorithm can determine, with 80 percent accuracy, whether video is running forward or backward." 

29 Incredibly Useful Websites You Wish You Knew Earlier

There are so many wonderful websites around, and it is difficult to know each and every one of them. The below list provides some of those websites that I find particularly helpful, even though they are not as famous or as prevalent as some of the big names out there.

1. BugMeNot

Are you bugged constantly to sign up for websites, even though you do not wish to share your email? If yes, then BugMeNot is for you. Instead of creating new logins, BugMeNot has shared logins across thousands of websites which can be used.

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2. Get Notify

This nifty little website tracks whether the emails sent by you were opened and read by the receiver. Moreover, it also provides the recipient’s IP Address, location, browser details, and more.

3. Zero Dollar Movies

If you are on a constant lookout of free full length movies, then Zero Dollar movies provides a collection of over 15,000 movies in multiple languages that are available to watch for free on Youtube. It indexes only full length movies and no trailers, or partial uploads. In addition, it has a clean interface, contributing to a good movie watching experience.

4. Livestream

Livestream allows you to watch and broadcast events live to viewers on any platform. For the next time when you want to share your company’s annual CEO speech live to employees who are on remote locations, Livestream serves as a perfect platform.

5. scr.im

scr.im converts your email address into a short custom URLs, that can be shared on public websites. This prevents your email id from getting picked up by spam robots, and email harvesters who are on a constant lookout from your email id.

6. TinEye

TinEye is a Reverse Image search tool which is as accurate as Google’s Reverse Image search tool. As opposed to Google, TinEye provides a set of APIs that can be used for personal and commercial purposes, which makes it very useful for developers.

7. Fax Zero

Fax Zero allows you to send faxes to US and Canada for free. Additionally, it enables you to send faxes to countries outside North America at a fixed pay per use cost.

8. Snopes

Do you believe that fingernails and hair continue to grow after death? Why don’t you check out if this is true, along with thousands of other urban folklore out there, at Snopes?

9. Stickk

Is it difficult for you to stick to goals ? If yes, then let Stickk help you reach your goals. It makes use of commitment contracts to empower you to better your lifestyle.

10. Boxoh

Boxoh can track the status of any shipment package on Google Maps.

11. PicMonkey

PicMonkey is an online Image editor, that allows you to touch up your images. Also, you can apply different effects, fonts, and designs to your images. It is a perfect tool to create pins for Pinterest and  awesome looking Facebook covers.

12. Trello

Trello is a great online tool for organizing just about anything using Kanban style cards. It provides a highly visual way for Online Collaboration, and is a simple free tool for Task and Project Management.

13. Short Reckonings

Short Reckonings is an online tool to keep track of shared expenses. It is deceptively simple, easy to use, and allows you to enter expenses with the fewest possible clicks. A clean, ad-free interface adds to the charm of this simple website.

14. Memrise

Do you fancy learning new things in small byte sized packages? If yes, then Memrise is for you. The additive nature of gaming combined with memory improvement makes this an excellent resource.

15. Instructables

Instructables provides instructions to help you build just about anything you can imagine. It provides a platform for people to explore, document, and share their creations.

16. join.me

In today’s world, where collaboration across multiple stakeholders is key,join.me provides an online platform to share desktop screens. Record audio for meetings conducted with participants not in the same room. In addition, it is a simple tool to share your screen with just about anybody on the web.

17. Sync.in

Sync.in allows multiple people to edit documents and notes in real time. It is a great tool for online collaboration.

18. Privnote

Do you wish to share notes and information that self destructs immediately after it is read ? Privnote does exactly that.

19. ScribbleMaps

Have you ever wanted to place your personal markers, shapes, and scribbles on Google Maps? Even though Google Maps does not allow that, ScribbleMaps does, and it does a great job at it.

20. TripIt

TripIt is a painless way to organize all the details of your vacation or business trip. Forget your flight time? Can’t find the e-mail with your hotel’s address? That won’t happen with TripIt, which keeps your itinerary in one place.

21. Skyscanner

Skyscanner is a leading global travel search site, providing instant online comparisons for millions of flights on over a thousand airlines, as well as car hire and hotels.

22. Hostel Bookers

Hostel Bookers is one of the best search engines to search for cheap hostels and hotels while backpacking or traveling around the globe.

23. Fitday

Fitday allows you to track you diet and weight loss through its journal. The personal dietician and free articles on nutrition and weight loss on their site are a great bonus.

24. Endomondo

Endomondo is a mobile app that allows you to track your workouts. The website allows detailed analysis of your training, that makes it a valuable tool to understand and plan your workouts.

25. My Fitness Pal

If counting calories is your main goal, then My Fitness Pal is the best web and mobile application out there. The service has a massive database of meals and exercises to make it easy to accurately count calories.

26. Fuelly

Fuelly tracks the gas mileage for your cars and helps you to analyze, share, and compare your vehicles fuel consumption.

27. 3-Minute Journal

3 Minute Journal is different than most other Journals out there. This application allows you to track your moods, achievements, failures, and moments of gratitude. In addition, it does great analysis over these parameters.

28. 750 Words

750 Words is based on the idea of “Morning Pages”; that advises aspiring creatives to start each morning with three pages of stream-of-consciousness writing to clear away the mental clutter, leaving you with a clearer mind to face the day.

29. Kiva

Kiva is a micro finance website, that attempts to leverage the Internet and a worldwide distribution of micro-finance institutions. It alleviates poverty by connecting lenders to people in need.

Do you have other favorite sites that you find incredibly useful?

Source: http://www.lifehack.org/