Playstation 4

A few days ago was leaked on the internet features of the new console PS4 or Orbis sounds like it will be the new name.
This, however, I made ​​a big impression was the RAM that you have.

 
Specs

CPU: New Cell processor, 22nm tech @3.2GHz, 16PPEs and 128 SPEs.
HDD: 250/320GB
OS: Sony Aether GUI.
Max Video O/P: 2160p for 2D - 1080p for 3D stereoscopic - All games running and Minimum 30FPS.
Audio channels: 11.1 (All games must support 5.1)
GPU: 22nm Custom Nvidia/SCEI 'Quantum Leap' GPU @2 Ghz based on GK104 X2 Kepler
RAM: 10GB XDR2 + 10GB GDDR6
Supported formats: PS4, PS3, PS2, PS1, BlueRay, DVD, CD.
Controller I/P: DualShock 4, DS 3, "Playstation Iris, PSVita, Playstation Omni, Xperia devices, Sony's tablets, PS Move, PS Eye, Keyboard and Mouse
Connectivity: USB 3.0, HDMI 1.4, Bluetooth 4.0, WiFi, DLNA, Ethernet, PSVita memorycards.

P.S

The Chromebook's New Ambitions

New hardware and software from Google's computing project.
Google, together with hardware partner Samsung, just announced the next iterations of Chromebook. Samsung’s putting out a new Chromebook laptop, as well as a “Chromebox”--essentially a desktop device.
You could be forgiven for forgetting what exactly a Chromebook is. Though they launched last year with much hype, Chromebooks did not become wildly popular, in a year dominated by the rise of the tablet computer. But to refresh your memory, the Chromebook is Google’s new vision of the personal computer--one that realizes that much of our actual computing is done in the cloud, and that our devices therefore might as well just be terminals feeding into that cloud.
Chromebooks don’t have hard drives, in the traditional sense. Rather, they have flash memory and USB ports that can connect to an external hard drive, if you’re one of those old-fashioned folks who insists on having storage offline. It’s no coincidence that Chromebooks share part of their name with Google’s popular web browser, Chrome; the idea is that the browser itself is, in a sense, your hard drive.
Some headliner specs on the new devices: The revamped Chromebook, called the “Series 5 550,” is said to run over twice as fast as the original Chromebooks; it also features HD video, offline docs editing, and a new focus on the Chrome Web store, which has 50,000 apps. It runs $449 for a Wi-Fi only model, $549 for a 3G-enabled one. (This is more or less on par with the pricing of the original Chromebooks.) The Series 5 550 has a 12.1” display, weighs 3.3 pounds, and runs on an Intel Core processor. Its battery life is 6 hours.
As for the Chromebox, it has certain similar specs (though naturally issues like battery life and screen size are no longer relevant), and starts at $329.
Google has clearly been affected by the tablet revolution in designing this new version of the Chromebook experience. Google’s blog post announcing the Chromebooks touts an “app-centric interface.” And any PR that spotlights an ability to play Angry Birds clearly has an eye on mobile market share.
The products are available online-only for now, but interestingly, Google plans a brick-and-mortar push shortly (stay tuned to learn just which Best Buy locations will be selling Chromebooks and the Chromebox). As the AP’s Michael Liedtke rightly puts it: “The expansion beyond Internet-only sales signals Google's determination to attract a mass audience to its Chromebooks, just as it's done with smartphones running on its Android software. More than 300 million mobile devices have been activated on Android since the software's 2008 release.”
If Chromebooks are going to take a real run at tablet computing--the iPad, specifically (versions of which still are priced lower than the Chromebook)-- then now indeed is the time to go after that mass audience. It’s interesting to compare the most recent Chromebook video with the one Google put out when the first devices launched last year. There was a quirkiness to original ads that I personally find more endearing, but that may have been more suitable for a niche, rather than mass, market.

Ref
TechReview

Facebook smartphone could come by next year

Facebook hopes to release its own smartphone by next year, as the newly public social networking giant looks to boost its revenue in the mobile Internet market, the New York Times reported Monday.

The company has hired more than half a dozen software and hardware engineers who have worked on Apple's bestselling iPhone and one engineer who has iPad experience, the paper said, citing employees and other unnamed sources.

It has also expanded a group working on a partnership with Taiwan's HTC to create a smartphone codenamed "Buffy" -- a project first revealed last year by technology blog All Things Digital.

One former Apple engineer who worked on the iPhone and was recruited said he had met with Facebook chief Mark Zuckerberg "who then peppered him with questions about the inner workings of smartphones," the report said.

"Mark is worried that if he doesn't create a mobile phone in the near future that Facebook will simply become an app on other mobile platforms," a Facebook employee told the New York Times.

When asked for comment, Facebook neither confirmed nor denied the report.

"Our mobile strategy is simple: we think every mobile device is better if it is deeply social," it said in a statement emailed to AFP.
"We're working across the entire mobile industry; with operators, hardware manufacturers, OS providers and application developers to bring powerful social experiences to more people around the world.
Facebook's initial public offering this month raised a record $16 billion but has sparked fury, and lawsuits, amid concerns that key information about the outlook for the company might not have been made widely available.

The company said earlier this month that it was launching an App Center for mini-programs that plug offerings such as Pinterest or Draw Something into the leading social network, which has more than 900 million members worldwide.

Ref:

Yahoo.news

The Quest for a Universal Memory Smartphones and tablets demand faster and more energy efficient memory, but a one-size-fits-all technology is proving elusive

 
Our smartphones and ultrathin laptops rely on a triumvirate of memory technologies—SRAM, DRAM, and flash—each customized for a specific purpose. They’ve all been fabulous workhorses, but now these memories are struggling to keep up with the steady demand for chips that are faster, cheaper, more reliable, and more energy efficient.
Industry watchers expect the most-advanced memory makers to hit a wall around 2015. So the question is, What comes next? Will we always be saddled with a complex hierarchy of different kinds of memory? Or is it possible we just might find the memory industry’s version of a cure-all—a single, universal memory format that can provide high-density storage, low-power operation, and unparalleled speed?
I’d like to say we have a clear candidate. Sadly, the laws of physics seem to rule against it. None of the 10 or so potential replacement technologies that are currently on the table meet every requirement. Despite plenty of hopeful talk in the research community about universal memories, it’s likely the memory hierarchy is only going to get more complicated.
Here’s where things stand now: Today’s memory technologies can’t do everything well, so they occupy specific niches. Static RAM (SRAM) is the speed demon, working in gigahertz lockstep with the microprocessor. We use it to cache a processor’s most frequently accessed instructions and data. But SRAM is expensive—each memory cell needs six or eight transistors. So if you wanted to make high-capacity SRAM, you’d need to sacrifice a lot of real estate on a silicon wafer just to make one chip.
As a result, dynamic RAM (DRAM) is the technology of choice when it comes to main computer memory. It’s moderately fast and quite compact. Each cell consists of just one transistor and one capacitor. But DRAM has a big limitation: Its capacitors are leaky. If you don’t take time to refill them every hundred milliseconds or so, the data simply evaporates.
So we have flash, which needs just one two-gated transistor for every memory cell. Flash’s low cost per bit and nonvolatility (unlike SRAM and DRAM, it holds onto data even when it’s powered down) has made it the technology of choice for mass storage in virtually all mobile devices. But flash has shortcomings of its own. Its data must be written and read in large blocks, making it several orders of magnitude slower than DRAM.
For a while, at least, each of these memories performed well for its specific purpose. But lately, they have all begun to suffer. The shrinking of chip features today doesn’t produce the same gains that it did a decade ago. As they shrink, SRAM and DRAM cells now need proportionally more power to operate, and flash is getting progressively leakier. In a few years, these memories will boast even smaller feature sizes, but they won’t perform much better than the previous generation.
Ideally, we’d like to replace SRAM, DRAM, and flash wholesale with a perfect “universal memory”—a single technology that can be ultrafast, very long lasting, low power, nonvolatile, scalable, and very, very cheap. That has certainly been the talk of the industry for a number of years. Next week, my company—Applied Materials—is sponsoring a panel at the 2012 IEEE International Memory Workshop in Milan, which we hope will take stock of some of the leading candidates. Chances are we won’t be able to identify one that does it all.
Of all the new memories being considered, magnetoresistive RAM (MRAM) seems to be closest to universal. MRAM replaces the capacitor of a DRAM cell with a pair of magnets, one with fixed polarity and one that can be “flipped.” The cell is programmed by flipping the movable magnet and read by sensing the resultant change in resistance. MRAM has much lower power consumption than DRAM, truly unlimited endurance, and should retain data for 20 years, twice as long as flash can. But even so, MRAM is only as speedy as the lowest-end SRAM and has less than a tenth of the cell density of flash. And it’s not really proven whether we’ll be able to come up with a cheap, speedy, and reliable way to put those tiny magnetic patches into circuits in the fab.
There are other candidates out there. One technology that often gets headlines is resistive RAM (which goes by either RRAM or ReRAM). RRAM works by forming or breaking minuscule conductive pathways through normally insulating materials. But RRAM currently has lower endurance than MRAM (the cell material gets fatigued over time) and has been held back by material and manufacturing difficulties.
Then there is phase-change RAM, or PCRAM, the most mature of the new emerging memory technologies. PCRAM cells work by switching a fragment of chalcogenide glass between amorphous and crystalline states, which have different resistivities. PCRAM underperforms DRAM in speed and flash in density, making it difficult to find compelling applications for it. But the memory will likely find some niches, such as aerospace applications, where its excellent resistance to radiation allows it to keep working when other memories would fail.
So far, each of the emerging memories is missing at least one key attribute that would make it a universal memory. And chances are that even if we do find one memory to rule them all, it will likely be manufactured in different ways so that it’s optimized for particular applications. Eking out the best speed, for example, would likely require compromising on storage capacity and vice versa. To make matters worse, even as new memory technologies emerge on the scene, memory makers will likely still be spitting out SRAM, DRAM, and flash chips for less advanced applications for years to come. In other words, brace yourself for an even more confusing landscape. Memory is about to get really messy.

About the Author

Gill Lee is a principal member of the technical staff at Applied Materials. He is based in Santa Clara, Calif.

Ref:
Spectrum

Facebook Knows Your Friends—Even if They’re Not on Facebook

Facebook can infer many things, even about people who deliberately stay away 

Hi, this is Steven Cherry for IEEE Spectrum’s “Techwise Conversations.”

We all have things we don’t want to put on Facebook, and for some, the loss of privacy is so large that they stay off the social network entirely. But it turns out that, to quote heavyweight champion Joe Louis, “You can run, but you can’t hide.”
To quote a research paper published last month on PLoS One, “With the help of machine learning, social network operators can make predictions regarding the acquaintance or lack thereof between two nonmembers with a high rate of success.”
It’s been known for a while that Facebook makes a shadow profile of people it learns about who aren’t on Facebook. What the researchers here found was that they could predict, with a surprising degree of accuracy, whether two such nonmembers were acquainted with each other.
The paper, entitled “One Plus One Makes Three (for Social Networks),” was written by a team from the University of Heidelberg, and my guest today is the corresponding author, Katharina Anna Zweig. Nina, as her colleagues call her, is a professor of theoretical computer science, specializing in network analysis and graph theory. She joins us by phone from her home in Heidelberg, where it’s late at night already.
Nina, welcome to the podcast and guten abend.
Katharina Anna Zweig: Thank you very much for this invitation to speak to you.
Steven Cherry: So, there’s maybe several parts to this. First, social networks often make guesses about whether two people know each other, and we’ve all seen this. LinkedIn, for example, has a “people you may know,” Facebook has a whole column on the side called “Know them?” Google + has something similar. How do these networks make those inferences?
Katharina Anna Zweig: So, I don’t know, actually. So we—actually, this was the starting point of our project. So my colleague, Professor Hamprecht, got such an e-mail, like “You are a nonmember, but you might know these members of”—in this case—“Facebook,” and he was startled. Because these people were not people that invited him personally. It was—it was a good set of people, and he actually knew almost all of them, and he’s in machine learning, so he—he was really startled, and so the...how? How do they know? And then we found out that whenever you register with Facebook, Facebook will ask you for your full e-mail address book. And so it’s quite easy for them to know that these members know him, even if they didn’t choose to invite him personally.
Steven Cherry: Okay, so tell us about your research and how you tried to figure out what Facebook was doing.
Katharina Anna Zweig: So as we teamed up with a machine learner and a network analysis guy; then we wanted to understand whether we could infer something about the relationship between nonmembers if we had information about the network between members and who they know outside of any social network. And so what we did is that for each two nonmembers, we looked at the people that are on the membership site and how they are connected to each other. And you can, you can call this “features.” So, for example, we looked at person A and person B, and these are nonmembers. And we looked at how connected...their friends on the social network platform would be connected. And this would give a vector of 15 features—properties. And then with a quite standard machine-learning algorithm, we tried to give a prediction value to these vectors, and what happens then is that for each two people we get a score. And we would then say that the highest 10 percent or 20 percent we would predict as being connected, and then we can check in our data whether they really are connected or not. And this is what we did. And yeah, we saw that we can under some assumptions, we can predict about 40 percent of the connections between nonmembers correctly.
Steven Cherry: We should point out that we, I guess, you were looking at both predicting that members were friends when they were and were not when they were not. And that both went into the 40 percent.
Katharina Anna Zweig: Yes, it did. In a way, because it’s always easy if you predict everyone to be connected, then you will always get a 100 percent score, but you will make a lot of wrong guesses.
Steven Cherry: Yeah, I guess we should talk about the machine learning a little bit. I gather this involves taking a training set, so you take a sample where you do know everything, and you figure out basically an algorithm, and then you—I’m sorry—you figure out an algorithm, and then you point it to a training set where you already know all of the answers.
Katharina Anna Zweig: And of course we also know it for doing the quantitative analysis. So we not only know the answers for the training set, but we also know it for the nontraining sets, and this information is used to assess the quality of the algorithms. So the training phase is used to find out—so I told you earlier about these 15 properties in the vector. And not all of these properties are really important. The machine-learning algorithm helps us to find out which of the properties is really important for predicting whether two nonmembers are connected or not. And the machine-learning algorithm understands, based on some training sets where we tell him, which of the nonmember pairs are really connected, are friends. Based on this, the machine-learning algorithm learns ways. So, for example, he learns that if two nonmembers have at least five friends together on the social network platform side, and these five friends are also connected with a probability of 0.5, then this is a very good indicator that the two nonmembers are also connected or are also friends. And so this is the first phase of the machine-learning algorithm, where we learn the ways of the teachers. And then [in] the second phase, we will then make our predictions on a set of samples that the algorithm has not seen before. And these two sets are really independent, so there’s no choice of doing anything spooky there. And in the second phase, we will then evaluate how good our algorithm was. So hopefully in the second phase we do have the real relationships, and we only try to predict as many relationships as there are really in the data sets. And based on this known “ground truth,” we call it, based on this, we can say how many of the ones that are really there were predicted by our algorithm.
Steven Cherry: So what was your—what was the set of people that you worked on?
Katharina Anna Zweig: Okay. So, this is a very good point, crucially, so we were able to get access to five real Facebook data sets from five universities. So this is data from 2005, and at this point almost 80 percent of all of the students in these five universities were really members of Facebook. And we know the total network structure there. What we did not know is who these people knew outside. Yeah, so we just had the data between the people on Facebook. So, what can you do if you don’t have the data that you need? You need to emulate it in a way. So we took this data and had five very different evolutionary models of how people decide to become a member of a new social network platform. So how did you decide to become a member of any social network platform? Maybe one of your friends was already there. Or you thought it was just a very cool social network platform. And so our five models scales between a totally dependent behavior, where your own decision depends on how many of your friends already are members, and the totally independent decision. And we have five different models, but we’re kind of scaling between these extremes. And we used these models to artificially divide this real data set into members and nonmembers. For this artificial division, we knew exactly who is connected to whom, but our algorithm would not know this. So for all five evolutionary models of how somebody decides to become a member, we got almost the same results qualitatively.
Steven Cherry: Now, it turns out that the prediction is more accurate, I gather, the more, the higher percentage of people are on Facebook. So I guess in the United States, for example, something like 70 percent of all adults in the U.S. are on Facebook, and that makes it relatively easy, I gather, to make predictions about the remaining 30 percent.
Katharina Anna Zweig: Yes it does. So this is one parameter we took into account. I really need to stress that we are not talking about Facebook because we are just talking about all social network platforms that have information about their members and the connection of members to nonmembers. So of course you’re totally right that this parameter is very important. If we were looking at a social network platform where only 5 percent or 10 percent of any population is a member of, then we couldn’t do much because basically machine learning is always depending on statistics. But what we saw is that, first of all, we don’t need to look at the full population. Sometimes we might be only interested in schoolkids or students of a given university. And in the subpopulation, as you said, the percentage might be very high and might go to 70, 80 percent. For our machine-learning algorithm, we don’t need much more than 50, 60 percent.
Steven Cherry: So I guess the lesson here is that if people stay off of a social network, whether it is Facebook or any other, because they basically don’t want that social network to have information about them, in a way, staying off may not be sufficient, because the social network may know about them through the people who are members and can start to collect information and make inferences about them, even if they’re not members.
Katharina Anna Zweig: You know, I’m a scientist, I always want to say that “What can I really infer, and what can I not infer?” Ah, yeah, there is an important point. The important point is that in our study, we only used the contact data. Because we didn’t have any other social information about the people. So if we were a social network platform, we would have additional information on the age, on the location, on the education, whatever. And if you take this additional information into account, it’s very likely that you can do a better inference than what we did now.
Steven Cherry: So these supplement the 15 features or replace some of them.
Katharina Anna Zweig: Totally.
Steven Cherry: And the accuracy could potentially be much, much higher than that 40 percent.
Katharina Anna Zweig: Yes. So this is what my experts, my colleagues in machine learning, say. They expect that if you take these additional information into account—we know that a connection between people is always driven by homophilies, so it’s much more likely that two people of the same age will be connected than two people that are very different in age, and so on. So taking this additional data into account would certainly improve the quality. And also the social network platform has a time, or dynamic, information, because they can see when a former nonmember...whether their predictions were right or not. So...and it’s very, very important to us that we don’t say that social network platforms are currently doing this, and I also wouldn’t know what type of harm can be done with it. And so our study is not about this. We don’t know anything about this. It’s just about this fact that staying away from social network platforms is not enough. And I was so much surprised, because when I thought about these results, I felt guilty. So we have a very famous German magazine, and I often sent an article to my brother, to my father, via this website, yeah? I just click on the link “send article to friend.” And so I’m actually doing the same because this platform can see what types of articles I like. And when I give an information or when I enter the e-mail address of my brother, then this platform also gets information that I have a relationship to this person and that I think this person is interested in the article. So I think actually our study hints at something much bigger. We are constantly leaving information about relationship to others, and relationship is by definition an information between two people. But if I give you the information that I know my brother, I know this person, can he use this information? Is this okay or not? And I think as a society we need to speak about this. What’s—what do we want companies or others to do with information about a relationship that is only revealed from one of the two sides?
Steven Cherry: And the information implied by these connections can be pretty personal. They could involve political beliefs; they could involve, perhaps, illnesses that a person might be suffering from; they could involve a person’s sexual orientation—or anything.
Katharina Anna Zweig: Yes, definitely. So I saw on some wall on a social network platform something like “Get well soon.” And this...this was an information that the other person revealed about the person whose wall it was. So I think we cannot—I think we cannot really prohibit that our friends are posting these informations, but we need to talk about whether this information can be used or should be free in any way.
Steven Cherry: Very good. Well Nina, as social networks become more powerful, it’s very important to figure out just how powerful they’re becoming, so thank you for your research, and thank you for joining us today.
Katharina Anna Zweig: Yeah, thank you very much.
Steven Cherry: We’ve been speaking with network researcher Katharina Anna Zweig about how when a social network gets big enough, it can figure out a lot about the people who stay off the network. For IEEE Spectrum’s “Techwise Conversations,” I’m
Steven Cherry.
Announcer: “Techwise Conversations” is sponsored by National Instruments.
This interview was recorded 9 May 2012.
Audio engineer: Francesco Ferorelli
Read more Techwise Conversations or follow us on Twitter.
NOTE: Transcripts are created for the convenience of our readers and listeners and may not perfectly match their associated interviews and narratives. The authoritative record of IEEE Spectrum’s audio programming is the audio version.

Ref:
Spectrum

 

Terascale computing-How to Stuff Five Universities Into One Computer Center

A multi-institutional Massachusetts computer center tests out terascale computing—and the social engineering needed to use it.

24 May 2012—When the Massachusetts Green High-Performance Computing Center launches at the end of this year, its energy efficiency and low carbon emissions (as IEEE Spectrum has detailed) may garner some headlines. But MGHPCC is a trailblazer in one other, perhaps more significant, way.
It puts to a high-stakes test what might be called the “Thanksgiving dinner” approach to academic computing: Put multiple outspoken and diversely opinionated entities under one roof, cross your fingers, and work to ensure that they all get along.
Each of MGHPCC’s five member institutions at the Thanksgiving table—Boston University, Harvard University, MIT, Northeastern University, and the University of Massachusetts system—will by year’s end begin transitioning research computing over to the center. MGHPCC, based in Holyoke, a once-thriving Massachusetts mill town two hours west of Boston, does not provide its own computers but rather the power, networking, and cooling infrastructure needed to remotely and inexpensively host members’ computers.
The US $95 million center’s executive director, John Goodhue, says teraflop-scale computing is blending into the background of a competitive university’s physical plant. Top research universities, he says, are increasingly obligated to make that number-crunching power as simple and seamlessly accessible as possible. So MGHPCC provides what Goodhue calls “ping, power, and pipe” for its members’ terascale computing needs.
“Getting high-performance computing cycles should not be any harder than getting 110 volts out of a wall socket,” Goodhue says of his center’s ultimate mission.
Getting there, of course, is the trick.
MGHPCC’s chief competitor is inertia. Many of the site’s future users, Goodhue says, will be moving their hardware from often-cramped and improvised on-campus locations—from server racks in the storage closet and dedicated labs (whose floor space could be put to better use).
“The nice thing about the computer cluster in the closet in your lab is that you have complete control,” he says. “When you need it, you get it. When you don’t need it, you know where it is.
“So locating the physical hardware [at the center] but making it as if you still have your private machine is the challenge for us. And the technologies that make that possible are now here: high bandwidth, inexpensive networking...and virtualization, building an environment that is totally private to the end user.”
In short, says Francine Berman, professor of computer science at Rensselaer Polytechnic Institute, MGHPCC is not trying to be a skyscraper but rather a city. And as with any city, getting all aspects of the infrastructure right is a crucial hurdle. But, adds Berman, “the first and perhaps biggest challenge is social engineering. As hard as the technical, computational, power, and software problems are, social engineering of the stakeholders is dramatically difficult.”
For instance, she says, different kinds of users and users from different institutions have sometimes conflicting standards of success. The tension at the table, as any veteran of contentious family meals knows, could simmer quietly or boil over.
Coalitions of ultracompetitive member research groups might only find MGHPCC worth supporting over the long run if it can contribute to a portfolio of highly cited papers and breakthrough discoveries. But such a high-powered course could also conflict with educators’ missions, for instance, to diversify the center’s user base by wooing arts and humanities faculty to try terascale computing for their research, or to reach out to students who might never otherwise program or use a supercomputer. “To get a broad user community is really challenging and often does not lead to the big bragging rights that the stakeholders need,” says Berman.
Having been director of the San Diego Supercomputer Center from 2001 to 2009, Berman is in a position to judge MGHPCC’s specs, and to her they look promising. Ultimately, MGHPCC and its cohorts perform more than just a technical duty, Berman says. The facility is the training and testing ground for tomorrow’s mainstream computing applications.
The same massive data centers powering today’s familiar online brands—including Amazon, Apple, eBay, Facebook, and Google—are applying the knowledge and expertise first acquired in the 1990s and early 2000s at cutting-edge centers like MGHPCC.
By 2020, Berman says, high-performance computing will be well into the exascale. And the terascale and petascale innovations developed today in academic and other research computer centers will, like the students currently developing them, move out into the real world. “Many of the applications that start in high-performance computing now, you’ll be seeing on your smartphone at the end of the decade,” she says. “These are important things for us to be doing, and if we do them right, they really do migrate to a much broader place.”

Ref:
Spectrum

End of empire for www.ellinadiko.com and www.music-bazaar.com, which had links for downloading illegal Greek music and movies.

Athens: The final blow to the movement of Greek music back through file sharing services was a decision of the First Instance Court of Athens on 16 May 2012 (ar.4658/2012). The Court ordered the Greeks to stop ISP access addresses ellinadiko.com and music-bazaar.com two addresses and IP 67.159.26.126 and 217.23.143.152 with a penalty EUR 5,000 for each violation.

However, ellinadiko.com was not properly working by that time. This followed the arrest in April of Greek DDL managers with hundreds of thousands of users. The downward spiral of websites allows registered members to download mainly Greek (and foreign) music was rather unexpected, as made reference to type websites Rapidshare, Megaupload etc. where there is sharing in the form of torrent, but as whole files. But, Rapidshare, Megaupload, FileSonic and other file sharing sites either do not exist or are no longer the same.

Typically, these web sites based on their reputation for publishing references to foreign sites where shared files are converted to forums and even online stores to cash in with a (ads) or otherwise (store) the reputation was already gained by their contribution to the movement of pirated material.

The Intellectual Property Organization issued a statement which identifies "catalytic" the judgment of Athens to tackle internet piracy. He stresses that, for the first time in Greece was ordered to take technical measures to prevent unauthorized user access to websites through which trafficked digital works were protected by copyright.

P.S.

Video: DARPA Demos Its ARM Robot

Ever wondered what it would look like if a robot grabbed you by the face?
On Monday we brought you an extensive video montage showing the most impressive robots we saw on the show floor of the 2012 IEEE International Conference on Robotics and Automation (ICRA), in St. Paul, Minn., last week. After seeing that video, many of you wrote in to ask for more footage of the DARPA ARM robot (the one that pried the camera from our weak human hands and spun it in the air). No problem!
In the video above, Patrick Rowe from RE², the Pittsburgh firm hired by DARPA to build the robot, which apparently is called "Robbie," describes its main components and capabilities. Later this year, a copy of the robot will be sent to the National Air and Space Museum in Washington, D.C., and RE² is developing games and tasks for visitors to interact with the robot. The challenge is to give visitors as much control of the robot as possible without allowing them to, er, destroy it.
The DARPA ARM program, which seeks to revolutionize robot manipulation, is coming to its final phase, in which the remaining teams will face some tough challenges, including changing a tire of a small car. It remains to be seen whether the program will fulfill its goal of significantly advancing robotic manipulation (whose progress has been glacially slow), but one thing's for sure: You don't want to get your face too close to Robbie.
 REF
IEEE Spectrum

The Biggest Cost of Facebook's Growth

Running the world's largest social network will be a technical and financial challenge as it grows.
Facebook is the gateway to the Internet for a growing number of people. They message rather than e-mail; discover news and music through friends, rather than through conventional news or search sites; and use their Facebook ID to access outside websites and applications.
As the keeper of so many people's social graph, Facebook is in an incredibly powerful position—one reason its IPO this week is expected to be the largest ever for an Internet company.
But potential investors should take note that there's a flip side to Facebook's explosive growth and power; that flip side, as one analyst put it, is its bid to become a core piece of the Internet's infrastructure. Facebook's own technology infrastructure is expensive to build and operate, and it must scale rapidly.
Infrastructure is Facebook's biggest cost, and to support growing traffic and network complexity, it will have to spend even more. What's less clear is whether Facebook's revenues will likewise increase—especially if additional traffic comes from less lucrative visitors, such as people accessing the site from their phones or from outside North America and Europe.

To date, Facebook has been up to the infrastructure challenge. In less than eight years it has grown to host 526 million daily users, 300 million daily photo uploads, and nine million applications.
Two metrics highlight Facebook's success in this respect.
First, Facebook spent $860 million, or about $1 per active monthly user, to deliver and distribute its products last year. The bulk of that money was related to data center equipment, staff, and operating costs. That is up from about 80 cents and 60 cents per user in the two previous years. For the moment, however, Facebook's revenue, currently at $4.30 per user, is growing at an even faster clip. That's a good sign for any potential investor.
Second, Facebook is not only the Web's biggest social media site, it is also consistently the fastest. In 2010, Facebook's response time averaged one second in the U.S., but had improved to 0.73 seconds by mid-2011, according to AlertSite. By comparison, LinkedIn, the next fastest, took nearly double the time to load. Twitter's site was a full two seconds slower.
Facebook has come a long way since it was first hosted in Mark Zuckerberg's dorm room and expanded as he rented additional servers for $80 a month. By late 2009, Facebook disclosed it was using about 30,000 servers, and since then, the number has more than doubled.
As it has grown, the company's engineers have had to innovate to keep costs down and process a growing volume of data. For example, Facebook designed minimalist custom servers that are cheaper for it to build and run than off-the-shelf ones. It also built a program to optimize the performance of its code, cutting the computing demand on its Web servers by 50 percent. It has open-sourced many of its software innovations and also created the Open Compute Project to widely share its new server designs, with the hope that others could contribute useful innovations. 
Today, Facebook is building its own data centers in Oregon, North Carolina, and Sweden. Last year it spent nearly a third of its revenues, $1.1 billion, in capital expenditures on networking equipment and infrastructure. It plans to spend as much as $1.8 billion on such costs this year.
These infrastructure investments are a good sign, says KC Mares, a data-center energy expert and the founder of MegaWatt Consulting; owning and operating rather than leasing data-center space will help Facebook save money in the long term. Other growing tech companies such as Google have pursued this same strategy.
But as Facebook's IPO filing makes clear, there is also a risk to investing in a global infrastructure to serve all users, regardless of their short-term profitability. It is a balancing act.
"If you add too much, it's a big cost that eats into your revenues. If you don't add fast enough, it's an opportunity cost of customers you can't serve," says John Pflueger, a board member of the Green Grid, an IT industry group.

Coming to the wrong conclusions about how to invest in infrastructure can have major consequences. Just look at Friendster, a social network founded before Facebook and MySpace. Friendster had more than 100 million users, but it quickly fell behind as Facebook came to dominate the landscape.
Jim Scheinman, head of business development at Friendster until 2005, says Friendster made product decisions that required too much computing power. For example, it tried to calculate up to six-degree connections between all users. As a result, the site slowed to a crawl. Today, big Web companies often calculate exactly how much revenue they lose when a page is slow to load, even down to tenths of a second.
Facebook, of course, is long past its early days and has more than a critical mass on its platform: almost half of the world's population of Internet users. But to stay relevant as it battles companies like Google, it'll have to stay on the cutting edge, and it will need the computing power to support that.
The question, says Scheinman, is less about costs and capital and more about engineering challenges: "When they have a billion people, and as people use the product more, does that create scaling issues they haven't yet seen before?"

Ref:
TechReview

Samsung Creates a Graphene Transistor with a Band Gap and Electron Mobility

Getting a graphene-based transistor to turn on and off has typically meant sacrificing its incredible electron mobility in the bargain. And the truth of it is that graphene's electron mobility—which is 200 times greater than that of silicon—is what has made it such an attractive alternative in a post silicon world.
Lately, research has been focused on coming up with different varieties of graphene better suited to electronics applications. A so-called “graphene monoxide (GMO)” looks promising, and an isotopically engineered graphene could find use in heat management applications for electronics.
Researchers at the Samsung Advanced Institute of Technology have taken a different approach. Instead of altering the graphene, they have re-engineered the basic operating principles of digital switches.
They developed a three-terminal active device (described in the journal Science) in which the key feature is a “an atomically sharp interface between graphene and hydrogenated silicon.” The device, capable of switching on and off via a Schottky barrier that controls the flow of current by changing its height, does so without the graphene losing any of its precious electron mobility.
Whenever you demonstrate a transistor, you get the usual refrain of: “Let me know when you make a simple logic circuit.” Ask and it shall be given. The Samsung researchers have reported the most basic logic gate (inverter) and logic circuits (half-adder) as part of their research, and demonstrated a basic operation (adding).
With nine patents already filed around this research, maybe this will be the way forward in bringing graphene to commercial electronics.

Ref:
Spectrum

Gmail's Security Hole Could Lead to Mass Harvesting of Accounts

Hackers could automate a social engineering trick that has already been proven to work.

A technique used by marketers to trick people into signing up for "free" merchandise could easily be re-deployed as an engine for harvesting untold numbers of Google account passwords. Fixing the issue won't be trivial for Google, because the exploit is fundamental to how Google allows users to recover access to their accounts when they lose or forget their passwords.
While others have reported on the use of this exploit by individual hackers, I believe what you're reading now is the first account of how it could be transformed into a mass phishing scam that could dragoon even relatively sophisticated users.
The Hack

•  Recently, my wife and I both received, within an hour of one another, a text like this:
• Your entry last month has WON! Goto http://xxxxxx enter your Winning Code: "1122" to claim your FREE $1,000 Best Buy Giftcard!

Our phone numbers are almost identical, so the fact that we both got this text in a short period of time suggests that someone is auto-SMSing it to every number in a certain range, one after another. Which would make it classic text spam, annoying but not dangerous on its own.
The URL contained in the text goes to this website, http://bestbuy.bestgiftcardsforu.com/ which asks for your email address. The site appears to be affiliated with (or at least is linking to and borrows text from) MyRewardsClub.com. I don't think these people are hackers, just marketers.
But here's how hackers could turn this marketing scheme into a password-harvesting scheme: After users enter their email address, if it's a gmail address, hackers could automatically request that Google send an account verification code to the cell phone of the owner of that Gmail address. This is what Google does when you tell it that you forgot your password -- one of the three options for recovering it is to have a verification code sent to the cell phone number associated with your account.
In order for the user to claim their "reward" (in this case, a fake $1000 gift card) the site could then direct them to enter the verification code that Google sent to the user's phone. As soon as the site has both a user's Gmail address and that verification code, it's game over -- hackers can use the code to log into that account and immediately change the password, giving them access and locking the user out of their own account.
Other Examples of The Hack
This exploit appears to be precisely the way that a hacker got access to a number of accounts in the course of obtaining images for the website Is Anyone Up, as described by Camille Dodero in a recent feature for the Village Voice:

Is it really so easy to hack a Gmail account? See for yourself: Go to the Gmail login screen and click on the frequently ignored link underneath the sign-in menu, "Can't access your account?" Three options appear; choose "I forgot my password." Type in a Gmail address—any active Gmail address—and if there's a phone number associated with the account, you're given three more options, one of which is "Get a verification code on my phone." You don't even need to know the phone number. Just hit "continue" and an unrelated six-digit code will appear in a text to the account owner's phone. Type in that verification code—a number easily obtained by a masquerading e-impostor—and you're in. The first thing you're prompted to do is immediately change your password, thereby blocking out the original owner.

In other words, if a hacker knows only your Gmail address and can figure out how to access your phone, he's already most of the way into your shit.

In the case of the hacker collecting images for Is Anyone Up, it appears that he or she chatted up targets via Facebook.
An Increasingly Common Phishing Scheme
This attack has been used by others, and may be widespread. Lokesh Singh, a "professional hacker," describes on the site HackingLoops how one of his clients fell victim to this same hack, only the attacker used Gchat to convince the victim to hand over the verification code that Google had texted to him.
What Google and its users are facing, in other words, is a phishing scheme that appears to work even on relatively sophisticated users, or at least the kind who are smart enough not to click on random links in spam emails. But what I described at the beginning of this piece potentially takes this attack to a whole new level, beyond labor-intensive hacks of individual accounts and into the realm of automated, large-scale password harvesting.
It's great that Google has a way for users to recover access to their Gmail accounts that relies on a secondary device that hackers almost never have access to -- a user's cell phone. The weak link, as always, is the human who already has access to all their supposedly secure touch points -- the user himself. Perhaps this attack can be stymied simply by raising awareness of the fact that no one should ever, ever hand over their google verification code.

Ref:
TecnologyReview

Facebook IPO Makes Zuckerberg Richer Than Google Founders

Facebook Inc. (FB)’s $16 billion initial public offering has made 28-year-old Mark Zuckerberg the 29th richest person on Earth.
Facebook, the world’s most popular social networking company, sold 421.2 million shares for $38 each. At that price, the 503.6 million shares and options Zuckerberg owns are valued at $19.1 billion, making him wealthier than Google Inc. (GOOG) co- founders Sergey Brin and Larry Page, according to the Bloomberg Billionaires Index. 
“Zuckerberg doesn’t think about his wealth,” David Kirkpatrick, author of “The Facebook Effect,” a history of the company, said in an May 17. “This is a huge success for everybody. There’s no way it can be seen otherwise.”
Facebook’s chief executive officer started the Menlo Park, California-based company for Harvard University classmates as a 19-year-old in his dorm room. It now has more than 900 million monthly users and generated $3.7 billion in revenue in 2011.
Zuckerberg sold 30.2 million shares for $1.15 billion during the offering. Most of the proceeds will be used to pay the taxes associated with exercising 60 million stock options.
Facebook’s $104.2 billion valuation crystallizes the fortunes of the company’s three other co-founders. Dustin Moskovitz, 27, who roomed with Zuckerberg at Harvard, is now worth $5.1 billion. He owns 133.7 million shares of the company’s Class B stock, and will sell 7.5 million shares if the underwriters exercise their option to buy additional stock.

Trio of Co-Founders

Moskovitz, the company’s first chief technology officer, left the social network with Facebook colleague Justin Rosenstein in 2008. The duo founded Asana Inc., a task management software company. The company received $9 million in funding from venture capital firms Andreessen Horowitz and Benchmark Capital in 2009, both of Menlo Park.
Eduardo Saverin, 30, has a $2.7 billion estimated fortune. He owned about 4 percent of the company’s outstanding shares prior to the offering, according to whoownsfacebook.com, which is published by Massinvestor Inc. and draws its information from Facebook’s filings with the SEC, press releases, news reports and other publicly available sources.
The Brazil-born Facebook co-founder renounced his U.S. citizenship last year and is now a permanent resident of Singapore. Saverin’s move could save him $67 million in federal taxes, according to data compiled by Bloomberg.
Saverin declined to discuss his stake in a telephone interview from his home in Singapore.
“I am obligated to and will pay hundreds of millions of dollars in taxes to the United States government,” he said in May 17 statement.

New Republic

Co-founder Christopher Hughes, 28, owns about 22 million shares of Facebook, according to a person familiar with his holdings who asked not to be named because the matter is private. At $38 per share, his stake is worth $836 million.
Hughes, who bought the Washington, D.C.-based magazine the New Republic in March 2012 for less than $5 million, has more than $100 million in cash and real estate after selling some of his Facebook hoard, according to data compiled by Bloomberg. Facebook shares would need to rise 7.9 percent to $41 for Hughes’s fortune to crest 10 figures.
Peter Thiel provided Facebook’s first outside investment: $500,000 in 2004. Today, he has a net worth of $2.7 billion. The Founders Fund Management LLC partner sold 16.8 million shares during the offering and owns almost 28 million shares of Facebook following the deal, according to regulatory filings.

Thiel, Parker

Thiel, who co-founded PayPal Inc., owns his Facebook shares through holding companies such as Rivendell One LLC and Lembas LLC. He also owns stakes in Palantir Technologies Inc., which makes data processing software, and Valar Ventures Management LLC, a venture fund that invests in New Zealand and Australian companies. The name of each of those entities was inspired by the writings of J.R.R. Tolkien.
Thiel was brought to Facebook by technology entrepreneur Sean Parker, who met Zuckerberg at a Manhattan restaurant in 2004. He persuaded Zuckerberg to move to California to focus on the company full time. The 32-year-old Parker has a $2.8 billion net worth, and will sell 10 million shares if the underwriters exercise their option to purchase additional shares.
Zuckerberg hired Parker, who had co-founded the Napster Inc. file sharing service with Shawn Fanning in 1999, as its first president. Parker left Facebook in 2005, after police found cocaine at a North Carolina beach house where Parker was hosting friends on vacation. No charges were filed.
For his early role in Facebook, Parker was given almost 70 million shares. He sold 3.65 million shares in the secondary market prior to the company’s IPO.

Sandberg Scores

The offering made Facebook’s chief operating officer Sheryl Sandberg a billionaire. Sandberg, 42, who was lured from Google in 2008, owns 27 million shares, including 25 million restricted stock units that have vested. She also owns more than 14 million unvested units that aren’t counted in her net worth calculation.
At the offering price, her vested stake in the company is worth $1 billion. Sandberg also owns small stakes in Starbucks Corp. (SBUX) and Walt Disney Co. (DIS); she is a director at both companies.
Facebook’s offering further elevated Mark Pincus, the founder of Zynga Inc. (ZNGA), the world’s largest social gaming company, in the billionaire ranks. Pincus, 46, owns 13 percent of Zynga, whose popular online games such as Mafia Wars and Farmville generated more than 10 percent of Facebook’s sales in 2011. Zynga shares are down more than 40 percent since March.
Pincus owns 4.3 million shares of Facebook. With his Zynga stock, he is worth $1.3 billion. He sold 1 million Facebook shares during the offering, earning him $38 million.

Russian Billionaires

Alisher Usmanov, Russia’s richest man, controls about 80 percent of the 85.6 million Facebook shares owned by investment company Digital Sky, which was founded by Yuri Milner. Digital Sky sold 45.7 million shares in the offering and may sell more in the coming months, according to regulatory filings.
Milner owns about 12.5 percent of the company’s Facebook shares and has a net worth of $1.1 billion, according to data compiled by Bloomberg. Digital Sky also owns stakes in Zynga, Groupon Inc. and Twitter Inc.
Usmanov -- whose most valuable asset is his 50 percent stake in Metalloinvest, Russia’s largest iron ore producer --has never met Zuckerberg.
Facebook, which makes 85 percent of its revenue from advertising, is valued at 25.8 times trailing 12-month sales, more than double Google’s valuation when the search engine debuted in 2004.
The company will begin trading on the Nasdaq today.

Ref:
Bloomberg

iPhone 5 Rumor Roundup

Like any good tech blog, Hello World likes to indulge itself in a bit of rumor mongering now and then. The past few weeks have led to a flurry of speculation about the next-gen iPhone, and who am I to ignore it?
The rumors can be divided into two types: rumors (claiming certain specs for the device) and counter-rumors (claiming that said specs are wrong, or at least not necessarily right).
The rumor mill started whirring in earnest at the beginning of May, when a site called iLounge reported that it had learned that the iPhone 5 (or whatever it may be called), would be longer and thinner than the iPhone 4 and iPhone 4S. iLounge also reported that their source told them that the rear casing of the phone would be metal, rather than ceramic, and flat, rather than curved. The site also reported that the forthcoming phone would use the next generation of Gorilla Glass, Gorilla Glass 2, which is able to withstand as much force as its predecessor, even if thinner. The site also claimed that the next-gen phone would use a new dock connector. ABC News, among others, thought the rumors credible.
Around the same time, sites began repeating rumors that Apple would use Liquidmetal amorphous metal in the next iPhone. Business Insider swiftly shot down that rumor, saying that while Liquidmetal was an exciting alloy and that Apple would indeed likely use it in a “breakthrough product” someday, that product probably won’t come along for years.
Also in the first days of May, MacRumors and other sites began speculating on what a leaked image of what was supposedly the iPhone 5’s SIM card tray might mean for the phone’s form factor. Added to these rumors were other ones that had accrued over the months--that the iPhone 5 would have a bigger screen (or that it wouldn’t); or that it would have a 16:9 aspect ratio (or that it wouldn’t). Not to mention the one about the A5X chip. Or the one about 4G LTE radio. Or the one--especially rapidly shot down--about the flexible display.
Finally, on Friday, the site iMore posted the counter-rumor to end all counter-rumors (or to end all rumors): it reported that Apple simply hadn’t decided yet on the final iPhone 5 design, so all speculation would have to remain just that--speculation. Yet even in deflating rumors, iMore stoked the fires of speculation a little, venturing that “if the screen size does change, it won’t be by a lot,” that the Home button would remain a fixture, and that October would be the launch month. (Other sources say September.)
My advice to you: don’t believe anything just yet, and try to be patient for Apple’s WWDC conference in mid-June, when much will be elucidated. Still, allow me to venture to pitch you one last rumor--the most credible and exciting one, I think. Wouldn’t it be cool if, as some sources are suggesting, the next iPhone will run iOS 6, complete with the astounding and gorgeous 3-D Apple Maps app that I’ve written about here before?

Ref:
TechReview
ilounge

 

What is Supercmputer? The K Computer is the number 1 on Top500.

A supercomputer is an ultra-fast computer that can be used for large-scale scientific calculations across a wide range of fields. Simulations using supercomputers have become vitally important as a third method of research and development, alongside experiments and theory. These supercomputer simulations can be extremely effective in a variety of situations in which experimental approaches are not applicable. Such situations include, for example, those where the object of study is too large or complex for analytical solutions, or in which experimental observation would require excessive time or high cost, or involve extreme or dangerous conditions (for instance radioactivity or high temperature). Simulations are also useful in cases where the subject of study is not amenable to experiment, such as in the case of the natural environment, geographic regions, or societies.

Supercomputers are now used in weather forecasting, in the design of cars and aircraft for analyzing structures and fluid flows, and for a wide range of other purposes. They are indispensable for the future of science and technology and the competitiveness of industry.

Comprehensive and high-level electronics skills in high-performance, low power dissipation semiconductor technology, optical communication technologies, network technologies and quality control technology, are all required in supercomputer development. Japan is at the top of the world in this field, but continued research development is needed to maintain and improve the country’s technological capacity.

K Computer
Japan’s Third Science and Technology Basic Plan (a Cabinet resolution adopted in March 2006) designated next-generation supercomputing as a key technology of national importance that should be the focus of investments in gestation periods for large-scale national projects. As a result, RIKEN along with the Ministry of Education, Culture, Sports, Science and Technology (MEXT) are putting full efforts into the development and advancement of such technology.

Under the guidance of MEXT and on the basis of the “Law on Joint Use Promotion of Large-Scale Advanced Research Facilities” (effective July 2006), RIKEN is currently managing the construction of the Next-Generation Supercomputer, the “K computer.” The K computer is to be a shared resource based on the above law and, with its wide accessibility, is expected to be put to a broad range of uses—from diverse fields of basic science and technology research to applications in industry. It was with this in mind that RIKEN established the Advanced Institute for Computational Science (AICS) on July 1, 2010. Through the use of its world-class supercomputer boasting 10 petaflops of computational power, and through collaboration and integration of the fields of computational science and computer science, AICS strives to create an international center of excellence dedicated to generating world-leading results.

Additionally, the K computer serves as the core of the “innovative high-performance computer infrastructure (HPCI)” concept currently promoted by the Administration. The RIKEN Advanced Institute for Computational Science promises to be the driving force behind the HPCI consortium.

*AICS: Advanced Institute for Computational Science

The K computer actually consists of more than 800 computer racks and an excess of 200,000 cables having a total length over 1,000km. In order　to efficiently configure the racks and cables, the third floor of the computer building ― a huge space covering 50m x 60m ― is free of structural pillars.

On one hand, the degree of freedom for layout has been attained, but on the other hand it is necessary to secure the floor load capacity　of 1t/m2 (avg.) in order to support the weight of racks, which can reach a maximum of 1.5t. High-technology design and construction have been　used to meet the requirements.

Additionally, the first floor, on which the global file system is placed, has just three pillars, all of which are located near its center point.

Cores                     548352
Rmax(GFlops)      8162000
Rpeak(GFlops)      8773632
Nmax                    10725120

Ref:
Top500
K-Computer

ARM servers and Intel smartphones are coming soon

There are two giants in the computer processor industry. One is Intel, which builds most of the processors in today’s PCs and servers. The other is ARM Holdings, in Cambridge, England, which thanks to its vast ecosystem of partners has established near-complete dominance of the market for the core logic inside smartphones and tablets.

But the demand for energy-efficient chips is reshaping the industry. As the PC market flattens, Intel aims to capture a sizable chunk of the rapidly growing mobile market, which rose to nearly half a billion smartphones in 2011. And chip designers in ARM’s camp are eyeing a US $50 billion server market, fueled by the rise of social networking and cloud computing. 

The coming months will see a number of volleys exchanged across the line that has traditionally divided the high-performance and low-power chip markets. One of the first will come from a small start-up in Austin, Texas, called Calxeda (pronounced cal-ZAY-dah). The fabless firm will begin shipping chips for servers based on 32-bit ARM mobile processor designs. They’ll soon be joined by AppliedMicro, in Sunnyvale, Calif., which is working on an even speedier, 64-bit ARM-based chip. At the same time, Intel will leap into the mobile game; two big companies—Lenovo and Motorola—plan to release phones based on Intel’s low-power Atom processor by the end of this year. 
(The very first Intel-based smartphone was launched in April by the India-based firm Lava International.)
Exactly how this competition shapes up will depend not on performance or power consumption but on the ratio between the two: performance per watt. And that metric is fueling a fiery debate over the fundamental differences between Intel’s x86 chips and ARM’s processors. 

But the most obvious difference between the two may not actually be the important one, according to experts. ARM processors use reduced instruction set computing (RISC), while x86 processors rely on an older approach, retroactively dubbed complex instruction set computing (CISC). 

Both RISC and CISC architectures govern the set of machine-level instructions, compiled from more complex code, that a chip can execute. CISC chips have a wider vocabulary—they can perform certain operations in one step that might require a series of commands on a RISC chip. But RISC chips can better handle speed-boosting tricks like allowing overlapping operations during each clock cycle.

As a result, over the years, Intel has incorporated decoders into its x86 chips to convert CISC instructions to RISC instructions to boost performance. This conversion process takes energy, but it’s unclear whether this added step gives ARM an advantage when it comes to efficiency.

Instead, other differences between ARM and Intel chips may have more of a bearing on the coming competition. One key difference is microarchitecture—the particular way that processor resources such as cache and registers are distributed and instructions are scheduled. Today’s high-performance processors, for example, are designed so instructions can be performed out of order. Every part of a computation is done as soon as possible to boost speed. Chips that employ this approach have built-in bookkeeping to make sure that the results are assembled in the right order at the end of the process. 

Such tricks can have a big impact on efficiency and performance, says Benjamin C. Lee, an assistant professor of electrical and computer engineering at Duke University, in Durham, N.C. While a researcher at Microsoft, Lee studied how well the company’s Bing Web search engine performed on Intel’s out-of-order Xeon server chip and its in-order Atom netbook processor. Each core on the Atom chip could handle queries at half the rate of a Xeon chip core but required just 20 percent of the energy per request. However, Atom wasn’t able to handle some of the more complex requests.

Microarchitecture will be a key battleground in any competition between Intel and ARM chips, says microprocessor industry analyst Linley Gwennap. Eking out even a slight improvement in performance can come at the expense of a large boost in power consumption. If ARM-based devices like AppliedMicro’s 64-bit server chip are to compete with Intel chips in the server market, developers will have to accept similar diminishing returns, he says. 

Ref:
Spectrum

Is College a Good Investment?

Does it make economic sense to invest in a college degree? If you’re studying engineering or can get yourself into a private research university, especially an Ivy League school, the answer is a whopping ‘Yes!’ according to Payscale’s return-on-investment calculations for over 850 schools.
Payscale calculates the value of a college degree by looking at earnings over a 30-year period, and deducting the earnings of a typical high school student. The net cost of college includes the amount spent over the entire time it takes students to graduate. And they factor in a college’s graduation rate.
The 2012 numbers were released last month, and the data show that 43 of the top 50 schools are private research universities. Close to a third of the top 30 schools are engineering schools, with Harvey Mudd College, Caltech, and MIT taking the report's top three spots. On average, engineering schools had ROIs of $603,362, more than double that for liberal arts schools ($245,943), and more than triple that of business schools ($141,014).
All eight Ivy Leagues are in the top 25 of the list, with an average ROI of more than $1 million. Although these schools are expensive, they provide generous financial aid and have high graduation rates, which bumps up their ROIs.
But private schools aren't the only good investments. Of the handful of public universities on the list, the Colorado School of Mines, Georgia Tech, and University of California, Berkeley take two spots each, for in-state and out-of-state tuition, respectively. And, at over $750,000 their ROIs aren't too shabby.
A college degree is worth the price “if you look at the wage premium that people with a college degree obtain over those without one,” says Nicole Smith, a senior economist at Georgetown University’s Center on Education and the Workforce. This is especially true for science, technology and engineering jobs, which are amongst the most highly paid, she says.
Still, with the rising cost of college, a four-year degree might not be the best choice for everyone from a purely economic standpoint, argues Richard Vedder, an economist at Ohio University. From Businessweek:

“As a general rule, I would say graduates in the top quarter of their class at a high-quality high school should go on to a four-year degree program, while those in the bottom quarter of their class at a high school with a mediocre educational reputation should not (opting instead for alternative methods of credentialing and training).
Those in between should consider perhaps doing a two-year program and then transferring to a four-year school. There are, of course, exceptions to this rule, but it is important for us to keep in mind that college is not for everyone.”

Ref:
Spectrum

Anonymous Hacktivists Hact Out

Members of the hacktivist group Anonymous have been active this week, striking out at Virgin Media, Vladimir Putin, and, allegedly, a Twitter spammer.
The Wednesday attacks on Virgin Media and Vladimir Putin were denial of service hacks, and temporarily brought down the sites. The motivations in these incidents were clear and stated.
According to a Reuters report, the group took credit for the Putin hack, a follow-through on an Anonymous threat that the group would target Russian government websites in support of opposition protests, via the Twitter account @Op_Russia. Details of the kremlin.ru attack were less clear. Reuters said it went down for several minutes on Wednesday. But the RT network reported that the site was down for almost an hour, and that other government sites were also attacked, though not as successfully. Several Russian media sites also experienced denial of service hacks, but those were not claimed by Anonymous members, RT said.
The denial of service Virgin Media hack was retaliation against the takedown of The Pirate Bay, a massive file-sharing site, Daily Tech reports. “#Anonymous have just taken down #VirginMedia website again because of their involvement in the #Censorship of The Pirate Bay,” @AnonUK tweeted Wednesday. Virgin Media had decided to cooperate with the U.K. High Court’s order to block the site.
Earlier in the week hackers claiming to be from Anonymous, took credit for publishing a list of 55 000 Twitter handles and passwords on Pastebin. Many of the accounts listed were spam handles. And many were duplicated on the list, bringing the total closer to 35 000. It appears that Twitter was not hacked – a spammer was hacked, and that list was released by another hacker.
Or a spammer posted the list in retaliation against Twitter for cancelling some of the accounts on the list, suggests The New York Times. An eWeek columnist went so far as to speculate that the cache of passwords were a honeypot laid out by Twitter itself to catch hackers intent on nabbing Twitter's user files.
In a debate about the list on Y Combinator’s Hacker News the consensus was that it was a white hat hack, an attempt to force Twitter to close a long list of fake accounts. When the handles and passwords went public, Twitter reset all of the passwords and sent out email notifications. That would kill a fake account, as the emails aren’t real.
But not all of the accounts posted in the Twitter list were fake. One Y Combinator user, felipe_csl, hacked into a real person’s Twitter account, then into that person’s Hotmail, and then emailed the owner to tell them their password had been posted online. Some users denounced felipe_csl's actions as illegal and invasive, while others called him conscientious.
This week’s hacktivist events may not be over. The BBC reports that tweets from Anonymous hackers have threatened a weekly repeat of a denial of service attack on Saturday in which the United Kingdom’s Home Office website was taken down for several hours.

Ref:
Spectrum

Psychologists Use Social Networking Behavior to Predict Personality Type

The ability to automatically determine personality type could change the way social networks target services to users.
One of the foundations of modern psychology is that human personality can be described in terms of five different forms of behavior. These are:

1. Agreeableness--being helpful, cooperative and sympathetic towards others
2. Conscientiousness--being disciplined, organized and achievement-oriented
3. Extraversion--having a higher degree of sociability, assertiveness and talkativeness
4. Neuroticism--the degree of emotional stability, impulse control and anxiety
5. Openness--having a strong intellectual curiosity and a preference for novelty and variety

Psychologists have spent much time and many years developing tests that can classify people according to these criteria.
Today, Shuotian Bai at the Graduate University of Chinese Academy of Sciences in Beijing and a couple of buddies say they have developed an online version of the test that can determine an individual's personality traits from their behavior on a social network such as Facebook or Renren, an increasingly popular Chinese competitor.
Their method is relatively simple. These guys asked just over 200 Chinese students with Renren accounts to complete online, a standard personality test called the Big Five Inventory, which was developed at the University of California, Berkeley during the 1990s.
At the same time, these guys analyzed the Renren pages of each student, recording their age and sex and various aspects of their online behavior such as the frequency of their blog posts as well as the emotional content of the posts such as whether angry, funny or surprised  and so on.
Finally, they used various number crunching techniques to reveal correlations between the results of the personality tests and the online behavior.
It turns out, they say, that various online behaviors are a good indicator of personality type. For example, conscientious people are more likely to post asking for help such as a location or e-mail address; a sign of extroversion is an increased use of emoticons; the frequency of status updates correlates with openness; and a measure of neuroticism is the rate at which blog posts attract angry comments.
Based on these correlations, these guys say they can automatically predict personality type simply by looking at an individual's social network statistics.
That could be extremely useful for social networks. Shuotian and comapny point out that a network might use this to recommend specific services. They give the rather naive example of an outgoing user who may prefer international news and like to make friends with others.
Other scenarios are at least as likely. For example, such an approach might help to improve recommender systems in general. Perhaps people who share similar personality characteristics are more likely to share similar tastes in books, films or each other.
There is also the obvious prospect that social networks would use this data for commercial gain; to target specific adverts to users for example. And finally there is the worry that such a technique could be used to identify vulnerable individuals who might be most susceptible to nefarious persuasion.
Ethics aside, there are also certain questions marks over the result. One important caveat is how people's response to psychology studies online differs from those done at other times. That could clearly introduce some bias. Then there are the more general questions of how online and offline behaviours differs and how these tests vary across cultures. These are things that Shuotian and Co. want to study in the future.
In the meantime, it is becoming increasingly clear that the data associated with our online behavior is a rich and valuable source of information about our innermost natures.

Ref:
arxiv.org/abs/1204.4809: Big-Five Personality Prediction Based on User Behaviors at Social Network Sites
Technologyreview

Social Networks : Can online communities manage their privacy problem?

Social networks are redefining the way people find and share information, they've provided a platform for a new wave of applications, and their impact has even spilled over into this year's revolutions in the Middle East. The operators of these networks make money primarily through ads, wooing advertisers with the prospect of campaigns better targeted than anything competitors like search engines or television can provide.
Consequently, social networks present the first serious challenge to the dominance of Google's search-based advertising business.
But to provide precisely targeted advertising, they must continue to make the most of the personal information provided by users. In the past, network operators have had a largely free hand in how they used that information, but on a number of occasions they provoked howls of protest by overstepping the mark. Now privacy concerns are becoming the focus of attention from consumer advocates and governments around the world (see "When Private Information Isn't").
These concerns will become even more pressing as social networking is integrated with more and more online services, such as personalized search tools that factor in what you and friends like or recommendation engines that suggest which movies to see, restaurants to visit, or news to read. But even as privacy issues linger, it's likely that before long, not having a profile and some connections on at least one social network will seem as strange as not having an e-mail address or a cell phone.

Ref:
Technologyreview