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05 Sep
P2C 150 Series
P2C 150 Series

Multi-Simple POS System P2C 150 Series

  • All-in-One POS with Internal Printer!
  • Low heat, near-silent, fanless method implemented!
  • Applied secured module to protect private information.(EMV Certified)
  • Multi-Simple POS System

P2C 150 Series!

  • Simple design with built-in printer to save space and easily maintain.
  • Change the body color for your store theme.
  • Simple design with COLOR point!
  • Simple Design with Two-Tone Color
  • Replaceable Cover for Color Matching




All In One POS


  • Save space with built-in printer
  • M/B (Mother board) equipped with ultra-thin body
  • Easy to replace colored covers

All In One POS

주변기기 연결용 케이블 하단 처리로 깔끔한 배선 정리

P2C forms natural business environment

  • Body and stand can be detached with One touch! Wall mount capable
  • Both VFD and 2nd monitor (10” or 15”) installation capable
  • Simple and clean wiring for peripheral connections

P2C's easy maintenance

  • Easily and conveniently equipped with Left and right I/O placement peripherals
  • Harness and cable to minimize maintenance costs and time savings
  • Easy to assemble and disassemble
  • Cool, quiet Fanless method implemented

편리한 유지보수


  • Simplified maintenance due to the harness and minimzed screws within the system


  • Cool, quiet Fanless method implemented


  • Simple and clean wiring for peripheral connections.

Customizable display

  • Replace Body and stand replacement with One Touch.
  • Instantly switched to wall-mount, after detaching from stand.

Secure Readability

  • Gently move the screen up, down, left, and right monitor for customers according to eye level improving readability.

Security Feature

  • Built-in protection for illegal use.(Site designation Possible)
  • Supports PC/SC support, IC Card Reader.
  • Magnetic Stripe Data Encryption Feature Support (AES).


  • Left and right I / O port access placement is easy and port placed in a dedicated MSR (MiniUSB), SR Interface (Serial & USB) cable changeable without altering
  • Control port-installed to support Cash drawer.
  • Default interface is USB and RS232 is available by CMOS setting
  • Smart all-in-one combination for MSR, SCR and Dallas key
  • (MSR + SCR + Dallas key as well as MSR, SCR and MSR + SCR)

2nd LCD display

  • 10inch and 15inch LCD display support
  • 1024 × 768 Resolution
  • Ultra slim bezel and case design
  • tilt and swivel adjustment bracket structure support


  • VFD type
  • 20 columns × 2 lines
  • MSR designed Body family look
  • Tilt & angle adjustment bracket structure
  • OPOS support
  • tilt and swivel support


  • Communication: UART (RS-232) or USB
  • Bu er Size: 1Mbytes
  • Paper roll : 80mm
  • High speed : 170mm/sec
18 Aug
Google is using AI to run its data center cooling systems
Google is using AI to run its data center cooling systems

The AI is finding better ways to reduce energy consumption.

Google's data centers hold thousands of servers and they power everything from Google Search to Gmail to YouTube. But those data centers need to be kept cool in order for those servers to run reliably. A couple of years ago, Google began applying AI to its data center cooling systems and it offered system controllers recommendations about how to boost energy efficiency while maintaining optimal temperatures. Now, Google says its AI is running the show.

When the company developed its AI-powered recommendation system, it said the thinking behind the move was simple. "Even minor improvements would provide significant energy savings and reduce CO2 emissions to help combat climate change," DeepMind said in a blog post today. After implementing the system, the company's data center operators reported that it was uncovering some improved techniques, but vetting and manually implementing they system's recommendations was requiring a fair amount of time and effort. That's when Google began to explore an automated system.

Now, the AI system is implementing actions on its own, though human operators are always there to supervise and take over if need be. Every five minutes, the system takes a snapshot of the cooling system and that information is fed into DeepMind's neural networks. The AI then assesses which actions should be taken to maintain temperature but minimize energy consumption, and after those actions are run through a number of safety checks, they're implemented.

Google says that the system has been in place for a few months and it's producing energy savings of around 30 percent. It's also continuing to find new techniques to satisfy energy saving goals. The company adds that data centers might not be the only places that could benefit from such a system. "In the long term, we think there's potential to apply this technology in other industrial settings, and help tackle climate change on an even grander scale," it said.

11 Aug
Even Anonymous Coders Leave Fingerprints
Even Anonymous Coders Leave Fingerprints

Casey Chin

Researchers who study stylometry—the statistical analysis of linguistic style—have long known that writing is a unique, individualistic process. The vocabulary you select, your syntax, and your grammatical decisions leave behind a signature. Automated tools can now accurately identify the author of a forum post for example, as long as they have adequate training data to work with. But newer research shows that stylometry can also apply to artificial language samples, like code. Software developers, it turns out, leave behind a fingerprint as well.

Rachel Greenstadt, an associate professor of computer science at Drexel University, and Aylin Caliskan, Greenstadt's former PhD student and now an assistant professor at George Washington University, have found that code, like other forms of stylistic expression, are not anonymous. At the DefCon hacking conference Friday, the pair will present a number of studies they've conducted using machine learning techniques to de-anonymize the authors of code samples. Their work could be useful in a plagiarism dispute, for instance, but it also has privacy implications, especially for the thousands of developers who contribute open source code to the world.

How To De-Anonymize Code

Here's a simple explanation of how the researchers used machine learning to uncover who authored a piece of code. First, the algorithm they designed identifies all the features found in a selection of code samples. That's a lot of different characteristics. Think of every aspect that exists in natural language: There's the words you choose, which way you put them together, sentence length, and so on. Greenstadt and Caliskan then narrowed the features to only include the ones that actually distinguish developers from each other, trimming the list from hundreds of thousands to around 50 or so.

The researchers don't rely on low-level features, like how code was formatted. Instead, they create "abstract syntax trees," which reflect code's underlying structure, rather than its arbitrary components. Their technique is akin to prioritizing someone's sentence structure, instead of whether they indent each line in a paragraph.

'People should be aware that it’s generally very hard to 100 percent hide your identity in these kinds of situations.'

Rachel Greenstadt, Drexel University

The method also need requires examples of someone's work to teach an algorithm to know when it spots another one of their code samples. If a random GitHub account pops up and publishes a code fragment, Greenstadt and Caliskan wouldn't necessarily be able to identify the person behind it, because they only have one sample to work with. (They could possibly tell that it was a developer they hadn't seen before.) Greenstadt and Caliskan, however, don't need your life's work to attribute code to you. It only takes a few short samples.

For example, in a 2017 paper, Caliskan, Greenstadt, and two other researchers demonstrated that even small snippets of code on the repository site GitHub can be enough to differentiate one coder from another with a high degree of accuracy.

Most impressively, Caliskan and a team of other researchers showed in a separate paper that it’s possible to de-anonymize a programmer using only their compiled binary code. After a developer finishes writing a section of code, a program called a compiler turns it into a series of 1s and 0s that can be read by a machine, called binary. To humans, it mostly looks like nonsense.

Caliskan and the other researchers she worked with can decompile the binary back into the C++ programming language, while preserving elements of a developer’s unique style. Imagine you wrote a paper and used Google Translate to transform it into another language. While the text might seem completely different, elements of how you write are still embedded in traits like your syntax. The same holds true for code.

“Style is preserved,” says Caliskan. “There is a very strong stylistic fingerprint that remains when things are based on learning on an individual basis.”

To conduct the binary experiment, Caliskan and the other researchers used code samples from Google’s annual Code Jam competition. The machine learning algorithm correctly identified a group of 100 individual programmers 96 percent of the time, using eight code samples from each. Even when the sample size was widened to 600 programmers, the algorithm still made an accurate identification 83 percent of the time.

Plagiarism and Privacy Implications

Caliskan and Greenstadt say their work could be used to tell whether a programming student plagiarized, or whether a developer violated a noncompete clause in their employment contract. Security researchers could potentially use it to help determine who might have created a specific type of malware.

More worryingly, an authoritarian government could use the de-anonymization techniques to identify the individuals behind, say, a censorship circumvention tool. The research also has privacy implications for developers who contribute to open source projects, especially if they consistently use the same GitHub account.

“People should be aware that it’s generally very hard to 100 percent hide your identity in these kinds of situations,” says Greenstadt.

For example, Greenstadt and Caliskan have found that some off-the-shelf obfuscation methods, tools used by software engineers to make code more complicated, and thus secure, aren't successful in hiding a developer's unique style. The researchers say that in the future, however, programmers might be able to conceal their styles using more sophisticated methods.

“I do think as we proceed, one thing we’re going to discover is what kind of obfuscation works to hide this stuff,” says Greenstadt. “I’m not convinced that the end point of this is going to be everything you do forever is traceable. I hope not, anyway.”

In a separate paper, for instance, a team led by Lucy Simko at the University of Washington found that programmers could craft code with the intention of tricking an algorithm into believing it had been authored by someone else. The team found that a developer may be able to spoof their "coding signature," even if they're not specifically trained in creating forgeries.

Future Work

Greenstadt and Caliskan have also uncovered a number of interesting insights about the nature of programming. For example, they have found that experienced developers appear easier to identify than novice ones. The more skilled you are, the more unique your work apparently becomes. That might be in part because beginner programmers often copy and paste code solutions from websites like Stack Overflow.

Similarly, they found that code samples addressing more difficult problems are also easier to attribute. Using a sample set of 62 programmers, who each solved seven "easy" problems, the researchers were able to de-anonymize their work 90 percent of the time. When the researchers used seven "hard" problem samples instead, their accuracy bumped to 95 percent.

Aylin Caliskan, George Washington University

In the future, Greenstadt and Caliskan want to understand how other factors might affect a person’s coding style, like what happens when members of the same organization collaborate on a project. They also want to explore questions like whether people from different countries code in different ways. In one preliminary study for example, they found they could differentiate between code samples written by Canadian and by Chinese developers with over 90 percent accuracy.

There’s also the question of whether the same attribution methods could be used across different programming languages in a standardized way. For now, the researchers stress that de-anonymizing code is still a mysterious process, though so far their methods have been shown to work.

“We’re still trying to understand what makes something really attributable and what doesn't,” says Greenstadt. “There’s enough here to say it should be a concern, but I hope it doesn’t cause anybody to not contribute publicly on things.”

11 Aug
Samsung's Galaxy Watch: A Scientist Weighs in on Its Features
Samsung's Galaxy Watch: A Scientist Weighs in on Its Features

On Thursday, Samsung revealed the latest addition to its collection of wearables. The Galaxy Watch looks a bit sleeker than an average fitness watch — a chic timepiece one might wear if one is not interested in advertising that they Seriously Work Out Regularly. Beneath the window dressing, the Galaxy successfully straddles two different worlds of fitness trackers: trackers that look good, and ones that can offer robust data.

The fitness watch spectrum ranges from hardcore workout trackers that don’t necessarily translate to daily wear (e.g., heart rate monitors that require a chest strap, like the [Polar M400 GPS(]) to watches that are designed to look good — or at least inconspicuous. The Galaxy watch appears to fall somewhere between the extremes if you take a look at the components, as Jonathan Peake, P.h.D, a senior lecturer at Queensland University’s Institute of Health and Biomedical Innovation points out to Inverse.

“There is plenty of window dressing to suit personal preference,” Peake says. “It is promoted as military grade hardware, I doubt many users would requite that level of durability. Likewise, it’s rated to five atmospheres of underwater pressure, thus it could be used while deep-sea diving.”

galaxy watch fitness

If you wanted to, you could take this watch deep sea diving 

How the Galaxy Watch Appears to Stack Up to Competition

Deep-sea diving capability isn’t the only elite-level sport aspect of the Galaxy watch. For instance, it has some advanced GPS technology — in particular capability to integrate with GLONASS, Russia’s satellite navigation system. But aside from those notable features, Peake, who conducted a systematic review of hundreds of consumer fitness trackers earlier in August and published his findings in Frontiers in Physiology, mentions that the Galaxy doesn’t have too much else under the hood to differentiate it from competitors.

“The sensors included in the watch are pretty standard for measuring activity levels, heart rate and daylight,” Peake says. “Monitoring activity levels at the wrist is most convenient and comfortable for users, But some activities don’t involve arm movement, and other activities involve only the arms while otherwise remaining sedentary.”

The Fitness Watch Still Has Two Big Problems

Still, even the Galaxy hasn’t solved two universal fitness tracking issues: the wrist is neither the best place on the body to even put a fitness tracker, nor is it the best place to get a heart rate reading. This is because it uses a system of measurement called plethysmography, which uses a light to sense the blood flow through the veins in the wrist, as opposed to chest-strap heart rate monitors, which measure the heart’s electrical activity.

fitness sport tracking

The different workout modes for the Apple Watch. Samsung's watch boasts more than three times as many sport choices. 

Galaxy Watch Appears to Be an Organizational Game-Changer

But what does seem unique to the Galaxy watch is not how it collects data, but rather how it allows you to organize it. At the unveiling, it was revealed that the watch has workout modes for 39 sports, from cycling to yoga. For comparison, the Apple watch’s newest line only has specific modes for ten different sports.

Having this level of labeling specificity with sport tracking is incredibly helpful when you’e looking to run any kind of analysis on your workout data. For instance, it’s good to know if that a data point showing low heart rate is due to a yoga class and not, say, a slow struggle through a spin class.

For now, the Galaxy watch is one of the first to really attempt to be a true lifestyle fitness tracker that straddles both the worlds of Russian satellite tracking and intense deep-sea diving but can also trick you into thinking it’s a fashion accessory. We won’t know much about how people react to the Galaxy watch’s interfaces until it launches officially on August 24, but if anyone takes it deep sea diving, Samsung would likely be happy to hear about it.

09 Aug
Artificial intelligence will soon power robots; replacing farmer
Artificial intelligence will soon power robots; replacing farmer

Artificial intelligence will soon be powering robots that will essentially replace farmers to pick vegetables. Thanks to the global warming, the increasing demand in agriculture, and the lack of ample land for farming, robotics is the solution seem by many to meet the demand of the exploding global population.

An American startup, Root AI, is working towards the direction of making indoor farming the next big thing in agriculture where robots, and not humans, help produce and harvest the crop with maximum optimisation of resources.

One of the first inventions coming from Root AI is a mobile robot for indoor farming facilities. Using the cameras and sensors on it, the robot is capable of picking tomatoes the right way and assess the health of the crops, conduct operations such as pruning vines, observing and controlling ripening to cultivate crops, TechCrunch reported.

Root AI is expected to begin the pilot tests from 2019. Given that the US, and other parts of the world, are facing a labour shortage in farming, the concept of indoor farming with AI at the core is likely to gain popularity.