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India’s Mswipe raises $30M to grow its smart point-of-sale terminal business

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Mswipe, an Indian fintech company that develops point-of-sale terminals for merchants, has pulled $30 million in new funding as it bids to triple its reach to 1.5 million merchants over the next year.

The company’s previous funding as a Series D in 2017 that ended up at just over $40 million, thanks to a $10 million extension from B Capital — the investment firm set up by Facebook co-founder Eduardo Saverin that’s backed by BCG. This time around, B Capital has provided the funding alongside other returning investors that include Falcon Edge, Epiq Capital and DSG Growth Partners. The deal takes the startup to $95 million raised to date.

We wrote extensively about the company’s strategy back at the time of that 2017 round, and essentially the thesis is that POS devices remain essential despite the proliferation of new fintech like mobile wallets. With that in mind, Mswipe makes its terminals cheaper than the competition while it can also work on more limited internet connections, even 2G, to help merchants and retailers in more remote areas or those on a modest budget.

More critically, Mswipe CEO and founder Manish Patel believes the country is “ripe for disruption” because it has so few terminals. With less than three million terminals in operation across the whole of India, even Turkey, with a significantly smaller population of 80 million, has more.

Right now, Mswipe claims to have reached over 400,000 merchants — up from 290,000 at the end of 2017 — and Patel said today that the aim is to grow that figure to 1.5 million over the next year.

To reach that ambitious target, Mswipe is once again trying to put more than just a terminal inside a terminal.

Beyond offering hardware that simply works and ties into newer types of payment, Mswipe has a vision of additional services for merchants. It is developing a new ‘smart’ POS — Wise POS Plus — that is developed on Android which allows applications like billing, inventory management and logistics to be pulled in, too. Indeed, the second piece to that is its own dedicated app store — MoneyStore — which is in development now and is aimed at housing a suite of productivity apps and related services for smaller retailers.

Mswipe is betting on a new Android-based smart terminal that will give its merchants access to productivity and management apps, too

“WisePOS Plus… powered by a suite of productivity apps, can enable a merchant to save thousands of rupees and hundreds of hours that go into running computer-based billing and inventory solutions with integrated payments. At the same time, we are also creating a huge opportunity for app developers with MoneyStore,” Patel said in a prepated statement.

The second major prong that he believes can bring this growth is the adoption of UPI, the government-backed real-time payments system in India. Mswipe said it is “all set to enable” the system which will allow QR payments at terminals. Mswipe is also working with lending startup Cashe on a co-branded card for consumers following a deal announced in December.

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Better than JPEG? Researcher discovers that Stable Diffusion can compress images

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Enlarge / These jagged, colorful blocks are exactly what the concept of image compression looks like.

Benj Edwards / Ars Technica

Last week, Swiss software engineer Matthias Bühlmann discovered that the popular image synthesis model Stable Diffusion could compress existing bitmapped images with fewer visual artifacts than JPEG or WebP at high compression ratios, though there are significant caveats.

Stable Diffusion is an AI image synthesis model that typically generates images based on text descriptions (called “prompts”). The AI model learned this ability by studying millions of images pulled from the Internet. During the training process, the model makes statistical associations between images and related words, making a much smaller representation of key information about each image and storing them as “weights,” which are mathematical values that represent what the AI image model knows, so to speak.

When Stable Diffusion analyzes and “compresses” images into weight form, they reside in what researchers call “latent space,” which is a way of saying that they exist as a sort of fuzzy potential that can be realized into images once they’re decoded. With Stable Diffusion 1.4, the weights file is roughly 4GB, but it represents knowledge about hundreds of millions of images.

Examples of using Stable Diffusion to compress images.
Enlarge / Examples of using Stable Diffusion to compress images.

While most people use Stable Diffusion with text prompts, Bühlmann cut out the text encoder and instead forced his images through Stable Diffusion’s image encoder process, which takes a low-precision 512×512 image and turns it into a higher-precision 64×64 latent space representation. At this point, the image exists at a much smaller data size than the original, but it can still be expanded (decoded) back into a 512×512 image with fairly good results.

While running tests, Bühlmann found that images compressed with Stable Diffusion looked subjectively better at higher compression ratios (smaller file size) than JPEG or WebP. In one example, he shows a photo of a candy shop that is compressed down to 5.68KB using JPEG, 5.71KB using WebP, and 4.98KB using Stable Diffusion. The Stable Diffusion image appears to have more resolved details and fewer obvious compression artifacts than those compressed in the other formats.

Experimental examples of using Stable Diffusion to compress images. SD results are on the far right.
Enlarge / Experimental examples of using Stable Diffusion to compress images. SD results are on the far right.

Bühlmann’s method currently comes with significant limitations, however: It’s not good with faces or text, and in some cases, it can actually hallucinate detailed features in the decoded image that were not present in the source image. (You probably don’t want your image compressor inventing details in an image that don’t exist.) Also, decoding requires the 4GB Stable Diffusion weights file and extra decoding time.

While this use of Stable Diffusion is unconventional and more of a fun hack than a practical solution, it could potentially point to a novel future use of image synthesis models. Bühlmann’s code can be found on Google Colab, and you’ll find more technical details about his experiment in his post on Towards AI.

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Apps can pose bigger security, privacy threat based on where you download them

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Google and Apple have removed hundreds of apps from their app stores at the request of governments around the world, creating regional disparities in access to mobile apps at a time when many economies are becoming increasingly dependent on them.

The mobile phone giants have removed over 200 Chinese apps, including widely downloaded apps like TikTok, at the Indian government’s request in recent years. Similarly, the companies removed LinkedIn, an essential app for professional networking, from Russian app stores at the Russian government’s request.

However, access to apps is just one concern. Developers also regionalize apps, meaning they produce different versions for different countries. This raises the question of whether these apps differ in their security and privacy capabilities based on region.

In a perfect world, access to apps and app security and privacy capabilities would be consistent everywhere. Popular mobile apps should be available without increasing the risk that users are spied on or tracked based on what country they’re in, especially given that not every country has strong data protection regulations.

My colleagues and I recently studied the availability and privacy policies of thousands of globally popular apps on Google Play, the app store for Android devices, in 26 countries. We found differences in app availability, security, and privacy.

While our study corroborates reports of takedowns due to government requests, we also found many differences introduced by app developers. We found instances of apps with settings and disclosures that expose users to higher or lower security and privacy risks depending on the country in which they’re downloaded.

Geoblocked apps

The countries and one special administrative region in our study are diverse in location, population and gross domestic product. They include the US, Germany, Hungary, Ukraine, Russia, South Korea, Turkey, Hong Kong, and India. We also included countries like Iran, Zimbabwe, and Tunisia, where it was difficult to collect data. We studied 5,684 globally popular apps, each with over 1 million installs, from the top 22 app categories, including Books and Reference, Education, Medical, and News and Magazines.

Our study showed high amounts of geoblocking, with 3,672 of 5,684 globally popular apps blocked in at least one of our 26 countries. Blocking by developers was significantly higher than takedowns requested by governments in all our countries and app categories. We found that Iran and Tunisia have the highest blocking rates, with apps like Microsoft Office, Adobe Reader, Flipboard, and Google Books all unavailable for download.

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Russia plans “massive cyberattacks” on critical infrastructure, Ukraine warns

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gwengoat | Getty Images

The Ukrainian government on Monday warned that the Kremlin is planning to carry out “massive cyberattacks” targeting power grids and other critical infrastructure in Ukraine and in the territories of its allies.

“By the cyberattacks, the enemy will try to increase the effect of missile strikes on electricity supply facilities, primarily in the eastern and southern regions of Ukraine,” an advisory warned. “The occupying command is convinced that this will slow down the offensive operations of the Ukrainian Defence Forces.”

Monday’s advisory alluded to two cyberattacks the Russian government carried out—first in 2015 and then almost exactly one year later—that deliberately left Ukrainians without power during one of the coldest months of the year. The attacks were seen as a proof-of-concept and test ground of sorts for disrupting Ukraine’s power supply.

The first attack repurposed a known piece of malware, called BlackEnergy, created by Kremlin-backed hackers. The attackers used this new BlackEnergy3 malware to break into the corporate networks of Ukrainian power companies and then further encroach into the supervisory control and data acquisition systems the companies used to generate and transmit electricity. The hack allowed the attackers to use legitimate functionality commonly found in power distribution and transmission to trigger a failure that caused more than 225,000 people to go without power for more than six hours.

The 2016 attack was more sophisticated. It used a new piece of malware written from scratch specifically designed for hacking electric grid systems. The new malware—which goes by the names Industroyer and Crash Override—was notable for its mastery of the arcane industrial processes used by Ukraine’s grid operators. Industroyer natively communicated with those systems to instruct them to de-energize and then re-energize substation lines.

“The experience of cyberattacks on Ukraine’s energy systems in 2015 and 2016 will be used when conducting operations,” the Ukrainian government said on Monday.

Monday’s advisory comes two weeks after Ukrainian forces recaptured vast swaths of territory in Kharkiv and other cities that had been under Russian control for months. Russian President Vladimir Putin last week called for the mobilization of 300,000 Russian citizens to bolster the country’s military invasion of Ukraine.

The move, which was the first time since World War II that Russia has done so, has prompted protests and a diaspora of mostly male Russians fleeing the country. A pivot to increased reliance on hacking by the country’s military could be seen as a way to achieve objectives without further straining the ongoing personnel shortage.

It’s hard to assess the chances of a successful hacking campaign against Ukraine’s power grids. Earlier this year, Ukraine’s CERT-UA said it successfully detected a new strain of Industroyer inside the network of a regional Ukrainian energy firm. Industroyer2 reportedly was able to temporarily switch off power to nine electrical substations but was stopped before a major blackout could be triggered.

“We don’t have any direct knowledge or data to make an assessment on Ukraine’s capability to defend its grid, but we do know that CERT-UA stopped the deployment of INDUSTROYER.V2 malware that targeted Ukraine’s electric substations earlier this year,” Chris Sistrunk, technical manager of Mandiant Industrial Control Systems Consulting, wrote in an email. “Based on that, and what we know about the Ukrainian people’s overall resolve, it’s increasingly clear that one of the reasons cyberattacks in Ukraine have been dampened is because its defenders are very aggressive and very good at confronting Russian actors.”

But researchers from Mandiant and elsewhere also note that Sandworm, the name for the Kremlin-backed group behind the power grid hacks, is among the most elite hacking groups in the world. They are known for stealth, persistence, and remaining hidden inside targeted organizations for months or even years before surfacing.

Besides an attack on electrical grids, Monday’s advisory also warned of other forms of disruptions the country expected Russia to ramp up.

“The Kremlin also intends to increase the intensity of DDoS attacks on the critical infrastructure of Ukraine’s closest allies, primarily Poland and the Baltic states,” the advisory stated. Since February, researchers have said pro-Russian threat actors have been behind a steady stream of distributed denial-of-service attacks targeting Ukraine and its allies.

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