Economics in Information Technology. Хрестоматия для студентов бакалавров направлений «Бизнес-информатика», «Прикладная информатика»

revealed by users is a source of savings on research costs for buyers and on canvassing costs for sellers, which increases the quality of matching of supply and demand. For this reason, operators may use their client base for the purpose of crossselling, by using data collected on their clients or users in order to sell another product or service to them. Intermediary marketing activities are also optimised by means of the collection and processing of personal data. For example, services based upon geolocation (searches for hotels and drivers) enable optimised matching of supply and demand by collecting data on users’ consumption habits. Apart from the provision of services properly speaking, platforms also create wealth by enabling the collection of a considerable mass of data, which can be put to profitable use on various different markets.

From the point of view of competition, personal data plays an ambivalent role. It constitutes special information which may be monopolised by private enterprises and block entry to new competitors. This is all the more true in cases where users prefer to belong to a single platform (single-homing), behaviour which the platforms moreover encourage by various different means such as loyalty programmes. Development of the portability of users’ personal data is a promising lever for the regulation of these situations. Personal data can also be used in the public interest. For example, in the health sector,

Internet users’ searches indirectly reveal information about their state of health, age, concerns and location: this data can be used by the public authorities in order to detect epidemics and the appearance of certain illnesses. Finally, digitalisation of personal data may serve the interests of the persons concerned,

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in particular when it makes it possible to reveal certain positive behaviours: drivers documenting their good behaviour behind the wheel, loan applicants making known their rigour in the management of their bank account.

Career roadmap: Cloud engineer

https://www.sciencedaily.com (December 3, 2020)

Instead of inserting a card or scanning a smartphone to make a payment, what if you could simply touch the machine with your finger?

A prototype developed by Purdue University engineers would essentially let your body act as the link between your card or smartphone and the reader or scanner, making it possible for you to transmit information just by touching a surface.

The prototype doesn't transfer money yet, but it's the first technology that can send any information through the direct touch of a fingertip. While wearing the prototype as a watch, a user's body can be used to send information such as a photo or password when touching a sensor on a laptop, the researchers show in a new study.

"We're used to unlocking devices using our fingerprints, but this technology wouldn't rely on biometrics -- it would rely on digital signals. Imagine logging into an app on someone else's phone just by touch," said Shreyas Sen, a Purdue associate professor of electrical and computer engineering.

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"Whatever you touch would become more powerful because digital information is going through it."

The study is published in Transactions on Computer-Human Interaction, a journal by the Association for Computing Machinery. Shovan Maity, a Purdue alum, led the study as a Ph.D. student in Sen's lab. The researchers also will present their findings at the Association for Computing Machinery's Computer Human Interaction (ACM CHI) conference in May.

The technology works by establishing an "internet" within the body that smartphones, smartwatches, pacemakers, insulin pumps and other wearable or implantable devices can use to send information. These devices typically communicate using Bluetooth signals that tend to radiate out from the body. A hacker could intercept those signals from 30 feet away, Sen said.

Sen's technology instead keeps signals confined within the body by coupling them in a so-called "Electro-Quasistatic range" that is much lower on the electromagnetic spectrum than typical Bluetooth communication. This mechanism is what enables information transfer by only touching a surface.

Even if your finger hovered just one centimeter above a surface, information wouldn't transfer through this technology without a direct touch. This would prevent a hacker from stealing private information such as credit card credentials by intercepting the signals.

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The researchers demonstrated this capability in the lab by having a person interact with two adjacent surfaces. Each surface was equipped with an electrode to touch, a receiver to get data from the finger and a light to indicate that data had transferred. If the finger directly touched an electrode, only the light of that surface turned on. The fact that the light of the other surface stayed off indicated that the data didn't leak out.

Similarly, if a finger hovered as close as possible over a laptop sensor, a photo wouldn't transfer. But a direct touch could transfer a photo.

Credit card machines and apps such as Apple Pay use a more secure alternative to Bluetooth signals -- called near-field communication -- to receive a payment from tapping a card or scanning a phone. Sen's technology would add the convenience of making a secure payment in a single gesture.

"You wouldn't have to bring a device out of your pocket. You could leave it in your pocket or on your body and just touch," Sen said.

The technology could also replace key fobs or cards that currently use Bluetooth communication to grant access into a building. Instead, a person might just touch a door handle to enter.

Like machines today that scan coupons, gift cards and other information from a phone, using this technology in real life would require surfaces everywhere to have the right hardware for recognizing your finger.

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The software on the device that a person is wearing would also need to be configured to send signals through the body to the fingertip -- and have a way to turn off so that information, such as a payment, wouldn't be transferred to every surface equipped to receive it.

The researchers believe that the applications of this technology would go beyond how we interact with devices today.

"Anytime you are enabling a new hardware channel, it gives you more possibilities. Think of big touch screens that we have today -- the only information that the computer receives is the location of your touch. But the ability to transfer information through your touch would change the applications of that big touch screen," Sen said.

Your next computer can be any colour, so long as it’s green

https://theconversation.com (May 16, 2013)

Do you have a computer on a desk somewhere? Fans whirring, screensaver flickering, left on for days. Would you leave your washing machine running for days? Because over time, a desktop computer draws on average a comparable amount of power to energy-hungry devices like a washing machine or kettle.

Yes, these use more power in the time taken to boil water or wash a load, but they are switched on and then off. A PC can be left to run for days, weeks, or months. And while your laptop may be more efficient, feel the heat blown from inside by high-

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speed fans or coming off the power adapter to appreciate the energy that is being used and lost.

You are likely to be spending £20 to £30 a year just for the convenience of not turning it on and off. Add to that all the other electronic devices in our homes, with televisions on standby, broadband routers, games consoles and phone chargers left running 24 hours a day, and the footprint grows.

Now take a step back and consider the IT infrastructure running in background: the network plumbing from the BT box in the street, through the local exchange, to warehouses full of whirring web servers. The scale is hard to comprehend. For example, giants such as Google, Microsoft, Facebook and

Amazon are are among the world’s most popular websites.

Behind these sites are datacentres packed with the hundreds or thousands of web servers needed to ensure their services stay snappy when millions of people Google or Facebook simultaneously. These datacentres are climate-controlled environments regulated by costly heating, ventilation and airconditioning.

All this data-pumping and computer-cooling requires huge amounts of power. That’s just a few of the big players; every one of the world’s millions of websites is a computer in a room somewhere, plugged into the mains and left running day and night. The internet doesn’t switch off the lights and go home at the end of the day.

As most electricity is still produced from burning fossil fuels like coal or gas, that means IT contributes to the production of

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greenhouse gases such as carbon dioxide and methane that cause climate change.

Is it possible to define a computer user’s carbon footprint? One Harvard academic, physicist Alex Wissner-Gross estimated running a couple of internet searches on a desktop computer generates 7g of CO2 - about the same as boiling a kettle.

More reliable estimates suggest the IT industry accounts for an estimated 4% (27 megatonnes of CO2 in 2011) of all UK greenhouse emissions, not far behind the airline industry which accounts for 6% (35 megatonnes in 2009). So the need to develop and move to greener, more efficient computing is very real – from handheld devices and desktop computers up to the data centres that feed our hunger for information.

This month, Greenpeace ranked Google and Cisco first on their Cool IT Leaderboard of energy efficiency. WHO Google recently pressured the local utility to provide it with clean energy. Last month, Hewlett Packard launched its HP Moonshot line of extra-low-power servers for use in datacentres.

This is green from an environmental perspective, but also preserves “green” of the folding kind - more efficient equipment requires less power and costs less to run.

To save battery life, your laptop is designed to power down the hard disk or slow the processor when the machine is idle. Despite all the energy expended at datacentres on power, cooling and lighting, servers typically run at only a third of

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their capacity. The rest of this power can be harnessed using virtualisation, where one computer can be configured as if it were several. This ramps up the usage on each machine and means fewer are required, a more efficient use of resources.

New manufacturing processes that use less resources to build computers, less toxic chemicals, and offer easier means to reuse components will cut pollution and wasted energy. Programmers can even write code that takes advantage of intelligent hardware to control processor speed and memory use in the most energy efficiently way possible.

But even if computers were twice as efficient, the national carbon footprint would only be 2% smaller. Far better to use computers to reduce the rest of our carbon footprint - the other 96% of energy usage.

Tele-conferencing, optimising travel and logistics, and just-in- time production are examples of where computers can reduce the need for resource intensive activity. The next generation of 3D printers will revolutionise manufacturing, leading to more efficient design, build, and transport of products.

In the meantime, buy the most energy efficient computer or portable device. Video-call with Skype instead of driving to a meeting (though convincing families to take a Skype holiday instead of flying abroad could pose a challenge).

And delete that message to “Please consider the environment before printing this email” from your email signature - because

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transmitting millions of those images of trees a day generates a carbon footprint of its own.

New machine learning tool tracks urban traffic congestion

https://www.sciencedaily.com (December 2, 2020)

UBER driver data helps track and potentially alleviate urban traffic congestion

A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic.

The tool, called TranSEC, was developed at the U.S. Department of Energy's Pacific Northwest National Laboratory to help urban traffic engineers get access to actionable information about traffic patterns in their cities.

Currently, publicly available traffic information at the street level is sparse and incomplete. Traffic engineers generally have relied on isolated traffic counts, collision statistics and speed data to determine roadway conditions. The new tool uses traffic datasets collected from UBER drivers and other publicly available traffic sensor data to map street-level traffic flow over time. It creates a big picture of city traffic using machine learning tools and the computing resources available at a national laboratory.

"What's novel here is the street level estimation over a large metropolitan area," said Arif Khan, a PNNL computer scientist

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who helped develop TranSEC. "And unlike other models that only work in one specific metro area, our tool is portable and can be applied to any urban area where aggregated traffic data is available."

UBER-fast traffic analysis

TranSEC (which stands for transportation state estimation capability) differentiates itself from other traffic monitoring methods by its ability to analyze sparse and incomplete information. It uses machine learning to connect segments with missing data, and that allows it to make near real-time street level estimations.

In contrast, the map features on our smart phones can help us optimize our journey through a city landscape, pointing out chokepoints and suggesting alternate routes. But smart phone tools only work for an individual driver trying to get from point A to point B. City traffic engineers are concerned with how to help all vehicles get to their destinations efficiently. Sometimes a route that seems efficient for an individual driver leads to too many vehicles trying to access a road that wasn't designed to handle that volume of traffic.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, the team reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes. The speed-up, accomplished with highperformance computing resources at PNNL, makes near-real- time traffic analysis feasible. The research team recently presented that analysis at the August 2020 virtual Urban

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