EESTEC Magazine 41st Issue Preview 2018

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MACHINE LEARNING: how to start? author: Helena Filić

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ave you ever wondered how people make decisions about who is the person that is walking towards you? Or how are we able to recognize all the words that someone is saying or signs and characters that we see? Or how the system for fingerprints identification works?

Those kinds of actions can be done by a machine. Machine learning represents a field of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It is not something new for the world, but algorithms have changed because of new computing technologies and requirements. The basic idea of ML (machine learning) is to teach a computer to perform specific tasks by only using data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt.

They learn from previous computations to produce reliable, repeatable decisions and results. Nowadays, researchers are trying to modify and apply complex algorithms to big data - repeating them over and over again,

faster and faster. There are two really popular examples of machine learning applications: Self-driving Google car - the system that should make online detection of all the cars and walkers that are nearby, traffic signs and then decide what to do; Online recommendation offers such as those from Amazon and Netflix. How does our music player make a playlist based on songs that we do or might like?

If you are now interested in Machine learning application enough and you want to improve your own ideas, here are the steps you should follow:

Step 1: Learn multivariable calculus and linear algebra As some people like to say that mathematics is the basis of everything, in order to prepare yourself for this challenge you should improve your knowledge in this field. ML is all about applying statistics and computer science to data. Don’t be scared! You really don’t need to be a professional programmer, or mathematician to learn ML.

Step 2: Choose your programming language There are many programming languages which provide ML capabilities. But Python and R are most commonly used languages. So before entering into this new world, try to make a smart choice and choose language that might be familiar to you.

Step 3: Machine learning courses There are many ways to make a progress with knowledge of ML. You can start by watching online tutorials for free. But if you want to build a strong machine learning foundation you should work harder. There are various courses available to learn about machine learning. They can be found online for free or maybe even at your university.

Step 4: Putting theory into practice Machine learning takes some time and you have to be patient and constant in what you do. For the advanced level, you need to spend a lot of time working on various machine learning and deep learning problems. And you need datasets to practice building and for tuning models.

Step 5: Challenge yourself in more complex projects After some time of practicing easier examples, you should try to test yourself in more complicated problems. It will upgrade your confidence to face more interesting projects.

In this Digital Age, everything is changing very fast and all the time. We can find out about a new discovery every day, especially in areas that are rapidly developing like machine learning. So try to sail into this world and achieve your goals.

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eestec magazine

MIXED REALITY

author: Marko Rajković

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ixed Reality (MR) is used as an independent concept or to classify the spectrum of reality technologies. As an independent concept, mixed reality combines the best of virtual reality (VR) and augmented reality (AR). When used to classify the larger scope of reality technologies, it refers to the coverage of all possible variations and compositions of real and virtual objects.

What is Mixed Reality?

Mixed Reality is the predominantly virtual spaces where real world objects or people are dynamically integrated into virtual worlds to produce new environments and visualizations where physical and digital objects co-exist and interact in real time.

The continuous scale ranging which covers all possible variations and compositions of real and virtual objects. The continuum ranges from a completely real and natural environment, to a completely virtual environment. The concept was first introduced

by Paul Milgram.

This spectrum (Mixed Reality Continuum) covers all possible variations of

real and virtual objects. The spectrum varies from where nothing is computer generated to the point of an environment where everything is computer generated.

Mixed reality, either has a standalone concept or used to refer to the entire spectrum of situations between actual reality (real world) and virtual reality, attempts to combine the best of both virtual reality and augmented reality.

When both real and virtual worlds are merged together, a new environment is born and it becomes possible for physical and digital objects to coexist and interact in real time.

ones, in the spectrum of reality technologies. This is because augmented reality users remain in the real world while experiencing enhanced virtual created visuals and feelings.

Augmented Virtuality: Augmented Virtuality describes the environment in which real objects are inserted into computer-generated virtual environments. It is best described as the inverse of augmented reality. By utilizing augmented virtuality technology, a homeowner could visualize and interact with virtual appliances and easily manipulate different layouts.

Real Environment:

Virtual Reality:

Real environment, also called natural environment, refers to the natural world we consume everyday. This natural environment encompasses all living and nonliving things occurring naturally on Earth.

Virtual reality seeks to provide users with the greatest level of immersion.

The total immersion experienced in virtual reality requires stimulation of all of the user’s senses in a fully immersive virtual experience, to the extent that the brain accepts the virtual environment as a real environment. In the virtual reality the user’s experience a completely synthetic world that may or may not mimic the properties of a real-world environment.

Augmented Reality: Augmented reality brings aspect of the virtual world into the real world. It is the real environment, as opposed to virtual

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ЊхWhat does Facebook know about us? author: Chiara Marzano

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ollowing a series of pieces published during the beginning of April 2018, the New York Times and the Guardian exposed Facebook, investigating the fallacies in the management of the data retrieved through their platform brought up by the scandal surrounding Cambridge Analytica, a consulting company for online marketing. The case is complex and still lacks a clear explanation, but the main accusations that the company is facing are abetment towards the Republican party during the latest American elections and a subsequent attempt to sway the voting process through targeted campaigns.

The red string connecting Facebook and Cambridge Analytica is a web application launched in 2014 by Alexandr Kogan called “thisisyourdigitallife” that gathered personal information whenever one of its users accessed it through their Facebook profile. This practice itself does not go against any law but, as at the time Facebook also granted access to details about each profile’s friends network, this application was able to reach an estimated number of 50 million people and so was Cambridge Analytica when Kogan later sold this data to the company, going against Facebook’s Terms of Service.

The subsequent process opened the user’s eyes regarding the value of personal information and brought many to rethink their online privacy and secrecy choices: Which of the data we share is Facebook storing and do we have control over them?

Besides the primary profile information provided at the moment of registration and interactions with public posts, the platform mainly gathers data using the following techniques:

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- 3rd Party Data: Data bought from third-party companies such as Acxiom, Epsilon, Oracle, Quantium, and Experian that help them profile users offline; - Facebook Pixels: Plug-ins for websites that help advertisers check on the effectiveness of their ads. In exchange for this service, Facebook tracks users’ interest in those ads and use them to target the following insertions better ; - Artificial Intelligence and Predictive Modelling: These methods are the active core of the deducting process.

In fact, the data currently stored cannot be examined effectively through any manual system and, where man fails, technology steps in. Artificial Intelli-

gence is able to select the ad that could better interest the reader and to show it to them in the best moment, both helping advertisers to receive a better outcome from their campaigns and to learn further about its users.

Considering the advertising activity undertaken by the networking platform in the past years, acting without any information backing their work up would consist in guesswork and, therefore, it would not be as effective.

The fact that Facebook owns so much data about its profiles should not scare users though, as social networks are delicately and accurately engineered and handle sensitive information by nature to assure its users the best possible online experience.

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World’s smallest transistor

author: Marko Rajković

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ransistor size is an important part of improving computer technology. The smaller the transistors, the more you can fit in a chip, and the faster and more efficient your processor can be. That’s why it is such a big news that a team at Lawrence Berkeley National Laboratory has successfully built a functional 1nanometer long transistor gate, which the lab claims is the smallest working transistor ever made! For years, the computing industry has been governed

by Moore’s Law. In 1965, Gordon Moore made a prediction that would set the pace for modern digital revolution. From careful observation of an emerging trend, Moore extrapolated that computing would dramatically increase in power, and decrease in relative cost, at an exponential pace. As well as that, a part of Moore’s law states that the number of transistors in a semiconductor circuit doubles every two years. Current generation technology uses 14-nanometer scale technology, with 10-nanometer semiconductors anticipated for release in 2018 or 2019 with products from big companies such as Intel.

But, there is a problem with Moore’s law. Nowadays it runs into trouble, and by trouble I mean laws of physics.

While the 7-nanometer node is technically possible to produce with silicon, after that point you reach problems, where silicon transistors smaller than 7-nanometer become so physically close together that electrons experience quantum tunnelling. So instead of staying in the intended logic gate, the electrons can continuously flow from one gate to the next, essentially making it impossible for the transistors to have an off state.

ing 7-nanometer semiconductors and beyond, the Berkeley Lab research team has beaten them in that race, using carbon nanotubes and molybdenum disulfide (MoS2) to create a sub-7 nanometer transistor. The MoS2 functions as the semiconductor, with the hollow carbon nanotube functioning as the gate to control the flow of electrons.

and reversible relocation of one single atom within a metallic quantum point contact. So far, the device operates by applying a small voltage to a control electrode within the aqueous electrolyte. The gel connects two small metallic strips containing silver and when a small electric field is applied it kickstarts the flow of electrons into the electrolyte gel.

Research is still in very early stages. At “By tan electric control pulse, we posi14 nanometer, a single die has over a tion a single silver atom into this gap billion transistors on it, and the Berkley and close the circuit” - said Thomas Lab team has yet to develop a viable Schimmel, co-author of the paper and a method to mass produce the new 1- professor at KIT. “When the silver atom nanometer transistor or even develop is removed again, the circuit is intera chip using them. But as proof of the rupted”. In this state the transistor is concept alone, the results are very turned off. This transistor can drive a important. tiny current of approx 1-8 microAmps.

It can conduct electricity more efficient In Karlsruhe Institute of Technology in than semiconductors and consumes Germany, a team of scientist had said less power since it is made from metal. that the world’s smallest 1-nanometer “This quantum electronics element transistor can be controlled by a single enables switching energies smaller atom. Unlike more traditional transis- that those of conventional silicon techtors it isn’t made from a semiconductor. nologies by a factor of 10.000” - said Instead, it’s crafted from metal and the Schimmel. gate – the part that acts as a switch turning the transistor on and off. The The advantage may seem promising, switch is a blob of electrolyte gel. but it’s still a prototype. It requires billions of these transistors to build The single atom transistor represents anything useful. The real challenge to a quantum electronic device at room making them applicable in the everytemperature, allowing the switching day life electronics lies in the scaling of an electric current by the controlled up the manufacturing process.

While companies like Intel had originally announced that they would be exploring other materials for produc-

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INTERNET OF THINGS author: Chiara Marzano

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he expression “Internet of - Post-internet: this second phase, Despite this technology being widely Things” can be used to refer can be divided further based on the spread and being present in over 2.5 to any device that is connect- features the devices are equipped with. billion pockets and purses around the ed to the Internet and that, world, it is still in its infancy. As a therefore, shares data and communi- The very nature of IoT relies heavily matter of fact, security and privacy are cates with other objects. These can on Big Data and on the efficiency of still open issues. Private networks and include any type of device starting from the DBMS of choice. Each physical VPNs security aspects are fundamental simple and research-oriented objects, device has embedded sensors and to ensure higher security. These devices such as sensors, passing through actuators that allow it to communi- often hold a weak and unstable connecsmart fridges and lightbulbs up to the cate remotely throughout the Internet. tion that could easily cause undesired growing trend of wearables. Depend- The data retrieved by the sensors is gateways for hackers as technology is ing on the task they were designed for, sent to designed receptors, analyzed running ahead of the game and some these devices collect data from their thanks to big data analysis techniques common means through which attacks environment, analyze it and then take and action is taken accordingly. This can be dismissed such as IPSec, DTLS action accordingly. technology is based on a four-stages or 6LoWPAN compression are often bottom-up architecture: impossible to implement on such This technology is full of potential and devices for memory, energy or compuoffers great advantages to its users. In tational reasons. The examples of Level 1 - Sensors: embedded fact, it can help with the little tasks of malicious access and manipulation are chips responsible for the retrieval everyday life, for instance, by warming many: unauthorized access to tags, tag of data from the environment; up our living room when it senses us cloning, Sybil attack, sinkhole attack, approaching the house or by buying DoS attack, malicious code injection or Level 2 - IoT Gateways and water and other groceries when it “man in middle” attack and so on. For Framework: responsible for the senses we are running out of them; it what concerns privacy, EIU (Economist transmission of the data collected can also have pivotal and life-saving Intelligence Unit) carried on a research to the internet infrastructure; applications. For instance, the compaon “What the Internet of Things means ny Concrete Sensors uses connectivity for consumer privacy” through which Level 3 - Cloud Server: Where and places sensors in the concrete that it was possible to realize that over the data collected is stored; collects and shares data regarding its 70% of users have some concerns state and conditions in order to prevent regarding how their data is retrieved Level 4 - Mobile App: Designed disaster and offer a more reliable and and processed while over 90% of the for end users in order to facilitate effective maintenance. subjects wished on more control on the their interaction with the device. storage of their personal information.

Formally, the first use of the phrase “Internet of Things” backs to the late 1990s, when a British engineer and researcher for the Massachusetts The impact of IoT on our daily life is Institute of Technology, Kevin Ashton, unmeasurable. Phones, cars, dishwashcoined it while working on the RFID ers, switches, clocks, toasters and any standard. Despite this, devices with object we can think of is connected to such characteristics already existed the Internet and communicates activebefore and many back the birth of IoT ly with others, collecting data that back to that of the Internet and of the concurs in making every small action semantic web. During this process, two easier and more accessible. main phases can be identified, with the genesis of the Internet indicating the transition: - Pre-internet: During this phase, sensors were heavily used and through the years technological development managed to obtain sharp precision in the process of data collection but the devices were unable to communicate;

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Google Analytics - What kind of data are they gathering? author: Helena Filić Probably, you have asked yourself a thousand times why whenever you enter a website some warning about ‘collecting cookies’ appears. What does it mean? And why have I never received some of those cookies to my home address?

Google Analytics is a free web analytics service and, now, the most widely used tool for following and reporting website traffic.

At its most basic level, Google Analytics consists of: * JavaScript code on each page of a website, * A data collection service on Google’s servers, * A processing engine that creates report data.

ASIMO by Honda: The world’s most advanced humanoid robot author: Chiara Marzano ASIMO was the first humanoid ever to be able to walk independently and to climb stairs, making him the most advanced robot to step on our Earth so far. The first prototype was released in 2000 and, since then, it has been refined on up until this summer, when Honda announced its retirement in order to concentrate on the development of more beneficial and promising projects. Nevertheless, the heredity left behind is unmeasurable: its locomotor system is already being used on some exoskeletons in order to facilitate rehabilitation, its balancing system is now used in the field of

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smart transportation in some self-balancing motorbikes.

This robot was designed in order to give help, in fact, it has grasping hands, it can understand some gestures and phrases that are present in its vocabulary or recognize faces and voices. Thanks to these features and considering its friendly appearance and small size, it easily serves its original purpose: ASIMO could be of assistance for any house chore, it could support the elderly, and even help people with handicaps that are confined to wheelchairs or beds.

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UBER neural network based forecasting system author: Chiara Marzano Any decision-making process in companies is nowadays centred around Business Intelligence, a series of strategies and processes apt to gather data and analyze strategic information in order to obtain knowledge from them. A fundamental component of these strategies is efficient forecasting regarding products, marketplace or expenses, which could serve several causes such as avoiding under or over-provisioning, determining prices for the service and improving customer experiences.

Uber, the American company offering, among others, a peer-to-peer ridesharing service, leverages forecasting to offer higher efficiency and quality to its

clients and to increase the rides while reducing costs.

Being heavily reliant on the actions and reactions of its clients, Uber employs these characteristics to create fitting models that can take into account human behaviours and other factors such as seasonality and conveys them in a fairly accurate forecast. Consequently, factors such as the uniqueness of human thought and fluctuations in their behaviour are not to be looked down on completely as they can easily be integrated into the model, and when in posses of a large database, can help deliver more accurate results thanks to statistical methods.

Bitcoin Mining Hardware ASICs author: Marko Rajković

Mining Bitcoin can till be a hobby, and even profitable if you have cheap electricity and get the most efficient Bitcoin mining hardware. It’s important to remember that Bitcoin mining is competitive. It’s not ideal for the average person to mine since the cheap price of electricity in China allows it to dominate the mining market.

It is practically impossible to profitably mine Bitcoin with a regular PC, you’ll need specialized hardware called ASICs. Originally, Bitcoin was created with an intent to be mined on CPUs (laptop or desktop computer). However, Bitcoin miners discovered they could get hashing power from graphics cards.

Graphics cards were then surpassed

by ASICs. Think of a Bitcoin ASIC as specialized Bitcoin mining computers, Bitcoin mining machines, or “bitcoin generators”.

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The Nobel Prizes in 2018 author: József Mák

THE NOBEL PRIZE IN PHYSICS This year Nobel prize in Physics is awarded to Arthur Ashkin ”for the optical tweezers and their application to biological systems” and to Gérard Mourou and Donna Strickland ”for their method of generating high-intensity, ultra-short optical pulses.” Optical tweezers use focused laser light to pick up and hold tiny objects in place. The basic working principle relies on light carrying momentum, and the light rays being bent by the object that is held in place. Total momentum is conserved in a closed system, and the small object acted on by the laser beam reacts with a momentum change compensating the momentum change of the light’s photons, which eventually moves it towards the focus point of the laser.

The inventor, Arthur Ashkin first constructed a device that was able to hold dielectric objects in the middle of a laser beam in 1970, but the actual optical tweezer setup using a focused beam was only born in 1986. The new technology quickly became popular both in atomic physics and biology:

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Ashkin and several more in his footsteps started capturing bacteria and viruses for studying them. As technology advanced, not only cells could be captured but also organelles and single molecules, which allowed such measurements as tracing the stepwise motion of motor proteins as they move along a filament. Optical tweezers continue to find applications in scientific and engineering research, such as in volumetric displays with the aim of once realizing the capabilities of R2-D2 displaying Princess Leia in real 3D space.

Before Gérard Mourou and Donna Strickland have invented Chirped Pulse Amplification (CPA) technology, high-power lasers were huge and costly, were limited to a few gigawatts, and could not give off more than a few pulses per day in order not to damage the amplifier material. However they have cleverly circumvented the problem of amplifying a high-intensity beam by first stretching it in time-domain, such that its peak-power is reduced, then amplifying it, and finally compressing it together again. Since its invention in 1985, CPA and its improvements

allowed for affordable lasers with peak-powers well in the terawatt range, and the ultra-short pulses and ultrahigh powers enabled a range of new experiments and inventions. Electronic movement in molecules and atoms happen on an attosecond (10−18 s) timescale, and CPA combined with a technology called High-Harmonic Generation (HHG) enables laser pulses of this temporal length, and as a consequence, direct observation of the electron dynamics. Perhaps more well-known and less obscure applications of femtosecond laser pulses are material etching, such as drilling holes or laser eye surgery, that has become widspread over the last decade. Ultrahigh power laser technology continues to improve, finding more and more applications, such as for making new types of particle accelerators for medical applications. What can be of special importance for European students, is that the Extreme Light Infrastructure (ELI) a co-operative project between the Czech Republic, Hungary and Romania, in which Gérard Mourou is also involved, is soon expected to be finished, and will further extend the limits of laser science.

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