DEV Community: RoksanaTanov

Proximity Marketing: How it Works and Why You Need It

RoksanaTanov — Tue, 21 Jan 2020 11:57:44 +0000

The evolving technological development often means more opportunities for businesses. This is the case with proximity marketing. This is a technology that takes the advertisement campaigns to a whole new level. Recently, the proximity marketing devices called beacons became a revelation for the retail stores and small start-ups. So, let’s take a look at what proximity marketing is and how it works.

The Concept of Proximity Marketing

Imagine you have opened a clothing store with the stock of great Scandinavian fashion. However, you do not have a base of loyal clients as your enterprise has just been born, and there are plenty of H&M’s in the district. How can you attract new clients and cope with the competition? The answer could be mobile proximity marketing.
The concept is straightforward. When the potential customer approaches your business asset, they automatically get the notification on their phone. It can be anything: new offers or an invitation to find out more about your business. What is great about this technology is that it sends the message at the right time, increasing the probability of customers actually becoming interested in the deal.
As in the modern era, people are inseparable from their mobile devices, this technology proves to be a useful addition to the firm’s advertising campaigns. According to JiWire, more than 53% of customers would not mind sharing their current location and in return receive advertising that is relevant to them at the moment.

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Digital Transformation in Banking

RoksanaTanov — Wed, 25 Dec 2019 09:55:56 +0000

Digital transformation in banking (retail and consumer banking) has so far been quite narrow. Some financial firms have made efforts to get out on social channels (to where the prospects are) and streamline common front-office operations. But that’s about where their modernization ended. Lending, deposits, and other core activities are still mostly run on complex and monolithic legacy systems, though a few companies now have a digital veneer over their antiquated architectures.

70% of banks are now actively reviewing their core banking platforms, according to a McKinsey survey from May 2019.

Types of transformation:

Rip and replace
Parallel cores
Modernize
New Base Platform
Passive observation

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Real-Time Big Data Analytics

RoksanaTanov — Wed, 25 Dec 2019 09:49:54 +0000

According to IDC, about 30% of data generated in the world will be real-time in nature by 2025. This has enterprises hastily experimenting with technical solutions and hoping to find a comprehensive platform that will help them tackle the challenges of real-time big data analytics.

IoT and Big Data are interrelated: Plenty of IoT applications have arisen in manufacturing, healthcare, smart energy, and agriculture in recent years, and they’ve been the main producers of big data for companies across these fields. The insights from big data analytics, on the other hand, is generally what firms use to enhance and streamline IoT services.

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How Fog Computing Helps Address IoT Business Challenges

RoksanaTanov — Thu, 28 Nov 2019 15:13:37 +0000

IoT, for all its momentum, is still quite nascent. It holds vast promise for businesses (particularly in the manufacturing, energy, healthcare, logistics, and utilities industries) in that it can provide visibility into assets and help react to events faster. But the overwhelming volume, variety, and velocity of data it produces often outstrips bandwidth and exceeds the storage, computing, and sophisticated analytical capabilities of even the most advanced enterprises. Also, the fact that it takes so long for sensor data to travel through a gateway into the cloud, where businesses usually store and analyze it, limits the utility of connected solutions to a large extent.

If an IoT device slapped onto an industrial piece of equipment sends out alerts indicating the possibility of a machine failure, the info is only of value if the firm is able to act on it immediately; if technicians are dispatched too late and fail to take corrective actions in time this might lead to an operational disruption and even a production line shutoff.

In this post, we’ll discuss how low latency, excessive bandwidth consumption, and other IoT business issues can be addressed efficiently through a computing paradigm known as Fog; we’ll explain how data processing can be brought closer to the network’s edge and the immense benefits this brings in terms of core network traffic offloading.

Let’s get to it!

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Detailed Analysis of Ukrainian IT Market in 2019

RoksanaTanov — Thu, 28 Nov 2019 15:05:15 +0000

It’s no secret that outsourcing software development has been a global trend for a while and is nowhere near stopping. In fact, a typical IT budget that goes to outsourcing vendors has risen sufficiently this year. According to a recent Outsourcing Study executed by Computer Economics, in 2019 it has grown by more than 30% compared with the previous year, from 9.4% of the total IT budget in 2018 to 12.7% in 2019. The reasons why these numbers keep progressing might be numerous but what they keep showing is that companies are familiar and comfortable with relying on outsourcing partners.

Another interesting shift in the IT outsourcing sector we are witnessing these days is the change of the primary reasons for outsourcing. Traditionally, it was deemed that companies opt for outsourcing as a way to cut budget costs, improve operational flexibility, maybe increase the quality of their services or just augment in-house capabilities. But none of these reasons implied long-term commitment. So what has changed? Today outsourcing is not about saving money and finding a quick fix, today companies are after real partners they can collaborate with, disrupt markets, implement new services companies are not capable of doing by themselves and generally drive innovation and leave their competitors far behind.

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AR vs MR vs VR: Intuitive Guide to Immersive Technologies and Tips on How to Secure AR Environments

RoksanaTanov — Thu, 24 Oct 2019 12:03:25 +0000

AR vs MR vs VR – same devices, different market value.

Augmented reality and mixed reality have already reached the usability phase thanks to the recent advances in computer vision, sensor fusion, and new-age display technologies. Their business applicability has been proven repeatedly, and now the community has moved on to researching, thoroughly, the security and privacy implications of AR’s adoption (which, turns out, are quite substantial.)

A person wearing a head-mounted device (HMD) captures lots of information about surrounding physical objects and locations and thus exposes, sometimes unwittingly, massive volumes of data to third-party applications that are meant to process the data and deliver relevant outputs. If these apps happen to be corrupted, sensitive (and potentially damaging) info can be leaked and used with malicious intent.

In this post, we shed light on the conceptual differences between the types of immersive technologies (since there’s still a great deal of confusion surrounding augmented reality vs virtual reality comparisons) and discuss how AR’s key security concerns can be addressed through the implementation of the latest protection approaches.

AR vs MR vs VR

The term Virtual Reality refers to software-generated simulations of real experiences. VR completely blocks out a person’s view of the world (typically through an HDM device) and immerses them into an artificial environment.

Augmented Reality, however, is about bringing digital information into a person’s view of the real world. Instead of shutting down reality, it superimposes computer-generated content onto the physical environment so that both can be experienced together.

Mixed Reality has a hybrid definition that blends aspects of both VR and AR. It is essentially an extension of AR that, on top of overlaying information, enables meaningful interaction between synthetic and physical objects to deliver rich, immersive experiences.

Though tech media loves lumping the two together, AR (MR) and VR are two distinct technologies that each have their own path. AR (MR) market is predicted to reach over $149b by 2025, while the global VR market is expected to grow to $33.8b in the same timeframe.

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Real-World Applications of Q-Learning: Gentle Introduction

RoksanaTanov — Mon, 14 Oct 2019 08:35:34 +0000

Reinforcement learning, as per our recent post, deals with a unique problem setup where an arbitrary agent is trying to learn the optimal way of interacting with an environment. In return for its actions, it receives delayed labels also known as rewards; the agent’s ultimate goal is to find an optimal policy that maximizes cumulative numerical return.

RL-based technologies have already been implemented by inventive companies to configure, in an optimal way, multi-tier web-networks, build robust recommendation algorithms, engineer sophisticated intrusion detection schemes for IoT networks, and so on.

In this post, we’ll delve a bit deeper into sequential decision making, discuss, in general terms, the promise and shortcomings of Reinforcement Learning and explain the principles of one of the most popular RL strategies – Q-learning.

Let’s get to it!

Mathematically, Reinforcement Learning problems are formulated as Markov Decision Processes (MDPs); they’re defined by:

set of states (S), set of actions (A);
distribution of rewards (R) – a function that maps a state-action pair to a reward;
transition probability distribution (P) over the next state (given a state-action pair);
discount factor (γ) that defines how important are the rewards we get now vs later in the episode.
In an MDP, everything begins with an environment’s sampling of initial state from initial state distribution. Then, a so-called reinforcement learning loop begins:

1) agent plays an action (a1) in a state (s1),

2) environment, having received the state-action pair, sends back a reward R1(a1,s1)

3) agent transitions into the next state (s2).

This continues iteratively until we reach the terminal state and the episode ends.

All agent’s actions are dictated by a policy π (a function that maps s at time step t to a at time step t). The objective is to find an optimal policy π* that will tell the agent, given the current state, which action brings the maximum cumulative discounted reward.

So, how is π* calculated?

First, let’s establish how the goodness (optimality) of states and actions is established in RL.

Each policy produces a certain trajectory of states, actions, and rewards.

We use a Value function (V) to measure how good a certain state is, in terms of expected cumulative reward, for an agent following a certain policy. The optimal value function (V*), therefore, is one that gives us maximum achievable value (return) for each state in given state space (set of all possible states).

A Q-value function (Q) shows us how good a certain action is, given a state, for an agent following a policy. The optimal Q-value function (Q*) gives us maximum return achievable from a given state-action pair by any policy.

One of the key properties of Q* is that it must satisfy Bellman Optimality Equation, according to which the optimal Q-value for a given state-action pair equals the maximum reward the agent can get from an action in the current state + the maximum discounted reward it can obtain from any possible state-action pair that follows. The equation looks like this:

Q* (s, a) = E [Rt+1 + γ max a′ Q*(s′,a′)]

Since Bellman Equation helps calculate Q* at each time step, it gives us a way to determine the optimal policy; we know the value of the following state-action pair, so the optimal strategy is to play actions that maximize the expected value of

R + γ Q*(s’,a’)

R is the immediate reward
Q*(s’,a’) – is the optimal Q-Value for the state we’ll end up in
γ– is the discounting factor
The optimal policy π, as we can infer from this, is to take the best action – as defined by Q – at each time step.

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Next-Generation IoT Architecture: How ML-based Techniques Boost IoT Security

RoksanaTanov — Wed, 02 Oct 2019 13:16:54 +0000

Generally, IoT systems can be divided into two groups: those applied merely for monitoring tasks (tend to be very data-intensive), and those meant to enable remote control of “intelligent” devices.

The first ones are usually analytics solutions that aggregate information from a multitude of IoT sensors (data comes up through gateways) and then format or visualize it to help users discern patterns and actionable insights from their datasets. They may have vast bandwidth requirements when there are plenty of devices incorporated, but only in terms of upstream bandwidth.

The latter, however, have much more difficult data models and both their upstream and downstream bandwidth are equally critical; there’s always a complex business logic involved in such platforms that’s used to 1) determine the needed adjustments based on received data and 2) send updated configurations to the devices to optimize performance.

The IoT networks of the second type, popular with enterprises, are typically comprised of a great many entities and scaling them safely, by introducing new devices and data streams, is acutely challenging. Vulnerabilities accumulate exponentially as new elements and relationships are added to the network.

In this post, we’ll discuss how to enhance a conventional IoT architecture through machine learning. We’ll explain how supervised, unsupervised, deep and reinforcement learning methods could help firms protect their connected solutions against various types spoofing, jamming, intrusion, man-in-the-middle, dos and other identity-based attacks.

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Building Your Own Uber: 9 Apps Like Uber to Get You Inspired

RoksanaTanov — Mon, 02 Sep 2019 12:38:41 +0000

Almost 10 years ago Uber disrupted the market of taxi services by creating a simpler and cheaper alternative to traditional cabs. Since then the company has spread to 250 cities and 80 countries around the globe and has more than 75 million active users. Their 2018 report states that the company’s full-year revenue for 2018 was $11.3 billion, with 43% increase from the previous year. Uber’s success has become a phenomenon, as well as an inspiration for others in the B2C field. Apps like Uber have become a global trend across various industries: from housecleaning, medical services to food delivery, and even dog walking.

Apps like Uber from various industries

Swifto

What is it?

Swifto is New York-based on-demand dog walking service. It incorporates mobile technologies, GPS tracking, photos and live notifications to offer the highest level of reliability and trust to pet owners. The service offers professional dog walkers who can help out either on the regular basis or if an occasional walk is needed.

How it works?

First, the app matches a user with a dog walker who is equipped with an app and whose background and experience are checked. From then on devoted dog owners can enjoy notifications about their pet’s walks, when they start and end, as well as can monitor the walk via GPS, browse pictures from the walks. At the end of every walk, owners will receive a message from the walker letting them know how the walk went. They can also watch their dog’s walk online.

Launched back in 2013, the app is still a success among dotting dog owners.

BlackJet

What is it?

Launched in 2012, BlackJet announced it’s like Uber but for jets. It allowed its customers to book seats on private jets immediately and with guaranteed seat availability. The service was originally backed by Uber co-founder and chairman Garrett Camp and an early Uber investor Shervin Pishevar who developed the idea. The startup attracted an array of celebrity investors from Ashton Kutcher, Will Smith and Ja Z’s Roc Nation to Marc Benioff and Tim Ferriss.

How it works?

Their idea was to become Uber but for the jet-set, where clients pay an annual fee and in exchange get access to a network of private jets on which they can book flights.

But it has been a bumpy ride for BlackJet. The company experienced massive layoff and service suspension in 2013 until they finally went out of business in 2016.

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How will the future of IoT look?

RoksanaTanov — Fri, 23 Aug 2019 16:00:20 +0000

The technological world we all have been dreaming about, and let’s face it, somewhat dreading as well is finally here. And what by now should come as a surprise to us, it is full of IoT connected devices. Per recent statistics, there are 26.66 billion IoT devices around the globe at the moment. And the number will only increase — 30 billion by 2025 and 75 billion by 2025 is what we are promised at the moment.

But the biggest surprise comes not from these numbers but from the fact that compared to the full potential of Internet of Things these are peanuts. Only 0,06 % of devices that could be connected, actually are at the moment. This leaves us thinking and hoping and the future of IoT has not fully unveiled at the moment and there’s more to come.

So let’s look how IoT will impact our lives in some of its major areas like home, work, transportation or living in our cities:

Home

Perhaps, one of the most promising applications of IoT. In the following years, the amount of IoT devices we will be sharing a living space with will only grow. To them point when these devices will get so intelligent that will be able to run and manage our homes for us.

And how will the smart home look? Pretty clear, actually. It will be built on the hierarchy of devices. The devices will fall into two subcategories: service bots (the ones that will take on more serious tasks and will have a set of functions) and niche bots (these will be capable of a single task, like vacuuming the home). And all of these IoT devices will be governed by the central platform, the “brain”, if you may.

The human residents of this ideal home will mainly be interacting with the central platform. So the main task for the developers here is to create logical and intuitive interface. This won’t come as a surprise that the main market players here are Apple, Amazon, Google and Samsung. As for the other bots, they will be developed much like apps — to be able to run on both iOS and Android.

Smart cities

Traffic congestion, parking problems, pollution and lack of any green spaces. These are all the issues we are facing in our cities daily. Now imagine living in the cities that are designed to fight these problems, and even more — building smart economy and government, improving resident safety, reducing environmental impact and energy usage.

The smart cities trends in currently happening most in Europe, where nearly half of the ongoing projects are being developed. But the popularity of smart cities keeps growing and 66% of US cities are either willing or investing in the technologies.

Work

Artificial intelligence and advanced analytics can turn your work into a dream workplace. Imagine an office where smart devices will set the right AC temperature or will help you not only book the meeting room everyone will like but customize it according to your preferences (setting the right temperature, lighting etc.). Who would want to quit?

Smart offices will transform our daily operation into more efficient ones, save some energy and other resources, and even increase our productivity.

So what’s holding these ideal work places back? The main struggle is communication between all these smart devices, data sharing, and what’s probably is the biggest issue today — security.

But there’s light at the end of the tunnel. Cisco is already and early adopter of smart office and controls some of its core functions with the help of IoT

Smart cars

In 2020 smart cars are planned to enter mass production. Google is already keeping itself busy by testing autonomous driving, while the new Tesla is already capable of driving on both the open roads and in traffic.

Driverless cars can prevent around 85% head-on collisions and also help with the traffic. The future of IoT has a vast possibility of unfolding in the automotive industry. The question is, are we ready to share the roads with self-driving cars? 54 % of US drivers don’t feel safe sharing the road with driverless cars. Moreover, 78% of Americans are afraid of riding in a self-driving car.

As we can see, the future of IoT development has numerous ways to unfold. The only big dark could on our road to happy and careless living is the issue of communication and connectivity between devices. But if manufactures manage to solve it, our lives are bound to become more interesting and smarter!

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Data Science Projects: Machine Learning process steps

RoksanaTanov — Fri, 23 Aug 2019 15:43:41 +0000

The popularity of AI and ML technologies is so widespread today that companies feel pressured to introduce them or otherwise they will look outdated or fall behind their competitors. But a mere desire to be innovative is not enough and not all are successful at harvesting the full potential of AI. Lots of firms simply lack the tools or AI lifecycle management experience to carry out their data science projects.

Trying to overlook key practices for BI and analytics and go straight to AI adoption is a common mistake of many AI enthusiasts and the one that leads to devastating failures. So what is the right lifecycle of building AI products and services (which, by the way, vary from traditional software engineering lifecycles)? And how to build a strong AI foundation? Let’s discuss

Reliable Data Flow at the Core of all Data Science Project

Companies that are familiar with Big Data technologies and have successfully adopted them for data integration / ETL, data governance, and other data services, have a strong foundation for their future AI and ML projects. Others need to get some basic BI and analytics going first. And here’s our step-by-step guide:

Step 1. Planning

Start by figuring out the key areas you want to focus AI technologies on. The general practice shows it’s better to involve data scientists at this stage to discuss which direction you want your project to take.

You need to decide which of the tasks you want to automate first. To help you decide answer these questions:

Is this particular task data-driven?

Is the scale the automation can bring here really worth the effort?

Is there enough relevant data to support the automation, is it clean enough, labeled well?

It’s important at this stage to clearly state your business objectives and make sure that the tasks you are putting in front of AI are achievable. we need to be certain we’re planning to task our AI with a problem that’s solvable with available data.

Step 2. Data Audit

Next, data science team will begin with cataloging all the data sources, and researching how clean the available data is, its relevance to the task, whether there’s a properly labeled training dataset (or if further annotation is needed), or whether the data scattered across disparate MES, RIP, SCADA platforms (in different formats) is somehow joinable.

This is when you might discover that a good chunk of relevant data is missing, or that some service logs are inconsistent and unusable.

Data cleansing will substantially reduce your initial dataset, which means you may have to reconsider which area to aim the AI model at.

Step 3. Picking / Creating Features

This stage is a teamwork between data science team and decision makers on compiling a list of features that have the strongest predictive signal. The data scientists then must score the predictive potential of these features against how hard they are to compute and pick a few optimal ones to kick off the experimentation.

Step 4. Modelling

Similar to engineering, start with the simple thing — build a baseline model, incorporate a few simple features, and iterate from there.

These common and on the surface simple algorithms (logistic regression, random forest) are the ones that often go into production; they have but a few parameters to tune, don’t require much training, and, in some cases, are surprisingly robust to overfitting.

Step 5. Deploying

Don’t rush into this. There should be an experimentation framework in place (even some primitive A/B testing will do) that would allow us to deploy gradually and minimize risks, as well as debug algorithms end-to-end.

The feedback from end-users should be incorporated into development early on, and we must remember that raw predictions solve nothing. We always need some post-processing functionality (APIs, workflow tools) to make the model’s outputs useful and explainable so that the company knows which factors are driving them.

Then, we’ll finally start working on a more advanced web app; sometimes making feature-extraction production grade is a time-consuming process and wrapping models into software packages that other applications can query for predictions requires a massive engineering effort.

Conclusion

Artificial Intelligence isn’t magic, it’s applied statistics and linear algebra.

Your models will be as good as the data you feed them. So if you only have databases that are full of inconsistencies, have gaps and are structured in some chaotic fashion, even the most advanced ML techniques won’t be able to help you desired results.

It’s a common practice to ignore the engineering part of all data science projects as they're not as exciting as modeling. But you must remember that the success of your AI endeavors depends on the quality of data your company generates and processes.

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