Bosch Future Mobility Challenge (BFMC) is an international student competition organized by Bosch Engineering Center Cluj that challenges teams of 2 to 5 students and a university mentor to demonstrate - after 6 months of development - the capabilities of the 1/10 scale vehicle provided by us, to navigate in a designated environment simulating a miniature smart city. Through this competition, we want to introduce the students to the automotive and autonomous driving (AD) fields, to see the challenges and complexities that these two face.

We live in a world of constant development and change. But, how can we adapt to all of these changes? The answer is quite simple, we must bring Digitalization in Engineering, Manufacturing and Industrial IoT. In this paper, we would aim to dive-in in a solution that could help us reach the desired level of digitalization, by changing the way we see the “world” and by changing the way the computers understand the data that they are manipulating.

In this article we’ll present the fundamentals of Solidity, the language of choice for developing Smart Contracts on Ethereum or Ethereum cloned blockchains. The theory and examples presented in this article are inspired primarily from two books. The first one, “Mastering Ethereum”, is written by two British computer scientists: Andreas M. Antonopoulos, a very known popularizer of Bitcoin and Gavin Wood, the creator of Solidity itself. The second book “Building Ethereum Dapps” written by Roberto Infante at Manning is my personal favorite: it is easy to understand but also tackles very complex aspects of Solidity and of the Ethereum ecosystem.

Our story begins in the seventeen century with Netherlands, the most advanced country of Europe at the time. Because of the adoption of Reformation in the preceding century (specifically the Calvinist ideology) and the proximity of the Atlantic Ocean, the Dutch people went through unprecedented developments. They were at the forefront of the geographical discoveries and fared brilliantly against Spain, the most powerful state at the moment, winning their formal independence from them after a struggle that lasted 80 years. But their most important discoveries with lasting impact even today were in the economic realm. They implemented the first modern stock-exchange and discovered first that a company could be split in tiny tranches called shares (societate pe actiuni in Romanian) which could be traded freely by all people, who thus became owners and sellers of small parts of a company. That company was the first mega-corporation, the Dutch East India Company.

A requirement that most of us have probably tackled already, or if not, most likely we will have in the future, is the migration of existing functionalities to a new and modern technology. Although there are multiple reasons why such a migration can be beneficial, we will try to expose those that have been most relevant to us, but first let's see which was the initial context. In our case, the goal was to migrate the existing functionalities from a monolithic application to microservices on the backend side and to microfrontends on the frontend side. Our objective for this article is to present the challenges encountered along the way and at the same time the solutions we have chosen.

AIoT is an acronym that stands for the use of Artificial Intelligence (AI) within the Internet of Things (IoT). The Internet of Things is not a new concept anymore. More and more intelligent products and services are being connected in order to create added value for their users. For example, 92% of all Bosch product classes are connectable. It is believed that there will be around 25 billion connected things by 2025 compared to only 14,2 billion in 2019.

To conclude what we have covered so far, it's clear that when building a model, the trainer selection is not the most difficult part. AutoML is able to suggest a list with the best models, due to the evaluation metrics which accompany every model. What is much more complex (and time-consuming) is the data preparation which, along with the training pipeline, builds a model ready to make predictions.

The beauty of open source software is that it allows you to create, experiment and transform code, and even give it a higher purpose. After discovering and deep diving into a new and exciting security scanning tool, with the help of our engineering team, we began making this tool into something more. What initially could have been used for red-teaming, bug bounty hunting or hacking in general was transformed into a tool that can help blue teams defend against the bad guys better.

Steganography is a technique whereby information is hidden inside otherwise innocuous- seeming information to preserve its secrecy. Etymologically, it comes from the combination of the Greek word steganos (which means concealed) and graphia (which means writing).

The purpose of these series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, and that is possible now using Microsoft ML.NET and Jupyter Notebooks. Moreover, you don't have to be a data scientist to do machine learning.