• Why code reviews are good, but not enough


      Code reviews are definitely necessary and useful. It's a way to impart knowledge, educate, control a task, improve code quality and formatting, fix bugs. Moreover, you can notice high-level errors related to the architecture and algorithms used. So it's a must-have practice, except that people get tired quickly. Therefore, static analysis perfectly complements reviews and helps to detect a variety of inconspicuous errors and typos. Let's look at a decent example on this topic.
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    • Crime, Race and Lethal Force in the USA — Part 3

      • Translation
      This is the concluding part of my article devoted to a statistical analysis of police shootings and criminality among the white and the black population of the United States. In the first part, we talked about the research background, goals, assumptions, and source data; in the second part, we investigated the national use-of-force and crime data and tracked their connection with race.
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    • InterSystems IRIS – the All-Purpose Universal Platform for Real-Time AI/ML

        Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

        Challenges of real-time AI/ML computations

        We will start from the examples that we faced as Data Science practice at InterSystems:

        • A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
        • An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
        • A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

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      • Crime, Race and Lethal Force in the USA — Part 1

        • Translation

        Do the police in the US really shoot black people more often than white people? Is use of lethal force connected with race? How is crime related to race? What are the odds of getting shot by the police if you are white and if you are black? We're taking public data and python with pandas to shed some light on these questions, propaganda and politics set far aside.
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      • Modern Google-level STT Models Released

          We are proud to announce that we have built from ground up and released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:

          • English;
          • German;
          • Spanish;

          You can find all of our models in our repository together with examples, quality and performance benchmarks. Also we invested some time into making our models as accessible as possible — you can try our examples as well as PyTorch, ONNX, TensorFlow checkpoints. You can also load our model via TorchHub.

          PyTorch ONNX TensorFlow Quality Colab
          English (en_v1) link Open In Colab
          German (de_v1) link Open In Colab
          Spanish (es_v1) link Open In Colab
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        • Molto-2 — a USB programmable multi-profile TOTP hardware token

            About a year ago, we released Token2 Molto-1, the world's first programmable multi-profile hardware token. While Molto-1 is still the only solution of its kind currently available on the market, we will be soon releasing a new variation of a multi-profile hardware token, in a different form-factor and with a different set of features available.

            While Molto-1 has its advantages, there were some shortcomings that we wanted to address, for example, it can only hold up to ten TOTP profiles, which is not enough for many users. Also, using NFC to program the device does not look very convenient for some users. There were also requests to have a backlight for the screen of the token, so it can be used in the dark. With Molto-2 we tried to address this and a few other concerns. So, we hereby present our new device model, Token2 Molto-2 with the following specifications:

            TOKEN2 MOLTO-2 multi-profile programmable TOTP hardware token:

            ▣ RFC 6238 compliant

            ▣ supports up to 50 accounts/profiles

            ▣ USB-programmable with a Windows app

            ▣ RTC battery life: 8 years

            ▣ LCD screen battery: 3-4 months (rechargeable)

            The table below shows the comparison between Molto-1 and Molto-2

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          • AdBlock has stolen the banner, but banners are not teeth — they will be back

          • How to write Palindrome Polyglot Quines

            • Translation

            I offer a solution to one beautiful task — writing code that outputs its text is valid for interpreters and compilers of different languages and is correctly executed when reversing its sources.

            Not so long ago I learned about code that can be both interpreted in PHP and compiled to Java: PhpJava.java. As it turned out, this idea is not new: code which is valid for several compilers or interpreters is called a polyglot. It is possible to write such code because of the peculiarities of processing strings and comments in different interpreters or compilers.

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          • The 2020 National Internet Segment Reliability Research

              The National Internet Segment Reliability Research explains how the outage of a single Autonomous System might affect the connectivity of the impacted region with the rest of the world. Most of the time, the most critical AS in the region is the dominant ISP on the market, but not always.

              As the number of alternate routes between AS’s increases (and do not forget that the Internet stands for “interconnected network” — and each network is an AS), so does the fault-tolerance and stability of the Internet across the globe. Although some paths are from the beginning more important than others, establishing as many alternate routes as possible is the only viable way to ensure an adequately robust network.

              The global connectivity of any given AS, regardless of whether it is an international giant or regional player, depends on the quantity and quality of its path to Tier-1 ISPs.

              Usually, Tier-1 implies an international company offering global IP transit service over connections with other Tier-1 providers. Nevertheless, there is no guarantee that such connectivity will be maintained all the time. For many ISPs at all “tiers”, losing connection to just one Tier-1 peer would likely render them unreachable from some parts of the world.
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            • Common misconceptions about space-grade integrated circuits

                Space exploration was always fascinating, and recent developments have reignited the interest to the heights never seen since the last man stood on the Moon. People argue about Mars exploration and features of spaceships as their grandparents would’ve done if the internet existed fifty years ago. I’m an electronics engineer working in the aerospace industry, so I know a thing or two about the technical background of this stuff — and I see that these things aren’t common knowledge, and people often have significantly skewed ideas about the reasons behind many things and decisions. Namely, I’d love to speak of some misconceptions about radiation hardened integrated circuits and the means of protection from radiation-induced damage.

                So, let's start our journey
              • The hunt for vulnerability: executing arbitrary code on NVIDIA GeForce NOW virtual machines


                  Against the backdrop of the coronavirus pandemic, the demand for cloud gaming services has noticeably increased. These services provide computing power to launch video games and stream gameplay to user devices in real-time. The most obvious advantage of this gaming type is that gamers do not need to have high-end hardware. An inexpensive computer is enough to run the client, spending time in self-isolation while the remote server carries out all calculations.

                  NVIDIA GeForce NOW is one of these cloud-based game streaming services. According to Google Trends, worldwide search queries for GeForce NOW peaked in February 2020. This correlates with the beginning of quarantine restrictions in many Asian, European, and North and South American countries, as well as other world regions. At the same time in Russia, where the self-isolation regime began in March, we see a similar picture with a corresponding delay.

                  Given the high interest in GeForce NOW, we decided to explore this service from an information security standpoint.
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                • Checking QEMU using PVS-Studio


                    QEMU is a rather well-known application for emulation. Static analysis can help developers of complex projects such as QEMU catch errors at early stages and generally improve quality and reliability of a project. In this article, we will check the source code of the QEMU application for potential vulnerabilities and errors using the PVS-Studio static analysis tool.
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                  • Plugins in Figma. From idea to 1000 installs

                      About a year ago, Figma added the ability to develop and publish your own plugins. Those who would like to please themselves and everyone around them with additional features or just to have the experience of lifetime with Figma have longed for this feature to appear. 

                      Inside the plugin, you can do whatever is available to the developer on the web. There are practically no restrictions on the possibilities, except for those related to the system. Fortunately, they are all detailed in the documentation. Many detailed articles have already been written on the topic of technical implementation of plugins, for example, here or here. As product designers, Lev Bruk and I wanted to go through all the stages of plugin release, from the idea and coding to promoting and working with feedback from real users. That’s exactly what we’re going to cover in the article.

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                    • How to display a gazillion of metrics and keep your sanity

                        Large scale equals distributed. Distributed equals inevitable complexity. Complexity at runtime equals extensive monitoring. At Hazelcast, doing distributed systems well is our bread and butter. It means we have no choice but to be huge fans of collecting all kinds of metrics to stay on guard of the data our users trust us with.

                        In Management Center 4.2020.08, we drastically changed the model of how we transfer the metric data from the cluster members to the Management Center, how we store it, and how we display it. In this post, we are going to talk about the latter bit of the triad.

                        We will discuss what to do when you want to display all the data at once, but your users have a limited number of monitors and only one pair of eyes. We will speculate about what users actually want to see when they look at a chart of a monitoring web app. We will go over different approaches to filter the data, and how an average, a median, and a definite integral play their key roles.

                        Cross-posted from hazelcast.com

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                      • Data Science vs AI: All You Need To Know

                          What do these terms mean? And what is the difference?


                          Data Science and Artificial Intelligence are creating a lot of buzzes these days. But what do these terms mean? And what is the difference between them?

                          While the terms Data Science and Artificial Intelligence (AI) comes under the same domain and are inter-connected to each other, they have their specific applications and meaning.

                          There’s no slowing down the spread of AI and data science. Many big tech giants are extensively investing in these technologies. As per the recent survey, it is estimated that artificial intelligence could add $15.7 trillion to the global economy by 2030.

                          Through this piece of writing, I will be explaining about the AI and data science concepts and their differences in detail. So, without wasting any more time, let’s get started!
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                        • Tensodrone that do not break, and what does architecture, robotic manipulator and copter have in common

                          • Translation
                          We had ten broken drones in a year, test flights twice a day, three PhDs in the team, a prototype of sushi sticks and a desire to find a way to stop hitting drones.

                          Very controversial, very unusual, very strange, but it works! At the intersection of architecture, collaborative robotics and unmanned aerial vehicles. 

                          Introducing: Tensodrone™.

                          Tensodrone is a multi-rotor UAV of a new design with collision protection, made on the principle of tensegrity. This approach allows for the combination of the protective cage and the airframe in one structure, increasing impact resistance with less weight. The project is a vivid example of the interaction of various teams of the Center for Competences of the National Technology Initiative in the field of «Robotics and Mechatronics» established at the Innopolis University in 2018.
                          Read more →