• SpaceFusion: Structuring the unstructured latent space for conversational AI

      A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence.


      A palette allows a painter to arrange and mix paints of different colors. SpaceFusion seeks to help AI scientists do similar things for different models trained on different datasets.
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    • How elliptic curve cryptography works in TLS 1.3

        image

        A couple of reader alerts:

        In order to (somewhat) simplify the description process and tighten the volume of the article we are going to write, it is essential to make a significant remark and state the primary constraint right away — everything we are going to tell you today on the practical side of the problematics is viable only in terms of TLS 1.3. Meaning that while your ECDSA certificate would still work in TLS 1.2 if you wish it worked, providing backwards compatibility, the description of the actual handshake process, cipher suits and client-server benchmarks covers TLS 1.3 only. Of course, this does not relate to the mathematical description of algorithms behind modern encryption systems.

        This article was written by neither a mathematician nor an engineer — although those helped to find a way around scary math and reviewed this article. Many thanks to Qrator Labs employees.

        (Elliptic Curve) Diffie-Hellman (Ephemeral)

        The Diffie–Hellman legacy in the 21 century

        Of course, this has started with neither Diffie nor Hellman. But to provide a correct timeline, we need to point out main dates and events.

        There were several major personas in the development of modern cryptography. Most notably, Alan Turing and Claud Shannon both laid an incredible amount of work over the field of theory of computation and information theory as well as general cryptanalysis, and both Diffie and Hellman, are officially credited for coming up with the idea of public-key (or so-called asymmetric) cryptography (although it is known that in the UK there were made serious advances in cryptography that stayed under secrecy for a very long time), making those two gentlemen pioneers.

        In what exactly?
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      • A City Without Traffic Jams


          Chapter 2.
          (the link to Chapter 1)

          The Art of Designing Road Networks


          Transport problems of a city through the eyes of a Computer Scientist


          If I were recommended an article with the title “The Art of Designing Road Networks,” I would immediately ask how many road networks were built with the participation of its author. I must admit, my professional activity was far from road construction and was recently associated with the design of microprocessors where I, among other responsibilities, was engaged in the resource consumption of data switching. At that time my table stood just opposite the panoramic window which opened up a beautiful view of the long section of the Volgograd Highway and part of the Third Transport Ring with their endless traffic jams from morning to evening, from horizon to horizon. One day, I had a sudden shock of recognition: “The complexities of the data switching process that I struggle with on a chip may be similar to the difficulties the cars face as they flow through the labyrinth of road network”.
          Probably, this view from the outside and the application of methods that were not traditional for the area in question gave me a chance to understand the cause of traffic jams and make recommendations on how to overcome the problem in practice.
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        • How we made landmark recognition in Cloud Mail.ru, and why



            With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

            One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.
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          • Automatic respiratory organ segmentation

              Manual lung segmentation takes about 10 minutes and it requires a certain skill to get the same high-quality result as with automatic segmentation. Automatic segmentation takes about 15 seconds.


              I assumed that without a neural network it would be possible to get an accuracy of no more than 70%. I also assumed, that morphological operations are only the preparation of an image for more complex algorithms. But as a result of processing of those, although few, 40 samples of tomographic data on hand, the algorithm segmented the lungs without errors. Moreover, after testing in the first five cases, the algorithm didn’t change significantly and correctly worked on the other 35 studies without changing the settings.


              Also, neural networks have a disadvantage — for their training we need hundreds of training samples of lungs, which need to be marked up manually.


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            • AI-Based Photo Restoration



                Hi everybody! I’m a research engineer at the Mail.ru Group computer vision team. In this article, I’m going to tell a story of how we’ve created AI-based photo restoration project for old military photos. What is «photo restoration»? It consists of three steps:

                • we find all the image defects: fractures, scuffs, holes;
                • we inpaint the discovered defects, based on the pixel values around them;
                • we colorize the image.

                Further, I’ll describe every step of photo restoration and tell you how we got our data, what nets we trained, what we accomplished, and what mistakes we made.
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              • Even more secret Telegrams

                  We used to think of Telegram as a reliable and secure transmission medium for messages of any sort. But under the hood, it has a rather common combination of a- and symmetric encryptions. Where's fun in that? And anyway, why would anyone trust their messages to the third-party?
                  Spy vs Spy by Antonio Prohías
                  TL;DR — inventing a private covert channel over users blocking each other.

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

                  More
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                • How does a barcode work?

                    Hi all!

                    Every person is using barcodes nowadays, mostly without noticing this. When we are buying the groceries in the store, their identifiers are getting from barcodes. Its also the same with goods in the warehouses, postal parcels and so on. But not so many people actually know, how it works.

                    What is 'inside' the barcode, and what is encoded on this image?



                    Lets figure it out, and also lets write our own bar decoder.
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                  • cyberd: Computing the knowledge from web3

                      The original post has been updated based on community input in order to remove confusion.


                      Final version of the whitepaper is available here:


                      https://github.com/cybercongress/cyber/releases

                      Only registered users can participate in poll. Log in, please.

                      Does web3 excites you as a developer?

                      • 40%Yes, I am crazy about it2
                      • 20%Well, will see how it goes1
                      • 0%Not so bad0
                      • 0%It is unlikely that everybody will use it0
                      • 40%No, it is just another hype2
                    • Ternary computing: basics

                        Balanced ternary


                        I am working on a computer architecture principles lectures for our university; and as an assignment I'd like to propose to my students to build a simple programmable machine working in ternary. The main reason is fun: as a lecturer I must bring a bit of entertainment, otherwise I won't be listened to. Besides, it is important for historic reasons. Any further «why?!» questions will be answered «Because I can».

                        This page describes the very basics, it won't go beyond a simple ternary adder (and its hardware implementation). Stay tuned for more.

                        I chose the balanced ternary system: every trit represents one of three possible states, -1, 0 or 1. A very extensive description of this system may be found here.


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                      • Kalman Filter

                        • Translation
                        • Tutorial


                        There are a lot of different articles on Kalman filter, but it is difficult to find the one which contains an explanation, where all filtering formulas come from. I think that without understanding of that this science becomes completely non understandable. In this article I will try to explain everything in a simple way.

                        Kalman filter is very powerful tool for filtering of different kinds of data. The main idea behind this that one should use an information about the physical process. For example, if you are filtering data from a car’s speedometer then its inertia give you a right to treat a big speed deviation as a measuring error. Kalman filter is also interesting by the fact that in some way it is the best filter. We will discuss precisely what does it mean. In the end of the article I will show how it is possible to simplify the formulas.
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                      • Real-time edge detection using FPGA

                        Introduction


                        Our project implements a real-time edge detection system based on capturing image frames from an OV7670 camera and streaming them to a VGA monitor after applying a grayscale filter and Sobel operator. Our design is built on a Cyclone IV FPGA board which enables us to optimize the performance using the powerful features of the low-level hardware and parallel computations which is important to meet the requirements of the real-time system.


                        We used ZEOWAA FPGA development board which is based on Cyclone IV (EP4CE6E22C8N). Also, we used Quartus Prime Lite Edition as a development environment and Verilog HDL as a programming language. In addition, we used the built-in VGA interface to drive the VGA monitor, and GPIO (General Pins for Input and Output) to connect the external hardware with our board.


                        ZEOWAA FPGA development board

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