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DIGURA is the tenant's digital friend. By utilizing digitization, efficient case processes, and high quality, we assist tenants with problems with their landlords. A complex platform was developed to compare 4,500 judgments and decisions with 200 predefined cases of tenants and landlords. We can provide a tenant with an assessment of the case within 24 hours using that solution. By doing this, we ensure that both ourselves and the tenants are in the best possible position for further litigation, and we become better with each case we review. For tenants who regularly need legal advice and sparring when they encounter challenges with their tenancy, we also offer a membership club called DIGURA Home. It is likely that there has not been a tenancy problem that our advisers have not encountered before in our membership club. Do you have questions or want to know more? Then feel free to send us a message - otherwise, you can read more about DIGURA at www.digura.dk/en

DIGURA

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44,000 startups. 4,553 cities. “4 billion votes” It was such a blessing to get to know so many of your ideas and innovations from every corner of the world in 2021. The startup community amazed all of us with your hard work and persistence despite all of the challenges that we faced globally throughout last year. Not gonna lie, the competition got our hearts racing like Eurovision. We won’t let you wait for much longer, below are long-awaited results! ✨✨✨ To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series 🔥 Central Europe Central Europe has always caught our attention with the talented pool of people in tech community. But were they able to get through 2021 despite the pandemic? Let’s cheers to the most outstanding amongst 4,500+ startups who have been working hard this year: Basel, Switzerland An overwhelming vote for ~ in their mission of helping machinery companies drive more service revenues from their installed base MachiQ Software Berlin, Germany We have with a whooping 538 votes! Let’s support Plan A with its mission to help companies to measure, monitor and reduce their environmental footprint. Plan A Düsseldorf, Germany It doesn’t get any easier with Kicking off in helping people organize online schedules more easily. EasyWeek! Geneva, Switzerland Manage code quality? We have to give props to for their startup! SonarSource Hamburg, Germany Taking over Hamburg is , an acoustic device to reduce noises in vehicle cabins. No more annoying sounds on the road 🚗 ReCalm Köln, Germany Small business? Hoping to manage your marketing campaign? is winning the vote! rankingCoach Lausanne, Switzerland is a Swiss-based company focused on cryptocurrency management solutions built for financial institutions. Metaco Leipzig, Germany empowers companies to operate and utilize drones for the most effective industrial data applications. FlyNex Mainz, Germany is growing sustainable digital mobility startups! Next Mobility Labs Munich, Germany wins for top-line, high-security international Identity Management! ORY Stuttgart, Germany Remote desktop application? Ask anywhere. AnyDesk Vienna, Austria , an IT company enabling privacy-preserving big data applications in the digital age. Mostly AI Zug, Switzerland Way to go 1762 votes for the first provably live pure Proof-of-Stake (PoS) CBC-Casper blockchain designed for real-world applications. CasperLabs! Nyon Calling for robust data solutions for impact-driven organizations! Havuta LLC Zürich is revolutionizing small business lending to give easy access to capital to those who need it most: SMEs ✨ Teylor Heidelberg, Germany is an innovative HR startup providing a solution for fast and efficient recruiting of employees with simple job profiles. Talk'n'Job Stuttgart, Germany Congratulations for winning this title! AnyDesk Moving on to our buddies in... 🔥 Eastern Europe Eastern Europe is home to many familiar big names in our start-up world. Trace history for Miro, Telegram, and Grammarly and you know where they are from. Who is the next generation for Eastern European startups? Budapest, Hungary is a platform for mobile app development, enabling tens of thousands of developers to automate integration, testing, and deployment. Bitrise I__ __ stanbul, Turkey Leading here is - a brand new software intelligence platform- helps organizations deliver faster, high-quality products by connecting their DevOps tools. Oobeya Kraków, Poland is a human cloud platform that connects global enterprises with European software houses, offering tech talent as a service. Talent Alpha Kyiv, Ukraine 281 votes for , an AI powered app where people can swap faces in the videos, GIFs and images in just a few seconds. Reface Sofia, Bulgaria 387 votes for __ __378, leading Blockchain and DLT solutions company that specializes in helping enterprises and blockchain startups design, build and scale innovative solutions using Blockchain & DLT! LimeChain Warsaw, Poland is a next-generation blockchain document management software (DMS). DoxyChain Yerevan, Armenia Whoever clicked to vote for was sure a vote, because it ended up winning as a full-service software provider delivering Artificial Intelligence & Machine Learning solutions for businesses. SmartClick smart Almaty Just like the name suggested, provides one pass to dozens of gyms and studios. 1FIT Debrecen got all the coders ranked figured out! CodersRank Kharkiv is complete solution digital assets management! Pics.io Minsk, Belarus with 243 votes is the first blockchain-powered sports media platform. Scorum Moscow, Russian Federation 1 e-grocery company in Russia with 26 votes and #1 on our HackerNoon board for Minsk. SberMarket Saint Petersburg, Russian Federation leads this area as a personal finance application that rewards responsible spending and savings. CoinKeeper Kazan, Russian Federation Smelling a sweet victory? 3d App designers for flower bouquets is in the house! Flowito Western Europe 🔥 Brexit? Pandemic? It seems like startups in Western Europe still keep their spirits regardless. Startups in Western Europe that stood out in votes this year go to: Antwerp is on a streak! As a bug bounty platform, it ensures online security through a crowd-sourced platform for computer experts. intigriti Belfast A record-breaking 10,003 votes? is an independent coffee shop. Come grab a coffee! Bobs & Berts Birmingham What’s happening in Birmingham? We have , an innovative UK-based advanced lithium battery developer aiming to accelerate the global shift to cleaner. Aceleron Bordeaux 30 is an open-source web framework for building modern apps and websites with Vue.js. NuxtJS Boulogne-billancourt is a software company that provides an in-store analytics platform to optimize operations. DiGEiZ Brighton In for some jam? offers musical education services. BIMM Bristol Another tasty victory for Western Europe is enabling the strongest digital encryption on the internet. KETS Quantum Security Brussels A fashionable win for , a social fashion network. You scan barcodes in fashion shops and see every influencer wearing those outfits! Share your wishlist with your friends and earn cashback. SCANDALOOK Cardiff is a technology company developing end-to-end digital connectivity for the point-of-care diagnostics market. Bond Digital Health London A whooping number of 1085 vote__ __ offers hardware based cyber-security solution for authentication and encryption in a wired local network. Crypto Quantique Edinburgh Hopping up on the crypto movement, just launched last year but they are already rocking it at making crypto accessible to everyone. Zumo Ghent 5379 votes for ! A Belgian InsurTech startup that helps insurance providers all over the world to better connect with their digital customers. WeGroup Glasgow specializes in single-cell analysis transforming the life sciences. Biomage Paris Show some love in the city of love for , a sports platform that is connected to a mobile sports coaching application. AirFit Aberystwyth enables artificial intelligence, data science & digital innovation solutions for the agriculture, biotech & life sciences industries. Agxio Norwich Congratulations for the amazing win of 1124 votes! WATR Charleroi Congratulations for your amazing win! Hirering Gibraltar Give a round of applause to Fortknoxter! Waltham Abbey is ruling the area with its amazing 72 votes! RPAIR Antwerpen Rocking up Antwerpen area is the rising startup ! Willow Egham What’s happening in Egham? Proudly announcing winning the Noonies this year! ATA.ONE Leicester Leicester is calling the name of as the winner for the region! DeadHappy Macclesfield Who else but ? Congratulations on dominating the award! AeroCloud Northern Europe 🔥 Want to listen to some music? Spotify. Need some games to kill time? Candy Crush. What about a quick call to someone you haven’t seen in a long time? Skype. These are all startups that originated from Northern Europe! Noonies, let’s take a look at the upcoming generation of startups from Northern Europe that hopefully will see an even bigger and brighter future. Gothenburg, Sweden Shipping was never made for small shippers, so took on the mission to built an end-to-end logistics platform to empower small businesses to spend less time and money on shipping and more time on growing their businesses. Sendify The Netherlands provides a virtual platform for many kinds of conferences and events. Let's Get Digital Helsinki, Finland A curated collection of public basketball courts in the Helsinki Metropolitan Area called . Everyone is welcome to submit to their local court. Koripallopaikat Lund, Sweden develops an antibiotic-free, safe and effective treatment of vaginal fungal infections. Gedea Biotech Luxembourg, Luxembourg 🚀 is a job-matching platform that connects developers and tech experts with CTOs and team leads from the best tech companies across Europe. 👾 nexten.io Malmö, Sweden Feeling studious, Noonies? __ __e is a tool for Google Classroom, that enables formative matrix assessment towards course learning objectives directly. StudyBe Oslo, Norway is the platform supports a unified API across all connected device categories, removing significant friction for app developers. Domos Labs Reykjavík, Iceland __ __s is a mobile-first technologically advanced neo-bank. Indo service #S__ __ tockholm, Sweden is the biggest blockchain start-up in Sweden. Double winner of the most prestigious nomination in Nordics 'NyTeknik 33-listan', winner of the Deloitte Fast 50, and now Noonies Award! ChromaWay AB T__ __ allinn, Estonia Passing 1000 votes is a fully digital and decentralized ecosystem for all actors in supply chain. OBORTECH Vilnius, Lithuania is an easy-to-use money app for cards and accounts. Via Genome, users can open a business or a merchant account or a personal one with dedicated IBANs, access transfers, exchange currency, process payments, and manage the funds. Genome Aalborg Digura A legaltech offering tenants fast, digital and reliable legal assistance Aarhus Partisia Market Design provides secure, simple, efficient solutions for auctions and exchanges realizing the potential of the market. Helsingborg is a hi-tech company developing cardiac occlusion devices and other vascular implants. Occlutech Tallinna is an AI-powered system for marketing automation in social networks. POSTOPLAN 🔥 Southern Europe Southern Europe’s economy went through a hard time this year due to a huge reduction in tourism. But our startups are of course always headstrong, from Spain to Italy and all the way through Greece. Who are the heroes rising amidst changes of Southern Europe? Skopje is a versatile software development company that solves the business challenges of SMBs, big brands, and brave hearts with awesome ideas. CodeChem Sliema Sounds like just a relaxing exhale. is a holistic health and wellbeing app built to allow users work on the development of their physical, mental and spiritual health Few Tajonar is a company of experienced business & industry entrepreneurs offering solutions to industries based on cutting-edge technologies. das-Nano Tarragona is a technology-based company focusing on the production of integrated microsystems. IMICROQ Terni is an ICT-based integrated solution that is able to modulate, respond, assist and learn to control most home activities. Mind Tirana is a video content and e-commerce platform for the Balkans built on top of an Albanian language specialized search engine. Gjirafa Torino develops and commercializes algorithms based on Artificial Intelligence to increase the autonomy of small satellite missions. AIKO T__ __ orres Buy online groceries like you would buy offline with personalized picking as fast as in 4 hours with Vedras 360hyper! Trento is a music-based platform built around streaming platforms that allow users to discover new emerging artists and make new friends. Brots 2021, what a YEAR. Congratulations to all of the startups that made it to the top winning list for Europe! Stay tuned for Startup awards in 2022 and let’s cheers to a new year of more innovative ideas, bold innovations, and robust creations from you, Europe 🌷 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by An Le and edited by Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Europe

The World Wide Web, thank you Sir Tim, was conceived as a technology designed to be “ ” It has devolved from that into a (cyber)space of moated castles, Disneylandish . a powerful source for social change and individual creativity. cathedrals, not bazaars While very entertaining and very useful in many ways the hegemonic Web platforms present as artificial rather than organic, hierarchical rather than egalitarian. The Big Platforms are owned and managed by a handful of dominant, supervalued, companies. Prominent are Alphabet (Google’s parent) and Meta (Facebook’s parent). Low cost and high profit, they are great at informing and entertaining us. Meanwhile, however, in consequential ways they are beggaring those who provide the content and the users who consume that content. The top platforms are richly paid to channel advertising. To us. The Web has evolved into a massive, and massively profitable, advertising engine. As such, the Web now might be (and is becoming) seen as a form of de facto feudalism, digital imperialism, and digerati aristocracy. The attention of us billions of serfs is the product sold to advertisers. No coercion there. More like… seduction. Note, this critique does not imply a call for legislation or government regulation. Both tend to be blunt instruments that land much harder on startups than on the hegemons of the digital megaverse. It calls for transformation. Which will come, as always, , as Microsoft dethroned IBM, Google (partially) dethroned Microsoft, Facebook (partially) dethroned Google, Apple (partially) dethroned Facebook, and so forth. from more innovation How did we get here? Can we, and if so how can we, once again unleash the imprisoned lighting ( ) of the colonized Web to create a new golden age of innovation and equitable prosperity? h/t Emma Lazarus Enter the blockchain. First, though, who am I to judge? I am one of the few surviving Original Gangsters of the Supply Side/Reaganomics revolution. We, relentlessly mocked by the right (“voodoo economics”) and the left (“trickle-down economics”), got policies enacted that, over time , pushed the Dow from 814 (on that day in November 1979 that Reagan officially declared his candidacy for the presidency) to its current flirtation with 40,000. Our voodoo doubled real per capita GDP (from $30,000 to $60,000). It raised nominal American and world GDP by a factor of ten. Pretty good voodoo. That said, what’s the next act? The juice from the original equitable prosperity policy formula -- of stabilizing the dollar (slaying the inflation dragon for two generations and cutting the top marginal income tax rate from 70% to, initially, 28%) -- got wrung out around the time Bill Clinton left the building. I was and am enthusiastic about reducing counterproductively high marginal tax rates across-the-board. Yet much of what ensued in the name of supply-side during the 21st century is a cargo-cult version of supply-side’s successes of the ‘80s and ‘90s. The post-Reagan/Clinton grotesque caricature of authentic supply-side economics has left the economy limping for a generation. Cumulatively, over 20+ years, sub-par growth rates are crippling. They leave us with an economy, both national and personal, far lower than what consistent 3+% supply-side growth rates would have delivered over time. As , compound interest is the eighth wonder of the world.  A one or two percent differential compounded over 20 years makes a huge difference. Einstein probably never said Is there a new, plausible, way to re-ignite vibrant, widely shared, rising living standards? As Nobel Prize winner Paul Romer has pointed out, the secret sauce is innovation. Tech. Enter A16z’s , “a General Partner and has been at Andreessen Horowitz since 2012. He founded and leads a16z crypto, which invests in web3 technologies through four dedicated funds with more than $7 billion under management.” Chris Dixon Dixon has delivered a splendid new book . It lays out in clear, practical terms how the wild wild Web was lost. And how to set it right in such a way that everyone, including us serfs, can prosper. Read Write Own: Building the Next Era of the Internet As , prosperity is too important to be left to the economists. Enter Dixon. war is too important to be left to the generals How to restore sizzling, equitable, prosperity? The blockchain. The blockchain is a revolutionary form of software which, when more fully developed, portends to allow most of the value created by the community and by its cultural creatives to be enjoyed by the full community and its cultural creatives. Not (as now) exclusively, or at least primarily, by the corporate royalty and tech aristocracy. At long last, Dixon gives us a presentation of the blockchain and its properties imbued with an enthusiasm leavened with shrewd practical insight devoid of hype. Dixon is one of the Masters of the Digital Universe (as coder, entrepreneur, and investor). He knows whereof he writes and has written the first really great, comprehensive, book on the blockchain, . Read Write Own: Building the Next Era of the Internet Eureka! Dixon provides an overview in his introduction. He there provides us a World-Wide-Webb-telescopic-eye view of the three eras of the internet, of a new movement, of seeing the truth and determining the internet’s future. Part One, “Read Write”—treats the early internet, which was effectively read only, and Web2, allowing us surfers to write and post through websites, blogs, podcasts, social media and so forth. He explains why networks matter and lays out a trenchant taxonomy of the two dominant incumbent networks (protocol, like the internet and email, and corporate, such as Google and Facebook) and their properties. In Part Two, “Own,” Dixon then goes on to explain what blockchains are, what “tokens” are, and what the – radically great – properties of blockchain networks are… or could and can be. “Own” is a short word with huge implications. Ownership is what has been lost, and which can be restored via a (better) blockchain. In Part Three, “A New Era,” Dixon explains and explores the fundamentals of the business model, community-created software, and take rates (meaning the vigorish that the corporate network proprietors extract from their communities, a process he aptly calls “attract and extract”). He lays out how building networks with token incentives can dramatically change the way the world works for the better, making it more prosperous, cultured, and equitable. He presents vividly the attendant “tokenomics” … and how to restore popular, rather than corporate-autocratic, governance. In Part Four, “Here and Now”, he contrasts the culture of the computer (blockchain as compute platform) with the casino (cryptocurrency), concluding with “What’s Next” … showing how mobile computing, starting with the iPhone, changed everything. He then lays out “some promising applications.” Dixon concludes with a pragmatic vision of the reinvention of the internet… and what grounds his cause for optimism. Until Dixon, in many years of my searching, like Diogenes with his lantern, I had encountered not one single really good, comprehensive, book on the blockchain. (I may have missed one, admittedly, for instance, not yet having gotten to Alex Tapscott’s critically acclaimed .) Web3 There I rested, baffled. So much sizzle. So little steak. So, together with a brilliant acquaintance, I helped write one: . That said, it mainly was focused on the potential finance applications of the blockchain, not the Big Picture. Redefining the Future of the Economy: Governance Blocks and Economic Architecture I thereafter co-authored a second book on this subject, , a compilation of ten superior policies designed to let the sector thrive while protecting the public. Blockchain With American Character It took a long odyssey to arrive at Dixon. I wandered around for years asking people vastly smarter than me what the blockchain was good for and seeking examples of killer applications (“use cases” in the prevailing jargon). In response, I got oodles of hype. And almost no satisfying answers … with one exception, though a largely unpublished one. noteworthy Cometh now Chris Dixon. I am not the only one impressed. Sam Altman, the godfather of ChatGPT, blurbs Read Write Own: “Read this book to understand a compelling vision of where the Internet should go and how to get there.” Shark Tank’s Mark Cuban: “Chris Dixon succeeds at explaining how crypto technology is vital to our technological future and how we all can benefit much as we have from the internet.” And many more captains of industry, tech titans, and nouveau billionaires discovered Dixon’s writings before I did. My own modest credentials? I have been working within and with the Blockchain ecosystem for, well, eons. One of my LinkedIn verticals is aggregating some of the most interesting stories about NFTs. Ralph Benko’s Cryptozoic Eon Geology nerds will recognize the original to be the pre-Cambrian era … before the trilobites and dinosaurs... comprising 80% of Earth’s geo-history. Are we trembling at the verge of the blockchain’s equivalent of the ? Cryptozoic Eon Cambrian explosion Dixon thinks so. So do I. Call me yet my odyssey through the blockchain ecosystem has brought me into contact with creatures and cultures as exotically beautiful and, sometimes, as weird as those encountered by Odysseus during his long return to Ithaka. Nobody My own pilgrimage: Senior advisor to the fabulous Perianne Boring’s Chamber of Digital Commerce, one of the sector’s earliest and most distinguished cheerleaders. Long-time active member of the Federal Blockchain Working Group. Privileged to present more than once at the conferences of the , the preeminent association of officials and international career civil servants. Government Blockchain Association Serving, for two years, as the senior counselor to the American Blockchain PAC, with its advisory board chaired by who generously contributed the introduction to . The breakthrough nature of Stornetta and Haber’s discovery is decisively evidenced by the fact that three of Satoshi’s eight footnotes referenced his and Stuart Haber’s . blockchain-co-inventor Dr. W. Scott Stornetta Blockchain With American Character seminal paper The high privilege of working with one of Satoshi’s earliest Bitcoin core devs (and major contributor to the Android OS) in establishing a DC think tank devoted to smart energy policy, . Jeff Garzik Washington Power and Light Voted Contributor of the Year in 2022 at for my blockchain writings in six categories: politics, economics, science, art, philosophy, and (with Greg Meredith) scaling. HackerNoon My blockchain-related efforts have migrated primarily to the , rather than policy, side. enterprise My primary focus is on the use of rho calculus (a refinement of Robin Milner’s Turing Award-winning pi-calculus) to deploy a blockchain that, unlike the dominant platforms, scales. Bitcoin does a mere 7 transactions per second, Ethereum, 70 transactions per second. Inadequate for practical utility. And expensive to operate. And storable. (Currently it costs almost half a million dollars to store one iTune on the dominant incumbent chains, hence the referenced data are mostly stashed behind someone else’s firewall, not at all optimal.) And searchable. (Currently the dominant blockchains are largely opaque, like the Web before search engines.) And secure. So … now what? Dixon is a true Master of the Blockchain Universe. Not (like me) a practiced propagandist. Thus he buries his lede on page 179: “The stakes are high. As I’ve argued here, blockchain networks are the only known technology that can re-establish an open, democratic internet.” Hello Congress my old friend, I’ve come to talk to you again…. This re-establishment is a very big deal indeed. What’s next? For the tantalizing promise of the blockchain to be realized we need smart policy that protects the public while not stifling innovation or exporting this cutting-edge tech, and the potentially trillions of associated dollars, to Berlin, Singapore or Zug. Imagine how worse off America would be today if Congress and our regulators had effectively deported the headquarters of the big Web success stories, like Google, to Europe or the Pacific? The need for a savvy legal and regulatory environment is something that legislators, regulators, and the ethical actors within the industry (like Dixon) all agree upon. Yet Washington has thus far proved mostly thrown, acting like the primates upon encountering the monolith at the beginning of . 2001: a Space Odyssey We’re at the precipice of deporting Web3 and its associated power, prestige and wealth. That may be about to change. The blockchain’s thought leaders are at long last gearing up to help guide the footsteps of the Washington elite back onto the Paths of Righteousness via smart policy, smart legislation, and smart regulation. Some very smart leaders of the blockchain ecosystem are now promising to , a modest investment in support a multi-billion dollar sector. invest $78M in lobbying and advocacy in 2024 The open question? Will they deploy their resources effectively? Or be taken in by the wily Beltway Bandits? What to do? Those of us who remember C.P. Snow’s seminal understand that the “physical scientists” (which would include software engineers, notwithstanding that software is, so to speak, metaphysical) among whom I count myself as a very lesser figure do not really understand the “literary intellectuals” (among whom I also count myself in a very minor way). The Two Cultures And The Scientific Revolution This misunderstanding causes a terrible disconnect. As Snow reported, in 1959: “They have a curious distorted image of each other. Their attitudes are so different that, even at the level of emotion, they can’t find much common ground.” This can be countered… if the blockchain thought leaders know how to adopt an effective approach. There is abundant potential common ground. Dixon’s book offers a “ ” – the brain’s bridge connecting the left (logical, dominating the physical scientists) and right (narrative-based, dominating the literary intellectuals) hemispheres – to enable both sides to understand each other. corpus callosum To their mutual, and our, great benefit. There’s a simple way to expedite this process, one with a high ROI. Were the thought leaders of the $78M blockchain advocacy effort to allocate an absurdly small fraction (less than 1%) of their dedicated resources to distributing two copies (one for the Congressperson or Senator, one for his or her most relevant legislative aide) of Read Write Own, the impact almost surely will be disproportionately valuable compared to the low cost of such an operation. How do I know? I’ve pretty much single-handedly gotten (non-blockchain related) legislation passed through the House. Twice. There are a lot of smart, idealistic, people working on Capitol Hill, many actively interested in the blockchain and in its tantalizing potential. Put Read Write Own in front of them! The power of can hardly be overestimated: Joseph Pulitzer’s principles “…put it before them briefly so that they will read it, clearly so that they will understand it, forcibly so that they will appreciate it, picturesquely so that they will remember it, and, above all, accurately so that they may be wisely guided by its light.” The late supply-side chief propagandist Jude Wanniski’s book “The Way the World Works” played an outsized role in igniting the worldwide wave of unprecedented equitable prosperity driven by supply-side economics. This arcane fact is mostly long forgotten. That said, I was there and remember. In the , “And I only am escaped alone to tell thee.” words of Ishmael Read Write Own, properly deployed, very well could catalyze a comparably magnificent equitable prosperity transformation. Putting Dixon’s Read Write Own in front of the right eyeballs in the right way on Capitol Hill, could very well release the djinn of “the only known technology that can re-establish an open, democratic internet” from its lamp. Which eyeballs? Don’t try to guess. Simply trust the process known as “ as described by James Surowiecki. the wisdom of crowds” Flood the zone and let the implicate order take it from there. And then … let a new golden age of equitable prosperity, via the road thereto so ably mapped out by Dixon, foment well-designed policy and thereby foster the next great manifestation of equitable prosperity for America. And the world. Read. Write. Own.

Here Comes Web3: Chris Dixon’s Read Write Own

এখানে আসে ওয়েব 3: ক্রিস ডিক্সনের পড়া লেখা নিজস্ব

Hier kommt Web3: Chris Dixons Read Write Own

Aquí viene Web3: Lectura y escritura propia de Chris Dixon

Voici le Web3 : Lire, écrire et posséder de Chris Dixon

यहाँ आता है वेब3: क्रिस डिक्सन का पढ़ें लिखें अपना

Web3 の登場: Chris Dixon の Read Write Own

Web3 출시: Chris Dixon의 읽기 쓰기

Aí vem a Web3: Read Write Own de Chris Dixon

А вот и Web3: книга Криса Диксона «Читайте, пишите сами»

İşte Web3 Geliyor: Chris Dixon'ın Oku Yaz Kendin Yaz

Đây là Web3: Tự đọc và viết của Chris Dixon

Web3 来了：Chris Dixon 的 Read Write Own

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Introduction A graph data structure defined is a non-linear data structure that has nodes and edges. A node in a graph has a value and all these nodes are interconnected with each other using vertices. Every graph has a finite set of nodes and vertices. Graphs are used to solve real-life problems. Some of the real-world examples where a graph is implemented are telephone lines or a circuit network. The concept of a mutual friend on a social media platform like LinkedIn, Facebook, Instagram, etc. internally implements a graph. We implement graphs in such a way that each node is a person in the network and it has attributes like id, name, age, etc. Just like graph theory in mathematics, every graph is an abstract type that is directed or undirected. Depending on the basis of nodes and vertices, there are many types of graph data structures mentioned below - Null Graph A null graph contains zero order. In this type of graph, there exist nodes but no edges or vertices. It means, there is no interconnection between the nodes. All the nodes are isolated in a null graph. If you want to import an item from a foreign to India, it is an example of the null graph as these are two different countries. Trivial Graph A graph that has only one vertex is known as a trivial graph. Such have only one vertex and no edges. graphs Non-directed Graph A graph is called non-directed if all the edges connected between the nodes have no connection flow between them. The edges are non-directed but connected. For example, A, B, C, and D are nodes of a graph and they are connected in a circle but the flow of edges is not defined. It can be ABCD or BCDA or DCBA, etc. Directed Graph A graph is called directed when a flow of direction is defined between two nodes. These are also known as digraphs. In directed graphs, there is a predefined start point and an endpoint. For example, A graph contains 4 nodes A, B, C, D. The directions are given are as follows - A → B, B → C, C → D, A → C If I want to go from A to D the path can be - A → B → C → D or A → C → D Connected Graph A graph comes under the category of the connected graph when there is an interconnection between two graphs. In short, there must be a vertex via which we can traverse from one graph to another and so on. For example, there are two graphs ABCD and EFGH. There is a vertex that connects Node D and Node E. Disconnected Graph A disconnected graph is just the opposite of a connected graph. A graph is said to be disconnected if there is no at least one edge that connects both the graphs. Both the graphs are separated and no interconnection exists between them. From taking the example of a connected graph, if we remove the vertex that is connecting D and E. It then becomes a disconnected graph having two graph structures (ABCD and EFGH). Regular Graph A graph falls under the category of the regular graph if it follows at least one in the below two conditions - a) The degree should be the same of all the vertices. If all the nodes of the graph have the same degree then it's called a regular graph. b) Every vertex of the graph must be adjacent. A graph is also called a ‘k-regular graph’ where k is the number of degrees all the nodes of the graph hold. For example, a graph having degree 6 is called a 6-regular graph. Complete Graph In a complete graph, each vertex in the graph is adjacent to all its vertices. It means there exists an edge connecting pair of vertices in a graph. A complete graph having n vertices has nC2 edges and this complete graph is represented as kn. For example, if a graph has 3 vertices and each vertex has at least one edge with the rest of the vertices then it is a k3 graph. Cyclic Graph If a graph consists of at least one cycle it is called a cyclic graph. A cycle here represents the loops by which we can be redirected to the starting node while traversing through other nodes. Acyclic Graph A graph is called acyclic if there is no cycle present in it. For example, if we start from a node and do not return back to it while traversing in the graph, it is acyclic in nature. Finite Graph A graph is called a finite graph if a definite number of nodes and vertices  exist in the graph. By finite, it means that the nodes and vertices present in the graph are countable. a graph ABCD has four nodes and four edges connected in a cyclic manner is a finite graph. For example, Infinite Graph An infinite graph is defined as a graph in which the number of nodes and edges are uncountable. Bipartite Graph For a graph to fall under the category of a bipartite graph, the following conditions must be satisfied - a) All the vertices of the graph should be divided into two distinct sets of vertices X and Y. b) All the vertices present in the set X should only be connected to the vertices present in the set Y with some edges. That means the vertices present in a set should not be connected to the vertex that is present in the same set. c) Both the sets that are created should be distinct, which means both should not have the same vertices in them. Planar Graph A graph is called planar if it can be embedded in the plane. It means that the graph is drawn in such a way that their edges intersect only at their endpoints. In this type of graph, no edges cross each other. It is used in rotation systems and combinatorial maps. Simple Graph If a graph contains no self-loops and parallel edges, it is termed a simple graph. For example, A graph having nodes ABC and edges as A → B, B → C and C → A, is a simple graph. Multi Graph A multi-graph is somewhat similar to a simple graph. The difference here is that a multi-graph can have parallel edges and no self-loops. If there exists more than an edge for a node, it comes under the category of a multigraph. Pseudo Graph A graph is called a pseudo graph if it contains self-loops but no parallel edges. A self-loop here is whose starting and endpoint is the node only. For example, A graph has nodes ABC and edges as A → B, B → C and C → A, and a self node from A → A is a pseudo graph. Euler Graph A graph is termed a Euler if all the vertices in the graph have a degree. In other words, there is an edge associated with every node in the graph. To be an Euler graph, all the vertices should have an even number of degrees of the node. To know in-depth about graphs read this . article by Scaler Topics Conclusion Graphs in Data structure have a variety and so is the application. There are several sorts of graphs each having different properties. It is used in computer networks, computational devices, sociology, geometry, conservation, and other fields. The most common uses of graphs are as follows: To demonstrate the flow of computation graphs are used. In google maps, graphs are used to locate the shortest distance by using Prim's and Kruskal's algorithms. Facebook mutual friend connection is an example of an undirected graph. Maps can be represented by graphs to show the roadways between various locations.

Different Types of Graphs in Data Structure

As we know data structures are very important, If we want to store data in an efficient way. So in this article, we will discuss about a data structure which is quite famous: 💡. In this article, we will discuss on the following topics : Graph Data Structure A short note on definition of Graph Data Structure Terminologies famous of Graph Different types of Graph Terms important for Undirected and Directed types of Graph Different ways to store a Graph Types of traversals in Graph Let we start our article !🤓 A short note on definition of Graph Data Structure is a popular non-linear data structure, consists of  finite number of vertices (nodes) and edges . Graph Data Structure And the main thing which makes so popular is its uses in real-world applications (or problems) . Examples of its applications are telephone networks, road networks, social networks, its use in circuit networks and in all those problems in which we need a shortest path between two points . Graph Data Structure It basically used for representing random connections, from anybody to anybody (or friendship relation, opposed of which denotes parent-child relation). Tree Data Structure Each graph is denoted by a pair of vertex set and edge set. , G ( V, E )
           ^  ^
           | Edge Graph |
       Vertex set set In the above graph, set of vertices are V = {1, 2, 3, 4} and set of edges are E = {(1, 2), (2, 3), (3, 4), (4, 2)} . Terminologies famous of Graph As we have discussed definition of Graph, let we now discuss some terminologies used in Graphs : two vertices are said to be adjacent, if an edge is present  between them . Like in above Graph, 1 and 2 are adjacent. Adjacent : a sequence of edges, we get while travelling through the vertices of Graph . In some places, walk is said path. And in this, repetition of edge ( hence of vertex also) is allow. Like in above Graph: 1- 2, 2- 3, 3- 2 is a walk. Walk (path) : It is a walk, without repeated edge and vertex . In some places, path is said simple path. Like in above Graph : 1- 2, 2- 3 is a path. Path (simple path) : if a path exists in the Graph, which starts and end with same vertex then Graph is said to be cyclic. It can be Directed or Undirected Graph. Cyclic : When a Graph is not Cyclic, then it is Acyclic . It also, can be Directed or Undirected Graph . Acyclic : Different types of Graph In this section, we are going to discuss different types of Graph🤔 . And we will mainly focus on four types of Graph, which are found very commonly : an Edge in Undirected Graph has no specific direction . They are bidirectional in nature . 1. Undirected Graph : For example : let a vertex represents an user of Facebook, and an edge as a connection between them. So, if 1 (vertex in graph) is a friend of 5 then 5 will also a friend of 1. an Edge in Directed Graph has a specific direction . They are also known as digraph. 2. Directed Graph: In the above graph, there is an edge between 0 and 4, but because arrow head points toward 4 . we can only traverse from 0 to 4, and not from 4 to 0 . It is same as of communication, in which communication is unidirectional . Simplex Mode In weighted graph, values are associated with edges known as weights . These weights can represent cost ( like traffic in computer networks ) or length ( like distance in road networks ) . 3. Weighted Graph : They can be Directed or Undirected . Graph in which no weights (or values) are associated with edges . By default, all graphs are unweighted, unless values are associated with edge. They can be Directed or Undirected also . 4. Unweighted Graph : Terms important for Undirected and Directed types of Graph There are some terms which are common for Undirected and Directed types of Graph but have different values, and they used frequently . For Undirected Graph : For Directed Graph : Different ways to store a Graph we have reached on discussion of the second last topic for this article, 😎. Let we discuss, different ways to store a Graph : Graph is represented by a 2D-matrix, called adjacency matrix . The size of matrix is V * V, where V is number of vertex . 1 . Adjacency Matrix : Each row and column represents a vertex, and value at row i column j, matrix[i][j] = 1 if there is an edge between vertex i and vertex j. otherwise matrix[i][j] = 0. Before we discuss second way to store Graph, let we first discuss some : Properties of Adjacency Matrix Space required : theta (V * V) Time required for Operations : check if vertices u and v are adjacent = theta (1) Find all vertices adjacent to u = theta (V) Find degree of vertex u = theta (V) Add / Remove an Edge = theta (1) Add / Remove a vertex = theta (v^2) If you look at the matrix, you find that it stores redundant information (information of vertex, having no connection). Problem with this method ? In this method, Graph is represented by an array of list. And an array list can be a dynamic size array or linked list . 2. Adjacency List : A single index, A[i] represents the list of nodes(or vertex) adjacent to the vertex i . The reason for using this method is that it stores only relevant information (information of only adjacent vertex). And a common operation, which used in many algorithms (like DFS and BFS), operation of finding all adjacent vertex is fast in this. Why this? Some are : properties of Adjacency Lists Space required : for undirected graph - theta (V+ 2*E) (as edges are bidirectional in it) for directed graph - theta (V + E) Time required for Operations : check if vertices u and v are adjacent = O(V) find all vertices adjacent to u = theta (degree (u)) [*outdegree are considered for directed graph ] find degree of vertex u = theta (1) Add / Remove an Edge = theta(1) Add / Remove a vertex = O(V) Types of traversals in Graph Searching for a or a require Graph traversal . There are two ways available,in which we can find a node or traverse a Graph : node path (simple path) ( Depth First Search ) DFS ( Breadth First Search ) BFS Reach The End ! Congrats 👏, you have reached at the end of the article. In this article, we discussed about definition of Graph Data Structure . Then we discussed some terminologies famous in Graph, and its types . We also discussed some terms of Undirected and Directed Graph . Then we discussed some storing methods of a Graph, and traversals types . If you want to see codes related to Graph, you may have a look at this on github. Thank you for reading it till the end 🙂 !

A Complete Introduction to Graph Data Structure

In the , we showed the character relationship for the by using NetworkX and Gephi. In this post, we will show you how to access data in Nebula Graph by using NetworkX. last post Game of Thrones NetworkX is a modeling tool for the graph theory and complex networks written by Python. With its rich, easy-to-use built-in graphs and analysis algorithms, it’s easy to perform complex network analysis and simulation modeling. NetworkX In NetworkX, a graph (network) is a collection of nodes together with a collection of edges. Attributes are often associated with nodes and/or edges and are optional. Nodes represent data. Edges are uniquely determined by two nodes, representing the relationship between the two nodes. Nodes and edges can also have many attributes so that more information can be stored. The following four basic graph types are provided in NetworkX: Graph: undirected graph.DiGraph: directed graph.MultiGraph: A flexible graph class that allows multiple undirected edges between pairs of nodes.MultiDiGraph: A directed version of a MultiGraph. Create an undirected graph in NetworkX: networkx nx
G = nx.Graph() import as Add nodes: G.add_node( )
G.add_nodes_from([ , , ])
G.add_node( ,name= ,age= ) 1 2 3 4 2 'Tom' 23 Add edges: G.add_edge( , )
G.add_edges_from([( , ),( , )])
g.add_edge( , , start_year= , end_year= ) 2 3 1 2 1 3 1 2 1996 2019 In the last post (part one of this series), we have displayed the community detection algorithm Girvan-Newman provided by NetworkX. Graph Database Nebula Graph NetworkX usually uses local files as the data source, which is totally okay for static network researches. But when the graph network changes a lot, for example, some central nodes are deleted or important network topology changes are introduced, it is a little troublesome to generate, load, and analyze the new static files. At this time, we’d better persist the entire change process in a database, and load subgraphs or whole graphs directly from the database for real-time analysis. This post uses Nebula Graph [3] as the graph database for storing the graph data. Nebula Graph provides two methods to get graph structures: Query to return a subgraph. Scan the underlying storage to get the whole graph. The first method is suitable for obtaining certain nodes and edges that meet the requirements through fine filtering and pruning in a large-scale graph network. Whereas the second method is more suitable for the whole graph analysis. It is usually used in the early stage of the project when conducting heuristic exploration. When you further understand the graph, you can use the first method for fine pruning analysis. By reading the code of the python client for Nebula Graph, we know that the and the file are the APIs that interact with the underlying storage. These files contain a series of interfaces to scan nodes, scan edges, read columns, and so on. The following two interfaces can be used to scan all nodes and edges: nebula-python/nebula/ngStorage/StorageClient.py nebula-python/nebula/ngMeta/MetaClient.py def scanVertex (self, space, returnCols, allCols, limit, startTime, endTime) def scanEdge (self, space, returnCols, allCols, limit, startTime, endTime) 1. Initialize a client and a ScanEdgeProcessor. The ScanEdgeProcessor decodes the edge data read from Nebula Graph: metaClient = MetaClient([( , )])
metaClient.connect()
storageClient = StorageClient(metaClient)
scanEdgeProcessor = ScanEdgeProcessor(metaClient) '192.168.8.16' 45500 2. Initialize the parameters for the scanEdge interface: spaceName = returnCols = {} returnCols[ ] = [ , ]
returnCols[ ] = [ ]
allCols = limit = startTime = endTime = sys.maxsize 'nba' # The name of the graph space to be read. # The edges (or nodes) and their property columns to be returned. 'serve' 'start_year' 'end_year' 'follow' 'degree' False # Return all the property columns or not. When set to False, only the specified property columns in the returnCols are returned. When set to True, all property columns are returned. 100 # Maximum data entry number returned. 0 3. Call the scanPartEdge interface to return the iterator for the canEdgeResponse object: scanEdgeResponseIterator = storageClient.scanEdge(spaceName, returnCols, allCols, limit, startTime, endTime) 4. Keep reading the data in the ScanEdgeResponse object which the iterator points to till all the data are read: scanEdgeResponseIterator.hasNext():
    scanEdgeResponse = scanEdgeResponseIterator.next() scanEdgeResponse :
        print( ) processEdge(space, scanEdgeResponse) while if is None "Error occurs while scaning edge" break In the preceding snippet, the process_edge is a custom function for processing the scanned edge data. This function first uses the scan_edge_processor to decode the data in the scan_edge_response. The decoded data can either be directly printed out or processed for other purposes such as importing these data into the calculation framework NetworkX. 5. Process data. Here we import all the scanned data into Graph G in the NetworkX: result = scanEdgeProcessor.process(space, scanEdgeResponse) edgeName, edgeRows result.rows.items(): row edgeRows:
            srcId = row.defaultProperties[ ].getValue()
            dstId = row.defaultProperties[ ].getValue()
            print( % (srcId, dstId))
            props = {} prop row.properties:
                propName = prop.getName()
                propValue = prop.getValue()
                props[propName] = propValue
            G.add_edges_from([(srcId, dstId, props)]) : def processEdge (space, scanEdgeResponse) # Get the corresponding rows by edge_name for in for in 0 2 '%d -> %d' for in # Add edges to Graph G in the NetworkX Nodes scanning is similar to the preceding process. In addition, for some of the distributed graph calculation frameworks [4], Nebula Graph provides batch reading and storing based on the partitions. We will show you in the later post. Applying Graph Analysis in NetworkX When importing all the nodes and edges into the NetworkX according to the preceding process, we can do some basic graph analysis and calculations: 1. Draw a plot: nx.draw(G, with_labels= , font_weight= ) matplotlib.pyplot plt
plt.show()
plt.savefig( ) True 'bold' import as './test.png' The plot you just drew looks like following: Print out all the nodes and edges in the plot: print( , list(G.nodes))
print( , list(G.edges)) 'nodes: ' 'edges: ' Results printed: nodes:  [ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]
edges:  [( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , )] 109 119 129 139 149 209 219 229 108 118 128 138 148 208 218 228 107 117 127 137 147 207 217 227 106 116 126 136 146 206 216 226 101 111 121 131 141 201 211 221 100 110 120 130 140 150 200 210 220 102 112 122 132 142 202 212 222 103 113 123 133 143 203 213 223 104 114 124 134 144 204 214 224 105 115 125 135 145 205 215 225 109 100 109 125 109 204 109 219 109 222 119 200 119 205 119 113 129 116 129 121 129 128 129 216 129 221 129 229 129 137 139 138 139 212 139 218 149 130 149 219 209 123 219 130 219 112 219 104 229 147 229 116 229 141 229 144 108 100 108 101 108 204 108 206 108 214 108 215 108 222 118 120 118 131 118 205 118 113 128 116 128 121 128 201 128 202 128 205 128 223 138 115 138 204 138 210 138 212 138 221 138 225 148 127 148 136 148 137 148 214 148 223 148 227 148 213 208 127 208 103 208 104 208 124 218 127 218 110 218 103 218 104 218 114 218 105 228 146 228 145 107 100 107 204 107 217 107 224 117 200 117 136 117 142 127 114 127 212 127 213 127 214 127 222 127 226 127 227 137 136 137 213 137 150 147 136 147 214 147 223 207 121 207 140 207 122 207 134 217 126 217 141 217 124 217 144 106 204 106 212 106 113 116 141 116 126 116 210 116 216 116 121 116 113 116 105 126 216 136 210 136 213 136 214 146 202 146 210 146 215 146 222 146 226 206 123 216 144 216 105 226 140 226 112 226 114 226 144 101 100 101 102 101 125 101 204 101 215 101 113 101 104 111 200 111 204 111 215 111 220 121 202 121 215 121 113 121 134 131 205 131 220 141 124 141 205 141 225 201 145 211 124 221 104 221 124 100 125 100 204 100 102 100 113 100 104 100 144 100 105 110 204 110 220 120 150 120 202 120 205 120 113 140 114 140 214 140 224 150 143 150 213 200 142 200 104 200 145 210 124 210 144 210 115 210 145 102 203 102 204 102 103 102 135 112 204 122 213 122 223 132 225 202 133 202 114 212 103 222 104 103 204 103 114 113 104 113 105 113 125 113 204 133 114 133 144 143 213 143 223 203 135 213 124 213 145 104 105 104 204 104 215 114 115 114 204 134 224 144 145 144 214 204 105 204 125 3. One of the common application is to calculate the shortest path between two nodes: p1 = nx.shortest_path(G, source= , target= )
print( , p1) 114 211 'The shortest path between Node 114 and Node 211: ' Results printed: The shortest path between Node Node :  [ , , , , ] 114 and 211 114 127 208 124 211 4. Calculate the pagerank value for each node in the graph to see their influences: print(nx.pagerank(G)) Results printed: { : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : } 109 0.011507076520104863 119 0.007835838669313514 129 0.015304593799331218 139 0.007772926737873626 149 0.0073896601012629825 209 0.0065558926178649985 219 0.014100908598251508 229 0.011454115940170253 108 0.01645334474680034 118 0.01010598371500564 128 0.01594717876199238 138 0.01671097227127263 148 0.015898676579503977 208 0.009437234075904938 218 0.0153795416919104 228 0.005900393773635255 107 0.009745182763645681 117 0.008716335675518244 127 0.021565565312365507 137 0.011642680498867146 147 0.009721031073465738 207 0.01040504770909835 217 0.012054472529765329 227 0.005615576255373405 106 0.007371191843767635 116 0.020955704443679106 126 0.007589432032220849 136 0.015987209357117116 146 0.013922108926721374 206 0.008554794629575304 216 0.011219193251536395 226 0.013613173390725904 101 0.016680863106330837 111 0.010121524312495604 121 0.017545503989576015 131 0.008531567756846938 141 0.014598319866130227 201 0.0058643663430632525 211 0.003936285336338021 221 0.009587911774927793 100 0.02243017302167168 110 0.007928429795381916 120 0.011875669801396205 130 0.0073896601012629825 140 0.01205992633948699 150 0.010045605782606326 200 0.015289870550944322 210 0.017716629501785937 220 0.008666577509181518 102 0.014865431161046641 112 0.007931095811770324 122 0.008087439927630492 132 0.004659566123187912 142 0.006487446038191551 202 0.013579313206377282 212 0.01190888044566142 222 0.011376739416933006 103 0.013438110749144392 113 0.02458154500563397 123 0.01104978432213578 133 0.00743370900670294 143 0.008011123394996112 203 0.006883198710237787 213 0.020392557117890422 223 0.012345866520333572 104 0.024902235588979776 114 0.019369722463816744 124 0.017165705442951484 134 0.008284361176173354 144 0.019363506469972095 204 0.03507634139024834 214 0.015500649025348538 224 0.008320315540621754 105 0.01439975542831122 115 0.007592722237637133 125 0.010808523955754608 135 0.006883198710237788 145 0.014654713389044883 205 0.014660118545887803 215 0.01337467974572934 225 0.009909720748343093 In addition, similar to the first post in the series, you can access to to make the visualization look better. Gephi The code used this post is placed for your reference. here Previously published at https://nebula-graph.io/posts/game-of-thrones-relationship-networkx-gephi-nebula-graph-part-two/

Effective Tools To Make A Great Relationships Analysis in Game of Thrones [Part 2]

So far we have seen how to start up a VM, emulate CUPID instruction, run arbitrary and learned about segmentation and paging in a CPU. This already allows us to do a lot of useful things with our VM. However, our CPU is in so called real mode. In this article we will go through setting up paging structure and enabling . code protected mode with paging When protected mode is switched on, CPU will remain in segmentation mode. This segmentation in protected mode is not the same as segmentation in (checkout my previous article to learn more about this -> ). Segmentation is not used a lot today and long mode uses paging exclusively. real mode https://hackernoon.com/segmentation-and-paging-in-x86-xts3yp1 In this article we will do the following: Configure for paging CPU Load bootstrap program (program A) at memory location 0x4000 Load another program (program B) at memory location which will be mapped to virtual address 0x6000 0xc000 Switch CPU to protected mode with paging enabled starting execution in program A and doing a jump to 0xc000 Even though program B resides at physical address we expect it to be correctly executed when we execute a jump to from the first program due to paging that will be set up. 0x6000 0xc000 1. Configure CPU for paging Before CPU can start using paging we need to set up what is called page tables. In this exercise we will set up a 2 level page table. In my opinion this is the simplest to set up when starting. After going through this process (and reading a bit of ) I hope that you will be able to set up 3, 4 or even 5 level page tables. Intel manual First level page table is known as and each entry in that table is known as . PD is 4Kb aligned*. page directory (PD) page directory entry (PDE) Address of is derived from value stored in register CR3[31:12]. Bits CR3[11:2] are filled bits LA[31:22] while the last two bits are set to zero. PD contains a pointer to . Elements of page table are called . 4Kb aligned page table is located at memory location specified by bits PDE[31:12]. Bits [11:2] are filled with bits LA[21:12] and the last two bits are set to zero (same as previous step). PDE page table (PT) page table entries (PTE) Each points to a . The physical address of that page frame is formed by using bits PTE[31:12] and filling bits [11:0] with zeros. PTE Page Frame Following figure depicts the process. Lower 12 bits in CR3, PDE and PTE are status bits which we can use to configure paging mechanism. 1.1. Game plan Following is the image that depicts page mapping that we will implement. On the left hand side are Logical/Virtual addresses while on the right hand size you can see page frames that represent physical memory. As you read the following sections feel free to refer to the map above to enrich your understanding. 1.1 Identity map a region of memory Firstly we will identity map a small region of memory [0x0000 - 0x5fff]. This region will be used to store our PD, PT, stack and some other CPU relevant stuff. Page tables will be stored in memory region ranging from [0x1000, 0x2fff]. This will ensure that we can access our page tables once we enable paging mode. Now we know that PD and PT each use 10 bits of the linear address (LA) to select an entry. Using that many bits we can select 2^10 entries. Given each entry is 4 bytes in size that means our PD and each of the PTs that we create will be 4kb in size. If we decide to place those sequentially in memory each of the tables will be 0x1000 (4kb) bytes apart. We also know that last 12 bits of LA are used to select specific byte ( ) in a page frame. That tells us that a single page frame can have 2^12 = 4096 bytes = 4kb = 0x1000 . physical address / PA Each of the PTEs covers 4096 bytes large region of memory. That means we will need three PTEs to cover region of memory from 0x0000 to 0x2fff. Going backwards we know that PDE can select one of 2^10 = 1024 entries in PD. Each of which is 4bytes in size (Because 10 bits of LA are combined to produce address of PTE and then last two bits are set to 0). Given we need to index only the first PTE from PT we need only one PDE. We will place our PD at memory location 0x1000 and PT at 0x2000. Our mapping so far looks like this: CR3 = 0x1000 [0x1000] = 0x2003 (at location 0x1000, which is our PD we have value 0x2000 which is location of our PT). This is our only PDE. [0x2000] = 0x0003 [0x2004] = 0x1003 [0x2008] = 0x2003 [0x200c] = 0x3003 [0x2010] = 0x4003 [0x2014] = 0x5003 Check the following section to understand why the last digit is 3. Hint - those come from status bits. 1.2 Map "program region" The next area that we will map is [0xc000, 0xefff] That is, three blocks of 4Kb in size - 0xc000, 0xd000 and 0xe000. The mapping will be as follows: 1. [0xc000, 0xcfff] -> [0x6000, 0x6fff] 1. [0xd000, 0xdfff] -> [0x7000, 0x7fff] 1. [0xe000, 0xefff] -> [0x8000, 0x8fff] 1. [0xf000, 0xffff] -> [0x9000, 0x9fff] This mapping is a bit trickier so I made the following digram to demonstrate how mapping from LA 0xc000 to PA 0x3000 works. From the image we can see that our PTEs should be as follows: [0x2030] = 0x6003 [0x2034] = 0x7003 [0x2038] = 0x8003 [0x203c] = 0x9003 As you can see in the figure, the last digit being 3 in PDE and PTEs is due to setting status bits 0 and 1. Setting those bits tells CPU that the the PTE (for PDE) and page frame (for PTE) are present, readable and writable. 2. Implementation Now that we know what the page tables should look like implementing this is easy: { pde = | ; (mem, &pde, ); pte_1 = | ; (mem + , &pte_1, ); pte_2 = | ; (mem + , &pte_2, ); pte_3 = | ; (mem + , &pte_3, ); pte_4 = | ; (mem + , &pte_4, ); pte_5 = | ; (mem + , &pte_5, ); pte_6 = | ; (mem + , &pte_6, ); pte_7 = | ; (mem + , &pte_7, ); pte_8 = | ; (mem + , &pte_8, ); pte_9 = | ; (mem + , &pte_9, ); pte_10 = | ; (mem + , &pte_10, ); ;
} * void createPageTable ( *mem) void uint32_t 0x2000 0x3 memcpy 4 uint32_t 0x0000 0x3 memcpy 0x1000 4 uint32_t 0x1000 0x3 memcpy 0x1004 4 uint32_t 0x2000 0x3 memcpy 0x1008 4 uint32_t 0x3000 0x3 memcpy 0x100c 4 uint32_t 0x4000 0x3 memcpy 0x1010 4 uint32_t 0x5000 0x3 memcpy 0x1014 4 uint32_t 0x6000 0x3 memcpy 0x1030 4 uint32_t 0x7000 0x3 memcpy 0x1034 4 uint32_t 0x8000 0x3 memcpy 0x1038 4 uint32_t 0x9000 0x3 memcpy 0x103c 4 return 0 Here we use type uint32_t as it's size is 4 bytes and it's unsigned.  The first PDE entry will be placed at the beginning of memory block passed into the function. PTEs will be placed into the same region but offset by 0x1000 bytes. In previous articles we have seen that host OS needs to allocate some memory for a VM. *mem = mmap( , , PROT_READ | PROT_WRITE, MAP_SHARED | MAP_ANONYMOUS, , ); void NULL 0x8000 -1 0 We will use our function to populate that block of memory with correct data structures: createPageTable createPageTable(mem); 3. Executable programs We will write two programs that we can use to test our mapping. The first program (a.asm) will output character 'A' and be loaded at memory address . This area of memory is identity mapped so physical and virtual addresses are the same. 0x4000 The other program ( ) is loaded at virtual memory address 0xc000 which is mapped to physical address 0x6000. . If we see output of the second program we can say that we set up paging correctly. b.asm We will use jump instruction in prog.a to jump to 0xc000 Here is our program: a.asm [ ] , , , , , BITS 32 mov ax 'A' add ax '0' mov dx 0x3f8 out dx al mov eax 0xc000 jmp eax And here is our program: b.asm [ ] , , , , BITS 32 mov ax 'B' add ax '0' mov dx 0x3f8 out dx al hlt We will use to compile both of the programs: nasm nasm -o a.bin a.asm
nasm -o b.bin b.asm If you are interested in looking at binary code that was generated you can use tools like . I hope I'll write a blog post at some point where I'll disassemble one of this (or similar programs) and go through the generated binary file. objdump 4. Loading binary files into memory Now that we have files generated, we will need to load them into memory. Here is one way to do this. First in order for us to know how much memory to allocate we need to find out size of our binaries. We can use host OS's facilities to do so. get_file_size( fd) { (fstat(fd, &s) < ) { ;
  } s.st_size;
} off_t int ; struct stat s if 0 return -1 return We will also need a function to copy those files into memory used by the VM: { temp[ ]; r = read(fd, &temp, ); total_bytes_copied = ; (r != ) { (buf + total_bytes_copied, temp, r);
		total_bytes_copied += r;
		r = read(fd, &temp, );
	} total_bytes_copied;
} int read_into_buffer ( fd, *buf) int uint8_t uint8_t 32 int 32 int 0 while 0 memcpy 32 return Next, let's open binary files: a_fd = open( , O_RDONLY); (a_fd == ) { ( ); ;
} b_fd = open( , O_RDONLY); (b_fd == ) { ( ); ;
    } int "a.bin" if -1 printf "Could not open a.bin.\n" return -1 int "b.bin" if -1 printf "Could not open b.bin.\n" return -1 And copy them into VM memory: bytes_copied = read_into_buffer(a_fd, mem + ); (bytes_copied != a_fs) { ( ); ;
}

bytes_copied = read_into_buffer(b_fd, mem + ); (bytes_copied != b_fs) { ( ); ;
} int 0x3000 if printf "Expected to copy as many bytes as there are in a.bin.\n" return -1 0x5000 if printf "Expected to copy as many bytes as there are in b.bin.\n" return -1 Notice how we have offset files a.bin and b.bin by 0x3000 and 0x5000 bytes respectively. Since this block of memory will be attached to the VM starting at address 0x1000, programs a.bin and b.bin will be found at addresses 0x4000 and 0x6000 from the perspective of VM. .slot = ,
    .guest_phys_addr = ,
    .memory_size = ,
    .userspace_addr = ( )mem,
};

ioctl(vmfd, KVM_SET_USER_MEMORY_REGION, &region); = { struct kvm_userspace_memory_region region 0 0x1000 0x8000 uint64_t 5. Running CPU in protected mode with paging enabled There are two more pieces of puzzle that we need to address: tell vCPU where to find page tables tell vCPU to start in protected mode with paging enabled. CPU looks in register CR3 for the address of page tables. This is a special register and its value can be retried and set using and ioctl pair of commands. KVM_GET_SREGS KVM_SET_SREGS Protected mode for a CPU is enabled by setting bit 0 in CR0 register while paging is enabled by setting bit 31 in the same register. struct kvm_sregs sregs;
ioctl(vcpufd, KVM_GET_SREGS, &sregs);

sregs.cr3 = ;
sregs.cr0 = sregs.cr0 | ;

ioctl(vcpufd, KVM_SET_SREGS, &sregs); 0x1000 0x80000001 6. Run VM Finally, set instruction pointer (IP/rip) to 0x4000 (location of a.bin program): .rip = ,
};
ret = ioctl(vcpufd, KVM_SET_REGS, &regs); = { struct kvm_regs regs 0x4000 And we are ready to kick off our VM: ...
ioctl(vcpufd, KVM_RUN, )
... NULL And hopefully you'll be greeted with the following output: Complete source code is available at GitLab: . Check out the README on how to run the code if you get stuck. https://gitlab.com/mvuksano/kvm-playground/-/tree/master/04-protected-mode-with-paging Conclusion And that's it! You reached the end of this article. I hope that by now you have better understanding of what it takes to configure CPU for what most engineers take for granted. In the upcoming articles I will talk more about compiling programs for different architectures (16/32/64) and configuring CPU to work in 64 bit mode. Stay tuned. assembly References: [1] Intel® 64 and IA-32 Architectures Software Developer’s Manual Volume 3A: System Programming Guide, Part 1, Chapter 4 Paging Glossary: PD - Page Directory PDE - Page Directory Entry PT - Page Table PTE - Page Table Entry VA - Virtual Address LA - Logical Address PA - Physical address

Enabling Paging for a Virtual Machine

Photo by Rajeshwar Bachu on Unsplash Predicting the future is oftentimes a foolhardy thing to do. If you were old enough to understand the internet in the late 90s, you might remember the Y2K scare; a small glitch in the internet matrix that experts said was going to mess things over temporarily. Similarly, technophiles have also been predicting the death of newspapers and magazines since the emergence of computers. But the print media — despite significant decline in readership — have stunned their critics by adapting to the taste of today’s digirati. Celebrity investor and contrarian thinker Peter Thiel, once famously quipped, “We wanted flying cars, instead we got 140 characters,” taking a dig at the 90’s predictions about technological innovation for the early 2000s that fizzled out in unexpected ways. Predictions are perhaps most rampant in the marketing realm because we (the marketers) like to assume some level of certainty — even if we are just hypothesizing. It’s become an annual ritual for a majority of marketing teams to come up with a round-up of things that are going to die in the coming year. Here is the marketoonist’s take on the annual apocalyptic tradition of marketing predictions: In defense of all marketers, I think it’s a good thing for us to try and get a feel of which way the wind is blowing. The world of marketing runs on experimentation and continued elimination of processes that become invalid after a certain period of time. Therefore, the marketing community is often very quick to jump to conclusions even when our judgment seem sloppy and exaggerated. As a faithful member of the tribe, I have my own strong opinions about the future of marketing. Now I am no Nostradamus or a marketing seer in any way (at least not yet). And expecting things to happen within 2020 would be short-sighted because trends don’t change by the calendar dates. Therefore, I try not to be prescriptive and myopic about my forecasts. My predictions are largely based on the current marketing norms that are indicative of certain trends and also certain aspects that are timeless. Therefore, I have tried to zoom out and envision these marketing trends to gradually take place over the next 10 years. With that said, let’s take a closer look at my crystal ball for the future revelations. Abracadabra…! 1. Google will face-off new rivals If TV ads and billboards were the past of marketing, Google is the present. Google is one of the most powerful platforms that helps millions of businesses worldwide market their products to their customers. If, by chance, an online apocalypse were to befall upon us tomorrow and Google ceased to exist, its consequences would be as big as the exodus of Noah’s Ark. It’s important to understand that Google is not just a tech conglomerate like Apple or Amazon. Google is not just in the business of making software products or fiber optics. If you look closely, you will see that Google is the world’s biggest advertising agency — its biggest source of revenue is online ads. Google aggregates ads to help businesses discover their customers and vice versa. But the consumer community are somewhat worried by a few worrying trends surrounding Google. The general consumers as well as businesses are coming to a bitter realization that while Google enjoys an absolute monopoly for their consumption needs, it’s not always their best friend. AngelList CEO and Silicon’s Valley philosopher-at-large Naval Ravikant says that the most powerful people in the world today are people who design algorithms, because they can influence people’s decisions and sway their behaviors towards whatever side they want. And that seems to be true with people working in the Alphabet Inc. It’s no secret that Google wants to expand their walled garden, just like others in the club do. So they deliberately shoot down businesses that pose a threat to one of their services and manipulate results that favor them. Google can especially be a femme fatale for you if your business poses some kind of threat to one of its revenue stream. FAANG Take Expedia for example. It’s one of the largest online travel aggregator (OTA) in the world. A few years back, Expedia’s CEO Mark Okerstrom publicly accused Google of trying to in the search results in order to promote their own flight recommendations. Google is supposedly down-ranking Quora results because it’s posing similar threats to Google’s monopoly of having a ready-answer for every online search query. kill Expedia’s pages Most importantly, a large section of the consumers are riled up about Google’s habit of hoarding users data and reportedly selling it to third-party vendors. Similarly, the U.S. senate and courts in the past have summoned Alphabet Inc’s executives time and gain for accusations against unethical manipulation of algorithms. Hence, the marketing ecosystem around Google looks a little different based on the present-day zeitgeist sentiments. I am inclined to believe that Google’s monopoly will soon end. It’s hard to imagine a world without Google at this point and it would be a gross exaggeration to say they might be completely obliterated. However, my prediction is that Google will get a few rivals who will take advantage of the distrust consumers have on Google. For instance, Duck Duck Go is already quite popular among the privacy-loving communities. With its CEO Gabriel Weinberg directly pitching his company’s promise about not snooping on people’s data across the internet forums, they might just be lucky (and prepare) for a widespread adoption. 2. The future is probably no marketing Consumers are already very sensitive and resistant to marketing and sales and this trend is likely going to grow bigger. People will antagonize the traditional marketing for the manipulative and shady practices it involves. I have to clarify though. When I say the future of marketing is no marketing, I don’t mean marketing as a discipline will cease to exist. I just think this form of marketing is going to live up to its true form, i.e., to bridge businesses with the ideal customers. The marketing that we know traditionally will pivot to become a conversation between business and its consumers. Given the growing distrust between businesses and consumers, marketing is going to be highly inbound and commoditized in a self-service manner. The current marketing trend already leans towards this direction. In B2B realm, for example, prospects complete 57% of their buyers journey on their own before even getting in touch with a company’s sales team. To compensate for the lack of such push marketing, businesses will have to come up with better, more genuine ways to communicate the value of their services. Marketing will have to become more conversational and less transactional, more DIY and less cryptic, more niched and less arbitrary. We will talk about the nuances of niche-based marketing in more detail in the next section. Naturally, the conversational aspect of marketing will also lend ways to accommodate dynamic interactions between business and consumers, powered by the likes of artificial intelligence, voice search, and augmented reality. 3. Community marketing will grow bigger This is my favorite prediction of all and I strongly believe this is more likely to happen because we already have proof for it. I am a long-time Redditor and I love the neatly-organized smorgasbord of knowledge that are scattered across the thousands of sub-reddits. Communities like Quora and Stack Overflow are other examples of niche-based communities that are go-to resources for many people who hang out in those watering holes to seek answers to their specific pursuits. Even to rejig their algorithms to encourage interest-based interactions and counter account-based bias. Topical communities are more helpful because they cater to a limited set of specific interests rather than being a massive motley of unorganized information. Twitter is contemplating Community marketing will also have direct implications on online search. Imagine dozens of niched-down Hacker News and threads emerging at the top of search results when you look for topics such as “best account based marketing software”. We are already seeing instances of this happening in forums such as Product Hunt and Indie Hackers. The conversations in these communities are highly moderated, and thus, cremier and meatier in quality. When done well, marketers will have better chances of reaching the right audience and wooing them. In other words, riches will be in the niches. Cake.co Let’s gear up Besides the aforementioned trends, the glocal approach of running business will also influence the marketing norms in the coming decade. For example, as we step closer to a gig-based economy, managing and/or collaborating with remote teams from across the world will be a highly sought-after skill for business professionals, including marketers. I think the coming years will also help businesses settle the row between which has been in a tricky spot for the last decade. Maybe permission-marketing will become the norm because of tighter data and privacy regulations all over the world. personalization and privacy William Gibson, who introduced the concept of cyberpunk through his sci-fi literature, once said, “The future is here. It’s just not widely distributed yet.” And that’s true for most of the future predictions — most of them are already happening around us albeit at a small scale, but they are far from being mainstream. And for all you know, most of them might fall out of fashion before we notice. But hey, if any of these forecasts come through — remember that I told you about it first. 🙂 Lastly, if you liked reading this blog post, I’m sure . you will love the 4th episode of our ABM Conversations Podcast

Three Trends that Will Define the Future of Marketing

Singapore is home to some of the best schools in the field of Computer Science, specifically . The going on there is unparalleled. Colleges like Nanyang Technological University (NTU) and have a great reputation all over the world for their CS programs. Artificial Intelligence cutting edge research National University of Singapore (NUS) An opportunity presented itself to me when I was in my college in SRMIST, Chennai, India. A Global Academic Internship Programme ( ) by Corporate Gurukul, which sends students interested/experienced in to study under world class faculty. I wasn't letting this pass, so I signed up for it, cleared the interview and got in! GAIP Artificial Neural Networks and Big Data to NUS It was December 2018 and I was on my flight to Singapore 🇸🇬. The was very exciting because of it's thorough covering the basics and advanced topics related to Artificial Neural Networks(ANN) and Big Data (more on these topics later). trying to implement whatever that was taught in the lectures keeping in mind the business value of the project that was being developed. Academic Internship syllabus In a span of 15 days we had to work on two projects We covered ANN for the first 8 days, followed by Big Data in the remaining. ANN Lectures were delivered by and . Dr. Lek Hsiang Hui , Dr. Tan Wee Kek Dr. Wang Wei Firstly, we had . It was a really good insight and added value to my understanding of the same concept. Dr. Lek Hsiang Hui who gave us an introduction to Data Analytics Explained the different types of and gave a few examples of how the data flows between different states, is modified and then a decision is taken based on the output. Decision Models also was covered with a great flow digram describing each of various stages involved like . Data mining Data Extraction, Data Cleaning, Data Aggregation, Data Representation, Data Interpretation Basics of and implementing various mathematical formulae was also taught, which included . R programming Measures of Location, Measures of Shape, Measures of Dispersion and Measures of Association Secondly, we had in a very intuitive manner, and I was able to grasp most of the concepts with ease. Things I understood and implemented: Dr. Tan Wee Kek who presented the concepts of Machine Learning Simple and Multi and it's problems Linear Regression Python Data Science Libraries like Numpy, Scipy, Matplotlib and Scikit Learn and it's types: Decision Trees, Bayesian Classifier, Logistic Regression, SVM (Support Vector Machines) Classification : K-Means, K-Mediods, Hierarchical Methods Clustering using Classification, Association and Clustering Text Mining and KDD An artificial neural network is designed to function like the neurons in the brain. Lastly, . These topics were harder to understand with the level of Calculus involved but Dr. Wei did a great job at teaching us the basics. Here's everything that I learned and implemented: Dr. Wang Wei introduced us to topics revolving around Artificial Neural Networks : Problems with Logistic Regression Why ANN and (GD) Algorithm Back Propagation Gradient Descent Some advanced GD Algorithms like , , and Stochastic GD Minibatch GD RMSProp Adam Training Techniques: Random Initialisation, ReLU, Dropout : Pooling, Padding, Strides and some common CNN Architectures Convolutional Neural Networks : Vanilla RNN and LSTM Recurrent Neural Networks We had to present our Artificial Neural Networks Course Project two days after our final lecture. Those days went by really quickly with little or no sleep as me and my group mates hustled to finish our project called Quick Draw. We wanted to make something that has great scope for real-world implementation and helps the society. . Our program tracks the strokes of the user and gives an output predicting what the user is trying to draw in real-time of various labeled hand drawn images in the Numpy Array format. We downloaded the data of 4 classes (20,000 images each) and started training it with different algorithms. First we started off with SVM, then K-Means Clustering, then Feed-Forward Neural Network, then Convolutional Neural Network and finally Long Short-term Memory (LSTM). . So we decided to use those two models and we made a front end for our project using OpenCV library which is basically taking input of our strokes from the keyboard. Quick Draw uses the existing Google's Dataset We found out that CNN and LSTM gave us the highest accuracies You can checkout the project here . Now moving on to the . This course was delivered by , or as we call him, . He helped us work on some really complicated linux commands, broke them down for us so that we find it easy. Here are the topics that I learned and implemented: Big Data Course, the second one of our internship Ravindra Kumar God of Linux What is and how it is changing the world Big Data Problems in Big Data and it's features, Hadoop vs RDBMS Hadoop The : HDFS, Yarn, MapReduce, HBase, ZooKeeper, Pig and Hive Hadoop Ecosystem : NameNode, DataNode, SSH-ing into these nodes without a password Setting up Node Cluster using Ambari Management , Commissioning and De-commissioning, Resource Manager, Scheduler Setting up HDFS using Shell Commands Basic Queries and Data Ingestion mechanisms using HIVE Sqoop Programming Mapreduce After the lectures, we had to work on a in which we set up a deployed on a platform and managed using . We had to do everything from scratch, set up between the three nodes, setup , creating a local repository, transferring data from the local repository to and then performing operations on that Data. We chose the book called 'Sherlock' from the Gutenberg Library and . Every step of the project was done mostly through shell commands which was really cool and helped us understand the working of Linux commands, when and where to use them. project three node cluster Ambari password-less SSH Java, JDBC, Hadoop environments on the .bashrc file HBASE performed Word-Count and MapReduce programs on it It was a to learn from the best in the world and work on projects under their guidance. I also would like to , they were always there to help us out with any difficulty that we would face and help us overcome it. really enriching experience thank the Teaching Assistants(TA) for the course Puru Sharma and Devvrit Khattri Thank you for reading!

My Time at NUS, Singapore

Exponential View Azeem Azhar ’s Weekly Wondermissive: Future, Tech & Society . This issue has been supported by our partner: Ocean Protocol sgCarMart is building Singapore's very first 'Know-Your-Vehicle' data marketplace on Ocean Protocol to help used car buyers make smarter buying decisions This week I am sending the same issue to free and Premium members. Like, comment or share this email by pressing the little buttons at the top of this email. Dept of the near future 🍏 After 23 years, Apple’s design chief Jony Ive, the “ ”, is stepping away from the empire he helped build. Ive’s departure, which according to insiders, was preceded by a long, steady withdrawal from the daily work of the company, is being read as a sign of the increasing shift away . most unremarkable remarkable person you could meet from the Steve Jobs era into one driven more by operations than inspiration 🤭 Despite all the early hype around voice assistants, usage is abysmal. Either the industry has yet to find the right use case for voice technology, or perhaps, , “[n]obody wants to walk around their house talking to a computer.” Fun fact: 10,000 Amazon employees work on the Alexa voice assistant. as one founder points out 🚨 An exceptional piece of research from Jordan Dworkin measures jobs, and concludes that service and industrial sector workers are likely to struggle with transitions more than those in business, scientific and medical sectors. the impact of automation in different professions against the ability of workers to transition to new Optimal recommendations should not only consider a new job’s automatability and growth potential, but also its similarity to the job a worker is leaving. Skills most associated with less automatable jobs differ across sectors, and skills that facilitate job transitions may be distinct from those that facilitate within-job skill redefinition. Automation will likely have complex effects on the job market that are not fully captured by the likelihood of individual jobs becoming automated. Instead, it is important that policy-makers, businesses and workers consider the relationships between jobs when determining who is at the highest risk of long-term displacement and which transition or reskilling opportunities they should pursue. ⚖️ . With those two countries removed from the dataset, income inequality has worsened in the remaining 143 countries measured in the study. Growth in regional income inequality in Asia outside of China and India is particularly significant. (Sorry, paywalled!) 📧 He explains: “What I see in Superhuman though is a company that has mistaken taking advantage of people for good design. They’ve identified a feature that provides value to some of their customers (i.e. seeing if someone has opened your email yet) and they’ve trampled the privacy of every single person they send email to in order to achieve that”. To his credit, Rahul Vohra of Superhuman, ending the practice. (This is part of the norming process we are going through as we grapple with these technologies, see the Dept of Surveillance below and my End Note.) Income inequality declined globally between 1988 and 2015, but only if we count India and China Mike Davidson raises an important question about Superhuman, an email client favoured by the digerati. Superhuman tracks when recipients open mail. responded effectively 🌡️ Climate crisis: 413.76ppm | 3,972 days Each week, we’re going to remind you of the CO2 levels in the atmosphere and the number of days until reaching the 450ppm threshold, the point at which significant portions of our planet’s ice will melt away. (as of July 1): 413.76 ppm; 12 months ago: 409ppm; 50 years ago: 326.66ppm; 250 years ago, est: 250ppm. Share this reminder with your community by forwarding this email or . 🔥 . European average temperatures were more than 2°C above normal, and temperatures were up to 10°C above normal over parts of France, Germany and Spain. Globally, the average temperature was about 0.1°C higher than during the previous warmest June. That was only three years ago. The speed at which temperature records are now being broken is an alarming sign of the future we may be facing. 🚫 The tide appears to be finally turning against fossil fuels, however. . Over 100 leading global financial institutions are pulling back from the sector, creating a “progressive strangulation” on access to capital markets and insurance for coal companies. Earlier this week, for example, insurer Chubb Ltd announced that and that it would phase out its coverage for existing risks which exceed this threshold by 2022. Researchers from the US and China find that . If we are to meet that target there is no room for new carbon-emitting energy infrastructure, and some existing infrastructure must be retired prematurely. as much as 200 gigatons of carbon from the atmosphere. The latest measurement tweeting this This June was the hottest ever recorded Capital is exiting the coal business, according to a recent report by the Institute for Energy Economics and Financial Analysis it would not underwrite new risks for companies which generate more than 30% of their revenue from coal committed emissions from existing and proposed energy infrastructure represent more than the entire remaining carbon budget for the 1.5 °C climate target Planting a trillion trees could capture Dept of artificial intelligence Unsupervised word embeddings capture latent knowledge from materials science literature. . Essentially, a machine learning system, requiring little training, was able to process more than three million abstracts in materials science. By looking at relationships between words and phrases, it was able to suggest hitherto unknown materials with thermoelectric properties. (Find a less technical .) The implications for science are significant. As a reminder, claimed: “on a per-dollar or per-person basis, science is becoming far less efficient.” If science is suffering diminishing returns, this might mean fewer insights to inspire breakthroughs that we need as we strain our planetary boundaries. AI is making progress in helping scientists. Think of Deepmind’s Alphafold predicting the 3-D structure of folded proteins or helping chemists understand millions of reaction pathways. And firms like Zymergen and LabGenius use machine learning tools to accelerate genomic research and drug development. The beauty of this current paper is that the technique, word2vec, is really rather simple compared to some breakthrough approaches. And the authors have . Elsewhere: This paper had scientists abuzz with the potential summary here this excellent essay on the stagnation of science made their code available for all . Sandhu draws out the point that some degree of bias is inherent and even necessary—the important thing is to explicitly recognise and understand which biases are present, how they're influencing the output and what the implications are. An excellent breakdown by Jaspreet Sandhu of the concepts and current state of knowledge about the role bias plays in machine learning MIT researchers . reckon they can predict emotional states by reflected wifi signals Dept of surveillance Worth noting just how surveillance, by government and private sector, has exploded as an issue. We covered this in , but there are a handful of more interesting developments. These indicate we need a new agreement around consumer and citizen privacy, and more clarity on how these technologies could be used. Partly it is about law and regulation. Partly it is about norms and behaviour. See my End Note. 👎 Dave Lee: . “Is Facebook fixing itself, or merely making it harder for us to see it’s broken?” Shoshana Zuboff doesn’t think we’ve failed to rein in Facebook and Google; . She argues that lawmakers need to “outlaw the secret theft of private experience”, disrupt revenue flows and business models based on surveillance, develop laws and regulations which protect users and empower new forms of grassroots collective action against the tech giants. 🔈Shoshana and I a couple of weeks ago. 🚓 The London Metropolitan Police’s facial recognition program continues to draw attention for all the wrong reasons. The system’s sky-high levels of inaccuracy (with a 98% false positive rate) have been repeatedly in the past. In that context, the revealed in the latest independent report commissioned by Scotland Yard almost looks like an improvement—although, in fact, it could be higher than that, as some of the “suspects” could not be found to verify whether or not the program had accurately identified them. The report also identified significant operational problems and issues around informed consent, or lack thereof. In the US, The group points to a well-known study which showed that major US retailers, Walmart and Home Depot, offered lower prices to users browsing anonymously. (Some anonymous users were offered 15% lower prices than identifiable users.) 🇪🇺 . The group are concerned about both state and commercial surveillance, but their report overall encourages the uptake and expansion of AI across multiple levels of society. last week’s special issue Facebook may be pivoting to something worse she thinks we haven’t really tried had a great conversation about surveillance capitalism highlighted 81% inaccuracy rate a consumer advocacy group has asked the Federal Trade Commission to investigate the practice of “surveillance scoring” customers’ financial status. An independent group advising the European Commission on whether and how to regulate AI has recommended an outright ban on the use of AI for mass-scale credit scoring and blanket surveillance Short morsels to appear smart at dinner parties 💔 🙈 Waymo has been given the to carry passengers in its self-driving vehicles on public roads. Women in most leadership skills. Could blockchain help ? 👀 Apple is trying to . 🙀 It wasn't your imagination: The world's of individual atoms. 👂 Five couples lined up at Russia’s largest fertility clinic for . Globalisation is already dead. go-ahead by Californian regulators score higher than men save politics from big money make eye contact using FaceTime way less awkward wiggling the mouse in Windows 95 really did make it run faster. smallest MRI visualises the magnetic field CRISPR babies to avoid deafness End note How should we make sense of the surveillance-privacy axis? Well, it is another instance of the ethics of invention. To quote Sheila Jasonoff from her book, (See what I did there?): The Ethics of Invention The challenge for modern societies is to develop sufficiently powerful and systematic understanding of technology for us to know where the possibilities lie for meaningful political action and responsible governance. The bargains struck in enhancing human capability do not have to be Faustian, ratified between unequal bargaining partners under conditions of blind ignorance or irreducible uncertainty. I want to talk about email tracking as such a technology. The story about Superhuman broke this week. The firm’s email client did, apparently, cross a boundary. The product had a tracking capability which meant that a sender could see a recipient’s rough location and online status, without opt-in or opt-out. But understand that elements of this capability are now foisted on us across the board. Superhuman was far from unique. Add-ons like Mailtrack and Boomerang have provided similar email tracking capabilities for close to a decade. Messengers like Signal, WhatsApp and iMessage also give away the recipient’s status information. Superhuman’s response has been rapid and significant. The firm put a moratorium on that particular technology while they figure out what the non-Faustian bargain should be. (I offered Rahul, the CEO, about what a product fix might look like.) some typo-ridden suggestions The reality is that we are all in a process where we figure out positive-sum arrangements for these enhancing technologies. The technologies will shape our norms, yes. But our norms should also shape the technologies. Incidentally, you may wonder what kind of tracking happens on emails sent by ? Well, in the case of this newsletter, every single recipient is on here via a double-opt-in. You have selected to receive the newsletter and confirmed you wanted to receive it. Exponential View In common with virtually every other newsletter, our mailing platform provides us with operational data that includes which users opened the mail, and which links they clicked on. We use this data for operational reasons. Most commonly, we use it to track open and click rates. If we see these changing, usually declining (!!), we can make course corrections. Often a declining open rate means we are being caught by spam filters, and need to take action. Sometimes a declining open rate means that I’ve started to bore you, and we need to shake things up. We have also drilled down into per-user open rates to understand cohort behaviours. This helps us to talk to you about improving . Exponential View We’ve also used open rate information as a way of identifying the most engaged readers. These users have become eligible for perks like early-access copies to new books or exclusive invites to events and conferences. Many hundreds of you have benefitted from this in the past few years. Of course, today those occasional goodies are now . concentrated on members of our Premium tier We don’t provide an opt-out for tracking these metrics. It is essential operating data for us. If you are uncomfortable with it, you could also use a separate email box to receive your weekly wondermissive. is rather good for those types of things. If your heebies are still jeebieing, I encourage you to use the unsubscribe link at the foot of the email. ProtonMail Have a great week! Azeem

🔮 AI & science; Apple & Ive; job transitions under automation; killing globalisation++ #225

I decided to focus full-time in this space towards the end of 2017. Seems like a decade ago now. I think it’s important to note that as an independent creative, I have the luxury to speak freely and I don’t have to tailor my thoughts to appease a venture capital investor or boss. I’m a Bitcoiner. I play with Ethereum too, but mainly because that’s where much of the cryptoArt experimentation has moved to over the past year but I would love to help some more Bitcoin-specific art/music projects get on their feet as well. (I’ve also fiddled with Waves, EOS and Tron but not enough to incorporate their protocols into my work yet.) So, I’ve recapped my contributions and content last year and hope to expand and continue learning more in 2019. Art On The Blockchain Podcast (AOTB) AOTB is the crypto-art and music education- focused podcast brought to you by Cynthia Gayton and myself. They are generally 60–120 minutes long and I produce and edit each episode with all of my own original music and try to keep it in line with the feel from my DJ mixtapes. Hopefully, they come across totally different than what you are used to. Episodes 9–26 were all recorded in 2018. That’s an average of 1.5 podcasts per month of conversations with some of the vanguards of the cryptoArt space. A big bump from our 8 episodes in 2017! Jason Bailey of Artnome.com Cryptograffiti (artist) Jessica Angel (artist) Tatiana Moroz (singer) Dan Anderson of Bitcorns Brian Hoffman of Open Bazaar Jess Houlgrave of Codex Protocol Kev & Jon from Rare Art Labs James Waugh & Trevor of Bidio Ruth Catlow of Furtherfield Sam Hart of Avant.com Poppy Simpson of Meural Grayson Cox, Ronald Gierlach, Brian Gruber & Hollis Witherspoon of Nash Cash Casino Yiying Lu (artist) Joe Chiapetta (artist) Manuel Palou, Moises Sanabria & Eddie Negron of Art404 Travis LeRoy Southworth (artist) Beatriz Ramos and Yehudit Mam of Dada.NYC Matt Hall of Choon Music and Cryptopunks Rob Myers (artist) DJ 3LAU (musician) Mack Flavelle of Cryptokitties Christian Moss of Indie Square Coin Artist of Neon District John Perkins and Jon Craine of Superrare.io Naturel (artist) On top of these long form episodes we also briefly branched out to FM radio with a bi-weekly 30 minute live radio show on WERA in Arlington, Virginia. For this show we focused on news and on local DC-area artists involved in cryptoArt. We had short interviews with artists like Vesa, Coldie and more. Although the show was a lot of fun to to do, we decided to discontinue it as it was hard to gauge how much it was being listened to and we could only repost to the archaic MixCloud website. Sometimes it’s hard being located in the cryptoArt-sparse Washington DC! lol AOTB In The DMV Mixcloud site Crypto-related Music I spent many years making rap music and somehow fell into audio visual memes this year. Here’s a sampling of some of the releases. DJ J-$crilla “ ” ft. Chris DeRose, Peter Schiff Cant Smoke A Bitcoin DJ J-$crilla “ ” I Wear Gucci (My Name’s Craig Wright) MikeInSpace “ ” ft. Theo Goodman (Produced by DJ J-$crilla) Pull The Latch “ ” Worst Podcast Ever Theme Song Junseth’s World uploads Choon Music uploads Ujo Music 2018 A3C Festival in ATL Educator, Speaker and Panelist I found myself looking to educate people more as the conferences and panels in cryptoArt took off after the historic RAREAF Conference at the top of 2018. I participated in a number panels, discussions and even a teaching gig. In 2019, my goal is to continue on this path by educating, learning more and discussing in this niche and exciting space. Here’s a list. Rare Art Festival RAREAF- January 13, 2018 NYC (panelist) Digital Assets- March 31, 2018 WDC (DJ/Speaker) Introduction to Crypto-art and Crypto-music- April 21, 2018 ATL (Speaker/Educator) Rare Digital Art Charity Crypto Auction @ POW DC- May 9, 2018 WDC (Speaker/Host) Creative Tech Week- May 10, NYC (Panelist/DJ) Ethereal Summit- May 11, Brooklyn, NY (Media) Washington Area Lawyers Association Presents DJ J-$crilla & Cynthia Gayton’s “Intro To Cryptocurrencies, CryptoArt and Music, and Blockchain for Creatives”- Sept 17- Oct 2 (Every Monday 7-9pm) WDC (Educator/Presenter) A3C Festival- “How Musicians and Artists Are Using Blockchain” Oct 5, 2018 ATL (Panelist/Speaker) The Rarest Show- Oct 25, 2018 Brooklyn, NY (DJ/Media) Art Decentralized- The World of Distributed Masterpieces- Dec 6, 2018 MIA (Media) a clip from Introduction To CryptoArt & CryptoMusic Writings I’ve always enjoyed writing and have a lofty goal of one day cooking up a novel. I started writing on cryptoArt in 2016 and I added a few more pieces in 2018 to the catalogue. Writing more will be near the top of the list of 2019. Introduction To CryptoArt & CryptoMusic Some NOTES on How To Start Earning Crypto With Music DON’T GO DOWN THAT RABBIT HOLE, SCRILLA “Bitcoin Honey Badger” Acrylic on canvas Crypto- Themed Artwork and Tokenized pieces In 2016/2017 I made a bunch of Counterparty (XCP/BTC) related artwork in the form of Rare Pepes. I made mostly hip-hop and Crypto-themed Pepes. As some in this space know now, I have an opinion- that the Rare Pepe Trading Group ignited this space faster than anything else. In 2018, as cryptoArt exploded, the Ethereum communities got behind the movement and hundreds of platforms seemed to pop up as you can witness in real time at dappradar.com. The Ethereum community is big on NFTs, (I personally dislike the name “NFT” to describe these collectibles but this article is not about that) crypto-collectibles and hackathons. I began experimenting with a few of these platforms but ultimately ended up mostly issuing my Ethereum-based art tokens through SuperRare. I really hope to try out Rare Art Labs more this year as well. I also continued using my go-to Counterparty platform to issue tokens using the Bitcoin blockchain. sprung up around the beginning of February and I’ve really enjoyed that community. Even met my fiancee through the community (Tinder is for boomers.) is one of the most innovative dark corners of the blockchain space and I really enjoy what goes on there. They basically use cryptoArt to break stuff or make you think. I also issued my own, and what I believe to be the first of it’s kind- Proof Of Work tokens to help facilitate my music- meme marketing for “Can’t Smoke A Bitcoin” and “I Wear Gucci.” This tokenizing experiment is certainly an interesting rabbit hole and I look to dive deeper this year! Bitcorns KaleidoscopeXCP Bitcoin Honey Badger (prints soon) Bitcorns.com- (sold out in a day) & (some still available) CNTSMOKECORN CORNTANGCLAN - I WEAR GUCCI (sold 1/1), CANT SMOKE A BITCOIN (sold 4/5), FACES (sold 1/1), CLOCKWORK GREEN (sold 1/1), MUH BAGS (unsold 1/1) SuperRare.io Proof Of Share (XCP Tokens)- and One-of-a-kind tokens that were both airdropped to wallet holders that provably helped promote the release through twitter. They are basically game-ified swag badges using Bitcoin. CANTSMOKEBTC IWEARGUCCI - I really like digitally finger-painting on this unique social media artist site, although I have yet to tokenize any of the pieces on there. dada.nyc - Cool website to monitor stats and artists using XCP Digirare.com Instructional Videos from rarescrilla.com Instructional/Education Videos I am definitely looking to expand more in the video side of things in 2019. Subscribe to my and pages. Youtube BitTube Art On The Blockchain Presents IPFS BATTLE ROYALE Art On The Blockchain Presents BATTLE ROYALE 2: Ethereum vs. Counterparty Blockchain: The Technology That’s Changing The Music Industry Pt. 1 Live at A3C How To Buy A Rare Pepe Using Bitcoin Clip from the “DJPEPE in 60 Seconds” video DJPEPE Maybe the hardest thing to explain to my friends and family is that I am a manager of a Bitcoin frog meme. It all started in 2016- Watch the 60 second video I produced in 2017 . It escalated quickly on January 13, 2018 as I somehow conjured up a 6 foot cardboard cutout to speak on a panel with industry professionals. Love him or hate him, here are some DJPEPE highlights from last year. #memetoo #UCIT here Tokenized cardboard cutout DJPEPE auctioned at RAREAF event calls DJPEPE the first blockchain performance artist Paris Review Rare Art Labs Interview Releases CANT SMOKE A BITCOIN and I WEAR GUCCI weeks ahead of release to DJPEPE token holders. DJPEPE becomes a blockchain farmer Pressed 500 stickers released to the wild. So rare that if you wash it you will destroy it. In fact, you have to pass interview questions in order to even be considered to purchase one. 30 tokenized DAMN shirts Featured in The RAREST BOOK Announced Presidential Candidacy for 2020 became a thing. (Keep it coming!) Fan Art Appearance 1/5 on the HODLCAST this past year. Press Sasha Hodler - Episode , , , & 13 21 30 53 60 Bitsesh- Episode 3 Paris Review The Tatiana Show- & Interview w/ Interview With AOTB J $crilla Rare Art Labs Interview Creative Crypto Magazine Bitsonline- & Artist DJ J $crilla Is Using Tokens to Promote a Music Video Rare Art Festival Canceled Following Crypto Market Crash “Funding Creative Projects with Non-Fungible Tokens” Steemit NewsBTC rarescrilla.com Launched RareScrilla.com _New Breaker Mag shoutout New Art On The Blockchain Epsiode 27 ft. Nanu Berks Another one! New intro too "I WEAR GUCCI…_rarescrilla.com Home page | DJ J Scrilla I am not a website developer but I do my best to keep it active! Hundreds of I’ve produced, 100s of I’ve been a part of, obviously the crypto stuff, my art and my all live on the site. I appreciate all the that link back to it- helps tremendously! songs rap videos mixtapes articles Conclusion I will continue down the rabbit hole I entered in 2014. Bear markets are where the innovation happens and I’m all aboard this . UCIT DLT P.S. There are no experts in this space.

My 2018 CryptoArt/Music ReCap

What most of you might know is that the Google Search that you currently know and use began as a Master’s thesis that Larry Page and Sergey Brin worked on back in 1996, that revolutionized the way people looked at search engines. However, what most do not know is that their initial idea was not to rank websites, rather to rank annotations on websites. “ One idea Page presented to Winograd, a collaboration with Brin, seemed more promising than the others: creating a system where people could make annotations and comments on websites. But the more Page thought about annotation, the messier it got. How would you figure out who gets to comment or whose comment would be the one you’d see first? For that, he says, “We needed a rating system.” Having a human being determine the ratings was out of the question. First, it was inherently impractical. Further, humans were unreliable. Only algorithms — well drawn, efficiently executed, and based on sound data — could deliver unbiased results. Page realized that such data already existed and no one else was really using it. He asked Brin, “Why don’t we use the links on the web to do that?” — In The Plex by Steven Levy Note: All the following quotes, unless otherwise mentioned, are also from ‘In The Plex’. And that’s how the was born! mispronounced name of Googol They worked on this idea for a year before giving it a name , in a way that every web page was given a ‘rank’. Brin, acting as the mathematical counterpart, produced the data mining system that scans web pages, and Page produced the algorithm that uses that information to rank the pages ( and not a web “page”, a slight but much deserved vanity). The original search engine wouldn’t have worked had either one of those components been missing. “BackRub”, a search engine that took advantage of the links that was incoming to a website named PageRank — the “Page” coming from his name And now, almost 20 years later, Google still uses PageRank as one of its . Some of the other ones being 200 components across which it evaluates a web page to give it a score keyword usage, domain age, domain history, country TLD extension, TF-IDF and so on. How Google Went From The Picture On Left To The Picture On Right Evolution of Google from 1998 to 2018 Below is the timeline of how Google went from a search engine with 25 million pages that lists the top 10 links to a search engine that with listing over billion results filled with text, image, video and a melange of other content. indexes over 1.8 billion pages Timeline of Google Search — Could find only till 2013 However, the journey was neither easy nor quick. After I began reading ‘In the Plex’ by Steven Levy, I understood just how massive a problem Google tackled — and made lives of over 2 billion people easier. Right now, if you type in ‘Houston Baker’ in Google, you get the links of an American Scholar who goes by that name ( ). However, about 18 years back that was not the case. The nascent algorithm did not understand the differences between names and keywords. I’m outlining below go ahead, try i t just 3 out of over 1000+ problems that Google overcame. 1. Index Faster, Damn It! How Crawling and Indexing Works Let’s assume, god forbid, there was a massive earthquake in Chennai, India today. The first instinct is to go on Google and learn about what happened and the casualties. However, instead of displaying the news, what if Google showed the best places to visit? What a sacrilege. “ But as the web kept growing, Google added more machines — by the end of 1999, there were eighty machines involved in the crawl (out of a total of almost three thousand Google computers at that time) — and the likelihood that something would break increased dramatically. Especially since Google made a point of buying what its engineers referred to as “el cheapo” equipment”. By 2000, the web was growing like a weed. Billions of new pages were added every year to the corpus. But what good is that if the engine cannot crawl them? Understanding the severity, the engineers built a system that a way for the index to hold its place if a calamity befell a server or hard disk. But the new system went further — it used a different way to handle a cluster of disks, more akin to the parallel-processing style of computing than the “sharding” technique Google had used. implemented “checkpointing,” Sanjay Ghemwat, with his team, rebuilt the entire file system. 2. Audrey Fino — Why Can’t I See My Name? We’ve all been there. We love typing our names into the search engines with the curiosity of a kid opening the Christmas gift. But how can an algorithm that is based on back links and keywords differentiate between a name and anything else? Haven’t we all been there? “ One unsuccessful search became a legend: Sometime in 2001, Amit Singhal learned of poor results when people typed the name “audrey fino” into the search box. Google kept returning Italian sites praising Audrey Hepburn. (Fino means fine in Italian.) “We realized that this is actually a person’s name,” Singhal says. “But we didn’t have the smarts in the system.” Even back then, over So how would you devise new signals to more skillfully identify names from queries and dig them out of the web corpus? Singhal and his colleagues began where they almost always did: with data. To improve search, Google licensed the White Pages, allowing it to use all the information contained in hundreds of thick newsprint-based tomes where the content consisted of nothing but names (and addresses and phone numbers). 8% of all searches were a name. Google’s search engine sucked up the names and analyzed them until it had an understanding of what a name was and how to recognize it in the system. 3. Hot dogs — Boiling Puppies or Sausage Sandwich? I couldn’t resist! Does a hot dog dignify a boiling puppy or a sausage sandwich? Even a (non-psychopathic) 5 year old would know the meaning. Google was smart enough to understand that ‘hot’ meant ‘boiling’ and ‘dog’ meant ‘puppy’. But it didn’t do well with context and combinations. That’s a stupid question. “ Google’s synonym system understood that a dog was similar to a puppy and that boiling water was hot. But it also concluded that a hot dog was the same as a boiling puppy. The problem was fixed in late 2002 by a breakthrough based on philosopher Ludwig Wittgenstein’s theories about how words are defined by context.” This meant, by crawling and indexing billions of pages, Google could understand which two words appeared closer to each other. ‘Hot dog’ appeared often with ‘bread’, ‘sausage’ and ‘baseball’. After millions of trials and clicks, it finally understood what to do with such queries. To make the engine smarter, Singhal realized he had to learn to master the art of ‘bigram breakage’. A or is a sequence of two adjacent elements from a of , which are typically letters, syllables, or words. He had to teach the system that ‘New York’ is in fact one entity, but that is different from ‘New York Times’ which is an entity on its own. bigram digram string tokens How Google Search Will Look In Future — My Idea Google has seen a tremendous shift in the way it indexes websites, ranks them and displays information. Here are my three ideas for the search engine: 1. One Stop Destination My two-cents is that Google Search has shifted from being a search engine to becoming a one-stop-destination to find answers. Here are some from the search results. stats on % of people who click on the various links Stats on % of people who visit different pages on Google It is said that 67% of people click on the top 5 links. And 95% click on the top 10 links. This gives a mere 5% chance that the links on the other millions of pages get clicked. This proves that people want immediate gratification — and they can get that from the main Search Engine Results Page (SERP) on Google. Knowing this, I would envision a future where people don’t need to click on any link, rather expand each result and perform actions on the main page. Simple prototype of how it could look like — with an ‘expand’ option present Simple (and shabby) prototype of what happens after clicking on ‘expand’ 2. Contextual Excellence People have begun searching for complex queries, and they expect Google to provide them the answers. With its resources and talent surrounding , I envision a future where Google can answer even queries such as the following. Natural Language Processing Currently the results from Google Search How it could look like in the future 3. Just Think, Don’t Type Imagine a world where you do not need to type anymore. All you have to do is , and the device in front of you understands that. Sounds too far-fetched? I fear not. Researchers have been working on this technology for more than 5 years now, and there are some very convincing prototypes like and to show that they are making progress. What a relief. think this this How AlterEgo works (like magic) I would envision a future where Google commercializes these devices, converting them into a non-intrusive (and not ridiculously looking) one so that people can buy them and control their Google Search, or with it. even their phone, ******************************************************************* Google might have begun as a search engine that displays links. However, 20 years has seen it grow tremendously, expand its reach into other fields such as autonomous cars , disease prevention , cyber-security , smart home devices and more. And I only see it growing further, and making our lives easier than we could ever imagine. ****************************************************************** 👏 💓_I love writing about social issues, products, the technology sector and my graduate school experience in the US. Here is my_ If you found this to be useful, do Follow me for more articles. Did you know you can more than once? Try it out! personal blog . The best way to get in touch with me is via Instagram and Facebook . I share some interesting content there. To know more about my professional life, check out my LinkedIn . Happy reading!

Google Search — How A Master’s Thesis Became An Idea Worth $70 Billion

Let’s talk about interpretability, transparency, explainability, and the trust headache Explainable (XAI) is getting a lot of attention these days and if you’re like most people, you’re drawn to it because of the conversation around AI and . If so, bad news: it can’t deliver the protection you’re hoping for. Instead, it provides a good source of incomplete . AI trust inspiration Before we get caught up in the trust hype, let’s examine the wisdom of a sentence I’ve been hearing a lot lately: “in order to trust AI, we need it to be able to explain how it made its decisions.” Complexity is the reason for all of it Some tasks are so complicated you instructions. cannot automate them by giving explicit AI is all about automating the ineffable, but don’t expect the ineffable to be easy to wrap your head around. The point of is that by explaining with , you can dodge the headache of figuring out the instructions yourself. That becomes the . AI examples instead AI algorithm’s job How could we possibly trust what we don’t understand? Now that we’re automating things where we couldn’t have handcrafted that model ( ) — because it’s way too complicated — are we seriously expecting to read it and fully grasp how it works? A recipe with a million boring items is something that a computer can remember easily, but it will overwhelm your limited human memory capacity. recipe/instructions So if we can’t just read that complicated tangle and figure out how the recipe is doing its decision-making, why should we it? trust Imagine choosing between two spaceships. Spaceship 1 comes with exact equations explaining how it works, but has never been flown. How Spaceship 2 flies is a mystery, but it has undergone extensive testing, with years of successful flights like the one you’re going on. Which spaceship would you choose? This is a philosophical question, so I can’t answer it for you. I know I have a personal preference — maybe that’s the in me — but I would choose careful testing as a better basis for . statistician trust Carefully testing your system, making sure that it works as it is supposed to — that’s what keeps you safe. Testing as a better basis for trust If we want a student to learn calculus, we want them to generalize beyond the textbook examples rather than overfit to them (“ ” translates approximately to “ ” in plain English). How do we check they’re competent? overfitting memorizing Please don’t take a scalpel and go poking around in your student’s cranial wet stuff to figure out they’re doing the calculus. That’s the equivalent of interpreting the model and I’m glad you don’t do that. You have no idea how the human brain implements calculus (since neither you nor neuroscience can describe the electrochemical signaling in there), but that’s okay — it’s not the best basis for trust anyway. how Craft exams to catch memorization and make sure they reflect the conditions where your student must perform. What you should do instead is design an exam for your student carefully and if the student — human or machine — passes your exam, then you know they’re qualified. That’s pretty much what testing means in AI too. The exam should be crafted to catch overfitting (using brand new data is the best way to foil those pesky memorizers) and relevant to the environment where the student must perform. Experts in take rigorous testing seriously and you should too. applied AI statistical Let’s talk about explainable AI Am I saying that interpretability, transparency, and explainability aren’t important? That’s not it at all. They have their place… in . analytics In many interpretability debates, you’ll notice the participants are talking past one another and misunderstanding the different areas of . They’re fundamentally interested in different classes of application. applied data science If the application involves — in other words, it’s advanced with AI — then of course you need interpretability. How are you going to use a black box for inspiration? With great difficulty, that’s how. generating inspiration analytics If you’re looking for inspiration with advanced , that’s a different goal from building a safe and reliable automated system for decisions at scale, where performance matters most. If your project truly demands both, you can blend two objectives for a price: your result will likely be worse at each of the objectives than if you’d stuck with only one. Don’t pay for things you don’t need. analytics It all boils down to how you the algorithms. There’s nothing to argue about if you frame the discussion in terms of the project goals. apply Often, the people arguing are . Their job is building general-purpose tools which don’t have a business project (or project goals) yet, so like salespeople peddling potato peelers, they are motivated to extoll the virtues of their wares to anyone who will listen, whether or not their captive audience is in danger of ever preparing food. “You this potato peeler” simply isn’t true for everyone — it really depends on your project. Same goes for interpretability and XAI. researchers need Fascination with mechanism If you’re fascinated with how something works (mechanism) for its own sake, that’s the instinct that got trained into you by your STEM classes. It helps you build a new student, a new brain, a new spaceship, a new . research microwave Much of the confusion comes from . Arguments that are appropriate for researchers (build better spaceships) make little sense for those who apply AI (solve problems using existing spaceships). not knowing which AI business you’re in In , a love of mechanism is a great instinct for ; seeing how something works can reveal potential threats and opportunities. If you’re mining data for inspiration, chuck all black boxes out with yesterday’s trash! applied data science analysts Unfortunately, that same instinct will lead you astray if your goal is performance. The mechanism inside the , or , of 1739. Canard Digérateur Digesting Duck Popular nonsense concerning humans Many people demand mechanism as a prerequisite for trust. They have a knee-jerk reaction to : AI “If I don’t know how it’s doing it, I can’t trust it to make decisions.” If you refuse to trust decision-making to something whose process you don’t understand, then you should fire all your human workers, because no one knows how the brain (with its hundred billion neurons!) makes decisions. Holding AI to superhuman standards If you require an interpretation of how a person came to a decision at the level, you should only be satisfied with an answer in terms of electrical signals and neurotransmitters moving from brain cell to brain cell. Do any of your friends describe the reason they ordered coffee instead of tea in terms of chemicals and synapses? Of course not. model Instead people do something else: examine the information and their choices, and then sing you a pretty song that tries to make sense of everything in hindsight. That’s XAI essentially, though human explanations aren’t always right. Behavioral economists get a kick out of planting decisions in their unsuspecting victims — er, experimental participants — and then enjoy listening to incorrect stories all about “why” the people made the decisions (which the experimenter actually made for them). Whenever humans make up a convenient, oversimplified story that fits the inputs and outputs in hindsight, good news! You always have access to the same level of explainability for any model; it can even be model-agnostic. Just look at your input and output data, then tell a fun story. That’s what is all about. analytics When it is simpler than the truth, your explanation is technically a lie. It might inspire you, but it’s closer to a safety blanket than a safety net. Adding a layer of is a good idea wherever you can afford it (remember, don’t take what you see ). The wiser efforts in XAI focus on with inputs and outputs. Sure, they make it sound like something brand new is being invented when it’s just good old fashioned “look at your data and check it makes sense” but I can’t fault the sensibility. The only issue I take with it is that it’s sold to you as a basis for . XAI is many good things, but the way it’s invoked in trust discussions is an audacious con. The explanation will always be horribly oversimplified, so it won’t be true. Oh dear. analytics too seriously analytics trust Explainability provides a cartoon sketch of a but it doesn’t provide the of decision-making. It’s not safe to take a cartoon sketch as more than inspiration, and you’d do well to remember that trust based on XAI is like trust based on a few pieces out of a giant puzzle. why, how Analytics without testing is a one-way ticket to a false sense of security. But let’s get back to the kind of AI where the is interpretable, where we’re not focused on what’s happening in the data. In other words, we want to understand the equivalent of the functioning of ~100,000,000,000 little cells in your skull to explain how you chose that coffee. model really Why can’t we have both? In a perfect world, you’d like perfect performance and perfect interpretability, but usually real life forces you to choose. You know what we call a task where you have both? can Simple, easy, and probably already solved without AI. So let’s talk about those hairy, complicated, and incomprehensible tasks. The ones your brain has evolved to do without telling you how it does them. Or the ones where the signal is a faint needle spread across a huge haystack of . The ones that force you to resort to AI in the first place. For those, you have to choose between: feature combinations you it but it well. Interpretability: do understand doesn’t work you it but it well. Performance: don’t understand does work Remember, explainability can be model-agnostic, so it’s almost a separate analytics project you can tack on if you have the time and energy. True model interpretability, however, will cost you performance if the task requires a complex recipe. When performance matters most, don’t limit your solutions to what the simple human mind can wrap itself around. You want to automate the ineffable, ? The whole point is that some tasks really are so complicated that a good solution will be a rat’s nest of complexity, so you won’t understand the model. At best you’ll be able to with . remember oversimplify analytics Don’t limit your solutions to what the simple human mind can wrap itself around. To really succeed at these tasks with high performance, you have to let go. If you wanted your senses to be controlled by a simple number of brain cells working understandably, they wouldn’t be very effective senses. A fruitfly’s simple brain is easier to understand than yours, but I bet you wouldn’t willingly trade places. You like your performance better. Requiring all models to be interpretable is like demanding that no helper’s brain is more complicated than a fruitfly’s. There’s only so much a fly can help you with. Instead, opt for the trust that comes from making sure that you’re able to verify that your system does, in fact, work. It’s nice to have both, of course, and some tasks are simple enough that they can be solved in a way that delivers all your wishes, but if you can’t have both, isn’t it’s best to go straight for what’s most relevant? That’s performance on well-crafted tests. Hubris and the danger of preferring mechanism Some people still prefer mechanism as their ideal basis for trust. They’d choose the untested spaceship whose physics they’ve read. Lest hubris get the better of us, it’s worth appreciating that mechanism is a step away from performance. People who prefer information about how something works may be trusting too much in their own human ability to leap from complex mechanism to expected performance. Let’s hope they’re clever enough to avoid a nasty splat. Not everything in life is simple In a nutshell, simple solutions didn’t work for tasks that need complicated solutions, so AI comes to the rescue with… complicated solutions. Wishing complicated things were simple does not make them so. If a law says your inherently complicated thing has to be simple, it’s a polite way of saying you can’t have the thing at all. (Which is sometimes for the best.) Why can make solutions that are more complicated than the code you handcraft? A computer can remember (save to disk) a billion examples perfectly in a way that you can’t, so it doesn’t fuzz out the nuances in the same way you do. It doesn’t get bored by writing out a million lines of instructions. Computer memory is nothing new, but with modern computing power we can exploit it at scale. You may have had a few millennia of simplistic recipes that fit in human memory, but now it’s time to turn a new leaf. AI algorithms Today, humanity’s playbook has expanded to automating tasks with complicated recipes. Some of them are so complicated you can’t explain them in three sentences over a beer. We’d best get used to it.

Explainable AI won’t deliver. Here’s why.

The vast majority of algorithms of interest operate on data. Therefore, there are particular ways of organizing data that play a critical role in the design and analysis of algorithms. From that, we can say that data structures are simply ways of organizing data. They are either . Arrays and linked lists are examples of linear data structures. On the other hand, graphs and trees are forms of non-linear data structures. linear or non-linear For example, one common data structure is the or , which is an ordered sequence of values. Here’s a list of numbers: 0, 1, 1, 2, 3, 5, 8, 13. The concept of the list isn’t particular to one language, and it’s also used outside of programming in everyday life — wish lists, shopping lists, and so on. list array Algorithms are recipes for logical execution of the problem. They are not the same as data structures. Algorithms are usually “better” if they work faster or more efficiently (using less time, memory, or both). But here in this article, it’s all about looking into non-linear data structures: graphs Diving into graphs A graph is a system in which there are potentially multiple ways to get from an arbitrary point, A, to another arbitrary point, B. A graph is normally defined as a pair of sets (V,E). V is a set of arbitrary objects called vertices or nodes, and E is a set of pairs of vertices, which we call edges or (more rarely) arcs. In an undirected graph, the edges are unordered pairs, or just sets of two vertices. I usually write instead of {u,v} to denote the undirected edge between u and v. u v In a directed graph, the edges are ordered pairs of vertices. I will use instead of (u,v) to denote the directed edge from u to v and vice versa for all edges in this article. u → v Graphs can also be undirected or directed, cyclic or acyclic (mostly directed), or weighted. Traversing a graph To visit each node or vertex which is a connected component, tree-based algorithms are used. You can do this easily by iterating through all the vertices of the graph, performing the algorithm on each vertex that is still unvisited when examined. Two algorithms are generally used for the traversal of a graph: Depth first search (DFS) and Breadth first search (BFS). Depth first search (DFS) algorithm Visualizing DFS traversal Depth-first Search (DFS) is an for searching a or data structure. The algorithm starts at the root (top) node of a tree and goes as far as it can down a given branch (path), and then backtracks until it finds an unexplored path, and then explores it. The algorithm does this until the entire graph has been explored. algorithm graph tree Many problems in computer science can be thought of in terms of graphs. For example, analyzing networks, mapping routes, scheduling, and finding spanning trees are graph problems. To analyze these problems, graph search algorithms like depth-first search are useful. - Source The simplest pseudo-code would be: Depth-first search is a common way that many people naturally use when solving problems like mazes. First, we select a path in the maze (for the sake of this example, let’s choose a path according to some rule we lay out ahead of time) and we follow it until we hit a dead end or reach the end of the maze. If a given path doesn’t work, we backtrack and take an alternative path from a past junction, and try that path. To turn this into a graph traversal algorithm, we basically replace “child” with “neighbor”. But to prevent infinite loops, we only want to visit each vertex once. Just like in BFS, we can use marks to keep track of the vertices that have already been visited, so we don’t visit them again. Also, just like in BFS, we can use this search to build a spanning tree with certain useful properties. dfs(vertex v){visit(v);for each neighbor w of vif w is unvisited{dfs(w);add edge vw to tree T}} Here’s the Python implementation using recursion: This was a basic overview of how DFS works. If you want to dive deeper, there is some great stuff available all over the internet and also on Medium. Breadth first search (BFS) algorithm It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a ‘search key’), and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. The only catch here is, unlike trees, graphs may contain cycles, so we may come to the same node again. To avoid processing a node more than once, we use a boolean visited array. For simplicity, it is assumed that all vertices are reachable from the starting vertex. What we do in a BFS is a simple step-by-step process: Start from a vertex . Let this vertex be at what is called…. “Level 0”. S Find all the other vertices that are immediately accessible from this starting vertex , that is they are only a single edge away (the adjacent vertices). S Mark these adjacent vertices to be at “Level 1”. You might be coming back to the same vertex due to a loop or a ring in the graph. If this happens, your BFS will take time. So, you will go only to those vertices who do not have their “Level” set to some value. ∞ Mark which is the parent vertex of the current vertex you’re at, i.e., the vertex from which you accessed the current vertex. Do this for all the vertices at Level 1. Now, find all those vertices that are a single edge away from all the vertices which are at “Level 1”. These new set of vertices will be at “Level 2”. Repeat this process until you run out of graph.\ See this — https://www.programiz.com/dsa/graph-bfs Python Implementation Using Queue Directed and Undirected graphs An is a graph in which edges have no orientation. The edge ( , ) is identical to the edge ( , ). That is, they are not ordered pairs, but unordered pairs — i.e., sets of two vertices { , } (or 2-multisets in the case of ). The maximum number of edges in an undirected graph without a loop is ( − 1)/2. undirected graph x y y x x y loops n n For any edge and in an undirected graph, we call u a neighbor of v and vice versa. The degree of a node is its number of neighbors. In directed graphs, we have two kinds of neighbors. For any directed edge , we call u a predecessor of v and v a successor of u. u v u — >v Cyclic and Acyclic graphs Among the many properties of graphs, two are important for a great number of applications : connectivity and acyclicity. Both are based on the notion of a path. A cyclic graph is a graph containing at least one . graph cycle Also remember that cyclic graphs cannot be a form of tree because tree’s nodes are only visited once via DFS or BFS(traversal methods). An acyclic graph is a graph without cycles (a cycle is a complete circuit). When following the graph from node to node, you will never visit the same node twice. What is Directed Acyclic Graph? A directed acyclic graph is an acyclic graph that has a direction as well as a lack of cycles. A tree is a formation of Directed acyclic graph Source — http://www.statisticshowto.com/directed-acyclic-graph/ The parts of the above graph are: = the set for the the Vertices. Integer = {1,2,3,4,5,6,7}. Vertices set = {(1,2), (1,3), (2,4), (2,5), (3,6), (4,7), (5,7), (6,7)}. Edge set A directed acyclic graph has a . This means that the nodes are ordered so that the starting node has a lower value than the ending node. A DAG has a unique topological ordering if it has a directed path containing all the nodes; in this case the ordering is the same as the order in which the nodes appear in the path. topological ordering In , DAGs are also called . When a DAG is used to detect a deadlock, it illustrates that a resources has to another process to continue. computer science wait-for-graphs wait for How can we detect cycles in a graph? As it turns out, the reason that the depth-first search algorithm is particularly good at detecting cycles is because of the fact that it is efficient at finding backward edges. We’ll look in to DFS algorithm later in this article Adjacency matrices and adjacency list representation Consider this graph as example for understanding adjacency lists and adjacency matrices Carrying out graph algorithms using the representation of graphs by lists of edges, or by adjacency lists, can be cumbersome if there are many edges in the graph. To simplify computation, graphs can be represented using matrices. Two types of matrices commonly used to represent graphs will be presented here. One is based on the adjacency of vertices, and the other is based on incidence of vertices and edges. Given an adjacency matrix, we can decide in Θ(1) time whether two vertices are connected by an edge just by looking in the appropriate slot in the matrix. We can also list all the neighbors of a vertex in Θ(V) time by scanning the corresponding row (or column). Understanding Adjacency matrix from above given image… Let’s understand how adjacency matrix is constructed from above given image. For simplicity I have discussed this only for vertex “a” . Same applies to all vertex. Due to page limit, I have not included analysis for and As we can conclude from image that there is an edge from a → d and a → e respectively. a → d a → e. And what about adjacency list? The adjacency list data structure should immediately remind you of hash tables with chaining;the two data structures are identical. An adjacency list is an array of linked lists, one list per vertex. Each linked list stores the neighbors of the corresponding vertex. For example for **a** vertex there are edges leading to neighbors as **b,d and e** . So its respective linked list contains vertex that are connected via edge. Reminder → For undirected graphs, each edge(u,v) is stored twice, once in u’s neighbor list and once in v’s neighbor list; for directed graphs, each edge is stored only once. Weighted Graphs Weighted graph. Source — https://cs.stackexchange.com A weighted graph (or weighted digraph) is a graph (or di-graph) with numbers assigned to its edges. These numbers are called weights or costs. An interest in such graphs is motivated by numerous real-world applications, such as finding the shortest path between two points in a transportation or communication network or the traveling salesman problem . Examples of graphs in real life Google Maps — it’s just one big graph! Where Edges represent streets and vertices represent crossings. Graph theory underlies the Internet. It’s very heavily used in networking code (building routing tables, etc), but it comes up all kinds of places like building an internet search engine, or a social media platform. For diving deep into trees, this article is one of the preferable I chose — https://medium.freecodecamp.org/all-you-need-to-know-about-tree-data-structures-bceacb85490c Facts that matters the most: All trees are graphs. Not all graphs are trees. A tree is a kind of graph, Only if it’s connected. For instance, the graph consisting of the vertices A and B and no edges is not a tree, although it is an acyclic graph. A single vertex is also considered a tree (no cycles, vacuously connected). So two unconnected vertices makes a forest of two trees. A tree is a special kind of graph where there are never multiple paths, that there is always only one way to get from A to B, for all possible combinations of A and B. Shot — https://dribbble.com/shots/3152667-Astronaut-Glove Resources: _Depth-first Search (DFS) is an algorithm for searching a graph or tree data structure. The algorithm starts at the root…_brilliant.org Depth-First Search (DFS) | Brilliant Math & Science Wiki _A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and…_www.geeksforgeeks.org GeeksforGeeks | A computer science portal for geeks _We’ve partnered with Dartmouth college professors Tom Cormen and Devin Balkcom to teach introductory computer science…_www.khanacademy.org Algorithms | Computer science | Computing | Khan Academy _Detailed tutorial on Linear Search to improve your understanding of Algorithms. Also try practice problems to test &…_www.hackerearth.com Linear Search Tutorials & Notes | Algorithms | HackerEarth http://www.cs.uiuc.edu/~jeffe/teaching/algorithms

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