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In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? 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. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva 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 The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. 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 Asia

They bait the hook and we fall for it every time. Want to use a free, massively popular public platform like Facebook or Google? Go for it. Nearly everyone with a smartphone uses these services — or their home country’s equivalent — every day. But like it or not, as you’re searching for a dinner recipe or peering into the exaggerated lifestyle of a nearly forgotten acquaintance, you’re being recorded. They know where you’ve been, how long you’ve been there, and what you were up to the whole time. It’s creepy and invasive and makes me uncomfortable. But what can anyone do about it? Well, the entire process, the entire industry of buying and selling personal data, is overdue for a blockchain-powered rattling. The system is broken but we can fix it. We can do better than this. But it’s going to take concessions, agreements, and persuasive mediators. One such blockchain startup — — wants to put personal data back into the hands of its rightful owners. And not in an anti-corporation manner, either. Datafund Instead, the team wants to create a win/win scenario in which data is ethically exchanged within a transparent marketplace. How will Datafund attempt to give today’s big-data industry a shakeup? We’ll cover that soon. But first, here’s a bit of important data for you to consume… Disclaimer : This is not investment or financial advice. I’m not a financial expert by any stretch of the imagination. Most information within this article is speculative and merely my own personal opinion. Always conduct your own research before contributing to any startup projects. Always remember that what you do with your money is your decision to make. And if that decision proves too difficult for you alone, seek guidance from a financial professional. I may or may not receive a small allocation of tokens for creating this content. That said, I will do my best to remain unbiased and fair. I try to avoid all FOMO and FUD and never wish to impart those emotions on my fellow crypto-heads. Reclamation FAMGA. You already know them but I’ll spell it out for you in case you’re unaware of their new, collective acronym. Coulda went with GAMAF or MAGFA but maybe those were already taken? Facebook. Amazon. Microsoft. Google. Apple. If you consider yourself human and have any connectivity to any kind of computational device, you use one or more of these companies’ products on a daily basis. Add up the global userbase for each — which would be tricky since there’s massive overlap — and we’re talking billions of people. A healthy chunk of the planet’s population knows, and in some cases, loves, these corporations. And what do all these behemoths do with the data they collect from us? How do these ultra-rich companies — — treat the personal data we freely hand over to them? already making money from us Frankly, they treat our data like a goddamn prostitute. They’ve built an enormously global, decentralized, data-driven red-light district, and the data pimps make us work there against our will. Datafund’s first order of business is to become a facilitator of consent. They want the people to be able to reclaim what’s rightfully theirs. And with consent, personal data develops into an easily-digitized asset. Creators of data — all us hoes — decide whether or not to open our legs. And if we do, we get paid accordingly. And purveyors of data — “Silky,” walking around in a multicolored fur coat and Phunk hat, prancing with a cane while balancing on 7" platform heels that may or may not contain goldfish — has to ask permission from us before selling our goods to randos. To kick things off, the project created its own PAC — Privacy Awareness Campaign. It’s live now and I’ll cover it in more detail in a future article. But here’s a link to if you want to go check it out for yourself now. Datafund’s PAC Monetization How DEX tokens flow through the Datafund protocol Like most blockchain protocols, Datafund runs on a native governance token. And in this case, the DEX utility token — ERC777/ERC20 — feeds the chain. When you consent to give your data to ethical companies who’ve elected to join the Datafund ecosystem, you’re rewarded with DEX tokens. Data Users These commercial organizations crunch data to enhance existing products and services, or invent new ones. They may even choose to enrich the data before turning around to sell it. These are the companies providing payments to data generators like you and me. Service Providers These are the companies that we — and also the Data Users — pay for the services they enable. Our business with these organizations is doubly beneficial for them — we pay them with funds and we pay them with data. We’re already doing this every day. But now, we’ll at least be compensated for it. In addition to rewards for selling data, there will be staking rewards for those who HODL their DEX tokens on the network. Yes, that’s passive income right there. Encrypted Curation You can’t just go leaving your data around anywhere. Well, truth be told, it’s already out there. Your personal data is already floating all throughout the Internet. It’s stored on servers and databases and in the cloud. Copies and copies are spread all throughout the world. But with the help of Datafund’s encrypted data storage solution — which you can pay for in DEX tokens — you can curate your own database of personal info. Secure storage can maintain your digital legacy. You control the keys and decide who gets to drive. And, you get to decide whether or not to charge them for the ride. Final thoughts… Datafund envisions — and is working to build — an environment in which the exchange of personal data is open and moral. No more stealing. No more going behind our collective backs. And no more free rides. Blockchain tech, at least for now, is our best shot of crawling out of this personal-data theft hole we’ve dug for ourselves. But the road to change is long and rocky. Bigass corporations have it good and have no intention of giving back control until they’re mandated to. But thankfully, projects like Datafund are working towards raising awareness. DataStolen, Inc. needs to wake up are realize how much better things would be if we could all just get along and play nice. We never would’ve had to endure that 2-week period of constant, incessant GDPR compliance emails if companies had morals. Are they could avoid costly ordeals like Facebook’s Cambridge Analytica embarrassment. Datafund seeks to be a moral compass for the big-data industry. The philosophy of personal freedom as a right runs deep throughout the project. I fully agree with that sentiment. Our data should to us, not them. And if we can all play by the rules, we can create an ecosystem in which everyone wins. belong When everyone wins, even within a closed ecosystem, the world becomes a little better. That very concept is why blockchain tech was invented in the first place. We have the power to take the sleaze and slime out of the big-data industry. And project Datafund wants to help make that dream a reality for us all. DYOR Now, just because you’ve read my opinion of the Datafund project doesn’t mean you’re now an expert. It’s always a great idea to investigate projects on your own. Conveniently, here are some resources so you can do just that: Datafund Website Datafund Whitepaper Get Social Medium Telegram Twitter By the way… If you like what you see here and need help getting your crypto message to the masses, I’d love to hear about your project. You can find me here: blockchainauthor at Gmail

Datafund ICO Review: Degreasing Big Data

The Best 50 Sites to Learn About Data Science

For all its talk of disrupting existing technologies and making our lives better, the tech industry hasn't been especially good at keeping its employees happy. In fact, Millennial tech workers are and more frequently than previous generations. There are several factors at play in both issues, but one big problem is that many Millennials are not finding meaning and purpose in their work. reaching alarming burnout rates leave their jobs Millennials, more so than their parents or grandparents, are looking to make an impact through their work. While traditional goals for money making are still relevant, Millennials when they find a job that provide them with meaningful work. This trend has already caused a shift in the type of work this generation seeks, and has resulted in the prominent development of social enterprises. are actually willing to take pay cuts When Disruption Isn’t Enough Although the idea of social enterprise is nothing new, mainstays in non-tech sectors, the tech industry often seems to place more value on “disruption” than effecting social change in the world. Technology companies place, somewhat disingenuously, more value on projects that offer the of disruption without any actual regard to genuine impact. brands like Toms appearance Companies that are are glorified and put forth as shining beacons of the “change” technology is providing. Even so, they offer little in terms of providing real meaning for Millennials. The industry continues to put its stock behind , where workplace culture has been described as “cult-like”, dissent is discouraged, and employees’ concerns over social impact are routinely ignored. overvalued and underperforming companies like Facebook Even newer more "friendly" seeming companies have been rated as terrible workplaces (with over 60% of employees at both firms noting they experience burnout at their jobs). The optics become even worse when billionaire CEOs like Elon Musk, glorify overwork as a means to “change the world”. like Snap and Lyft Musk’s comments on how employees should be happy seem out of touch seeing as mental health problems are on the rise among tech company employees.  Moreover, when coming from executives that pay themselves tens, if not hundreds of millions of dollars in bonuses and salaries, these calls seem like little more . to put in 100+ hour work weeks than invitations to indentured servitude Tech for Social Good Luckily, the rat-race mentality is not the only option for young tech workers. As a reaction to tech executives instilling a culture of overwork, more Millennials are instead focusing on work that is both meaningful and helps produce positive social impact. Social enterprise, the idea that companies can make a profit and provide an actual solution to social needs, is experiencing a revival in the 21st century. There has been a real push from within the tech world to abandon the “disruption at all costs” mentality and embrace projects that produce real change to a pernicious status quo. In fact, there has been a rise in the number of companies where the main focus is resolving social issues. , which connects data scientists with NGOs working on humanitarian problems; , which supplies an educational platform exclusively for refugees across the world, demonstrate that technology can create profitable solutions that make the world a better place. Smaller projects like DataKind or Kiron Some companies are providing more immediate help to address pressing problems such as clean technology (cleantech) firms. , seeks to provide reliable off-grid electricity to rural populations across Africa and Asia. Lumos Global, for instance Others, , work on reducing food waste by helping sellers distribute food which would have otherwise gone to waste to those in need. like LastMinuteSottoCasa This tech-for-good philosophy is also attracting investors with major names like noting that social enterprises are the best bet for long-term financial returns. Projects that employ technology are also more likely to attract workers who will be more motivated to work. BlackRock CEO Larry Fink 76% of Millennials care about a company’s social and environmental commitments when considering whether to work for it, and 64% won’t work for a company that doesn’t have strong corporate social responsibility practices. A 2016 study found that The Future is Social As the veneer of the Silicon Valley dream wears off, tech entrepreneurs and employees are finding that the companies they work for are not the benevolent institutions they purported to be. Even Google, directive has been scrubbed from its code of conduct, can no longer claim to care about anything other than profits. whose  “Don’t Be Evil” As more Millennial tech workers face burnout and depression they are  likely to look for employment elsewhere. It therefore makes sense that companies who ditch the growth-and-profit-above-all-else mentality and care about social good are almost certainly more likely to be sought out by Millennials striving for happiness and fulfillment instead of simply pursuing money. These companies, while less profitable in the short term will be significantly rewarded in the long term.

Millennials are Leaving Tech’s Old Guard in Favor of Social Enterprise Startups

we explored one of the most complex topics I’ve presented on this blog. We examined potential runtime failures that can occur when using Tensor Flow. These included mismatched dimensions and missing placeholders. In an ideal world, we would catch these issues at compile time instead. At its current stage, the Haskell Tensor Flow library doesn’t support that. But we demonstrated that it is possible to add a layer to do this by using dependent types. In the last couple weeks Now, I’m still very much of a novice at dependent types, so the solutions I presented were rather clunky. This week I’ll show a better example of this concept from a different library. The library uses dependent types everywhere. It allows us to build verifiably-valid neural networks with extreme concision. So let’s dive in and see what it’s all about! Grenade Shapes and Layers The first thing to learn with this library is the two concepts of Shapes and Layers. Shapes are best compared to tensors from Tensor Flow, except that they exist at the type level. In Tensor Flow we could build tensors with arbitrary dimensions. Grenade currently only supports up to three dimensions. So the different shape types either start with , or , depending on the dimensionality of the shape. Then each of these type constructors take a set of natural number parameters. So the following are all valid “Shape” types within Grenade: D1, D2 D3 D1 5D2 4 12D3 8 10 2 The first represents a vector with 5 elements. The second represents a matrix with 4 rows and 12 columns. And the third represents an 8x10x2 matrix (or tensor, if you like). The different numbers represent those values , not the term level. If this seems confusing, here’s a that goes into more depth about the basics of dependent types. The most important idea is that something of type can have 5 elements. A vector of 4 or 6 elements will not type-check. at the type level good tutorial D1 5 only So now that we know about shapes, let’s examine layers. Layers describe relationships between our shapes. They encapsulate the transformations that happen on our data. The following are all valid layer types: ReluFullyConnected 10 20Convolution 1 10 5 5 1 1 The layer describes a layer that takes in data of any kind of shape and outputs the same shape. In between, it applies the activation function to the input data. Since it doesn’t change the shape, it doesn’t need any parameters. Relu relu A layer represents the canonical layer of a neural network. It has two parameters, one for the number of input neurons and one for the number of output neurons. In this case, the layer will take 10 inputs and produce 20 outputs. FullyConnected A layer represents a 2D convolution like we saw with our network. This particular example has 1 input feature, 10 output features, uses a 5x5 patch size, and a 1x1 patch offset. Convolution MNIST Describing a Network Now that we have a basic grasp on shapes and layers, we can see how they fit together to create a full network. A network type has two type parameters. The second parameter is a list of the shapes that our data takes at any given point throughout the network. The first parameter is a list of the layers representing the transformations on the data. So let’s say we wanted to describe a very simple network. It will take 4 inputs and produce 10 outputs using a fully connected layer. Then it will perform an activation. This network looks like this: Relu type SimpleNetwork = Network  ‘[FullyConnected 4 10, Relu]  ‘[ ‘D1 4, ‘D1 10, ‘D1 10] The apostrophes in front of the lists and terms indicated that these are promoted constructors. So they are types instead of terms. To “read” this type, we start with the first data format. We go to each successive data format by applying the transformation layer. So for instance we start with a 4-vector, and transform it into a 10-vector with a fully-connected layer. Then we transform that 10-vector into another 10-vector by applying . That’s all there is to it! We could apply another layer onto this that will have 3 outputs like so: D1 relu FullyConnected type SimpleNetwork = Network  ‘[FullyConnected 4 10, Relu, FullyConnected 10 3]  ‘[ ‘D1 4, ‘D1 10, ‘D1 10, `D1 3] Let’s look at MNIST to see a more complicated example. We’ll start with a 28x28 image of data. Then we’ll perform the convolution layer I mentioned above. This gives us a 3-dimensional tensor of size 24x24x10. Then we can perform 2x2 max pooling on this, resulting in a 12x12x10 tensor. Finally, we can apply an layer, which keeps it at the same size: Relu type MNISTStart = MNISTStart  ‘[Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu]  ‘[D2 28 28, D3 24 24 10, D3 12 12 10, D3 12 12 10] Here’s what a full MNIST example might look like (per the on the library’s Github page): README type MNIST = Network    '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu     , Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, FlattenLayer, Relu     , FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]    '[ 'D2 28 28, 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10     , 'D3 8 8 16, 'D3 4 4 16, 'D1 256, 'D1 256     , 'D1 80, 'D1 80, 'D1 10, 'D1 10] This is a much simpler and more concise description of our network than we can get in Tensor Flow! Let’s examine the ways in which the library uses dependent types to its advantage. The Magic of Dependent Types Describing our network as a type seems like a strange idea if you’ve never used dependent types before. But it gives us a couple great perks! The first major win we get is that it is very easy to generate the starting values of our network. Since it has a specific type, we can let type inference guide us! We don’t need any term level code that is specific to the shape of our network. All we need to do is attach the type signature and call ! randomNetwork randomSimple :: MonadRandom m => m SimpleNetworkrandomSimple = randomNetwork This will give us all the initial values we need, so we can get going! The second (and more important) win is that we can’t build an invalid network! Suppose we try to take our simple network and somehow format it incorrectly. For instance, we could say that instead of the input shape being of size 4, it’s of size 7: type SimpleNetwork = Network  ‘[FullyConnected 4 10, Relu, FullyConnected 10 3]  ‘[ ‘D1 7, ‘D1 10, ‘D1 10, `D1 3]-- ^^ Notice this 7 This will result in a compile error, since there is a mismatch between the layers. The first layer expects an input of 4, but the first data format is of length 7! Could not deduce (Layer (FullyConnected 4 10) ('D1 7) ('D1 10))        arising from a use of ‘randomNetwork’      from the context: MonadRandom m        bound by the type signature for:                   randomSimple :: MonadRandom m => m SimpleNetwork        at src/IrisGrenade.hs:29:1-48 In other words, it notices that the chain from to using a layer is invalid. So it doesn’t let us make this network. The same thing would happen if we made the layers themselves invalid. For instance, we could make the output and input of the two fully-connected layers not match up: D1 7 D1 10 FullyConnected 4 10 -- We changed the second to take 20 as the number of input elements.type SimpleNetwork = Network   '[FullyConnected 4 10, Relu, FullyConnected 20 3]  '[ 'D1 4, 'D1 10, 'D1 20, 'D1 3]

…

/Users/jamesbowen/HTensor/src/IrisGrenade.hs:30:16: error:    • Could not deduce (Layer (FullyConnected 20 3) ('D1 10) ('D1 3))        arising from a use of ‘randomNetwork’      from the context: MonadRandom m        bound by the type signature for:                   randomSimple :: MonadRandom m => m SimpleNetwork        at src/IrisGrenade.hs:29:1-48 So Grenade makes our program much safer by providing compile time guarantees about our network’s validity. Runtime errors due to dimensionality are impossible! Training the Network on Iris Now let’s do a quick run-through of how we actually train this neural network. Readers with a keen eye may have noticed that the we’ve built is the same network we used to train the Iris data set. So we’ll do a training run there, using the following steps: SimpleNetwork Write the network type and generate a random network from it Read our input data into a format that Grenade uses Write a function to run a training iteration. Run it! 1. Write the network type and generate the network So we’ve already done this first step for the most part. We’ll adjust the names a little bit though. Note that I’ll include the imports list as an appendix to the post. Also, the code is on the of my repository in ! [grenade](https://github.com/jhb563/HTensor/tree/grenade) branch Haskell Tensor Flow IrisGrenade.hs type IrisNetwork = Network   '[FullyConnected 4 10, Relu, FullyConnected 10 3]  '[ 'D1 4, 'D1 10, 'D1 10, 'D1 3]

randomIris :: MonadRandom m => m IrisNetworkrandomIris = randomNetwork

runIris :: FilePath -> FilePath -> IO ()runIris trainingFile testingFile = do  initialNetwork <- randomIris  ... 2. Take in our input data We’ll make use of the function we used back when we . Then we'll make a dependent type called , which uses the type. This type is a container for a shape. We want our input data to use for the 4 input features. Then our output data should use for the three possible categories. readIrisFromFile first did Iris IrisRow S S D1 4 D1 3 -- Dependent type on the dimensions of the rowtype IrisRow = (S ('D1 4), S ('D1 3)) If we have malformed data, the types will not match up, so we’ll need to return a to ensure this succeeds. Note that we normalize the data by dividing by 8. This puts all the data between 0 and 1 and makes for better training results. Here's how we parse the data: Maybe parseRecord :: IrisRecord -> Maybe IrisRowparseRecord record = case (input, output) of  (Just i, Just o) -> Just (i, o)  _ -> Nothing  where    input = fromStorable $ VS.fromList $ float2Double <$>      [ field1 record / 8.0, field2 record / 8.0, field3 record / 8.0, field4 record / 8.0]    output = oneHot (fromIntegral $ label record) Then we incorporate these into our main function: runIris :: FilePath -> FilePath -> IO ()runIris trainingFile testingFile = do  initialNetwork <- randomIris  trainingRecords <- readIrisFromFile trainingFile  testRecords <- readIrisFromFile testingFile

  let trainingData = mapMaybe parseRecord (V.toList trainingRecords)  let testData = mapMaybe parseRecord (V.toList testRecords)

  -- Catch if any were parsed as Nothing  if length trainingData /= length trainingRecords || length testData /= length testRecords    then putStrLn "Hmmm there were some problems parsing the data"    else … 3. Write a function to train the input data This is a multi-step process. First we’ll establish our learning parameters. We’ll also write a function that will allow us to call the function on a particular row element: train learningParams :: LearningParameterslearningParams = LearningParameters 0.01 0.9 0.0005

-- Train the network!trainRow :: LearningParameters -> IrisNetwork -> IrisRow -> IrisNetworktrainRow lp network (input, output) = train lp network input output Next we’ll write two more helper functions that will help us test our results. The first will take the network and a test row. It will transform it into the predicted output and the actual output of the network. The second function will take these outputs and reverse the process to get the label out (0, 1, or 2). oneHot -- Takes a test row, returns predicted output and actual output from the network.testRow :: IrisNetwork -> IrisRow -> (S ('D1 3), S ('D1 3))testRow net (rowInput, predictedOutput) = (predictedOutput, runNet net rowInput)

-- Goes from probability output vector to labelgetLabels :: (S ('D1 3), S ('D1 3)) -> (Int, Int)getLabels (S1D predictedLabel, S1D actualOutput) =   (maxIndex (extract predictedLabel), maxIndex (extract actualOutput)) Finally we’ll write a function that will take our training data, test data, the network, and an iteration number. It will return the newly trained network, and log some results about how we’re doing. We’ll first take only a sample of our training data and adjust our parameters so that learning gets slower. Then we’ll train the network by folding over the sampled data. run :: [IrisRow] -> [IrisRow] -> IrisNetwork -> Int -> IO IrisNetworkrun trainData testData network iterationNum = do  sampledRecords <- V.toList <$> chooseRandomRecords (V.fromList trainData)  -- Slowly drop the learning rate  let revisedParams = learningParams         { learningRate = learningRate learningParams * 0.99 ^ iterationNum}  let newNetwork = foldl' (trainRow revisedParams) network sampledRecords  .... Then we’ll wrap up the function by looking at our test data, and seeing how much we got right! run :: [IrisRow] -> [IrisRow] -> IrisNetwork -> Int -> IO IrisNetwork    run trainData testData network iterationNum = do      sampledRecords <- V.toList <$> chooseRandomRecords (V.fromList trainData)      -- Slowly drop the learning rate      let revisedParams = learningParams             { learningRate = learningRate learningParams * 0.99 ^ iterationNum}      let newNetwork = foldl' (trainRow revisedParams) network sampledRecords      let labelVectors = fmap (testRow newNetwork) testData      let labelValues = fmap getLabels labelVectors      let total = length labelValues      let correctEntries = length $ filter ((==) <$> fst <*> snd) labelValues      putStrLn $ "Iteration: " ++ show iterationNum      putStrLn $ show correctEntries ++ " correct out of: " ++ show total      return newNetwork 4. Run it! We’ll call this now from our main function, iterating 100 times, and we’re done! runIris :: FilePath -> FilePath -> IO ()runIris trainingFile testingFile = do ...  if length trainingData /= length trainingRecords || length testData /= length testRecords    then putStrLn "Hmmm there were some problems parsing the data"    else foldM_ (run trainingData testData) initialNetwork [1..100] Comparing to Tensor Flow So now that we’ve looked at a different library, we can consider how it stacks up against Tensor Flow. So first, the advantages. Grenade’s main advantage is that it provides dependent type facilities. This means it is more difficult to write incorrect programs. The basic networks you build are guaranteed to have the correct dimensionality. Additionally, it does not use a “placeholders” system, so you can avoid those kinds of errors too. This means you’re likely to have fewer runtime bugs using Grenade. Concision is another major strong point. The training code got a bit involved when translating our data into Grenade’s format. But it’s no more complicated than Tensor Flow. When it comes down to the exact definition of the network itself, we do this in only a few lines with Grenade. It’s complicated to understand what those lines mean if you are new to dependent types. But after seeing a few simple examples you should be able to follow the general pattern. Of course, none of this means that Tensor Flow is without its advantages. As we saw a couple weeks ago, it is not too difficult to add very thorough logging to your Tensor Flow program. The Tensor Board application will then give you excellent visualizations of this data. It is somewhat more difficult to get intermediate log results with Grenade. There is not too much transparency (that I have found at least) into the inner values of the network. The network types are composable though. So it is possible to get intermediate steps of your operation. But if you break your network into different types and stitch them together, you will remove some of the concision of the network. Also, Tensor Flow also has a much richer ecosystem of machine learning tools to access. Grenade is still limited to a subset of the most common machine learning layers, like convolution and max pooling. Tensor Flow’s API allows approaches like support vector machines and linear models. So Tensor Flow offers you more options. One question I may explore in a future article would be to compare the performance of the two libraries. My suspicion is that Tensor Flow is faster due to how it gets all its math down to the C-level. But I’m not too familiar yet with HMatrix (which Grenade depends on for its math) and its efficiency. So I could definitely be wrong. Conclusion Grenade provides some truly awesome facilities for building a concise neural network. A Grenade program can demonstrate at compile time that the network is well formed. It also allows an incredibly concise way to define what layers your neural network has. It doesn’t have the Google level support that Tensor Flow does. So it lacks many cool features like logging and visualizations. But it is quite a neat library for its scope. One thing I haven’t mentioned is its mechanics for Generative/Adversarial networks. I’d definitely like to try that out soon! Grenade is a simpler library to incorporate into Stack compared to Tensor Flow. If you want to compare the two, you should check out our so you can install TF and get started! Haskell Tensor Flow guide If you’ve never written a line of Haskell before, never fear! Download our for some free resources to start your Haskell education! Getting Started Checklist Appendix: Compiler Extensions and Imports {-# LANGUAGE DataKinds #-}{-# LANGUAGE BangPatterns #-}{-# LANGUAGE TupleSections #-}{-# LANGUAGE GADTs #-}

import           Control.Monad (foldM_)import           Control.Monad.Random (MonadRandom)import           Control.Monad.IO.Class (liftIO)import           Data.Foldable (foldl')import           Data.Maybe (mapMaybe)import qualified Data.Vector.Storable as VSimport qualified Data.Vector as Vimport           GHC.Float (float2Double)import           Grenadeimport           Grenade.Core.LearningParameters (LearningParameters(..))import           Grenade.Core.Shape (fromStorable)import           Grenade.Utils.OneHot (oneHot)import           Numeric.LinearAlgebra (maxIndex)import           Numeric.LinearAlgebra.Static (extract)

import           Processing (IrisRecord(..), readIrisFromFile, chooseRandomRecords)

Grenade! Dependently Typed Neural Networks

Last week we dove into the world of dependent types. We linked tensors with their shapes at the type level. This gave our program some extra type safety and allowed us to avoid certain runtime errors. This week, we’re going to solve another runtime conundrum: missing placeholders. We’ll add some more dependent type machinery to ensure we’ve plugged in all the necessary placeholders! But we’ll see this is not as straightforward as shapes. To follow along with the code in this article, take a look at on my Haskell Tensor Flow Github repository! All the code for this article is in . As usual, I’ve listed the necessary compiler extensions and imports at the bottom of the article. If you want to run the code yourself, you’ll have to get Haskell and Tensor Flow running first. Take a look at our guide for that! this branch DepShape.hs Haskell Tensor Flow Now to start, let’s remind ourselves what placeholders are in Tensor Flow and how we use them. Placeholder Review Placeholders represent tensors that can have different values on different application runs. This is often the case when we’re training on different samples of data. Here’s our very simple example in Python. We’ll create a couple placeholder tensors by providing their shapes and no values. Then when we actually run the session, we’ll provide a value for each of those tensors. node1 = tf.placeholder(tf.float32)node2 = tf.placeholder(tf.float32)adderNode = tf.add(node1, node2)sess = tf.Session()result1 = sess.run(adderNode, {node1: 3, node2: 4.5 }) The weakness here is that there’s nothing forcing us to provide values for those tensors! We could try running our program without them and we’ll get a runtime crash: ...sess = tf.Session()result1 = sess.run(adderNode)print(result1)…

Terminal Output:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float   [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] Unfortunately, the Haskell Tensor Flow library doesn’t actually do any better here. When we want to fill in placeholders, we provide a list of “feeds”. But our program will still compile even if we pass an empty list! We’ll encounter similar runtime errors: (node1 :: Tensor Value Float) <- placeholder [1](node2 :: Tensor Value Float) <- placeholder [1]let adderNode = node1 `add` node2let runStep = \node1Feed node2Feed -> runWithFeeds [] adderNoderunStep (encodeTensorData [1] input1) (encodeTensorData [1] input2)…

Terminal Output:

TensorFlowException TF_INVALID_ARGUMENT "You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [1]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[1], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]" In the and examples, we bury the call to within our neural network API. We only provide a object. This model object forces us to provide the expected input and output tensors. So anyone using our model wouldn't make a manual call. Iris MNIST runWithFeeds Model runWithFeeds data Model = Model  { train :: TensorData Float          -> TensorData Int64          -> Session ()  , errorRate :: TensorData Float              -> TensorData Int64              -> SummaryTensor              -> Session (Float, ByteString)  } This isn’t a bad solution! But it’s interesting to see how we can push the envelope with dependent types, so let’s try that! Adding More “Safe” Types The first step we’ll take is to augment Tensor Flow’s type. We’ll want it to have shape information like and . But we’ll also attach a name to each piece of data. This will allow us to identify which tensor to substitute the data in for. At the type level, we refer to this name as a . TensorData SafeTensor SafeShape Symbol data SafeTensorData a (n :: Symbol) (s :: [Nat]) where  SafeTensorData :: (TensorType a) => TensorData a -> SafeTensorData a n s Next, we’ll need to make some changes to our type. First, each will get a new type parameter. This parameter refers to a mapping of names (symbols) to shapes (which are still lists of naturals). We'll call this a placeholder list. So each tensor will have type-level information for the placeholders it depends on. Each different placeholder has a name and a shape. SafeTensor SafeTensor data SafeTensor v a (s :: [Nat]) (p :: [(Symbol, [Nat])]) where  SafeTensor :: (TensorType a) => Tensor v a -> SafeTensor v a s p Now, recall when we substituted for placeholders, we used a list of feeds. But this list had no information about the names or dimensions of its feeds. Let’s create a new type containing the different elements we need for our feeds. It should also contain the correct type information about the placeholder list. The first step of to define the type so that it has the list of placeholders it contains, like the . SafeTensor data FeedList (pl :: [(Symbol, [Nat])]) where This structure will look like a linked list, like our . Thus we’ll start by defining an “empty” constructor: SafeShape data FeedList (pl :: [(Symbol, [Nat])]) where  EmptyFeedList :: FeedList '[] Now we’ll add a “Cons”-like constructor by creating yet another type operator . Each “piece” of our linked list will contain two different items. First, the tensor we are substituting for. Next, it will have the data we’ll be using for the substitution. We can use type parameters to force their shapes and data types to match. Then we need the resulting placeholder type. We have to append the type-tuple containing the symbol and shape to the previous list. This completes our definition. :--: data FeedList (pl :: [(Symbol, [Nat])]) where  EmptyFeedList :: FeedList '[]  (:--:) :: (KnownSymbol n)    => (SafeTensor Value a s p, SafeTensorData a n s)     -> FeedList pl    -> FeedList ( '(n, s) ': pl)

infixr 5 :--: Note that we force the tensor to be a tensor. We can only substitute data for rendered tensors, hence this restriction. Let's add a quick so we can render our items. Value safeRender SafeTensor safeRender :: (MonadBuild m) => SafeTensor Build a s pl -> m (SafeTensor Value a s pl)safeRender (SafeTensor t1) = do  t2 <- render t1  return $ SafeTensor t2 Making a Placeholder Now we can write our function. We’ll add a as a type constraint. Then we’ll take a to give ourselves the type information for the shape. The result is a new tensor that maps the symbol and the shape in the placeholder list. safePlaceholder KnownSymbol SafeShape safePlaceholder :: (MonadBuild m, TensorType a, KnownSymbol sym) =>   SafeShape s -> m (SafeTensor Value a s '[ '(sym, s)])safePlaceholder shp = do  pl <- placeholder (toShape shp)  return $ SafeTensor pl This looks a little crazy, and it kind’ve is! But we’ve now created a tensor that stores its own placeholder information at the type level! Updating Old Code Now that we’ve done this, we’re also going to have to update some of our older code. The first part of this is pretty straightforward. We’ll need to change so that it has the type information. It will have an empty list for the placeholders. safeConstant safeConstant :: (TensorType a, ShapeProduct s ~ n) =>   Vector n a -> SafeShape s -> SafeTensor Build a s '[]safeConstant elems shp = SafeTensor (constant (toShape shp) (toList elems)) Our mathematical operations will be a bit more tricky though. Consider adding two arbitrary tensors. They may share placeholder dependencies but may not. What should be the placeholder type for the resulting tensor? Obviously the union of the two placeholder maps of the input tensors! Luckily for us, we can use from the library to represent this concept. Union type-list safeAdd :: (TensorType a, a /= Bool, TensorKind v)  => SafeTensor v a s p1  -> SafeTensor v a s p2  -> SafeTensor Build a s (Union p1 p2)safeAdd (SafeTensor t1) (SafeTensor t2) = SafeTensor (t1 `add` t2) We’ll make the same update with matrix multiplication: safeMatMul :: (TensorType a, a /= Bool, a /= Int8, a /= Int16,               a /= Int64, a /= Word8, a /= ByteString, TensorKind v)   => SafeTensor v a '[i,n] p1 -> SafeTensor v a '[n,o] p2 -> SafeTensor Build a '[i,o] (Union p1 p2)safeMatMul (SafeTensor t1) (SafeTensor t2) = SafeTensor (t1 `matMul` t2) Running with Placeholders Now we have all the information we need to write our function. This will take a , and it will also take a with the same placeholder type. Remember, a contains a series of items. They must match up symbol-for-symbol and shape-for-shape with the placeholders within the . Let’s look at the type signature: safeRun SafeTensor FeedList FeedList SafeTensorData SafeTensor safeRun :: (TensorType a, Fetchable (Tensor v a) r) =>  FeedList pl -> SafeTensor v a s pl -> Session r The constraint enforces that we can actually get the “result” out of our tensor. For instance, we can "fetch" a vector of floats out of a tensor that uses as its underlying value. Fetchable r Float We’ll next define a tail-recursive helper function to build the vanilla “list of feeds” out of our . Through pattern matching, we can pick out the tensor to substitute for and the data we’re using. We can combine these into a feed and append to the growing list: FeedList safeRun = ...  where    buildFeedList :: FeedList ss -> [Feed] -> [Feed]    buildFeedList EmptyFeedList accum = accum    buildFeedList ((SafeTensor tensor_, SafeTensorData data_) :--: rest) accum =       buildFeedList rest ((feed tensor_ data_) : accum) Now all we have to do to finish up is call the normal function with the list we’ve created! runWithFeeds safeRun :: (TensorType a, Fetchable (Tensor v a) r) =>  FeedList pl -> SafeTensor v a s pl -> Session rsafeRun feeds (SafeTensor finalTensor) = runWithFeeds (buildFeedList feeds []) finalTensor  where  ... And here’s what it looks like to use this in practice with our simple example. Notice the type signatures do get a little cumbersome. The signatures we place on the initial placeholder tensors are necessary. Otherwise the compiler wouldn’t know what label we’re giving them! The signature containing the union of the types is unnecessary. We can remove it if we want and let type inference do its work. main3 :: IO (VN.Vector Float)main3 = runSession $ do  let (shape1 :: SafeShape '[2,2]) = fromJust $ fromShape (Shape [2,2])  (a :: SafeTensor Value Float '[2,2] '[ '("a", '[2,2])]) <- safePlaceholder shape1  (b :: SafeTensor Value Float '[2,2] '[ '("b", '[2,2])] ) <- safePlaceholder shape1  let result = a `safeAdd` b  (result_ :: SafeTensor Value Float '[2,2] '[ '("b", '[2,2]), '("a", '[2,2])]) <- safeRender result  let (feedA :: Vector 4 Float) = fromJust $ fromList [1,2,3,4]  let (feedB :: Vector 4 Float) = fromJust $ fromList [5,6,7,8]  let fullFeedList = (b, safeEncodeTensorData shape1 feedB) :--:                     (a, safeEncodeTensorData shape1 feedA) :--:                     EmptyFeedList  safeRun fullFeedList result_

{- It runs![6.0,8.0,10.0,12.0]-} Now suppose we make some mistakes with our types. Here we’ll take out the “A” feed from our feed list: -- Let’s take out Feed A!main = …  let fullFeedList = (b, safeEncodeTensorData shape1 feedB) :--:                     EmptyFeedList  safeRun fullFeedList result_

{- Compiler Error!• Couldn't match type ‘'['("a", '[2, 2])]’ with ‘'[]’      Expected type: SafeTensor Value Float '[2, 2] '['("b", '[2, 2])]        Actual type: SafeTensor                       Value Float '[2, 2] '['("b", '[2, 2]), '("a", '[2, 2])]-} Here’s what happens when we try to substitute a vector with the wrong size. It will identify that we have the wrong number of elements! main = …  -- Wrong Size!  let (feedA :: Vector 8 Float) = fromJust $ fromList [1,2,3,4,5,6,7,8]  let (feedB :: Vector 4 Float) = fromJust $ fromList [5,6,7,8]  let fullFeedList = (b, safeEncodeTensorData shape1 feedB) :--:                     (a, safeEncodeTensorData shape1 feedA) :--:                     EmptyFeedList  safeRun fullFeedList result_

{- Compiler Error!Couldn't match type ‘4’ with ‘8’        arising from a use of ‘safeEncodeTensorData’-} Conclusion: Pros and Cons So let’s take a step back and look at what we’ve constructed here. We’ve managed to provide ourselves with some pretty cool compile time guarantees. We’ve also added de-facto documentation to our code. Anyone familiar with the codebase can tell at a glance what placeholders we need for each tensor. It’s a lot harder now to write incorrect code. There are still error conditions of course. But if we’re smart we can write our code to deal with these all upfront. That way we can fail gracefully instead of throwing a random run-time crash somewhere. But there are drawbacks. Imagine being a Haskell novice and walking into this codebase. You’ll have no real clue what’s going on (I wouldn’t have 2 months ago). The types are very cumbersome after a while, so continuing to write them down gets very tedious. Though as I mentioned, type inference can deal with a lot of that. But if you don’t track them, the type union can be finicky about the ordering of your placeholders. We could fix this with another type family though. All these factors could present a real drag on development. But then again, tracking down run-time errors can also do this. Tensor Flow’s error messages can still be a little cryptic. This can make it hard to find root causes. Since I’m still a novice with dependent types, this code was a little messy. Next week we’ll take a look at a more polished library that uses dependent types for neural networks. We’ll see how the library allows us to specify a learning system in just a few lines of code! Grenade If you’re new to Haskell, I hope none of this dependent type madness scared you! The language is much easier than these last couple posts make it seem! Try it out, and download our . It’ll give you some instructions and tools to help you learn! Getting Started Checklist If you’re an experienced Haskeller and want to try out Tensor Flow, download our ! It will walk you through incorporating the library into a Stack project! Tensor Flow Guide Appendix: Compiler Extensions and Imports {-# LANGUAGE GADTs                #-}{-# LANGUAGE DataKinds            #-}{-# LANGUAGE KindSignatures       #-}{-# LANGUAGE TypeOperators        #-}{-# LANGUAGE ScopedTypeVariables  #-}{-# LANGUAGE TypeFamilies         #-}{-# LANGUAGE FlexibleContexts     #-}{-# LANGUAGE UndecidableInstances #-}

import           Data.ByteString (ByteString)import           Data.Int (Int64, Int8, Int16)import           Data.Maybe (fromJust)import           Data.Proxy (Proxy(..))import           Data.Type.List (Union)import qualified Data.Vector as VNimport           Data.Vector.Sized (Vector, toList, fromList)import           Data.Word (Word8)import           GHC.TypeLits (Nat, KnownNat, natVal)import           GHC.TypeLits

import           TensorFlow.Coreimport           TensorFlow.Core (Shape(..), TensorType, Tensor, Build)import           TensorFlow.Ops (constant, add, matMul, placeholder)import           TensorFlow.Session (runSession, run)import           TensorFlow.Tensor (TensorKind)

Checking it’s all in Place: Placeholders and Dependent Types

In the , one primary point I made was that Haskell is a safe language. There are a lot of errors we will catch at compile time, rather than runtime. Runtime errors can often be catastrophic to a system, so being able to reduce these is paramount. This is especially true when programming an autonomous car or drone. These objects will be out in the real world where they can hurt people if they malfunction. introduction to this series So let’s take a look back at some of the code we’ve written over the last 3 or 4 weeks. Is it actually any safer? We’ll find the answer is, well, not so much. It’s hard to verify certain properties about code. But the facilities for making this code safer do exist in Haskell! In the next couple articles we’ll do some serious hacking with dependent types. We’ll be able to prove some of these difficult properties of AI programs at compile time! The next three articles will focus on dependent type programming. This is a difficult topic, so don’t worry if you can’t follow all the code examples completely. The main idea of making our machine learning code safer is what’s important! So without further ado, let’s rewind to the beginning to see where runtime issues can appear. If you want to play with this code yourself, check out the on my Github repository! All the code for this article is in . Though if you want to get the code to run, you’ll probably also need to get Haskell Tensor Flow working. Download our for instructions on that! dependent shapes branch DepShape.hs Haskell Tensor Flow Guide Issues with Python Python, as an interpreted language, is definitely subject to runtime bugs. As I was first learning Tensor Flow, I came across a lot of these that were quite common. The two that stood out to me most were placeholder failures and dimension mismatches. For instance, let’s think back to one of the first examples. Our code will have a couple of placeholders, and we submit values for those when we run the session: node1 = tf.placeholder(tf.float32)node2 = tf.placeholder(tf.float32)adderNode = tf.add(node1, node2)sess = tf.Session()result1 = sess.run(adderNode, {node1: 3, node2: 4.5 }) But there’s nothing stopping us from trying to run the session without submitting values. This will result in a runtime crash: ...sess = tf.Session()result1 = sess.run(adderNode)print(result1)…

Terminal Output:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float   [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] Another issue that came up from time to time was dimension mismatches. Certain operations need certain relationships between the dimensions of the tensors. For instance, you can’t add two vectors with different lengths: node1 = tf.constant([3.0, 4.0, 5.0], dtype=tf.float32)node2 = tf.constant([4.0, 16.0], dtype=tf.float32)additionNode = tf.add(node1, node2)

sess = tf.Session()result = sess.run(additionNode)print(result)

…

Terminal Output:

ValueError: Dimensions must be equal, but are 3 and 2 for 'Add' (op: 'Add') with input shapes: [3], [2]. Again, we get a runtime crash. These seem like the kinds of problems we can solve at compile time. Does Haskell Solve these Issues? But anyone who takes a close look at the Haskell code I’ve written so far can see that it doesn’t solve these issues! Here’s a review of our basic placeholder example: runPlaceholder :: Vector Float -> Vector Float -> IO (Vector Float)runPlaceholder input1 input2 = runSession $ do  (node1 :: Tensor Value Float) <- placeholder [1]  (node2 :: Tensor Value Float) <- placeholder [1]  let adderNode = node1 `add` node2  let runStep = \node1Feed node2Feed -> runWithFeeds         [ feed node1 node1Feed        , feed node2 node2Feed        ]         adderNode  runStep (encodeTensorData [1] input1) (encodeTensorData [1] input2) Notice how the function takes a list of objects. The code would still compile fine if we supplied the empty list. Then it would face a fate no better than our Python code: runWithFeeds Feed …let runStep = \node1Feed node2Feed -> runWithFeeds [] adderNode…

Terminal Output:

TensorFlowException TF_INVALID_ARGUMENT "You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [1]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[1], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]" For the second example of dimensionality, we can also make this mistake in Haskell. The following code compiles and will crash at runtime: runSimple :: IO (Vector Float)runSimple = runSession $ do  let node1 = constant [3] [3 :: Float, 4, 5]  let node2 = constant [2] [4 :: Float, 5]  let additionNode = node1 `add` node2  run additionNode…

Terminal Output:TensorFlowException TF_INVALID_ARGUMENT "Incompatible shapes: [3] vs. [2]\n\t [[Node: Add_2 = Add[T=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Const_0, Const_1)]]" At an even more basic level, we don’t even have to tell the truth about the shape of our vectors! We can give a bogus shape value and it will still compile! let node1 = constant [3, 2, 3] [3 :: Float, 4, 5]…

Terminal Output:invalid tensor length: expected 18 got 3CallStack (from HasCallStack):  error, called at src/TensorFlow/Ops.hs:299:23 in tensorflow-ops-0.1.0.0-EWsy8DQdciaL8o6yb2fUKR:TensorFlow.Ops Can we do better? Now, we did do some things right. Let’s think back to our type when we made neural networks. Model data Model = Model  { train :: TensorData Float          -> TensorData Int64          -> Session ()  , errorRate :: TensorData Float              -> TensorData Int64              -> SummaryTensor              -> Session (Float, ByteString)  } We exposed our training step as a function. This function forced the user to supply both of the tensors for the placeholders. This is good, but doesn’t protect us from dimension issues. When trying to solve these, we could write wrappers around every operation. Functions like and could return values. But this would be clunky. We could take this same step in Python. Granted, monads would allow the Haskell version to compose better. But it would be nicer if we could check our errors all at once, up front. add matMul Maybe If we’re willing to dig quite a bit deeper, we can solve these problems! In the rest of this post, we’ll explore using dependent types to ensure dimensions are always correct. Getting placeholders right turns out to be a little more complicated though! So we’ll save that for next week’s post. Checking Dimensions Currently, the Tensor Types we’ve been dealing with have no type safety on the dimensions. Tensor Flow doesn’t provide this information when interacting with the C library. So it’s impossible to enforce it at a low level. But this doesn’t stop us from writing wrappers that allow us to solve this. To write these wrappers, we’re going to need to dive into dependent types. I’ll give a high level overview of what’s going on. But for some details on the basics, you should check out . I’ll also give a shout-out to , author of the and other great Haskell things. He helped me a lot in crossing a couple big knowledge gaps for implementing dependent types. this tutorial Renzo Carbonara Exinst library Intro to Dependent Types: Sized Vectors The simplest example for introducing dependent types is the idea of sized vectors. If you read the tutorial above, you’ll see how they’re implemented from scratch. A normal vector has a single type parameter, referring to what type of item the vector contains. A sized vector has an extra type parameter, and this type refers to the size of the vector. For instance, the following are valid sized vector types: import Data.Vector.Sized (Vector, fromList)

vectorWith2 :: Vector 2 Int64...vectorWith6 :: Vector 6 Float... In the first type signature, does not refer to the term 2. It refers to the 2. That is, we’ve taken the term and it to a type which has only a single value. The mechanics of how this works are confusing, but here’s the result. We can try to convert normal vectors to sized vectors. But the operation will fail if we don’t match up the size. 2 type promoted import Data.Vector.Sized (Vector, fromList)import GHC.TypeLits (KnownNat)

-- fromList :: (KnownNat n) => [a] -> Maybe (Vector n a)

-- This results in a “Just” value!success :: Maybe (Vector 2 Int64)success = fromList [5,6]

-- The sizes don’t match, so we’ll get “Nothing”!failure :: Maybe (Vector 2 Int64)failure = fromList [3,1,5] The constraint allows us to specify that the type refers to a single natural number. So now we can assign a type signature that encapsulates the size of the list. KnownNat n A “Safe” Shape type Now that we have a very basic understanding of dependent types, let’s come up with a gameplan for Tensor Flow. The first step will be to make a new type that puts the shape into the type signature. We’ll make a type that mimics the sized vector type. Instead of storing a single number as the type, it will store the full list of dimensions. We want to create an API something like this: SafeShape -- fromShape :: Shape -> Maybe (SafeShape s)

-- Results in a “Just” valuegoodShape :: Maybe (SafeShape ‘[2, 2])goodShape = fromShape (Shape [2,2])

-- Results in NothingbadShape :: Maybe (SafeShape ‘[2,2])badShape = fromShape (Shape [3,3,2]) So to do this, we first define the type. This follows the example of sized vectors. See the below for compiler extensions and imports used throughout this article. In particular, you want GADTs and DataKinds. SafeShape appendix data SafeShape (s :: [Nat]) where  NilShape :: SafeShape '[]  (:--) :: KnownNat m => Proxy m -> SafeShape s -> SafeShape (m ': s)

infixr 5 :-- Now we can define the function. This will take our and turn it into a normal using proxies. toShape SafeShape Shape toShape :: SafeShape s -> ShapetoShape NilShape = Shape []toShape ((pm :: Proxy m) :-- s) = Shape (fromInteger (natVal pm) : s')  where    (Shape s') = toShape s Now for the reverse direction, we first have to make a class . This class encapsulates all the types that we can turn into the type. We’ll define instances of this class for all lists of naturals. MkSafeShape SafeShape class MkSafeShape (s :: [Nat]) where  mkSafeShape :: SafeShape sinstance MkSafeShape '[] where  mkSafeShape = NilShapeinstance (MkSafeShape s, KnownNat m) => MkSafeShape (m ': s) where  mkSafeShape = Proxy :-- mkSafeShape Now we can define our function using the class. To check if it works, we’ll compare the resulting shape to the input shape and make sure they’re equal. Note this requires us to define a simple instance of . fromShape MkSafeShape Eq Shape instance Eq Shape where  (==) (Shape s) (Shape r) = s == r

fromShape :: forall s. MkSafeShape s => Shape -> Maybe (SafeShape s)fromShape shape = if toShape myShape == shape  then Just myShape  else Nothing  where    myShape = mkSafeShape :: SafeShape s Now that we’ve done this for Shape, we can create a similar type for that will store the shape as a type parameter. Tensor data SafeTensor v a (s :: [Nat]) where  SafeTensor :: (TensorType a) => Tensor v a -> SafeTensor v a s Using our Safe Types So what has all this gotten us? Our next goal is to create a function. This will let us create a wrapping a constant tensor and storing the shape. Remember, takes a shape and a vector without ensuring correlation between them. We want something like this: safeConstant SafeTensor constant safeConstant :: (TensorType a) => Vector n a -> SafeShape s -> SafeTensor Build a ssafeConstant elems shp = SafeTensor $ constant (toShape shp) (toList elems) This will attach the given shape to the tensor. But there’s one piece missing. We also want to create a connection between the number of input elements and the shape. So something with shape should force you to input a vector of length 18. And right now, there is no constraint between and . [3,3,2] n s We’ll add this with a type family called . The instances will state that the correct natural type for a given list of naturals is the product of them. We define the second instance with recursion, so we'll need . ShapeProduct UndecidableInstances type family ShapeProduct (s :: [Nat]) :: Nattype instance ShapeProduct '[] = 1type instance ShapeProduct (m ': s) = m * ShapeProduct s Now we’re almost done with this part! We can fix our function by adding a constraint on the between and . safeConstant ShapeProduct s n safeConstant :: (TensorType a, ShapeProduct s ~ n) => Vector n a -> SafeShape s -> SafeTensor Build a ssafeConstant elems shp = SafeTensor $ constant (toShape shp) (toList elems) Now we can write out a simple use of our function as follows: safeConstant main :: IO (VN.Vector Int64)main = runSession $ do  let (shape1 :: SafeShape '[2,2]) = fromJust $ fromShape (Shape [2,2])  let (elems1 :: Vector 4 Int64) = fromJust $ fromList [1,2,3,4]  let (constant1 :: SafeTensor Build Int64 '[2,2]) = safeConstant elems1 shape1  let (SafeTensor t) = constant1  run t We’re using as a shortcut here. But in a real program you would read your initial tensors in and check them as values. There's still the possibility for runtime failures. But this system has a couple advantages. First, it won't crash. We'll have the opportunity to handle it gracefully. Second, we do all the error checking up front. Once we've assigned types to everything, all the failure cases should be covered. fromJust Maybe Going back to the last example, let’s change something. For instance, we could make our vector have length 3 instead of 4. We’ll now get a compile error! main :: IO (VN.Vector Int64)main = runSession $ do  let (shape1 :: SafeShape '[2,2]) = fromJust $ fromShape (Shape [2,2])  let (elems1 :: Vector 3 Int64) = fromJust $ fromList [1,2,3]  let (constant1 :: SafeTensor Build Int64 '[2,2]) = safeConstant elems1 shape1  let (SafeTensor t) = constant1  run t

…

    • Couldn't match type ‘4’ with ‘3’        arising from a use of ‘safeConstant’    • In the expression: safeConstant elems1 shape1      In a pattern binding:        (constant1 :: SafeTensor Build Int64 '[2, 2])          = safeConstant elems1 shape1 Adding Type Safe Operations Now that we’ve attached shape information to our tensors, we can define safer math operations. It’s easy to write a safe addition function that ensures that the tensors have the same shape: safeAdd :: (TensorType a, a /= Bool) => SafeTensor Build a s -> SafeTensor Build a s -> SafeTensor Build a ssafeAdd (SafeTensor t1) (SafeTensor t2) = SafeTensor (t1 `add` t2) Here’s a similar matrix multiplication function. It ensures we have 2-dimensional shapes and that the dimensions work out. Notice the two tensors share the dimension. It must be the column dimension of the first tensor and the row dimension of the second tensor: n safeMatMul :: (TensorType a, a /= Bool, a /= Int8, a /= Int16, a /= Int64, a /= Word8, a /= ByteString)   => SafeTensor Build a '[i,n] -> SafeTensor Build a '[n,o] -> SafeTensor Build a '[i,o]safeMatMul (SafeTensor t1) (SafeTensor t2) = SafeTensor (t1 `matMul` t2) Here are these functions in action: main2 :: IO (VN.Vector Float)main2 = runSession $ do  let (shape1 :: SafeShape '[4,3]) = fromJust $ fromShape (Shape [4,3])  let (shape2 :: SafeShape '[3,2]) = fromJust $ fromShape (Shape [3,2])  let (shape3 :: SafeShape '[4,2]) = fromJust $ fromShape (Shape [4,2])  let (elems1 :: Vector 12 Float) = fromJust $ fromList [1,2,3,4,1,2,3,4,1,2,3,4]  let (elems2 :: Vector 6 Float) = fromJust $ fromList [5,6,7,8,9,10]  let (elems3 :: Vector 8 Float) = fromJust $ fromList [11,12,13,14,15,16,17,18]  let (constant1 :: SafeTensor Build Float '[4,3]) = safeConstant elems1 shape1  let (constant2 :: SafeTensor Build Float '[3,2]) = safeConstant elems2 shape2  let (constant3 :: SafeTensor Build Float '[4,2]) = safeConstant elems3 shape3  let (multTensor :: SafeTensor Build Float '[4,2]) = constant1 `safeMatMul` constant2  let (addTensor :: SafeTensor Build Float '[4,2]) = multTensor `safeAdd` constant3  let (SafeTensor finalTensor) = addTensor  run finalTensor And of course we’ll get compile errors if we use incorrect dimensions anywhere. Let’s say we change to use as its type: multTensor [4,3] • Couldn't match type ‘2’ with ‘3’      Expected type: SafeTensor Build Float '[4, 3]        Actual type: SafeTensor Build Float '[4, 2]    • In the expression: constant1 `safeMatMul` constant2… • Couldn't match type ‘3’ with ‘2’      Expected type: SafeTensor Build Float '[4, 2]        Actual type: SafeTensor Build Float '[4, 3]    • In the expression: multTensor `safeAdd` constant3…• Couldn't match type ‘2’ with ‘3’      Expected type: SafeTensor Build Float '[4, 3]        Actual type: SafeTensor Build Float '[4, 2]    • In the second argument of ‘safeAdd’, namely ‘constant3’ Conclusion In this exercise we got deep into the weeds of one of the most difficult topics to learn about in Haskell. Dependent types will make your head spin at first. But we saw a concrete example of how they can allow us to detect problematic code at compile time. They are a form of documentation that also enables us to verify that our code is correct in certain ways. Types do not replace tests (especially behavioral tests). But in this instance there are at least a few different test cases we don’t need to worry about too much. Next week, we’ll see how we can apply these principles to verifying placeholders. If you want to learn more about the nuts and bolts of using Haskell Tensor Flow, you should check out our . It will guide you through the basics of adding Tensor Flow to a simple Stack project. Tensor Flow Guide Maybe you’ve never used Haskell before but I’ve convinced you that dependent types are the future. If you want to try it out, download our . You can also learn how to create and organize Haskell projects using Stack! Checkout our ! Getting Started Checklist Stack mini-course Appendix: Extensions and Imports {-# LANGUAGE GADTs                #-}{-# LANGUAGE DataKinds            #-}{-# LANGUAGE KindSignatures       #-}{-# LANGUAGE TypeOperators        #-}{-# LANGUAGE ScopedTypeVariables  #-}{-# LANGUAGE TypeFamilies         #-}{-# LANGUAGE UndecidableInstances #-}

import           Data.ByteString (ByteString)import           Data.Constraint (Constraint)import           Data.Int (Int64, Int8, Int16)import           Data.Maybe (fromJust)import           Data.Proxy (Proxy(..))import qualified Data.Vector as VNimport           Data.Vector.Sized (Vector(..), toList, fromList)import           Data.Word (Word8)import           GHC.TypeLits (Nat, KnownNat, natVal)import           GHC.TypeLits

import           TensorFlow.Coreimport           TensorFlow.Core (Shape(..), TensorType, Tensor, Build)import           TensorFlow.Ops (constant, add, matMul)import           TensorFlow.Session (runSession, run)

Deep Learning and Deep Types: Tensor Flow and Dependent Types

We hear the horror stories of data leaks almost every day. Some require a sophisticated attack to gain shell access to the servers. Some happen over the web using the in the web apps. As a result of this attack, your customer data can be dumped and . SQL injections personal data leaked Regarding database encryption, let’s face it. Most of the solutions provide a . Data is encrypted on the storage or disk level. In case, your website has an SQL injection, the bad actor will get your . fake sense of security data in cleartext Here comes the solution. is an open-source, self-hosted, GDPR compliant, secure database for personal data or PII. Databunker Databunker Instaed of storing your customer data in a regular database you can use Databunker. Instead of talking with Databunker using SQL, your backend will have to call an API function to retrieve specific user details. Databunker does not have an API to enumerate all users. Databunker encrypts customer records and builds a secure search index for quick user lookup (i.e. using email, token, etc…). What’s Next? Databunker is an open-source project. You can start palying with it right now. Pre-built Docker container available. https://databunker.org/ Free takeaway I run and architects. It is a free training. privacy training for startup founders About the author Yuli Stremovsky is a world-class software and security architect. Founder of and privacy products. Former Checkpoint, and RSA Security employee. An expert in marrying technological solutions with privacy. PrivacyBunker.io DataBunker.org

Data Leak Prevention with Databunker

Hey everyone! and just like you, I’ve been fascinated with the recent progress of artificial intelligence. Realizing that I needed to stay abreast with all the developments happening, I decided to embark on a personal journey of learning, thus was born! With this series, I will be learning about LLMs and share ideas, experiments, opinions, trends & learnings through my blog posts. You can follow along the journey on HackerNoon or my personal website . In today’s article, we’ll be looking to build a Semantic Kernel with the help of GPT-4. , I’m Nataraj 100 days of AI here here If you are reading the 100 Days of AI series I am writing a common theme that you will notice is most of the posts have some kind of an experiment or a demo. At the end of posts I also list ideas that are possible with the technologies I explored. So the obvious next question is, is there a way to easily wrap these experiments into demo-able products and share them with people and get feedback? In this post we will explore how to easily convert an AI experiment into a demo that can be and get feedback. Let’s say you want to create an AI app that takes in text and returns a summary. And you want to share it with people and get feedback. If you have to create a web app for this app, you have to code the UI make sure you find a hosting solution and figure out what the entire stack is going to be. Of course there are started webapp stacks that will allow to do it more faster but you don’t need all that complexity for your use case. Instead we will use Gradio which create a web app like interface with out writing any code for this app. What is Gradio? Gradio is a python library that allows you to easily create demos of your AI apps and share it with an audience in the form of a web app. To understand how to easily demo a gen AI experiment with Gradio, we will build a summarization web app where you can give the app a long text and the app will output the summary of that long text. Step 1: Setup Along with Gradio, we will use hugging face app so to follow along make sure you keep your hugging face api key handy. To get started lets load up the API key from .env file and add required python modules. import os
import io
from IPython.display import Image, display, HTML
#from PIL import Image
import base64 
import openai

env_path = '../.env'
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv(env_path)) # read local .env file

hf_api_key = os.environ['HF_API_KEY'] Step 2: Define Summarization Function The core logic of what we want to achieve is to send a long text to a model and get a summary of the text. We will define a function that takes in the long text as an input and calls a model on hugging face called . Here is the function to do this. distilbart def get_completion(inputs, parameters=None, ENDPOINT_URL=os.environ['HF_API_SUMMARY_BASE']): 
    headers = {
      "Authorization": f"Bearer {hf_api_key}",
      "Content-Type": "application/json"
    }
    data = { "inputs": inputs }
    if parameters is not None:
        data.update({"parameters": parameters})
    response = requests.request("POST",
                                ENDPOINT_URL, headers=headers,
                                data=json.dumps(data)
                               )
    return json.loads(response.content.decode("utf-8")) Step 3: Use Gradio to Demo Summarization App The main way to interact with Gradio is through function. We need to pass a function and its corresponding inputs and outputs. Note that we defined a new function called for this purpose. This function takes an input, which will be the long text which is to be summarized and the function will call the function from step 2 to get the summary of the input text and return that as the . In the final line we are asking gradio to launch the demo of this app. By providing we are telling gradio to create a public link that can be shared to others and also giving the server_port on which the local webhost should run on. In the below code we also customize the title, description and labels of input and output fields which you will notice in the out put. interface summarize get_completion output share=True import gradio as gr
def summarize(input):
    output = get_completion(input)
    return output[0]['summary_text']
    
gr.close_all()
demo = gr.Interface(fn=summarize, 
                    inputs=[gr.Textbox(label="Text to summarize", lines=6)],
                    outputs=[gr.Textbox(label="Result", lines=3)],
                    title="Text summarization with distilbart-cnn",
                    description="Summarize any text using the `shleifer/distilbart-cnn-12-6` model under the hood!"
                   )
demo.launch(share=True, server_port=int(os.environ['PORT1'])) Once you run this, here is what the output now looks like in your browser at http://127.0.0.1:<your port number>. It is that easy to demo a project with Gradio. The beauty here is you as a developer didn’t have to come up with UI stack and go through with the complexity of learning that stack and taking it to production. Gradio does it for you. Gradio offers different input and output types using which you can create more complex and interesting demos that can be easily shared. We will explore more demos with Gradio and other tools in the coming posts. That’s it for Day 12 of 100 Days of AI. . I write a newsletter called Above Average where I talk about the second order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, subscribe to it Follow me on , or for latest updates on 100 days of AI. If you are in tech you might be interested in joining my community of tech professionals . Twitter LinkedIn HackerNoon here

100 Days of AI, Day 12: How to Quickly Demo Your Gen AI Experiments to Get Feedback

এআই-এর 100 দিন, 12 দিন: প্রতিক্রিয়া পেতে আপনার জেনারেল এআই পরীক্ষাগুলি কীভাবে দ্রুত ডেমো করবেন

100 Tage KI, Tag 12: So demonstrieren Sie schnell Ihre Gen-KI-Experimente, um Feedback zu erhalten

100 días de IA, día 12: Cómo realizar una demostración rápida de sus experimentos de IA de generación para obtener comentarios

100 jours d'IA, jour 12 : comment faire une démonstration rapide de vos expériences Gen AI pour obtenir des commentaires

एआई के 100 दिन, दिन 12: फीडबैक प्राप्त करने के लिए अपने जनरल एआई प्रयोगों का त्वरित प्रदर्शन कैसे करें

100 日間の AI、12 日目: Gen AI 実験をすばやくデモしてフィードバックを得る方法

AI 100일, 12일차: Gen AI 실험을 빠르게 시연하여 피드백을 받는 방법

100 dias de IA, dia 12: como demonstrar rapidamente seus experimentos de Gen AI para obter feedback

100 дней искусственного интеллекта, день 12: как быстро продемонстрировать свои эксперименты с искусственным интеллектом, чтобы получить обратную связь

100 Gün Yapay Zeka, 12. Gün: Geri Bildirim Almak İçin Nesil Yapay Zeka Deneylerinizi Hızlı Bir Şekilde Nasıl Tanıtabilirsiniz?

100 ngày AI, Ngày 12: Cách demo nhanh các thử nghiệm AI thế hệ của bạn để nhận phản hồi

人工智能 100 天，第 12 天：如何快速演示您的新一代人工智能实验以获得反馈

Multi-Class Classification: Understanding Activation and Loss Functions in Neural Networks

মাল্টি-ক্লাস শ্রেণীবিভাগ: নিউরাল নেটওয়ার্কগুলিতে সক্রিয়করণ এবং ক্ষতি কার্যকারিতা বোঝা

Mehrklassenklassifizierung: Aktivierungs- und Verlustfunktionen in neuronalen Netzen verstehen

Clasificación de clases múltiples: comprensión de las funciones de activación y pérdida en redes neuronales

Classification multiclasse : comprendre les fonctions d'activation et de perte dans les réseaux de neurones

मल्टी-क्लास वर्गीकरण: तंत्रिका नेटवर्क में सक्रियण और हानि कार्यों को समझना

マルチクラス分類: ニューラル ネットワークの活性化関数と損失関数を理解する

다중 클래스 분류: 신경망의 활성화 및 손실 함수 이해

Classificação multiclasse: Compreendendo as funções de ativação e perda em redes neurais

Классификация нескольких классов: понимание функций активации и потерь в нейронных сетях

Çok Sınıflı Sınıflandırma: Sinir Ağlarında Aktivasyon ve Kayıp Fonksiyonlarını Anlamak

Phân loại nhiều lớp: Tìm hiểu các chức năng kích hoạt và mất mát trong mạng thần kinh

多类分类：了解神经网络中的激活函数和损失函数

Let's learn about via these 44 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Gdpr /Learn Repo 1. How Does the GDPR Affect B2B Data? - A Simple Explanation You can reach out to prospects and collect B2B data even under the GDPR. However, there are some rules and regulations. 2. Bye-bye Google Fonts: German Court Rules That Google Fonts Is Not in Compliance with GDPR Google Fonts is a data privacy concern. Learn why you should care. 3. Cloud Data Security and the Need for Confidential Containers Why should you choose Confidential Containers for your business and how do they stand out from the "vanilla" version? Check it out in our article! 4. The Digital Iceberg: How Third-Party Code Can Break Your Business By Chris Olson, CEO of The Media Trust 5. The Future of Innovation: How Will Entrepreneurs Adapt To This Decade? Futureproofing is at the forefront of every entrepreneur’s mind. But putting ideas into practice can be a different challenge altogether. 6. Businesses are Tackling Data Privacy Restrictions with Hybrid Cloud If a business deals with personal data, it inevitably faces data protection and privacy legislation issues. Here is how the hybrid cloud helps address those. 7. Handling Sensitive Data: A Primer Properly securing sensitive customer data is more important than ever. 8. Why Are Businesses Raising Equity with Crowdfunding? Equity crowdfunding was not the easiest choice to make, but it kept us true to our core values of trust, transparency, and user-centricity. 9. New Data Privacy Laws Are an Opportunity, Not a Threat The New York Times declared the 2010s as “The Decade Tech Lost Its Way.” And it’s easy to agree when you look back at the Cambridge Analytica scandal, tech companies who consistently got off easy after privacy violations and the rise of sweeping new regulations to protect personal data. 10. How to Properly Implement GDPR for Customer Communications If you are considering sending notifications to the users of your SaaS application, whether via email, push, or a Slack bot, you need to keep GDPR in mind. 11. Data Leak Prevention with Databunker We hear the horror stories of data leaks almost every day. Some require a sophisticated attack to gain shell access to the servers. Some happen over the web using the SQL injections in the web apps. As a result of this attack, your customer data can be dumped and personal data leaked. 12. How to Create an Effective Incident Response Plan When it comes to security incidents, it’s not a question of if, but when they will happen. 80% of organizations say that they have experienced some kind of cybersecurity incident in the last year. With this in mind, it’s essential to have a security incident response plan in place before you need one. 13. What are the Essential Healthcare IT Standards & Regulations? Still, despite the array of communication technologies available, there is no universal way of managing and transferring healthcare data. 14. Redefining Privacy and Data Ownership: An Interview with ARPA Founder, Felix Xu A conversation with Felix Xu, CEO of ARPA, on data utility and ownership, the NFT ecosystem, and much more. 15. Data Privacy is Becoming More Important for Users in 2022 A look at how data privacy is becoming more important for users in 2022 16. Best Practices for Cold Emailing Under GDPR As a growth hacker I do cold emailing. A lot! 17. The Independent Phone :  More Privacy, Less Freedom? Freedom and privacy tend to go together, but there is a difference. With a more private phone, does it really mean you have more freedom? 18. Playing Hide and Seek with Big Brother We live in the age of constant surveillance, unlike the one described by George Orwell in his seminal book 1984, but the one which might have bought willingly. We regularly surrender information in exchange for free services and convenience. Often, we skim over the fine print of user agreements, and the act of disseminating private information becomes a voluntary task that absolves the media giants from any legal burden [3]. Ian Bogost of The Atlantic calls this “the age of privacy nihilism” and says that the changes in escaping surveillance capitalism are bleak [4]. 19. Announcing our Data Breach website on Data Privacy Day We are happy to announce our Data Breach website Databreach.es, our latest community project. With this project, we strive to create insights into global data breaches. Which companies have been breached, what kind of data got lost, what's the impact on your privacy and how did these companies handle the situation. 20. Will The New Wave of Data Laws Affect Fintech? Fintech, otherwise known as the financial technology market, is on a very strong growth trajectory, having already disrupted the banking world in a powerful way. From mobile payments, to artificial intelligence, insurance and investment services, Fintech has gradually transformed the way we bank since the day the US Federal Reserve introduced the Fedwire Funds Service back in 1918, which used morse code and telegraph to transfer funds electronically. 21. GDPR vs CCPA: Data Privacy On The Lead [Infographic] A quick look at the two approaches to privacy 22. How Software Accountability shifts Technology Dynamics for Today’s Startups For a long time now, software-based startups have been launching their products and thrived by having users’ data as the very foundation of their business models. Social media platforms are the perfect examples of how a business can profit from a well of consumer data. While users had traditionally been in the dark about how the information they share when interacting with digital products is used, various notorious scandals have changed the narrative. 23. GDPR's Two-Tier Internet: One Year On It’s been over a year since the GDPR came into effect, and at least five since it was first debated seriously. For much of that time it was discussed as a global game-changer, yet the predicted earth-shaking effects of its implementation — billion-dollar fines, the end of data exploitation by rogue firms, high-profile court cases — have largely yet to materialize. Instead, the landscape of the internet has changed in more subtle and complex ways which could not have been predicted by pundits, and it’s affecting businesses of every size. 24. Best Practices for GDPR Compliance Implementation In the new digital age we face unprecedented times of wide opportunities. Internet is just about everywhere and can be connected to just about any device generating diverse forms of data circulation, exchange and accumulation. 25. The Cost of Overlooking Patch Management Software firms are not responsible for data breaches - you are! Make sure you're keeping up with the latest patches whenever updates are released. 26. SaaS Apps Are Less Compliant Than You Think: An Analysis of The Top 1,000 SaaS Apps Today’s IT and operations professionals might find themselves feeling like the opposite of Spiderman. With great responsibility comes … no power? The stakes are higher than ever to ensure that their organizations are protected from a security and compliance perspective, but new survey data from Blissfully shows that the most popular SaaS applications are less compliant than you’d think. 27. What are the Key Stages of Data Protection Impact Assessment? A Data Protection Impact Assessment which is also referred to as Privacy Impact Assessments is a mandatory requirement for organizations to comply with. 28. Research Suggests That a Hard Brexit Could Lead to Significant Confusion within the UK Cloud Market An inquiry into public opinion suggests that fear and confusion could push sensitive personal data out of the country. 29. Hiring Remote Devs? How to be GDPR Compliant As the demand for high-quality software engineers increases, employers hiring remotely will find themselves ahead of the competition. This is because hiring remotely allows employers and startup founders to expand their talent pool and hire the best candidate no matter where they live. It also leads to reducing costs and improving employees’ productivity and retention. 30. Exploring Ethical User Data with Datawallet’s New SDK On July 8, the Information Commissioner’s Office (ICO) announced the highest GDPR fine ever of £183 million over last year’s data breach at British Airways. The UK’s data watchdog elected to fine the airline as its “poor security arrangements” led to the breach of credit card information, names, addresses, travel booking details, and logins of around 500,000 customers.  In recent years, consumers have become wearily accustomed to data breaches of this magnitude. 31. 5 Reasons Why Privacy by Design is More Critical Than Ever in 2023 The more manual work is involved in managing data privacy, the greater the risk of costly data and compliance breaches. 32. Cloud Compliance And Regulation Resources Customers and Cloud Service Provider (CSP) share the responsibility of security and compliance. Thus, the organization would have the freedom to have architect their security and compliance needs, according to the services they utilize from the CSP and the services they intend to achieve. CSP has the responsibility to provide services securely and to provide physical security of the cloud. If, however, a customer opts for Software-as-a-service, then the CSP provides standard compliance. Still, the organization has to check if it meets its regulations and compliance levels to strive to achieve. All Cloud services (such ad different forms of databases) are not created equal. Policies and procedures should be agreed upon between CSP and client for all security requirements and operations responsibility. 33. Why Should Websites Adopt Passwordless Authentication? Brands have started to shift away from the old, password-centric world. While it sounds like a bad idea, there are some benefits to this change to passwords. 34. Conquer GDPR Article 32: Locking Down Data Segregation and Identity-Based Access Understand the importance and tools of implementation for data segregation through access control GDPR compliance to address Article 32. 35. 8 Cloud Compliance Frameworks and Why Cloud-based Organizations Need Them Cloud compliance frameworks help an organization to keep its database secure and private. In this article, you will eight such frameworks that can guide you. 36. Identity Management Unchained - Taking the Metaverse Red Pill Technology should serve humans. But when combined with the wrong business model, it can turn individuals into non-player characters in a game dominated by tech. 37. What the GDPR and California Privacy Act Means for Tech Companies You may not know it, but companies are using your data as a commodity. For decades, all of your online habits have been stored, bought, shared, transferred, and maybe even stolen by businesses and individuals. 38. How to Ensure your SaaS is Compliant - For Developers This article reviews what kinds of compliance certifications and regulations exist, what they mean for developers, and how to begin the certification process. 39. Time Travel Through 2010s Technology: Part 2 In the first part of the series “Time travel through 2010s technology” we looked at how operating systems, phones, tablets, smartwatches and smartglasses changed through the last decade. The 2010s changed how we interact with technology, but more importantly, how we think about the impact it has in our lives. 40. We Need To Build A Human-Centered Future Because Regulations Alone Aren't Enough Emerging data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act (CCPA) in the USA, have sparked global discussion around: 41. Blockchaining the Internet of Identity In May 2018, the most far-reaching legislation for the protection of personal data ever conceived took effect: The European Union's General Data Protection Regulation (GDPR). 42. Identity and Blockchain’s Digital Vending Machines In a previous article, we outlined investment opportunities in companies utilizing decentralized software solutions such as blockchains. 43. Issues With Private Data and Confidentiality in Hyperledger Fabric [Deep Dive] In many blockchain platforms such as Ethereum and Bitcoin, data confidentiality is a kind of excluded item in their blockchain framework. In these distributed blockchain platform transactions are executed in every participant node in the network. So, every transaction in the network can be visible to all the peers. The ledger update process through all the endorsed peers and has to reach an agreement among all before it committed successfully to the ledger. So, in this scenario creating a private record and comprises within a certain group of participants in the network is a complete “No”. 44. A Marketer's Guide to GDPR In God we trust, all others must bring data – W. Edwards Deming Thank you for checking out the 44 most read stories about Gdpr on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

44 Stories To Learn About Gdpr

102 Stories To Learn About Encryption

379 Stories To Learn About Data Privacy

Underground: Hacking, Madness and Obsession on the Electronic Frontier by Suelette Dreyfus, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Introduction. here Introduction. My great aunt used to paint underwater. Piling on the weighty diving gear used in 1939 and looking like something out of 20000 Leagues Under the Sea, Lucie slowly sank below the surface, with palette, special paints and canvas in hand. She settled on the ocean floor, arranged her weighted painter's easel and allowed herself to become completely enveloped by another world. Red and white striped fish darted around fields of blue-green coral and blue-lipped giant clams. Lionfish drifted by, gracefully waving their dangerous feathered spines. Striped green moray eels peered at her from their rock crevice homes. Lucie dived and painted everywhere. The Sulu Archipelago. Mexico. Australia's Great Barrier Reef. Hawaii. Borneo. Sometimes she was the first white woman seen by the Pacific villagers she lived with for months on end. As a child, I was entranced by her stories of the unknown world below the ocean's surface, and the strange and wonderful cultures she met on her journeys. I grew up in awe of her chosen task: to capture on canvas the essence of a world utterly foreign to her own. New technology—revolutionary for its time—had allowed her to do this. Using a compressor, or sometimes just a hand pump connected to air hoses running to the surface, human beings were suddenly able to submerge themselves for long periods in an otherwise inaccessible world. New technology allowed her to both venture into this unexplored realm, and to document it in canvas. I came upon the brave new world of computer communications and its darker side, the underground, quite by accident. It struck me somewhere in the journey that followed that my trepidations and conflicting desires to explore this alien world were perhaps not unlike my aunt's own desires some half a century before. Like her journey, my own travels have only been made possible by new technologies. And like her, I have tried to capture a small corner of this world. This is a book about the computer underground. It is not a book about law enforcement agencies, and it is not written from the point of view of the police officer. From a literary perspective, I have told this story through the eyes of numerous computer hackers. In doing so, I hope to provide the reader with a window into a mysterious, shrouded and usually inaccessible realm. Who are hackers? Why do they hack? There are no simple answers to these questions. Each hacker is different. To that end, I have attempted to present a collection of individual but interconnected stories, bound by their links to the international computer underground. These are true stories, tales of the world's best and the brightest hackers and phreakers. There are some members of the underground whose stories I have not covered, a few of whom would also rank as world-class. In the end, I chose to paint detailed portraits of a few hackers rather than attempt to compile a comprehensive but shallow catalogue. While each hacker has a distinct story, there are common themes which appear throughout many of the stories. Rebellion against all symbols of authority. Dysfunctional families. Bright children suffocated by ill-equipped teachers. Mental illness or instability. Obsession and addiction. I have endeavoured to track what happened to each character in this work over time: the individual's hacking adventures, the police raid and the ensuing court case. Some of those court cases have taken years to reach completion. Hackers use `handles'—on-line nicknames—that serve two purposes. They shield the hacker's identity and, importantly, they often make a statement about how the hacker perceives himself in the underground. Hawk, Crawler, Toucan Jones, Comhack, Dataking, Spy, Ripmax, Fractal Insanity, Blade. These are all real handles used in Australia. In the computer underground, a hacker's handle is his name. For this reason, and because most hackers in this work have now put together new lives for themselves, I have chosen to use only their handles. Where a hacker has had more than one handle, I have used the one he prefers. Each chapter in this book is headed with a quote from a Midnight Oil song which expresses an important aspect of the chapter. The Oilz are uniquely Australian. Their loud voice of protest against the establishment—particularly the military-industrial establishment—echoes a key theme in the underground, where music in general plays a vital role. The idea for using these Oilz extracts came while researching Chapter 1, which reveals the tale of the WANK worm crisis in NASA. Next to the RTM worm, WANK is the most famous worm in the history of computer networks. And it is the first major worm bearing a political message. With WANK, life imitated art, since the term computer `worm' came from John Brunner's sci-fi novel, The Shockwave Rider, about a politically motivated worm. The WANK worm is also believed to be the first worm written by an Australian, or Australians. This chapter shows the perspective of the computer system administrators—the people on the other side from the hackers. Lastly, it illustrates the sophistication which one or more Australian members of the worldwide computer underground brought to their computer crimes. The following chapters set the scene for the dramas which unfold and show the transition of the underground from its early days, its loss of innocence, its closing ranks in ever smaller circles until it reached the inevitable outcome: the lone hacker. In the beginning, the computer underground was a place, like the corner pub, open and friendly. Now, it has become an ephemeral expanse, where hackers occasionally bump into one another but where the original sense of open community has been lost. The computer underground has changed over time, largely in response to the introduction of new computer crime laws across the globe and to numerous police crackdowns. This work attempts to document not only an important piece of Australian history, but also to show fundamental shifts in the underground —to show, in essence, how the underground has moved further underground. Suelette Dreyfus March 1997 About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Suelette Dreyfus (2003). Underground: Hacking, Madness and Obsession on the Electronic Frontier. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4686/pg4686.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

My great aunt used to paint underwater

The Promise and Potential of LLM in Crypto

Building an Invoice PDF system with , , and can be a complex task, but I'm here to guide you through the process. React.js Redux Node.js Here's a step-by-step tutorial on how you can create such a system: You can find the Github Repository for this project here: https://github.com/idurar/idurar-erp-crm Content Overview Step 1: Setting up the environment Step 2: Setting up the server (Node.js/Express) Step 3: Building the React.js application Step 4: Integrating React.js with Node.js Step 5: Styling and enhancing the user interface Step 6: Testing and debugging Step 7: Deployment Step 1: Setting up the environment Make sure you have Node.js installed on your machine. Create a new directory for your project and navigate into it using the terminal. Initialize a new Node.js project by running . npm init Install required dependencies by running . npm install react redux react-redux Step 2: Setting up the server (Node.js/Express) Create a new file called and set up a basic Express server. server.js Import the necessary dependencies ( , ) in the server file. express html-pdf Define routes for generating and downloading invoices. const express = require('express');

const helmet = require('helmet');
const path = require('path');
const cors = require('cors');

const cookieParser = require('cookie-parser');
require('dotenv').config({ path: '.variables.env' });

const helpers = require('./helpers');

const erpApiRouter = require('./routes/erpRoutes/erpApi');
const erpAuthRouter = require('./routes/erpRoutes/erpAuth');
const erpDownloadRouter = require('./routes/erpRoutes/erpDownloadRouter');

const errorHandlers = require('./handlers/errorHandlers');

const { isValidAdminToken } = require('./controllers/erpControllers/authJwtController');

// create our Express app
const app = express();
// serves up static files from the public folder. Anything in public/ will just be served up as the file it is

// Takes the raw requests and turns them into usable properties on req.body

app.use(helmet());
app.use(cookieParser());
app.use(express.json());
app.use(express.urlencoded({ extended: true }));
app.use(express.static(path.join(__dirname, 'public')));



// pass variables to our templates + all requests

app.use((req, res, next) => {
  res.locals.h = helpers;
  res.locals.admin = req.admin || null;
  res.locals.currentPath = req.path;
  const clientIP = req.socket.remoteAddress;
  let isLocalhost = false;
  if (clientIP === '127.0.0.1' || clientIP === '::1') {
    // Connection is from localhost
    isLocalhost = true;
  }
  res.locals.isLocalhost = isLocalhost;
  next();
});

// app.use(function (req, res, next) {
//   if (req.url.slice(-1) === "/" && req.path.length > 1) {
//     // req.path = req.path.slice(0, -1);
//     req.url = req.url.slice(0, -1);
//   }
//   next();
// });

// Here our API Routes

var corsOptionsDelegate = function (req, callback) {
  var corsOptions;
  const clientIP = req.socket.remoteAddress;
  let isLocalhost = false;
  if (clientIP === '127.0.0.1' || clientIP === '::1') {
    // Connection is from localhost
    isLocalhost = true;
  }
  if (isLocalhost) {
    corsOptions = {
      origin: '*',
      credentials: true,
    };
  } else {
    corsOptions = {
      origin: true,
      credentials: true,
    };
  }
  callback(null, corsOptions); // callback expects two parameters: error and options
};

app.use(
  '/api',
  cors({
    origin: true,
    credentials: true,
  }),
  erpAuthRouter
);

app.use(
  '/api',
  cors({
    origin: true,
    credentials: true,
  }),
  isValidAdminToken,
  erpApiRouter
);

app.use('/download', cors(), erpDownloadRouter);

// If that above routes didnt work, we 404 them and forward to error handler
app.use(errorHandlers.notFound);

// Otherwise this was a really bad error we didn't expect! Shoot eh
if (app.get('env') === 'development') {
  /* Development Error Handler - Prints stack trace */
  app.use(errorHandlers.developmentErrors);
}

// production error handler
app.use(errorHandlers.productionErrors);

// done! we export it so we can start the site in start.js
module.exports = app; Step 3: Building the React.js application In the root directory of your project, create a new folder called . client Navigate into the folder and run to generate a new React.js application. client npx create-react-app . Replace the contents of the generated folder with your own code. src Create components for the invoice form, invoice list, and invoice detail view. Use Redux to manage the state of your application, including the invoice data. import React from 'react';
import dayjs from 'dayjs';
import { Tag } from 'antd';
import InvoiceModule from '@/modules/InvoiceModule';
import { useMoney } from '@/settings';

export default function Invoice() {
  const { moneyRowFormatter } = useMoney();

  const entity = 'invoice';
  const searchConfig = {
    displayLabels: ['name', 'surname'],
    searchFields: 'name,surname,birthday',
  };
  const entityDisplayLabels = ['number', 'client.company'];
  const dataTableColumns = [
    {
      title: '#N',
      dataIndex: 'number',
    },
    {
      title: 'Client',
      dataIndex: ['client', 'company'],
    },
    {
      title: 'Date',
      dataIndex: 'date',
      render: (date) => {
        return dayjs(date).format('DD/MM/YYYY');
      },
    },
    {
      title: 'Due date',
      dataIndex: 'expiredDate',
      render: (date) => {
        return dayjs(date).format('DD/MM/YYYY');
      },
    },
    {
      title: 'Total',
      dataIndex: 'total',
      render: (amount) => moneyRowFormatter({ amount }),
    },
    {
      title: 'Balance',
      dataIndex: 'credit',
      render: (amount) => moneyRowFormatter({ amount }),
    },
    {
      title: 'status',
      dataIndex: 'status',
      render: (status) => {
        let color = status === 'draft' ? 'cyan' : status === 'sent' ? 'magenta' : 'gold';

        return <Tag color={color}>{status && status.toUpperCase()}</Tag>;
      },
    },
    {
      title: 'Payment',
      dataIndex: 'paymentStatus',
      render: (paymentStatus) => {
        let color =
          paymentStatus === 'unpaid'
            ? 'volcano'
            : paymentStatus === 'paid'
            ? 'green'
            : paymentStatus === 'overdue'
            ? 'red'
            : 'purple';

        return <Tag color={color}>{paymentStatus && paymentStatus.toUpperCase()}</Tag>;
      },
    },
  ];

  const PANEL_TITLE = 'invoice';
  const dataTableTitle = 'invoices Lists';
  const ADD_NEW_ENTITY = 'Add new invoice';
  const DATATABLE_TITLE = 'invoices List';
  const ENTITY_NAME = 'invoice';
  const CREATE_ENTITY = 'Save invoice';
  const UPDATE_ENTITY = 'Update invoice';

  const config = {
    entity,
    PANEL_TITLE,
    dataTableTitle,
    ENTITY_NAME,
    CREATE_ENTITY,
    ADD_NEW_ENTITY,
    UPDATE_ENTITY,
    DATATABLE_TITLE,
    dataTableColumns,
    searchConfig,
    entityDisplayLabels,
  };
  return <InvoiceModule config={config} />;
} Step 4: Integrating React.js with Node.js In your React.js application, make HTTP requests to the server endpoints created in Step 2 using libraries like or . axios fetch When submitting the invoice form, send the form data to the server and handle the creation of the PDF invoice on the server side. Retrieve the generated PDF from the server and display a link or button to let users download it. import React, { useState, useEffect } from 'react';
import { Form, Divider } from 'antd';

import { Button, PageHeader, Row, Statistic, Tag } from 'antd';

import { useSelector, useDispatch } from 'react-redux';
import { erp } from '@/redux/erp/actions';
import { selectCreatedItem } from '@/redux/erp/selectors';

import { useErpContext } from '@/context/erp';
import uniqueId from '@/utils/uinqueId';

import Loading from '@/components/Loading';
import { CloseCircleOutlined, PlusOutlined } from '@ant-design/icons';

function SaveForm({ form, config }) {
  let { CREATE_ENTITY } = config;
  const handelClick = () => {
    form.submit();
  };

  return (
    <Button onClick={handelClick} type="primary" icon={<PlusOutlined />}>
      {CREATE_ENTITY}
    </Button>
  );
}

export default function CreateItem({ config, CreateForm }) {
  let { entity, CREATE_ENTITY } = config;
  const { erpContextAction } = useErpContext();
  const { createPanel } = erpContextAction;
  const dispatch = useDispatch();
  const { isLoading, isSuccess } = useSelector(selectCreatedItem);
  const [form] = Form.useForm();
  const [subTotal, setSubTotal] = useState(0);
  const handelValuesChange = (changedValues, values) => {
    const items = values['items'];
    let subTotal = 0;

    if (items) {
      items.map((item) => {
        if (item) {
          if (item.quantity && item.price) {
            let total = item['quantity'] * item['price'];
            //sub total
            subTotal += total;
          }
        }
      });
      setSubTotal(subTotal);
    }
  };

  useEffect(() => {
    if (isSuccess) {
      form.resetFields();
      dispatch(erp.resetAction({ actionType: 'create' }));
      setSubTotal(0);
      createPanel.close();
      dispatch(erp.list({ entity }));
    }
  }, [isSuccess]);

  const onSubmit = (fieldsValue) => {
    if (fieldsValue) {
      // if (fieldsValue.expiredDate) {
      //   const newDate = fieldsValue["expiredDate"].format("DD/MM/YYYY");
      //   fieldsValue = {
      //     ...fieldsValue,
      //     expiredDate: newDate,
      //   };
      // }
      // if (fieldsValue.date) {
      //   const newDate = fieldsValue["date"].format("DD/MM/YYYY");
      //   fieldsValue = {
      //     ...fieldsValue,
      //     date: newDate,
      //   };
      // }
      if (fieldsValue.items) {
        let newList = [...fieldsValue.items];
        newList.map((item) => {
          item.total = item.quantity * item.price;
        });
        fieldsValue = {
          ...fieldsValue,
          items: newList,
        };
      }
    }
    dispatch(erp.create({ entity, jsonData: fieldsValue }));
  };

  return (
    <>
      <PageHeader
        onBack={() => createPanel.close()}
        title={CREATE_ENTITY}
        ghost={false}
        tags={<Tag color="volcano">Draft</Tag>}
        // subTitle="This is create page"
        extra={[
          <Button
            key={`${uniqueId()}`}
            onClick={() => createPanel.close()}
            icon={<CloseCircleOutlined />}
          >
            Cancel
          </Button>,
          <SaveForm form={form} config={config} key={`${uniqueId()}`} />,
        ]}
        style={{
          padding: '20px 0px',
        }}
      ></PageHeader>
      <Divider dashed />
      <Loading isLoading={isLoading}>
        <Form form={form} layout="vertical" onFinish={onSubmit} onValuesChange={handelValuesChange}>
          <CreateForm subTotal={subTotal} />
        </Form>
      </Loading>
    </>
  );
} Step 5: Styling and enhancing the user interface Utilize CSS and any CSS framework of your choice (e.g., Ant Design) to style your application. Enhance the user interface with features like pagination, sorting, searching, and filtering invoices. import React, { useState, useEffect, useRef } from 'react';
import dayjs from 'dayjs';
import { Form, Input, InputNumber, Button, Select, Divider, Row, Col } from 'antd';

import { PlusOutlined } from '@ant-design/icons';

import { DatePicker } from '@/components/CustomAntd';

import AutoCompleteAsync from '@/components/AutoCompleteAsync';
import ItemRow from '@/components/ErpPanel/ItemRow';

import MoneyInputFormItem from '@/components/MoneyInputFormItem';

export default function InvoiceForm({ subTotal = 0, current = null }) {
  const [total, setTotal] = useState(0);
  const [taxRate, setTaxRate] = useState(0);
  const [taxTotal, setTaxTotal] = useState(0);
  const [currentYear, setCurrentYear] = useState(() => new Date().getFullYear());
  const handelTaxChange = (value) => {
    setTaxRate(value);
  };

  useEffect(() => {
    if (current) {
      const { taxRate = 0, year } = current;
      setTaxRate(taxRate);
      setCurrentYear(year);
    }
  }, [current]);
  useEffect(() => {
    const currentTotal = subTotal * taxRate + subTotal;
    setTaxTotal((subTotal * taxRate).toFixed(2));
    setTotal(currentTotal.toFixed(2));
  }, [subTotal, taxRate]);

  const addField = useRef(false);

  useEffect(() => {
    addField.current.click();
  }, []);

  return (
    <>
      <Row gutter={[12, 0]}>
        <Col className="gutter-row" span={9}>
          <Form.Item
            name="client"
            label="Client"
            rules={[
              {
                required: true,
                message: 'Please input your client!',
              },
            ]}
          >
            <AutoCompleteAsync
              entity={'client'}
              displayLabels={['company']}
              searchFields={'company,managerSurname,managerName'}
              // onUpdateValue={autoCompleteUpdate}
            />
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={5}>
          <Form.Item
            label="Number"
            name="number"
            initialValue={1}
            rules={[
              {
                required: true,
                message: 'Please input invoice number!',
              },
            ]}
          >
            <InputNumber style={{ width: '100%' }} />
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={5}>
          <Form.Item
            label="year"
            name="year"
            initialValue={currentYear}
            rules={[
              {
                required: true,
                message: 'Please input invoice year!',
              },
            ]}
          >
            <InputNumber style={{ width: '100%' }} />
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={5}>
          <Form.Item
            label="status"
            name="status"
            rules={[
              {
                required: false,
                message: 'Please input invoice status!',
              },
            ]}
            initialValue={'draft'}
          >
            <Select
              options={[
                { value: 'draft', label: 'Draft' },
                { value: 'pending', label: 'Pending' },
                { value: 'sent', label: 'Sent' },
              ]}
            ></Select>
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={9}>
          <Form.Item label="Note" name="note">
            <Input />
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={8}>
          <Form.Item
            name="date"
            label="Date"
            rules={[
              {
                required: true,
                type: 'object',
              },
            ]}
            initialValue={dayjs()}
          >
            <DatePicker style={{ width: '100%' }} format={'DD/MM/YYYY'} />
          </Form.Item>
        </Col>
        <Col className="gutter-row" span={7}>
          <Form.Item
            name="expiredDate"
            label="Expire Date"
            rules={[
              {
                required: true,
                type: 'object',
              },
            ]}
            initialValue={dayjs().add(30, 'days')}
          >
            <DatePicker style={{ width: '100%' }} format={'DD/MM/YYYY'} />
          </Form.Item>
        </Col>
      </Row>
      <Divider dashed />
      <Row gutter={[12, 12]} style={{ position: 'relative' }}>
        <Col className="gutter-row" span={5}>
          <p>Item</p>
        </Col>
        <Col className="gutter-row" span={7}>
          <p>Description</p>
        </Col>
        <Col className="gutter-row" span={3}>
          <p>Quantity</p>
        </Col>
        <Col className="gutter-row" span={4}>
          <p>Price</p>
        </Col>
        <Col className="gutter-row" span={5}>
          <p>Total</p>
        </Col>
      </Row>
      <Form.List name="items">
        {(fields, { add, remove }) => (
          <>
            {fields.map((field) => (
              <ItemRow key={field.key} remove={remove} field={field} current={current}></ItemRow>
            ))}
            <Form.Item>
              <Button
                type="dashed"
                onClick={() => add()}
                block
                icon={<PlusOutlined />}
                ref={addField}
              >
                Add field
              </Button>
            </Form.Item>
          </>
        )}
      </Form.List>
      <Divider dashed />
      <div style={{ position: 'relative', width: ' 100%', float: 'right' }}>
        <Row gutter={[12, -5]}>
          <Col className="gutter-row" span={5}>
            <Form.Item>
              <Button type="primary" htmlType="submit" icon={<PlusOutlined />} block>
                Save Invoice
              </Button>
            </Form.Item>
          </Col>
          <Col className="gutter-row" span={4} offset={10}>
            <p
              style={{
                paddingLeft: '12px',
                paddingTop: '5px',
              }}
            >
              Sub Total :
            </p>
          </Col>
          <Col className="gutter-row" span={5}>
            <MoneyInputFormItem readOnly value={subTotal} />
          </Col>
        </Row>
        <Row gutter={[12, -5]}>
          <Col className="gutter-row" span={4} offset={15}>
            <Form.Item
              name="taxRate"
              rules={[
                {
                  required: false,
                  message: 'Please input your taxRate!',
                },
              ]}
              initialValue="0"
            >
              <Select
                value={taxRate}
                onChange={handelTaxChange}
                bordered={false}
                options={[
                  { value: 0, label: 'Tax 0 %' },
                  { value: 0.19, label: 'Tax 19 %' },
                ]}
              ></Select>
            </Form.Item>
          </Col>
          <Col className="gutter-row" span={5}>
            <MoneyInputFormItem readOnly value={taxTotal} />
          </Col>
        </Row>
        <Row gutter={[12, -5]}>
          <Col className="gutter-row" span={4} offset={15}>
            <p
              style={{
                paddingLeft: '12px',
                paddingTop: '5px',
              }}
            >
              Total :
            </p>
          </Col>
          <Col className="gutter-row" span={5}>
            <MoneyInputFormItem readOnly value={total} />
          </Col>
        </Row>
      </div>
    </>
  );
} Step 6: Testing and debugging Use tools like React DevTools and Redux DevTools to debug your application. Write unit tests using libraries like Jest or Enzyme to ensure the stability of your codebase. Step 7: Deployment Deploy your Node.js server and React.js application to a hosting platform like Heroku, AWS, or Netlify. Configure the necessary environment variables and ensure that everything is working as expected in a production environment. You can find the Github Repository here: https://github.com/idurar/idurar-erp-crm This tutorial provides a high-level overview of building an Invoice PDF system using React.js, Redux, and Node.js. Good luck with your project! Also published . here

How to Build an Invoice PDF System Using React.js, Redux and Node.js

Binary Classification: Understanding Activation and Loss Functions with a PyTorch Example

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