In other news, BLACKPINK's Jisoo and her bandmates will have the "Born Pink" finale concerts at the Gocheok Sky Dome in Seoul on Sept. 16 and 17. This will close the group's sold-out world tour, which kicked off in October 2022 at the KSPO Dome in Seoul.

WATCH: BLACKPINK's Jisoo Plays 'Akinator,' Tries To See If It Can Guess Herself

Read more about Is AppleCare Worth It for Macs: MacBook Warranty 101 (2023)

iPhone Life - Best Apps, Top Tips, Great Gear

Akinator has been floating around on the internet in his ephemeral genie form since 2007, and continues to wow people even today. Think of any character, object, or place, and just answer Akinator’s volley of questions. And, hey presto, the genie has ...

The Tipping Point where Algorithm Turns into AI

Both Akinator and 20Q are online guessing games that attempt to guess what you are thinking 20Q is a multiple choice game while Akinator is a series of questions. Guess the Mystery is an interactive puzzle game that takes you on a journey of discovery and ...

10 Amazing Alternatives to Akinator for Guessing Who You Are Thinking Of

Akinator is a mind-reading game that will blow your mind! You can play it on your phone, enable the Alexa skill or play on your computer.

This Mind-Reading Game Will Blow Your Mind

Fans have shared videos of Amber Heard being named in a guessing game played on Amazon Echo devices, after the app asks if the person in question is considered "abusive" and "a liar."Heard, 36, on Monday returned to court for the final week of her defamation trial with ex-husband Johnny Depp,

Amazon Alexa Guessing Game Calls Amber Heard 'Abusive,' 'Liar'

Aiator

aiator

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

Measuring engineering productivity is a complicated process — it’s hard to get a full picture of how developers spend their time. A common way to measure productivity is to analyze system metrics like DORA or SPACE. These can be extremely useful metrics to understand the productivity of the team compared to the industry standards. Diving into each of those metrics can also provide insights into what’s slowing down the team. But sometimes, there are also “hidden pockets” of time that developers spend throughout their day that may not be perceived as impacting productivity. However, when we start adding those things up, the numbers can be alarming. For instance, consider the amount of time a developer spends debugging a flaky test trying to figure out if it failed because of their change or not. Or the time spent by a developer who is trying to resolve a mainline build failure. To provide that perspective, we built a calculator that makes a run at assessing engineering efficiency. By no means does this provide a full analysis of your engineering team’s efficiency. What it does provide is a glimpse of the “hidden pockets” of time wasted that typically do not show up in more common productivity metrics. The calculator focuses on how much time you and your team lose due to build and test failures in developer workflows. If you compare this to DORA metrics, the lead time for changes is significantly impacted by build and test instability. That impact can be assessed using this calculator. How it works We ask you to input data based on your GitHub activities and how you use GitHub branches. To explain the actual calculations below, let’s assign variables to each of them: M – PRs merged per day X – mainline failures in a week T – average CI time F – flakiness factor % Based on these inputs, we estimate how much time your engineering team wastes weekly on managing build failures and dealing with flaky tests. Let’s go over the results one by one. Hours wasted to fix This calculates how many hours are wasted to identify, triage, fix, and have the build pass again when a mainline build failure is detected. Typically, in a large team, someone will notice and report the broken mainline build. We assume that a mainline build failure involves an average of 1-3 developers to debug and fix. If we consider an average of one hour for the time it takes for the issue to be reported and a fix to be pushed, we are spending to track, investigate, and resolve the issue. (2*T + 1) hours That means if there are X failures a week, we are spending (( in developer time to fight mainline build failures every day. 2 devs * X/5 * (2*T + 1)) hours Hours wasted in resolving merge conflicts Rollbacks and merge conflicts can cause further issues. Assuming that there are roughly 2% PRs that have merge conflicts during the window of broken build time , and PRs coming in every hour, we will spend wasted in resolving these conflicts. ((2*T + 1) * X/5) M/8 ((2*T + 1) * X/5) * 0.02 * M/8 Weekly CI failures due to broken builds If the team is not using a golden branch to base their feature branches on, they would likely create feature branches on top of a failed mainline branch. Since the number of PRs created during any time would be similar to the average number of feature branches based out of mainline, this would cause number of CI failures happening every day. (2*T + 1 hour) * X/5 * M/8 Time to resolve CI With approximately fifteen minutes of context switching the handle every build failure, that’s hours of developer time wasted every day with CI failures. (2*T + 1 hour) * X/5 * M/8 * 0.25 Time spent rerunning flaky tests. Similarly, with the flaky tests, the context switching time required to investigate whether the test was flaky or real, and rerunning the tests itself takes an average of fifteen minutes per run. Depending on the flakiness factor, the developers would waste hours every day. (0.25 * M * F / 100) Improving efficiency Although most of these impact the DORA metrics associated with we are still just scratching the surface in terms of measuring the inefficiencies in engineering team workflows. The impact of build and test failures also leads to delayed releases impacting the other DORA metrics like and persistence of flaky tests in the system, which can lead to a higher chance of Failure rate. . Lead time for changes, deployment frequency, time to restore service, Learn more about DORA metrics Or learn more about their disadvantages. We built to solve some of these hidden challenges for large engineering teams. Today, using Aviator MergeQueue, many engineering organizations can scale their merge workflow without breaking builds. Combining that with a flaky test suppression system like , teams can save hundreds of engineering hours every week. Aviator TestDeck Also published . here

New From Aviator: The Calculator Assessing Engineering Efficiency

এভিয়েটর থেকে নতুন: দ্য ক্যালকুলেটর অ্যাসেসিং ইঞ্জিনিয়ারিং দক্ষতা

Neu von Aviator: Der Rechner zur Bewertung der technischen Effizienz

Lo nuevo de Aviator: la calculadora que evalúa la eficiencia de la ingeniería

Nouveau chez Aviator : le calculateur évaluant l'efficacité de l'ingénierie

एविएटर से नया: इंजीनियरिंग दक्षता का आकलन करने वाला कैलकुलेटर

Aviator の新機能: エンジニアリング効率を評価する計算機

Aviator의 새로운 기능: 엔지니어링 효율성을 평가하는 계산기

Novidade do Aviator: a calculadora que avalia a eficiência da engenharia

Новинка от Aviator: калькулятор для оценки инженерной эффективности

Aviator'dan Yenilik: Mühendislik Verimliliğini Değerlendiren Hesap Makinesi

Tính năng mới từ Aviator: Máy tính đánh giá hiệu quả kỹ thuật

Aviator 的新功能：评估工程效率的计算器

13 Cybercrime Facts That Will Give You Chills

13 факта за киберпрестъпността, които ще ви побият тръпки

13টি সাইবার অপরাধের তথ্য যা আপনাকে শান্ত করবে

13 hechos sobre delitos cibernéticos que te pondrán los pelos de punta

Hotzikara emango dizuten ziberkrimenaren 13 gertakari

ゾッとするサイバー犯罪の13の事実

13 ការពិតអំពីឧក្រិដ្ឋកម្មតាមអ៊ីនធឺណិតដែលនឹងផ្តល់ឱ្យអ្នកនូវភាពត្រជាក់

13 Fakta Jenayah Siber Yang Akan Menggigil Anda

13 faktów na temat cyberprzestępczości, które wywołają u Ciebie dreszcze

13 fatos sobre crimes cibernéticos que vão te dar arrepios

Sizi Tüyler Ürpertecek 13 Siber Suç Gerçeği

13 Sự thật về tội phạm mạng sẽ khiến bạn rùng mình

Iinyani ezili-13 zoBugebenga be-Cyberculos eziya kukunika ukuzola

Deployments: The Irrational Fear of Them

ማሰማራቶች፡ የእነርሱ ምክንያታዊ ያልሆነ ፍርሃት

Yerləşdirmələr: Onlardan İrrasional Qorxu

Despliegues: el miedo irracional a ellos

派遣：それに対する不合理な恐怖

Орналастыру: олардан иррационалды қорқыныш

परिनियोजनहरू: तिनीहरूको तर्कहीन डर

Implantações: O medo irracional delas

Kohereza: Ubwoba Bidafite ishingiro

Hawlgelinta: Cabsida aan caqligal ahayn ee iyaga laga qabo

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CulturaX: A High-Quality, Multilingual Dataset for LLMs - Conclusion and References

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Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 8. RELATED WORK Multiple projects have tried to improve upon or create new formats for storing unstructured datasets including TFRecord extending Protobuf [5], Petastorm [18] extending Parquet [79], Feather [7] extending arrow [13], Squirrel using MessagePack [75], Beton in FFCV [39]. Designing a universal dataset format that solves all use cases is very challenging. Our approach was mostly inspired by CloudVolume [11], a 4-D chunked NumPy storage for storing large volumetric biomedical data. There are other similar chunked NumPy array storage formats such as Zarr [52], TensorStore [23], TileDB [57]. Deep Lake introduced a typing system, dynamically shaped tensors, integration with fast deep learning streaming data loaders, queries on tensors and in-browser visualization support. An alternative approach to store large-scale datasets is to use HPC distributed file system such as Lustre [69], extending with PyTorch cache [45] or performant storage layer such as AIStore [26]. Deep Lake datasets can be stored on top of POSIX or REST API-compatible distributed storage systems by leveraging their benefits. Other comparable approaches evolve in vector databases [80, 8, 80] for storing embeddings, feature stores [73, 16] or data version control systems such as DVC [46], or LakeFS [21]. In contrast, Deep Lake version control is in-built into the format without an external dependency, including Git. Tensor Query Language, similar to TQP [41] and Velox [59] approaches, runs n-dimensional numeric operations on tensor storage by truly leveraging the full capabilities of deep learning frameworks. Overall, Deep Lake takes parallels from data lakes such as Hudi, Iceberg, Delta [27, 15, 10] and complements systems such as Databarick’s Lakehouse [28] for Deep Learning applications. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 8. RELATED WORK Multiple projects have tried to improve upon or create new formats for storing unstructured datasets including TFRecord extending Protobuf [5], Petastorm [18] extending Parquet [79], Feather [7] extending arrow [13], Squirrel using MessagePack [75], Beton in FFCV [39]. Designing a universal dataset format that solves all use cases is very challenging. Our approach was mostly inspired by CloudVolume [11], a 4-D chunked NumPy storage for storing large volumetric biomedical data. There are other similar chunked NumPy array storage formats such as Zarr [52], TensorStore [23], TileDB [57]. Deep Lake introduced a typing system, dynamically shaped tensors, integration with fast deep learning streaming data loaders, queries on tensors and in-browser visualization support. An alternative approach to store large-scale datasets is to use HPC distributed file system such as Lustre [69], extending with PyTorch cache [45] or performant storage layer such as AIStore [26]. Deep Lake datasets can be stored on top of POSIX or REST API-compatible distributed storage systems by leveraging their benefits. Other comparable approaches evolve in vector databases [80, 8, 80] for storing embeddings, feature stores [73, 16] or data version control systems such as DVC [46], or LakeFS [21]. In contrast, Deep Lake version control is in-built into the format without an external dependency, including Git. Tensor Query Language, similar to TQP [41] and Velox [59] approaches, runs n-dimensional numeric operations on tensor storage by truly leveraging the full capabilities of deep learning frameworks. Overall, Deep Lake takes parallels from data lakes such as Hudi, Iceberg, Delta [27, 15, 10] and complements systems such as Databarick’s Lakehouse [28] for Deep Learning applications. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Related Work

গভীর লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: সম্পর্কিত কাজ

Deep Lake, ein Lakehouse für Deep Learning: Verwandte Arbeiten

Deep Lake, una casa de lago para el aprendizaje profundo: trabajo relacionado

Deep Lake, un Lakehouse pour le Deep Learning : travaux connexes

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: संबंधित कार्य

ディープラーニングのためのレイクハウス「Deep Lake」: 関連研究

딥러닝을 위한 호수집 Deep Lake: 관련 연구

Deep Lake, um Lakehouse para Deep Learning: Trabalhos Relacionados

Deep Lake, домик у озера для глубокого обучения: соответствующая работа

Deep Lake, Derin Öğrenme için Bir Göl Evi: İlgili Çalışma

Deep Lake, Lakehouse cho Deep Learning: Công việc liên quan

Deep Lake，一个用于深度学习的Lakehouse：相关工作

Transductive Learning for Textual Few-Shot: Limitations, Acknowledgements, & References

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 6 Conclusion The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield . Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures. 7 Acknowledgments We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. We would also like to thank Omdena organization for giving us this valuable opportunity References Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, and Ophir Frieder. Hate speech detection: Challenges and solutions. PloS one, 14(8):e0221152, 2019. Mirela P Bogdani, Federico Faloppa, and Xheni Karaj. Beyond definitions. a call for action against hate speech in albania. a comprehensive study november 2021. 2021. JT Nockleby. hate speech in encyclopedia of the american constitution. electronic journal of academic and special librarianship. 2000. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Ara-women-hate: An annotated corpus dedicated to hate speech detection against women in the arabic community. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 68–75, 2022. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017. Paula Fortuna and Sérgio Nunes. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1–30, 2018. Anna Schmidt and Michael Wiegand. A survey on hate speech detection using natural language processing. In Proceedings of the fifth international workshop on natural language processing for social media, pages 1–10, 2017. Delphine Battistelli, Cyril Bruneau, and Valentina Dragos. Building a formal model for hate detection in french corpora. Procedia Computer Science, 176:2358–2365, 2020. Fatimah Alkomah and Xiaogang Ma. A literature review of textual hate speech detection methods and datasets. Information, 13(6):273, 2022. Flor Miriam Plaza-del Arco, M Dolores Molina-González, L Alfonso Urena-López, and M Teresa Martín-Valdivia. Comparing pre-trained language models for spanish hate speech detection. Expert Systems with Applications, 166: 114120, 2021. Nuha Albadi, Maram Kurdi, and Shivakant Mishra. Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 69–76. IEEE, 2018. Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, and Halima Alshabani. L-hsab: A levantine twitter dataset for hate speech and abusive language. In Proceedings of the third workshop on abusive language online, pages 111–118, 2019. Fréha Mezzoudj, Mourad Loukam, and Fatma Zohra Belkredim. Arabic algerian oranee dialectal language modelling oriented topic. International Journal of Informatics and Applied Mathematics, 2(2):1–14, 2019. Oussama Boucherit and Kheireddine Abainia. Offensive language detection in under-resourced algerian dialectal arabic language. arXiv preprint arXiv:2203.10024, 2022. Djamila Menifi, Wiam Moussa, and Ahmed Cherif Mazari. Transfer Learning and Deep Learning for Multilingual Algerian Dialect Hate Speech Detection. PhD thesis, 2022. Adel Abdelli, Fayçal Guerrouf, Okba Tibermacine, and Belkacem Abdelli. Sentiment analysis of arabic algerian dialect using a supervised method. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), pages 1–6. IEEE, 2019. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Sexism detection: The first corpus in algerian dialect with a code-switching in arabic/french and english. arXiv preprint arXiv:2104.01443, 2021. Ona De Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. Hate speech dataset from a white supremacy forum. arXiv preprint arXiv:1809.04444, 2018. Areej Al-Hassan and Hmood Al-Dossari. Detection of hate speech in social networks: a survey on multilingual corpus. In 6th international conference on computer science and information technology, volume 10, pages 10–5121, 2019. Naaima Boudad, Rdouan Faizi, Rachid Oulad Haj Thami, and Raddouane Chiheb. Sentiment analysis in arabic: A review of the literature. Ain Shams Engineering Journal, 9(4):2479–2490, 2018. Nizar Y Habash. Introduction to Arabic natural language processing. Springer Nature, 2022. Abdul-Baquee M Sharaf and Eric Atwell. Qurana: Corpus of the quran annotated with pronominal anaphora. In Lrec, pages 130–137, 2012. Kheireddine Abainia, Kenza Kara, and Tassadit Hamouni. A new corpus and lexicon for offensive tamazight language detection. In Proceedings of the 7th International Workshop on Social Media World Sensors, pages 1–6, 2022. Ahmed Cherif Mazari and Hamza Kheddar. Deep learning-based analysis of algerian dialect dataset targeted hate speech, offensive language and cyberbullying. International Journal of Computing and Digital Systems, 2023. Imane Guellil, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. Detecting hate speech against politicians in arabic community on social media. International Journal of Web Information Systems, 16(3):295–313, 2020. Djamila Mohdeb, Meriem Laifa, Fayssal Zerargui, and Omar Benzaoui. Evaluating transfer learning approach for detecting arabic anti-refugee/migrant speech on social media. Aslib Journal of Information Management, 74(6): 1070–1088, 2022. Reem ALBayari and Sherief Abdallah. Instagram-based benchmark dataset for cyberbullying detection in arabic text. Data, 7(7):83, 2022. Monirah A Al-Ajlan and Mourad Ykhlef. Optimized twitter cyberbullying detection based on deep learning. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–5. IEEE, 2018. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. Arabic cyberbullying detection: Enhancing performance by using ensemble machine learning. In 2019 international conference on internet of things (ithings) and ieee green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (smartdata), pages 323–327. IEEE, 2019. Hamdy Mubarak, Kareem Darwish, and Walid Magdy. Abusive language detection on arabic social media. In Proceedings of the first workshop on abusive language online, pages 52–56, 2017. Azalden Alakrot, Liam Murray, and Nikola S Nikolov. Dataset construction for the detection of anti-social behaviour in online communication in arabic. Procedia Computer Science, 142:174–181, 2018. Hatem Haddad, Hala Mulki, and Asma Oueslati. T-hsab: A tunisian hate speech and abusive dataset. In International conference on Arabic language processing, pages 251–263. Springer, 2019. Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, and Ahmed Abdelali. Arabic offensive language on twitter: Analysis and experiments. arXiv preprint arXiv:2004.02192, 2020. Hala Mulki and Bilal Ghanem. Let-mi: an arabic levantine twitter dataset for misogynistic language. arXiv preprint arXiv:2103.10195, 2021. Rehab Duwairi, Amena Hayajneh, and Muhannad Quwaider. A deep learning framework for automatic detection of hate speech embedded in arabic tweets. Arabian Journal for Science and Engineering, 46:4001–4014, 2021. Wassen Aldjanabi, Abdelghani Dahou, Mohammed AA Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, and Robertas Damaševicius. Arabic offensive and hate speech detection using a cross-corpora multi-task learning ˇ model. In Informatics, volume 8, page 69. MDPI, 2021. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6): 275–284, 2017. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations, pages 11–16, 2016. Amine Abdaoui, Mohamed Berrimi, Mourad Oussalah, and Abdelouahab Moussaoui. Dziribert: a pre-trained language model for the algerian dialect. arXiv preprint arXiv:2109.12346, 2021. Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. Peft: State-of-the-art parameterefficient fine-tuning methods. https://github.com/huggingface/peft, 2022. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. E Moatez Billah Nagoudi, A Elmadany, and M Abdul-Mageed. Arat5: Text-to-text transformers for arabic language understanding and generation. arXiv preprint arXiv:2109.12068, 2021.

Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion,  Acknowledgments

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