投稿時間:2021-11-24 09:29:04 RSSフィード2021-11-24 09:00 分まとめ(32件)

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ROBOT	ロボスタ	遠隔操作ロボットでレーサー佐藤琢磨選手とレースカー見学ツアー開催！avatarinの新企画、気になるお値段は	https://robotstart.info/2021/11/24/takuma-avatar-tour.html	avatarin	2021-11-23 23:30:50
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「売り上げがゼロになった」　埼玉県の町工場が、畑違いの医療分野でヒット商品を生み出せたワケ	https://www.itmedia.co.jp/business/articles/2111/24/news025.html	ITmediaビジネスオンライン「売り上げがゼロになった」埼玉県の町工場が、畑違いの医療分野でヒット商品を生み出せたワケ埼玉県の小さな町工場から医療分野における「世界のスタンダード」が誕生するかもしれない。	2021-11-24 08:30:00
AWS	AWS Networking and Content Delivery	Introducing IPv6-only subnets and EC2 instances	https://aws.amazon.com/blogs/networking-and-content-delivery/introducing-ipv6-only-subnets-and-ec2-instances/	Introducing IPv only subnets and EC instancesIn June we announced our continued commitment and innovation towards the enablement of IPv on AWS Today we take a monumental step forward with the ability to create an IPv only architecture on AWS With this launch Amazon Virtual Private Cloud VPC now allows you to create IPv only subnets in your dual stack VPCs and launch …	2021-11-23 23:07:42
AWS	AWS Japan Blog	EC2 Image builder とイメージキャッシュ戦略による Windows コンテナ起動時間の高速化	https://aws.amazon.com/jp/blogs/news/speeding-up-windows-container-launch-times-with-ec2-image-builder-and-image-cache-strategy/	これは部分的には真実ですが、「大きなイメージ」を解明し、ディスク上での高コストな操作前処理、原文ではextract・ionという言葉を本記事では「前処理」としていますを回避し、Windowsコンテナの起動を高速化するためのキャッシュ戦略をどのように実施するかが重要です。	2021-11-23 23:49:56
AWS	lambdaタグが付けられた新着投稿 - Qiita	ヘキサゴナルアーキテクチャを使ってドメインモデルをAWS Lambdaファンクションで実装してみた	https://qiita.com/afukui/items/c705aca2cb46e182c5e4	そこで自分なりに考えた結果、ヘキサゴナルアーキテクチャの概念を利用してドメインモデルを外部から隔離し、また制御の反転IoCを利用することで、クラス間を疎結合にしてユニットテストを容易にするのが良いのではないかと仮説を立て、実際にサンプルアプリケーションを実装してみることにしました。	2021-11-24 08:22:00
js	JavaScriptタグが付けられた新着投稿 - Qiita	Javascript カレンダー作成	https://qiita.com/jeronimo34/items/3a14b1acbd88c28ca690	Javascriptカレンダー作成はじめに本稿では下記動画にあるソースコードを解説します。	2021-11-24 08:26:58
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Realmに保存したデータをListviewに表示したい	https://teratail.com/questions/370729?rss=all	Realmに保存したデータをListviewに表示したい前提・実現したいことRealmを使ったシンプルなメモアプリを作ろうと考えております。	2021-11-24 08:56:33
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	xcodeではクロームで言う所の「開発ツール」で見られる物はどこにあるでしょうか。	https://teratail.com/questions/370728?rss=all	xcodeではクロームで言う所の「開発ツール」で見られる物はどこにあるでしょうか。	2021-11-24 08:30:12
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【docker+wordpress,+mysql 】localhostにアクセスすると「Error establishing a database connection」のエラー	https://teratail.com/questions/370727?rss=all	【dockerwordpressmysql】localhostにアクセスすると「Errorestablishingadatabaseconnection」のエラーdocker勉強中の者です。	2021-11-24 08:02:02
AWS	AWSタグが付けられた新着投稿 - Qiita	list_objects_v2 の 1000 オブジェクトの罠	https://qiita.com/intrajp/items/0ad12b4ed387b308b2d4	listobjectsvのオブジェクトの罠Sバケットから、数テラのデータをダウンロードしていて、ハマりました。	2021-11-24 08:57:50
AWS	AWSタグが付けられた新着投稿 - Qiita	ヘキサゴナルアーキテクチャを使ってドメインモデルをAWS Lambdaファンクションで実装してみた	https://qiita.com/afukui/items/c705aca2cb46e182c5e4	そこで自分なりに考えた結果、ヘキサゴナルアーキテクチャの概念を利用してドメインモデルを外部から隔離し、また制御の反転IoCを利用することで、クラス間を疎結合にしてユニットテストを容易にするのが良いのではないかと仮説を立て、実際にサンプルアプリケーションを実装してみることにしました。	2021-11-24 08:22:00
Docker	dockerタグが付けられた新着投稿 - Qiita	【随時更新】メモ〜Dockerとコンテナ【自分用】	https://qiita.com/keishi04hrikzira/items/ee05d428159c53cdec12	DockerHubとはコンテナを作成するのに必要なコマンドやライブラリのセットOSイメージが保管されているリポジトリのこと。	2021-11-24 08:29:04
技術ブログ	Developers.IO	礼節を大切にする Slack bot を作ってみた	https://dev.classmethod.jp/articles/civility-by-slack-bot/	slack	2021-11-23 23:46:04
技術ブログ	Developers.IO	Security Hub を PagerDuty に統合してみた	https://dev.classmethod.jp/articles/security-hub-pagerduty/	pagerduty	2021-11-23 23:00:39
海外TECH	MakeUseOf	What Is Rembrandt Lighting? How to Use It for Portrait Photography	https://www.makeuseof.com/what-is-rembrandt-lighting-photography-how-to/	rembrandt	2021-11-23 23:01:02
海外TECH	DEV Community	Controlled vs Uncontrolled Components	https://dev.to/katelynjewel/controlled-vs-uncontrolled-components-44e0	Controlled vs Uncontrolled Components What is the difference between controlled and uncontrolled components in react To put simply controlled components have their data being handled with a react component whereas an uncontrolled component s data is being handled with the DOM itself Let s dig into this answer a bit more though starting at the basics and including a few visual examples What are controlled and uncontrolled components Form elements are rendered with HTML within React components where data is being accessed and manipulate When we are discussing uncontrolled and controlled components these are terms that are specifically discussing the way in which the form created is handling and accessing that said data The data handling can be done a few different ways but is commonly seen using typed elements like lt input gt and lt textarea gt or selected elements such as lt checkbox gt lt select gt lt radiobutton gt Controlled ComponentsAs we stated earlier controlled components handle their updated data using use state This would look like setting the value for the input form element to this state value or to a use state When setting these element s value to use state we have wrapped up the control for both the rendering of the form as well as future input of the form into the same React component Another way to think of is is that the React state will always act as the source of truth As users interact with the form handleChange will run on every keystroke or interaction which then updates the React state The React documentation acknowledges that writing out controlled components can feel banal since you do need to create an event handler for each way the data can change while also containing that in the React component use state Uncontrolled ComponentsA helpful tidbit to remember about uncontrolled components is that the part of the reason it s uncontrolled is because the value is set by the user and not by the program With this in mind the input lt input type file gt will always be uncontrolled without the value being set This will render the for element s where the form element s data is handled by the DOM In this way it functions similarly to traditional HTML code Due to uncontrolled components keeping their source of truth in the DOM it is sometimes easier to integrate React and non React code when using uncontrolled components TL DRThe primary difference between a controlled component vs an uncontrolled component is related to how they handle their value Uncontrolled components pass down the value through props In contrast controlled components use state to handle the value internally For most use cases controlled components are the best option in code	2021-11-23 23:49:22
海外TECH	DEV Community	Build an Image Classification API Using Django Rest Framework.	https://dev.to/paulwababu/build-an-image-classification-api-using-django-rest-framework-4ned	Build an Image Classification API Using Django Rest Framework Overview Machine Learning ML and data science applications are in high demand When ML algorithms offer information before it is known the benefits for business are significant Integrating machine learning algorithms for inference into production systems is a technological barrier hence the need for deploying ML models as API S IntroductionIn this short article we implement AI based models to detect COVID in Chest X rays and CT Scans using four Deep Learning Algorithms VGG ResNet InceptionV and Xception Note we shall focus mainly on implementing the api and not the model creation To test my live endpoint send a POST request to the following URL with an x ray image appended to the body of the request You will get the following sample output if request was successful for the x ray images for CT scans status success data asset id ebafbaffbffe public id vacpxfywfohgfprwhrso version version id ccdbceedefecbd signature ecbecbbcbfabadedbf width height format png resource type image created at T Z tags bytes type upload etag dcbdbcd placeholder false url secure url original filename covid pneumonia api key url xception chest pred COVID inception chest pred COVID vgg chest pred COVID resnet chest pred COVID ML Model BuildingThe dataset for the project was gathered from two open source Github repositories Chest X ray images images were obtained from CT Scan images images were obtained from Four algorithms VGG ResNet InceptionV and Xception were trained separately on Chest X rays and CT Scans giving us a total of deep learning models of the images were used for training the models and the remaining for testing the accuracy of the models The code for training the models is available on my github repository The model for the project can be found on the following google drive Turning the Model into an RESTFUL APIFollowing Python best practices we will create a virtual environment for our project and install the required packages First create the project directory mkdir djangoapp cd djangoappNow create a virtual environment and install the required packages For macOS and Unix systems python m venv myenv source myenv bin activate myenv pip install django requests djangorestframework tensorflow cloudinary opencv pythonFor Windows python m venv myenv myenv Scripts activate myenv pip install django requests djangorestframework tensorflow cloudinary opencv python Setting Up Your Django ApplicationFirst navigate to the directory djangoapp we created and establish a Django project myenv django admin startproject mainappThis will auto generate some files for your project skeleton mainapp manage py mainapp init py settings py urls py asgi py wsgi pyNow navigate to the directory you just created make sure you are in the same directory as manage py and create your app directory myenv python manage py startapp monitorThis will create the following monitor init py admin py apps py migrations init py models py tests py views pyOn the mainapp settings py file look for the following line and add the app we just created above INSTALLED APPS django contrib admin django contrib auth django contrib contenttypes django contrib sessions django contrib messages django contrib staticfiles rest framework new line monitor new line Ensure you are in the monitor directory then create a new directory called templates and a new file called urls py Your directory structure of monitor application should look like thismonitor init py admin py apps py migrations templates init py models py tests py urls py views pyEnsure your mainapp urls py file add our monitor app URL to include the URLs we shall create next on the monitor app from django contrib import adminfrom django urls import path includeurlpatterns path admin admin site urls path include monitor urls monitor app url Now on the monitor urls py file add our website there from django urls import pathfrom views import urlpatterns path api upload xray UploadView as view name prediction path api upload ct CTUploadView as view name ct prediction Let s create another directory to store our machine learning model I ll also add the dataset to the project for those who want to achieve the whole dataset It is not compulsory to create a data folder venv mkdir ml venv mkdir ml models venv mkdir ml dataWe also need to tell Django where our machine learning model is located and also add our cloudinary configuration there Add these lines to settings py file import osimport cloudinarycloudinary config cloud name prometheusapi api key GETYOURAPIKEY api secret GETYOURAPIKEY MODELS os path join BASE DIR ml models Load Keras Model through apps pyLoad your machine learning models in apps py so that when the application starts the trained model is loaded only once Otherwise the trained model is loaded each time an endpoint is called and then the response time will be slower Let s update apps pyimport osfrom django apps import AppConfigfrom django conf import settingsfrom tensorflow keras models import load modelfrom tensorflow import kerasclass ResNetModelConfig AppConfig name resnetAPI MODEL FILE os path join settings MODELS resnet chest h model keras models load model MODEL FILE class ResNetCTModelConfig AppConfig name resnetCTAPI MODEL FILE os path join settings MODELS resnet ct h model keras models load model MODEL FILE class VGGModelConfig AppConfig name vggAPI MODEL FILE os path join settings MODELS vgg chest h model keras models load model MODEL FILE class VGGCTModelConfig AppConfig name vggCTAPI MODEL FILE os path join settings MODELS vgg ct h model keras models load model MODEL FILE class InceptionModelConfig AppConfig name inceptionv chestAPI MODEL FILE os path join settings MODELS inceptionv chest h model keras models load model MODEL FILE class InceptionCTModelConfig AppConfig name inceptionv chestCTAPI MODEL FILE os path join settings MODELS inception ct h model keras models load model MODEL FILE class ExceptionModelConfig AppConfig name xception chestAPI MODEL FILE os path join settings MODELS xception chest h model keras models load model MODEL FILE class ExceptionCTModelConfig AppConfig name xception chestCTAPI MODEL FILE os path join settings MODELS xception ct h model keras models load model MODEL FILE Edit views pyThe last step is to update views py The views will be mainly responsible for two tasks Process incoming POST requests Make a prediction with the incoming data and give the result as a Response import urllibfrom django shortcuts import renderimport numpy as npfrom apps import from rest framework views import APIViewfrom rest framework response import Responsefrom rest framework parsers import MultiPartParser JSONParserimport cloudinary uploaderimport matplotlib pyplot as pltimport cv Create your views here class UploadView APIView parser classes MultiPartParser JSONParser staticmethod def post request file request data get picture upload data cloudinary uploader upload file print upload data img upload data url load models resnet chest ResNetModelConfig model vgg chest VGGModelConfig model inception chest InceptionModelConfig model xception chest ExceptionModelConfig model req urllib request urlopen img arr np asarray bytearray req read dtype np uint image cv imdecode arr Load it as it is image cv imread upload chest jpg read file image cv cvtColor image cv COLOR BGRRGB arrange format as per keras image cv resize image image np array image image np expand dims image axis resnet pred resnet chest predict image probability resnet pred print Resnet Predictions if probability gt resnet chest pred str f probability COVID else resnet chest pred str f probability NonCOVID print resnet chest pred vgg pred vgg chest predict image probability vgg pred print VGG Predictions if probability gt vgg chest pred str f probability COVID else vgg chest pred str f probability NonCOVID print vgg chest pred inception pred inception chest predict image probability inception pred print Inception Predictions if probability gt inception chest pred str f probability COVID else inception chest pred str f probability NonCOVID print inception chest pred xception pred xception chest predict image probability xception pred print Xception Predictions if probability gt xception chest pred str f probability COVID else xception chest pred str f probability NonCOVID print xception chest pred return Response status success data upload data url img xception chest pred xception chest pred inception chest pred inception chest pred vgg chest pred vgg chest pred resnet chest pred resnet chest pred status class CTUploadView APIView parser classes MultiPartParser JSONParser staticmethod def post request file request data get picture upload data cloudinary uploader upload file print upload data img upload data url load models resnet chest ResNetCTModelConfig model vgg chest VGGCTModelConfig model inception chest InceptionCTModelConfig model xception chest ExceptionCTModelConfig model req urllib request urlopen img arr np asarray bytearray req read dtype np uint image cv imdecode arr Load it as it is image cv imread upload chest jpg read file image cv cvtColor image cv COLOR BGRRGB arrange format as per keras image cv resize image image np array image image np expand dims image axis resnet pred resnet chest predict image probability resnet pred print Resnet Predictions if probability gt resnet chest pred str f probability COVID else resnet chest pred str f probability NonCOVID print resnet chest pred vgg pred vgg chest predict image probability vgg pred print VGG Predictions if probability gt vgg chest pred str f probability COVID else vgg chest pred str f probability NonCOVID print vgg chest pred inception pred inception chest predict image probability inception pred print Inception Predictions if probability gt inception chest pred str f probability COVID else inception chest pred str f probability NonCOVID print inception chest pred xception pred xception chest predict image probability xception pred print Xception Predictions if probability gt xception chest pred str f probability COVID else xception chest pred str f probability NonCOVID print xception chest pred return Response status success data upload data url img xceptionCT chest pred xception chest pred inceptionCT chest pred inception chest pred vggCT chest pred vgg chest pred resnetCT chest pred resnet chest pred status Testing our APICreate the necessary migrations then run the server myenv python manage py makemigrations myenv python manage py migrate myenv python manage py runserverFire up Postman and make a POST request with an image appended to the body Thanks for staying tuned	2021-11-23 23:43:26
Apple	AppleInsider - Frontpage News	Apple Watch credited with saving woman's life after detecting serious heart condition	https://appleinsider.com/articles/21/11/23/apple-watch-credited-with-saving-womans-life-after-detecting-serious-heart-condition?utm_medium=rss	Apple Watch credited with saving woman x s life after detecting serious heart conditionA Missouri woman is crediting Apple Watch with saving her life after the wearable s low heart rate detection feature was triggered twice in as many weeks revealing an underlying cardiac condition Oakville resident Patti Sohn has been working to close her rings ーMove Exercise and Stand ーsince receiving Apple Watch as a gift from her son for Mother s Day reports local NBC affiliate KSDK A few months ago the retired nurse practitioner was surprised to receive a low heart rate alert from the device a feature she did not know existed My watch buzzed my wrist and read something like your heart rate has been below for the last minutes and I thought What that can t be right Sohn told the outlet Read more	2021-11-23 23:23:45
海外TECH	Engadget	T-Mobile will pay $19.5 million settlement for 12-hour 911 outage	https://www.engadget.com/t-mobile-911-outage-fcc-settlement-230802122.html?src=rss	T Mobile will pay million settlement for hour outageT Mobile is once again on the hook for a outage The carrier has agreed to pay million to settle an FCC investigation of a hour service outage in June that led to call failures While the FCC didn t know exactly how many emergency calls were affected due to some overlapping issues it recorded tens of thousands of issues Over calls suffered a quot complete quot failure the FCC said while a similar amount didn t include location data Roughly another didn t include callback info The outage began when a leased fiber link in the T Mobile network went awry and a single location routing flaw magnified the crisis T Mobile also had problems remotely accessing the fiber link This isn t the first time T Mobile has dealt with a outage It settled to the tune of million over failures in We ve asked T Mobile for comment The FCC said the carrier responded to outage related questions in a quot timely quot fashion however so this wasn t a hotly disputed issue Not that the company was likely to fight a settlement that won t significantly impact its finances And like it or not this won t do much to help people who couldn t get full help in a moment of crisis	2021-11-23 23:08:02
Cisco	Cisco Blog	Announcing Cisco Networking Academy’s Be the Bridge Award winners!	https://blogs.cisco.com/csr/announcing-cisco-networking-academys-be-the-bridge-award-winners	Announcing Cisco Networking Academy s Be the Bridge Award winners Cisco Networking Academy Be the Bridge Awards celebrates the incredible things our education partners have made possible and their achievements	2021-11-23 23:20:41
海外科学	NYT > Science	Watch NASA Launch DART, a Mission to Crash Into an Asteroid	https://www.nytimes.com/2021/11/23/science/nasa-dart-launch-asteroid.html	space	2021-11-23 23:53:37
金融	日本銀行：RSS	国内銀行の資産・負債等（銀行勘定）（9月末）	http://www.boj.or.jp/statistics/asli_fi/ald2109.pdf	銀行	2021-11-24 08:50:00
金融	日本銀行：RSS	貸出約定平均金利（9月）	http://www.boj.or.jp/statistics/dl/loan/yaku/yaku2109.pdf	貸出	2021-11-24 08:50:00
ニュース	BBC News - Home	'He has all the talent in the world' - how Man Utd's Sancho made most of his 'big night'	https://www.bbc.co.uk/sport/football/59396684?at_medium=RSS&at_campaign=KARANGA	x He has all the talent in the world x how Man Utd x s Sancho made most of his x big night x After a tough start to life at Manchester United Jadon Sancho has his first goal and is tipped to become a vital part of the side	2021-11-23 23:12:31
ニュース	BBC News - Home	Fleck 'conscious in hospital' after collapsing during Sheff Utd win	https://www.bbc.co.uk/sport/football/59395982?at_medium=RSS&at_campaign=KARANGA	Fleck x conscious in hospital x after collapsing during Sheff Utd winSheffield United boss Slavisa Jokanovic confirms midfielder John Fleck is conscious in hospital after collapsing during their win at Reading	2021-11-23 23:15:09
ビジネス	ダイヤモンド・オンライン - 新着記事	FRBバランスシートの行方、リバースレポにも影響 - WSJ発	https://diamond.jp/articles/-/288539	行方	2021-11-24 08:07:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	【参加者募集】ART PUB NIGNT #2 開催　－アート×ビジネスの第一線で活躍するゲストが、未来を妄想する	https://dentsu-ho.com/articles/7980	artpubnignt	2021-11-24 09:00:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	【参加者募集】「答えは必ず見つかる 100案思考」12月2日開催	https://dentsu-ho.com/articles/7978	wasedaneo	2021-11-24 09:00:00
LifeHuck	ライフハッカー［日本版］	マウスをストレスなく快適に使う方法7つ	https://www.lifehacker.jp/2021/11/246196how-configure-mouse-comfort.html	普段	2021-11-24 08:30:00
ビジネス	プレジデントオンライン	中小企業の6割に｢ゾンビ企業化｣の恐れ…これから日本のサービス業を見舞う"借金地獄" - ｢コロナ対人4業種｣は影響が長期化	https://president.jp/articles/-/52052	中小企業	2021-11-24 09:00:00
ビジネス	プレジデントオンライン	｢生命保険は今すぐ見直しなさい｣お金のプロが教える本当に必要な保険の選び方 - 手厚い社会保険で十分カバーできる	https://president.jp/articles/-/51946	医療保険	2021-11-24 09:00:00
ニュース	THE BRIDGE	おとり物件無しのオフィス不動産ポータル「R・SQUARE」が82億円調達など——韓国スタートアップシーン週間振り返り（11月15日~11月19日）	https://thebridge.jp/2021/11/startup-recipe-nov-15-nov-19	おとり物件無しのオフィス不動産ポータル「R・SQUARE」が億円調達などー韓国スタートアップシーン週間振り返り月日月日本稿は、韓国のスタートアップメディア「StartupRecipe스타트업레시피」の発表する週刊ニュースを元に、韓国のスタートアップシーンの動向や資金調達のトレンドを振り返ります。	2021-11-23 23:00:41