投稿時間:2022-07-11 04:17:32 RSSフィード2022-07-11 04:00 分まとめ(26件)

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海外TECH	MakeUseOf	11 Amazon Fire Tablet Tips and Tricks Everyone Should Use	https://www.makeuseof.com/tag/amazon-fire-tablet-tips/	tablet	2022-07-10 18:49:14
海外TECH	DEV Community	Visual Studio Code For Absolute Beginner	https://dev.to/codewithmmak/visual-studio-code-for-absolute-beginner-21mm	Visual Studio Code For Absolute BeginnerIn this course I ll walk you step by step about Visual Studio Code and some of its extensions The complete playlist of Visual Studio Code For Absolute Beginner gives you basic setup knowledge to advance usage of the tool youtube com We will be covering the following topics Introduction to Visual Studio Code and Installation Format On Save in Visual Studio Code Git Integration in Visual Studio Code GitLens Extension for Visual Studio Code Code Icons Extension for Visual Studio Code You can follow my content here as well WebsiteGitHubLinkedInTwitterFacebookInstagram I love coffees And if this post helped you out and you would like to support my work you can do that by clicking on the button below and buying me a cup of coffee Buy me a coffeeYou can also support me by liking and sharing this content Thanks for reading	2022-07-10 18:34:22
海外TECH	DEV Community	How to create simple email form in your NodeJS website	https://dev.to/pavel_polivka/how-to-create-simple-email-form-in-your-nodejs-website-3end	How to create simple email form in your NodeJS websiteMost webpages have some contact form usually not very fancy and the backend behind it can be SalesForce any other variation of lead generation software but usually it just sends email Sending emails is not trivial you need to have an SMTP server and that alone will add to your costs Fortunately we live in an age where everything is a service and API There are tons of services that can send emails to you My goal was to do it as cheaply as possible preferably for free I was expecting at most about emails per day After some research I decided to use Send In Blue Their free plan has emails per day more than enough for me you do not need a credit card to sign up no DNS shenanigans and their API is very straightforward API endpoint in NextJSNextJS offers a friendly and easy way to create API endpoints from where you can call any external APIs without actually exposing your secret keys to the browser I got the API key from Send in Blue with the help of this awesome help page and add the key into my env local file NEXT PUBLIC SEND IN BLUE KEY xxxxapikeyxxxxI also put the key into the Vercel Environment variables manager with the same name I created a new file api email js from where I will send the email via Send In Blue API const sendInBlueKey process env NEXT PUBLIC SEND IN BLUE KEYexport default async function handler req res const body req body const resp await fetch method POST headers Accept application json Content Type application json api key sendInBlueKey body JSON stringify sender name body name email body email to email myemail ppolivka com name Pavel Polivka subject Contact Form Eamil From body name htmlContent lt html gt lt head gt lt head gt lt body gt From body name lt br gt Message body message lt br gt lt body gt lt html gt res status end From the form I am getting the name and email of the sender and a message I am sending that the sender is the email from the form to my email address so that I can reply to my email client Contact FormThen I created a contact form component It s a simple form with three fields and the submit button All the sending magic is happening in the handleSubmit function import TextField from mui material TextField import Button from mui material Button import useState from react export default function Contact const send setSend useState false async function handleSubmit event event preventDefault const data email event target email value name event target name value message event target message value const JSONdata JSON stringify data const endpoint api email const options method POST headers Content Type application json body JSONdata const response await fetch endpoint options setSend true let content if send content lt gt lt p gt Thank you for contacting me lt p gt lt gt else content lt gt lt form onSubmit handleSubmit gt lt TextField id email label Email variant outlined required gt lt TextField id name label Name variant outlined required gt lt TextField id message label Message variant outlined required multiline rows gt lt Button variant contained type submit gt Send lt Button gt lt form gt lt gt return lt gt content lt gt I am using React Material UI but you can use any UI or just normal inputs This is all you need Simple and elegant It was done in an hour Good luck coding	2022-07-10 18:30:22
海外TECH	DEV Community	Building Serverless with SAM	https://dev.to/aws-builders/building-serverless-with-sam-396o	Building Serverless with SAMAWS Serverless Application Model SAM is a framework for developing and deploying Serverless applications on AWS Key takeaways from the blogUnderstanding SAM and SAM templateDifferent features of SAM CLI Understanding SAM AWS Serverless Application Model SAM is an open source framework which consists of SAM templatesSAM CLISAM helps developers by providing different bootstrapped starting points when creating a new SAM application SAM CLI supports various programming runtimes as a choice for developers to get started with The recent update being the NodeJS x SAM templates This is an abstracted version of AWS CloudFormation templates which promotes Infrastructure as Code IaC practice for your serverless applications and uses AWS CloudFormation under the hood for all AWS resources provisioning SAM Transforms the template into AWS CloudFormation template so that it could be later deployed by creating a new CloudFormation stack or updating the existing CloudFormation stack SAM template also makes it easier to defined different AWS Serverless services with pre defined resource types AWS Serverless FunctionAWS Serverless LayerVersionAWS Serverless ApiAWS Serverless HttpApiAWS Serverless SimpleTable an equivalent of AWS DynamoDB TableAWS Serverless ApplicationAWS Serverless StateMachine SAM template uses various pre managed AWS IAM policies for authorization to various resources One of the managed policies for DynamoDB CRUD operations The above sample SAM Template describes a AWS Lambda function and AWS API Gateway REST API with a deployment stage SAM CLI The CLI is the tooling which facilitates developers to get started with Serverless application development and helps developers to build deploy test monitor and also package the artifacts for production ready deployments using CI CD pipelines The SAM CLI has different CLI commands such as sam init The init command is used to create a new SAM application the CLI command helps developers with an interactive questionnaire based walkthrough to setup a SAM app with steps involving choosing from a template choice of language and type of Lambda function deployment You can find the documentation here sam build The build CLI command is used to build your SAM workstation with the template and validating it against the right resources This builds the artifacts in aws sam build directory which can be used to deploy into an AWS Account You can find the documentation here sam deploy The deploy CLI command is used to deploy the built artifacts to an AWS Account This under the hood uses AWS CloudFormation to either deploy the resources with a CreateStack operation or UpdateStack operation The sam deploy has different flags sam deploy guided or sam deploy changeset for either walking through the deploy with interactive questionnaire or deploying the changeset alone You can find the documentation here sam local generate event Whenever working with AWS Lambda functions locally you would need to generate different events to test the AWS Lambda function when the Lambda fn is getting invoked from a specific event source such as Amazon S operations AWS API Gateway AWS SNS AWS SQS and many more You can find the documentation here sam local invoke The local invoke command invokes the specific Lambda function with an event and exits This helps in testing Lambda function logic against the simulated event You can find the documentation here sam package The package CLI command creates a zip file of the code and dependences and uploads to the designated AWS S build artifacts bucket This is internally performed by sam deploy You can find the documentation heresam pipeline bootstrap SAM helps in a CLI interactive way to setup CI CD pipelines with SAM Pipelines This setups different staging environments and the also CLI generates the needed CI CD workflow template in the project directory based on the CI CD provider You can find the documentation heresam sync The SAM Accelerate feature is used for immediate code deploys to development AWS Account and also real time monitoring You can find the documentation here sam logs The logs command is used to fetch the CloudWatch logs generated by AWS Lambda functions You can find the documentation here sam delete If you want to delete your SAM application you can delete it with this command which uses a CloudFormation DeleteStack operation under the hood Wrapping upSAM helps building Serverless apps easily and for developers who are familiar with AWS CloudFormation it s even more easier with the SAM template even otherwise SAM init facilities you to get started with one of the templates and build on top of it and also expand it with different SAM CLI commands You can refer to my session on Building Serverless Apps with IaC at AWS UG Colombo April meet up where I demonstrate how we can use IaC with AWS SAM Building Serverless Apps with IaC Jones Zachariah Noel N YouTube The session focuses on Serverless apps that are built with AWS SAM for IaC In this session we will understand what is IaC and how IaC helps build Serverles youtube com You can also refer to my AWS Summit India session about Building Serverless Apps with SAM Accelerate and SAM Pipelines slide deck	2022-07-10 18:23:50
海外TECH	DEV Community	I was featured on the Scrimba podcast 🎉🎉🎉	https://dev.to/beetlehope/i-was-featured-on-the-scrimba-podcast-3406	I was featured on the Scrimba podcast Check out the episode Nadia Zhuk Anybody Can Code and Your Background Doesn t Define You	2022-07-10 18:19:07
海外TECH	DEV Community	Defect Detection in Manufacturing With Unsupervised Learning	https://dev.to/viktoriaakhremenko/defect-detection-in-manufacturing-with-unsupervised-learning-4aod	Defect Detection in Manufacturing With Unsupervised LearningAs the American Society of Quality reports many organizations have quality related costs of up to of their total production revenue A large part of this cost comes from inefficiency of manual inspection which is the most common way to provide quality control in manufacturing The application of artificial intelligence for quality control automation presents a more productive and accurate way of doing visual inspection in production lines However traditional machine learning methods present several limitations to how we can train and utilize models for defect detection So in this article we ll discuss the advantages of unsupervised learning for defect detection and elaborate on the approaches MobiDev uses in our practical experience What is AI defect detection and where it is used AI defect detection is based on computer vision that provides capabilities for automating the whole AI quality inspection process using machine learning algorithms Defect detection models are trained to visually examine items that pass the production line and recognize anomalies on their surface and spot inconsistencies in dimensions shape or color The output depends on what the model is trained to do but in the case of defect detection the flow typically looks like this How AI defect detection works in a nutshellApplied to quality control processes defect detection AI is efficient at inspecting large production lines and spotting faults even on the smallest parts of a final product This relates to a large spectrum of manufactured products that may contain surface defects of different natures Defect detection in different branches of manufacturingImage sourceIntel describes a case of implementing computer vision to automate tire quality inspection As it is stated in the report the accuracy of quality control rose up from to percent while approximately labor costs were cut by production line But such systems are not bound to stationary hardware in the factory For instance drones with cameras can be used for inspecting pavement defects or other outdoor surfaces which significantly decreases the time required for covering large city areas The pharmacy industry also benefits from inspecting manufacturing lines for different products For instance Orobix applies defect detection to drug manufacturing with a specific type of camera that can be used by an untrained human operator The same principle is applied to inspecting pharmaceutical glass defects like cracks and air blobs caught in the glass Such examples can be found in the food industry textile electronics heavy manufacturing and other branches But there are some specific problems in how we can approach defect detection algorithms with traditional machine learning As manufacturers inspect thousands of products daily it becomes difficult to collect sample data for training and also to label it This is where unsupervised learning comes into play What is unsupervised learning The majority of machine learning applications rely on supervised machine learning methods Supervised learning entails that we provide ground truth information to the model by manually labeling collected data In terms of a production line collecting and labeling data can be impossible since there is no way we can gather all the variations of cracks or dents on a product to ensure accurate detection by the model Here we face four problems difficulties with obtaining a large amount of anomalous datapossibility of very small difference between a normal and an anomalous samplesignificant difference between two anomalous samplesinability to know in advance the type and number of anomaliesSupervised vs unsupervised defect detectionUnsupervised machine learning algorithms allow you to find patterns in a data set without pre labeled results and discover the underlying structure of the data where it is impossible to train the algorithm the way you normally would Unlike supervised learning the flow of training becomes less labor intensive as we expect the model to discover patterns in data with higher threshold for variations Anomaly detection reveals previously unseen rare objects or events without any prior knowledge about them The only available information is that the percentage of anomalies in the dataset is small Concerning defect detection this helps to resolve the problem with data labeling and collecting huge amounts of samples So let s see how unsupervised learning methods can be used for defect detection model training How does unsupervised learning apply to defect detection Defect detection relates to the problem of anomaly detection in machine learning While we don t rely on labeling there are other approaches in unsupervised learning that aim at grouping data and providing hints to the model Clustering is grouping unlabeled examples according to similarity Clustering is widely used for recommender engines market or customer segmentation social network analysis or search result clustering Association mining aims to observe frequently occurring patterns correlations or associations from datasets Latent variable models are designed to model the distribution probability with latent variables It is mainly used for data preprocessing reduction of features in a dataset or decomposing dataset into multiple components based on features Uncovered patterns with unsupervised learning can be used for implementing traditional machine learning models For instance we might apply clustering on the available data and then use those clusters as a training dataset for supervised learning models Concrete Crack Detection with Unsupervised MLHaving a vast experience in machine learning we ve conducted an experiment using Concrete Crack dataset The goal was to create a model capable of recognizing images with defects and normal ones using unsupervised learning Additionally the study checks how the number of defect images affects certain algorithms used in this project Concrete cracks dataset examplesIn the use case we selected we assume that image labels cannot be known in advance during training Only a test dataset is labeled in order to verify the quality of model prediction as training occurs by unsupervised approach So here we used five different approaches to get classification results from an unsupervised learning model CLUSTERINGSince we don t have any labeled ground truth data grouping unlabeled examples is done with clustering In our case there are two clusters of images we need to single out from the dataset This was performed with a pre trained VGG convolutional neural network for feature extraction and K means for clustering What clustering did here is grouped images with and without cracks based on their visual similarities In a nutshell clustering looks something like this K means clusteringClustering methods are easy to implement and usually are considered as a baseline approach for further deep learning modeling BIRCH CLUSTERINGUpon this approach images were clustered based on visual similarity with a pre trained ResNet neural network for feature extraction and Birch for clustering This algorithm constructs a tree data structure with the cluster centroids being read off the leaf It is a memory efficient online learning algorithm The results of clustering were visualized with Principal Component Analysis Birch clustering resultsAs we can see birch clustering shows a pretty good distribution of classes even on points where the sample is quite far from its centroid CUSTOM CONVOLUTIONAL AUTOENCODERCustom convolutional autoencoder contains two blocks encoder and decoder It helps to obtain features in the encoder part and reconstruct images from them in the decoder part Encoder decoder visualizationAs we don t have labels for network training we need to choose another approach to obtain classes for example an adaptively selectable threshold The purpose of an adaptively selectable threshold is to divide two distributions crack free images and images with cracks as precisely as possible Autoencoder distribution results DCGANDCGAN generates images from z space with the help of adversarial loss BCALoss Finally we have three losses generator loss discriminator loss and MSE loss to compare generated images and ground truth We can build our classification on the same approach as in custom autoencoder through comparison between losses on images with cracks and without with the help of an adaptive selectable threshold For threshold it will be appropriate to use discriminator loss or MSE loss depending on their distributions GANOMALYGANomaly uses the conditional GAN approach to train a Generator to produce images of the normal data During inference when an anomalous image is passed it is not able to capture the data correctly It leads to poor reconstruction for defected images and good reconstruction for normal and gives the anomaly score GANomaly architectureImage source How to approach unsupervised anomaly detectionPerhaps the most beneficial side of unsupervised learning techniques is that we can avoid gathering tremendous amounts of sample data and labeling it for training Applying unsupervised learning techniques to derive data patterns we re not limited towards which model can be used for actual classification and defect detection However unsupervised learning models are suited better for segmenting existing data into classes since it s quite difficult to check the model prediction accuracy especially without a labeled dataset	2022-07-10 18:15:22
Apple	AppleInsider - Frontpage News	First Touch Bar MacBook Pro models will become vintage on July 31	https://appleinsider.com/articles/22/07/10/first-touch-bar-macbook-pro-models-will-become-vintage-on-july-31?utm_medium=rss	First Touch Bar MacBook Pro models will become vintage on July Apple will be placing its first models of MacBook Pro equipped with the Touch Bar to its list of vintage products at the end of July Apple regularly designates its hardware as vintage or obsolete over time with a small list of much loved products consigned to the vintage collection each year For July the additions include the first Touch Bar equipped products The list of products that will be vintage includes the editions of the inch and inch MacBook Pro along with the inch MacBook and the MacBook Air an internal memo obtained by MacRumors states The list also includes the inch iMac and Retina K inch iMac Read more	2022-07-10 18:21:26
海外TECH	Engadget	Asteroid NASA’s OSIRIS-REx mission landed on had a surface like a ‘pit of plastic balls’	https://www.engadget.com/nasa-osiris-rex-bennu-finding-plastic-ball-pit-184718806.html?src=rss	Asteroid NASA s OSIRIS REx mission landed on had a surface like a pit of plastic balls Nearly two years ago NASA made history when its OSIRIS REx spacecraft briefly “tagged Bennu to collect a regolith sample from the surface of the asteroid While the mission won t return to Earth until late next year NASA shared new information about the celestial body In an update published this week via Mashable the agency revealed OSIRIS REx would have sunk into Bennu had the spacecraft not immediately fired its thrusters after touching the asteroid s surface quot It turns out that the particles making up Bennu s exterior are so loosely packed and lightly bound to each other that if a person were to step onto Bennu they would feel very little resistance as if stepping into a pit of plastic balls that are popular play areas for kids quot NASA said OSIRISREx data gathered during sample collection show that asteroid Bennu s exterior is made up of loosely packed amp lightly bound rock So standing on its surface would feel like being in a plastic ball pit Ready set jump pic twitter com PxxItejNーNASA NASA July That s not what scientists thought they would find on Bennu Observing the asteroid from Earth the expectation was that its surface would be covered in smooth sandy beach like material Bennu s reaction to OSIRIS REx s touchdown also had scientists puzzled After briefly interacting with the asteroid the spacecraft left a foot meter wide crater In lab testing the pickup procedure “barely made a divot nbsp After analyzing data from the spacecraft they found it encountered the same amount of resistance a person on Earth would feel while squeezing the plunger on a French press coffee carafe “By the time we fired our thrusters to leave the surface we were still plunging into the asteroid said Ron Ballouz a scientist with the OSIRIS REx team According to NASA its findings on Bennu could help scientists better interpret remote observations of other asteroids In turn that could help the agency design future asteroid missions “I think we re still at the beginning of understanding what these bodies are because they behave in very counterintuitive ways said OSIRIS REx team member Patrick Michel	2022-07-10 18:47:18
ニュース	BBC News - Home	Novak Djokovic beats Nick Kyrgios to win Wimbledon title	https://www.bbc.co.uk/sport/tennis/62109725?at_medium=RSS&at_campaign=KARANGA	straight	2022-07-10 18:02:09
ニュース	BBC News - Home	Austrian Grand Prix: Formula 1 to investigate claims of abuse	https://www.bbc.co.uk/sport/formula1/62110154?at_medium=RSS&at_campaign=KARANGA	abuse	2022-07-10 18:32:03
ニュース	BBC News - Home	England v India: Hosts win third T20 despite Suryakumar Yadav's hundred	https://www.bbc.co.uk/sport/cricket/62114765?at_medium=RSS&at_campaign=KARANGA	England v India Hosts win third T despite Suryakumar Yadav x s hundredEngland hold on for a consolation victory in the third Twenty against India despite Suryakumar Yadav s sensational century at Trent Bridge	2022-07-10 18:01:30
ニュース	BBC News - Home	Euro 2022: Belgium and Iceland play out entertaining draw	https://www.bbc.co.uk/sport/football/62109954?at_medium=RSS&at_campaign=KARANGA	Euro Belgium and Iceland play out entertaining drawIceland and Belgium settle for a point each after an entertaining Euro Group D encounter in front of a raucous crowd at Manchester City s Academy Stadium	2022-07-10 18:33:45
ニュース	BBC News - Home	Sue Barker's final Wimbledon ends with tributes from Andy Murray, Roger Federer & Billie Jean King	https://www.bbc.co.uk/sport/tennis/62116017?at_medium=RSS&at_campaign=KARANGA	Sue Barker x s final Wimbledon ends with tributes from Andy Murray Roger Federer amp Billie Jean KingRoger Federer and Andy Murray leads the tributes as Sue Barker s final Wimbledon ends with an emotional send off in front of cheering fans	2022-07-10 18:43:15
ビジネス	ダイヤモンド・オンライン - 新着記事	前向きに生きることに疲れたら…禅僧が「夢や希望はなくていい」と語る理由 - 要約の達人　from flier	https://diamond.jp/articles/-/306172	前向きに生きることに疲れたら…禅僧が「夢や希望はなくていい」と語る理由要約の達人fromflierポジティブシンキングが大切だ。	2022-07-11 03:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	日産新型「サクラ」、毎日の気分が“きゅん”と上がる軽の本格BEV【試乗記】 - CAR and DRIVER 注目カー・ファイル	https://diamond.jp/articles/-/306162	caranddriver	2022-07-11 03:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	三菱新型「eKクロスEV」、日産サクラとメカニズム共用のSUV風・軽BEV - CAR and DRIVER 注目カー・ファイル	https://diamond.jp/articles/-/306177	bevampldquoek	2022-07-11 03:47:00
ビジネス	ダイヤモンド・オンライン - 新着記事	リモート勤務の求人、思わぬ落とし穴に要注意 - WSJ PickUp	https://diamond.jp/articles/-/306173	wsjpickup	2022-07-11 03:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【サンジャポで話題！金融界の鬼才】「部下を育てると自らの首を締めると思う人」と「チームで成果を上げる人」の差が生まれる根本的な理由 - 東大金融研究会のお金超講義	https://diamond.jp/articles/-/306186	【サンジャポで話題金融界の鬼才】「部下を育てると自らの首を締めると思う人」と「チームで成果を上げる人」の差が生まれる根本的な理由東大金融研究会のお金超講義年月に発足した東大金融研究会。	2022-07-11 03:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	進む円安と円買い介入の限界、金融緩和の手じまい議論を - 数字は語る	https://diamond.jp/articles/-/306138	企業物価指数	2022-07-11 03:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	損保代理店から注目集める結心会「モーター部会」、生保販売や統合で成果 - ダイヤモンド保険ラボ	https://diamond.jp/articles/-/306179	業界団体	2022-07-11 03:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	英首相転落　政界の花形から与党の「お荷物」に - WSJ PickUp	https://diamond.jp/articles/-/306174	wsjpickup	2022-07-11 03:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	景気後退の足音ここでも、銅価格が急落 - WSJ PickUp	https://diamond.jp/articles/-/306175	wsjpickup	2022-07-11 03:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	50代前半の「ねんきん定期便」の年金見込み額は、あてにならない？【書籍オンライン編集部セレクション】 - 知らないと大損する！　定年前後のお金の正解	https://diamond.jp/articles/-/305867	代前半の「ねんきん定期便」の年金見込み額は、あてにならない【書籍オンライン編集部セレクション】知らないと大損する定年前後のお金の正解何歳までこの会社で働くのか退職金はどうもらうのか定年後も会社員として働くか、独立して働くか年金を何歳から受け取るか住まいはどうするのか定年が見えてくるに従い、自分で決断しないといけないことが増えてきます。	2022-07-11 03:05:00
北海道	北海道新聞	物価高、賃金底上げ鍵　アベノミクス継承も焦点	https://www.hokkaido-np.co.jp/article/704099/	岸田文雄	2022-07-11 03:53:00
北海道	北海道新聞	改憲発議、現実味増す　緊急事態４党ほぼ一致　首相、世論の反応見定め	https://www.hokkaido-np.co.jp/article/704098/	憲法改正	2022-07-11 03:51:00
北海道	北海道新聞	重視した政策　社会保障２６％、経済２３％　道内出口調査	https://www.hokkaido-np.co.jp/article/704097/	出口調査	2022-07-11 03:23:00