投稿時間:2022-04-21 08:43:38 RSSフィード2022-04-21 08:00 分まとめ(53件)

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IT	気になる、記になる…	Microsoft、｢Outlook for iOS｣にAI校正機能｢Microsoft エディター｣を搭載	https://taisy0.com/2022/04/21/156035.html	microsoft	2022-04-20 22:54:09
IT	気になる、記になる…	｢MagSafeバッテリーパック｣、最新のファームウェアアップデートで7.5Wでの充電に対応	https://taisy0.com/2022/04/21/156032.html	apple	2022-04-20 22:30:50
IT	気になる、記になる…	Apple、テスター向けに｢macOS Monterey 12.4 Public Beta 2｣をリリース	https://taisy0.com/2022/04/21/156030.html	apple	2022-04-20 22:18:18
IT	気になる、記になる…	Apple、テスター向けに｢iOS 15.5 Public Beta 2｣と｢iPadOS 15.5 Public Beta 2｣をリリース	https://taisy0.com/2022/04/21/156028.html	applebetasoftwar	2022-04-20 22:17:50
IT	ITmedia 総合記事一覧	[ITmedia News] Meta（旧Facebook)、Questゲームイベントで「Ghostbuters VR」など多数のゲームを紹介	https://www.itmedia.co.jp/news/articles/2204/21/news081.html	amongusvr	2022-04-21 07:24:00
AWS	AWS Partner Network (APN) Blog	Delivering Closed Loop Assurance with Infosys Digital Operations Ecosystem Platform on AWS	https://aws.amazon.com/blogs/apn/delivering-closed-loop-assurance-with-infosys-digital-operations-ecosystem-platform-on-aws/	Delivering Closed Loop Assurance with Infosys Digital Operations Ecosystem Platform on AWSA closed loop assurance system predicts network events such as faults and congestions that are highly probable of causing service degradation or interruption and automatically take preventive actions to avert service disruptions Learn how Infosys leveraged AWS data streaming data analytics and machine learning services to ingest process and analyze high volumes of data from disparate sources and to build ML models to predict network events that cause service degradation	2022-04-20 22:07:52
AWS	AWS Media Blog	AWS for M&E Video Tutorials: CDN Authorization for AWS Elemental MediaPackage	https://aws.amazon.com/blogs/media/aws-for-me-video-tutorials-cdn-authorization-for-aws-elemental-mediapackage/	AWS for M amp E Video Tutorials CDN Authorization for AWS Elemental MediaPackageAWS for Media amp Entertainment Video Tutorials are a series of short videos that cover best practices and actionable tips to build media workflows on AWS In these on demand videos AWS media and entertainment experts answer common questions from customers and offer valuable insight about how to architect deploy and optimize your media workflows In …	2022-04-20 22:07:45
AWS	AWS	Roche: Enabling Enterprise-Wide Analytics and ML with Automated Compliance	https://www.youtube.com/watch?v=oTtPNgcZ05I	Roche Enabling Enterprise Wide Analytics and ML with Automated ComplianceRoche allows their internal teams to create Analytics and ML environments to experiment with data that is shared through a precisely controlled and audited catalog They achieve this by leveraging AWS Service Catalog and AWS CloudFormation to automate the creation of such environments with built in compliance Check out more resources for architecting in the AWS​​​cloud ​ AWS AmazonWebServices CloudComputing ThisIsMyArchitecture	2022-04-20 22:37:20
AWS	AWSタグが付けられた新着投稿 - Qiita	ネットワーク&セキュリティ勉強（using AWS)	https://qiita.com/cultivate/items/6365027c3b19d4729d10	apache	2022-04-21 07:05:46
海外TECH	MakeUseOf	How to Switch to Ubuntu Rolling Rhino: A Rolling Release Version of Ubuntu	https://www.makeuseof.com/install-ubuntu-rolling-rhino/	rhino	2022-04-20 22:07:14
海外TECH	DEV Community	🔐 Authentication: Is Identity-aware Proxy enough for most use cases?	https://dev.to/hunghvu/authentication-is-identity-aware-proxy-enough-for-most-use-cases-3hi9	Authentication Is Identity aware Proxy enough for most use cases Assuming an application is for internal use only containing sensitive information there are ways to implement an authentication system Self implementation Using third party auth providers like Google or Auth Using Identity aware Proxy Between the choices self implementation is too much of a hassle so only and are most likely the way to go Using third party providers is the most well known choice e g Auth and it also offers ability to setup authorization mechanism e g I can see my folder but am I allowed to see admin panel Identity aware Proxy on the other hands is an authentication layer to prevent your request from reaching the server It is used to replace VPN in cloud environment In a sense it also offers simple authorization as you can declare which group of users can reach the web server not down to endpoint level though You can have both authentication mechanism implemented in your application and an Identity aware Proxy enabled With that said for an internal web application with sensitive information is using only Identity aware Proxy enough if there is no complex authorization involved What are the downsides If you are using both at the same time why Also if both Identity aware Proxy and application login page use the same authentication provider e g Google SSO does it make the login page become redundant	2022-04-20 22:21:47
海外TECH	DEV Community	Predicting Drought in Illinois	https://dev.to/varsha_thatte_4d49e1853a6/predicting-drought-in-illinois-5836	Predicting Drought in Illinois	2022-04-20 22:15:52
海外TECH	DEV Community	AWS Well-Architected Framework - Security Pillar	https://dev.to/sebastiantorres86/aws-well-architected-framework-security-pillar-2jhc	AWS Well Architected Framework Security Pillar What Is the Security Pillar The Security pillar encompasses the ability to protect data systems and assets to take advantage of cloud technologies to improve your security Why is Security important to improving my architecture CustomersYour customers may be internal to your organization or external Legal and regulatory requirementsYou will have legal and regulatory requirements that appropiate security controls and architecture can help address What are the design principles of the Security Pillar Implement a strong identity foundationImplement the principle of least privilege and enforce separation of duties with appropiate authorization for each interaction with Amazon Web Services AWS resources Enable traceabilityMonitor alert and audit actions and changes to your environment in real time Integrate log and metric collection with systems to automatically investigate and take action Apply security at all layersApply a defense in depth approach with multiple security controls Apply to all layers for example edge of network VPC load balancing every instance and compute service operating system application and code Automate security best practicesUse automated software based security mechanisms to improve your ability to securely scale more rapidly and cost effectively Create secure architectures including the implementation of controls that are defined and managed as code in version controlled templates Protect data in transit and at restClassify your data into sensitivity levels and use mechanisms such encryption tokenization and access control where appropiate Keep people away from dataUse mechanisms and tools to reduce or eliminate the need for direct access or manual processing of data This reduces the risk of mishandling or modification and human error when handling sensitive data Prepare for security events Prepare for an incident by having incident management and investigation policies and processes that align to your organizational requirements Run incident response simulations and use tools with automation to increase your speed for detection investigation and recovery What are the best practice areas of security Security FoundationsIdentity and Access ManagementDetectionInfrastructure ProtectionData ProtectionIncident Response	2022-04-20 22:09:33
海外TECH	DEV Community	BIG O NOTATION IN DATA STRUCTURES AND ALGORITHM	https://dev.to/iemmanuel104/big-o-notation-in-python-kdk	BIG O NOTATION IN DATA STRUCTURES AND ALGORITHMIn computer science the Big O notation is described as a metric for analyzing system performance it measures the run time of a function or code block as the input size increases hence it determines the complexity of your algorithm using algebraic terms This concept is peculiar to asymptotic notation as it was invented by Paul Bachmann Edmund Landau and others collectively called Bachmann Landau notation or asymptotic notation The arithmetic notation is as shown above there exists several asymptotic notations that describes the overall complexities of a computer algorithms Big O As earlier stated it gives the function limit that describes whether the complexity will be less than or equal to another function outcome or to the worst case Big Ω Big Omega This provides the bounds of which a function will be greater than or equal to another It gives a complexity that is to be at least more than the best case Big Θ Big theta This asympttic notation gives a complexity between the worst and best case Algorithmic complexity is a measure of how long and how much memory space is required for an algorithm to complete a given an input of size n This can be viewed in two distinct sections Time complexities of AlgorithmsThis signifies the total time required by an algorithm to run till its completion Time Complexity is most commonly estimated by counting the number of elementary steps performed by any algorithm to finish execution There are different algorithm run time complexities O Constant Time complexity Here there would be no change in the run time for any given input Hence the output time depends on a constant value and does not depend on the input size For example accessing any single element in an array takes constant time as only one operation has to be performed to locate it TimeComplex TimeComplex accessing any element takes constant timeO N Linear Time complexity The run time increases as the size of the input increases hence the time complexity is directly proportional to the size of the input data It is suitable in algorithms that require a loop through array elements or where the algorithm has to sequentially read its entire input For example a function that adds up all elements of a list requires time proportional to the length of the list TimeComplex for i in TimeComplex print i O N Oh N squared Quadratic Time complexity This represents and algorithm whose run time performance is directly proportional to the square size of its input data a common example is the nested for loop that looks attt every index in an array twice TimeComplex for i in TimeComplex for j in TimeComplex print i O LogN Logarithmic Time complexity This represents and algorithm whose run time performance is proportional to the logarithm of its input data size This is an algorithm to break a set of numbers into halves to search a particular field and does not need to access all elements of its input Algorithms taking logarithmic time are commonly found in operations on binary trees or when using binary search TimeComplex for i in range len TimeComplex print TimeComplex i O n Exponential Time Complexity They are referred to as Brute force algorithms whereby the algorithm growth rate doubles with each addition to the input n often iterating through all subsets of the input elements This is obviously a not optimal way of performing a task since it will affect the time complexity Brute Force algorithms are used in cryptography as attacking methods to defeat password protection by trying random strings until they find the correct password that unlocks the system Space Complexity of AlgorithmsSpace complexity is the amount of working storage needed by an algorithm it describes the memory used by the algorithm including the input values to the algorithm to execute and produce the result It is a parallel concept to Time complexity and determines how the size of storage grows as input grows To calculate the space complexity of an algorithm only the data space is considered The data space refers to the amount of space used by the variables and constants Space complexity like time complexity also plays a crucial role in determining the efficiency of an algorithm program If an algorithm takes up a lot of time you can still wait run execute it to get the desired output But if a program takes up a lot of memory space the compiler will not let you run it	2022-04-20 22:05:12
海外TECH	DEV Community	Angular Universal ENV Variables with Webpack and Dotenv	https://dev.to/jdgamble555/angular-universal-env-variables-with-webpack-and-dotenv-3i6o	Angular Universal ENV Variables with Webpack and DotenvIn the last week for some reason my Vercel Hack stopped working The variables were not importing as expected In case this does not work for you here is the webpack version I wonder if using webpack actually slows down the build process or not Should it be avoided Comment if you know please I am thinking you should avoid any external dependencies when possible but this makes using process env seemless Here are instructions with Angular Universal I had to compile different results on Google as I tried to simply things that others seem to over complicate as usual ͡° ͜ʖ͡° Install Dependenciesnpm i D angular builders custom webpacknpm i D angular builders dev servernpm i D dotenv Create custom webpack config tsAlways use Typescript This should be a rule for all JS developer companies Put this in your root directory import EnvironmentPlugin from webpack import config from dotenv config module exports plugins new EnvironmentPlugin FIREBASE API DEV FIREBASE API PROD Of course I am using a Firebase example here as you can import it as a JSON file Edit Environment Filesenvironment prod tsexport const environment production true firebase JSON parse process env FIREBASE API PROD as string If you are just importing a string you don t need JSON parse here Do the same for all environment version files Edit Angular jsonReplace projects architect build builder From angular devkit build angular browserTo angular builders custom webpack browserReplace projects architect serve builder From angular devkit build angular dev serverTo angular builders custom webpack dev serverReplace projects architect test builder From angular devkit build angular karmaTo angular builders custom webpack karmaReplace projects architect server builder From angular devkit build angular serverTo angular builders custom webpack serverAdd projects architect server optionsAnd projects architect build options customWebpackConfig path custom webpack config ts Create Your env FilePut your variables in your env file as usual FIREBASE API DEV apiKey FIREBASE API PROD And done Here is my repository if you need an example J	2022-04-20 22:05:09
海外TECH	DEV Community	Amazon SageMaker GroundTruth	https://dev.to/aws-builders/amazon-sagemaker-groundtruth-ff7	Amazon SageMaker GroundTruthWe will review possible methods provided by Amazon GroundTruth to label our data Amazon GroundTruth is a service within Amazon Sagemaker that labels datasets for further use in building machine learning models Three options are available when using this service Mechanical TurkPrivate labelling workforceVendorMechanical Turk workforce is a team of global on demand workers from Amazon that work around the clock on labelling and human review tasks Your data should be free of any personally identifiable information PII as this is a public workforce You should use this workforce if you want to save time on the labelling work which anyone could do and if there are no PII within your data Private labelling workforce is a team of workers which you choose They could be employees of your company or a group of subject matter experts For example if you have a dataset containing X ray images and you want to classify those images whether they contain a certain disease or not Another situation is when your data contains PII and you want a private workforce to label them Vendor workforce is a selection of experienced vendors who specialize in providing data labelling services They could be found at the AWS marketplace Let us now take a look at the different types of labelling jobs available for the image data type ImagesImage classification Single label In this task the employees are categorising images into individual classes class per image In this example we are either choosing Basketball OR Soccer as a label for this image Image classification Multi label In this task the employees are categorising images into one or more classes In this example we are choosing ALL labels present within the image Bounding boxIn this task the employees should draw bounding boxes around specified objects in the images In this example we want to specify the location of the birds within the image by drawing bounding boxes which surrounds them Semantic segmentationIn this task the employees should draw pixel level labels around specific objects and segments in the image In this example we are classifying EACH PIXEL within the image So you can see that the pixels of the plane are coloured in red and the rest are in black Label verificationIn this task the employees should verify existing labels in the dataset This could be used to check prior work by human workers or automated labeling jobs In this example we want to verify the car s label as being correct or incorrect	2022-04-20 22:04:56
海外TECH	DEV Community	How we built our tiered storage subsystem	https://dev.to/redpanda-data/how-we-built-our-tiered-storage-subsystem-at-redpanda-320e	How we built our tiered storage subsystem IntroductionOne of the key premises of Redpanda is unification of real time and historical data by giving users the ability to store infinite data However in the modern cloud the price of storage often dominates the price of all computing resources The cost of object storage is vastly lower than the cost of the local disk attached to a compute node Furthermore object storage is often more reliable than the nodes themselves To these ends we created Shadow Indexing a capability available as a tech preview as of this writing that allows us to capitalize on the guaranteed reliability of the tier datacenter What is Shadow Indexing Shadow Indexing is a subsystem that allows Redpanda to move large amounts of data between brokers and cloud object stores efficiently and cost effectively without any human intervention This allows Redpanda to overcome the data center reliability limitations by storing data in object stores such as Amazon Simple Storage Service S or Google Cloud Storage GCS The S storage is more cost effective compared to attached storage in EC around six times cheaper compared to provisioned IOPS io EBS storage and three to five times cheaper than GP GP It is also more available than EC instances providing a whopping nines durability guarantee For the end user Shadow Indexing means the ability to fetch both historical and real time data using the same API transparently and without much performance overhead With Shadow Indexing enabled Redpanda migrates data to an object store asynchronously without any added complexity This makes for a seamless transition of data from hot in memory to warm local storage and to lukewarm object storage allowing users to create topics with infinite retention while maintaining high performance By allowing for tiered storage Shadow Indexing decouples the cluster s load capacity from storage capacity This allows operators to deploy Redpanda on fewer smaller brokers and with less storage thereby reducing infrastructure costs and administrative overhead By eliminating total storage capacity as a constraint operators can freely size their clusters strictly according to the live load Deploying brokers with less storage also has the benefit of improving mean time to recovery MTTR as it greatly reduces the amount of log data that needs to be replicated in the event of a broker failure Finally Redpanda has the ability to restore topic data from the archive giving administrators an additional method for disaster recovery in case of accidental deletion or in the unlikely event of a cluster wide failure Next we ll go into detail about the key components of Shadow Indexing and how we built it Understanding the Shadow Indexing architectureThe Shadow Indexing subsystem has four main components The scheduler service that uploads log segments and Shadow Indexing metadata to the object storeThe archival metadata stm which stores information about segments uploaded to the object store locally in the Redpanda data directory default var lib redpanda data The cache service that temporarily stores data downloaded from the object storeThe remote partition which is a component responsible for downloading data from the object store and serving it to the clientsThe scheduler service and archival metadata stm are the main components of the write path which uploads data to the object store bucket The cache service and remote partition are elements of the read path which is used to download data from the object store to satisfy a client request How write path worksThe scheduler service component is responsible for scheduling uploads It creates an ntp archiver object for every partition and invokes individual archivers periodically in a fair manner to guarantee that all partitions are uploaded evenly Note that uploads are always done by the partition leader The archiver follows a naming scheme that defines where the log segments should go in the bucket It is also responsible for maintaining the manifest which is a JSON file that contains information about every uploaded log segment Object stores shard workloads based on an object name prefix so if all log segments have the same prefix they will hit the same storage server This will lead to throttling and limit the upload throughput To ensure good upload performance Redpanda inserts a randomized prefix into every object The prefix is computed using a xxHash hash function The archival subsystem uses a PID regulator to control the upload speed in order to prevent uploads from overwhelming the cluster The regulator measures the size of the upload backlog the total amount of data that needs to be uploaded to the object store and changes the upload priority based on that If the upload backlog is small the priority of the upload process will be low and the occasional segment uploads won t interfere with any other activities in the cluster However if the backlog is large the priority will be higher and the uploads will use more network bandwidth and CPU resources Redpanda maintains some metadata in the cloud so the data can be used without the cluster For every topic we maintain a manifest file that contains information about said topic for instance the retention duration number of partitions segment size etc For every topic partition we also maintain a separate partition manifest that has a list of all log segments uploaded to the cloud storage This metadata and individual object names make the bucket content self sufficient and portable It can be used to discover and recreate the topics The content of the bucket can also be accessed from different AWS regions When the archiver uploads a segment to the object store it adds segment metadata e g segment name base and last offsets timestamps etc to the partition manifest It also adds this information to the archival metadata stm ーthe state machine that manages the archival metadata snapshot The partition leader handles the write path and manages archival state For consistency durability and fault tolerance this state needs to be replicated to the followers as well Redpanda does this by sending state changes via configuration batches in the same raft log as the user data This allows the followers to update their local snapshot In case of a leadership transfer this ensures that any replica that takes over as a leader has the latest state and can start uploading new data immediately The snapshot is stored within every replica of the partition Every time the leader of the partition uploads the segment and the manifest it adds a configuration batch with information about the uploaded segment to the raft log This configuration batch gets replicated to the followers and then it gets added to the snapshot Because of that every replica of the partition “knows the whereabouts of every log segment that was uploaded to the object store bucket In other words we re tracking the data stored inside the object store bucket using a Raft group This is the same Raft group that is used to store and replicate the user data This solution has some nice benefits For example when the replica is not a leader it still has the up to date archival snapshot When the leadership transfer happens the new leader can start uploading new data based on a snapshot state without downloading the manifest from the object store Another benefit that snapshot enables is smarter data retention Because the archival metadata is available locally the partition can use it to figure out what part of the log is already uploaded and can be deleted locally This constitutes a safety mechanism in Redpanda which prevents retention policy from deleting log segments that were not uploaded to the object store yet How read path worksThe remote partition component is responsible for handling the reads from cloud storage The component uses data from the archival metadata snapshot to locate every log segment in the object store bucket It also knows the offset range that it can handle based on the snapshot data When an Apache Kafkaclient sends a fetch request Redpanda decides how the request should be served It will be served using local data stored in the Raft log if it is possible However if the data is not available locally the data will be served using the remote partition component This means that even if the partition on the Redpanda node stores only recent data the client will see that the offsets are available starting from offset zero When Redpanda is processing a fetch request it checks if the offsets are available locally and if this is the case serves the local data back to the client Alternatively if the data is only available in cloud storage it uses the remote partition to retrieve the data The remote partition checks the archival snapshot to find a log segment that hosts the required offsets and copies that segment into the cache Then the log segment is scanned to get the record batches to the client However the remote partition can t serve the fetch request using data in the cloud storage directly First it has to download the log segments to the local cache The cache is configured to store only a certain number of log segments simultaneously and evicts unused segments if there is not enough space left Internally the remote partition object stores a collection of lightweight objects that represent uploaded segments When the segment is accessed this lightweight representation is used to create a remote segment The remote segment deals with a single log segment in the cloud It can download the segment to the local storage and can be used to create reader objects which are used to serve fetch requests Readers can fetch data from the log segment and materialize record batches Think of them as something loosely similar to database cursors that can scan data in one direction The remote segment also maintains a compressed in memory index which is used to translate Redpanda offsets to offsets in the file The remote partition also hosts the reader cache This cache stores unused readers that can be reused by fetch requests if the offset range requested by the fetch request matches one of the readers Also the remote partition can evict unused remote segment instances by transitioning them to the offloaded state in which they re not consuming system resources The latency profile might be different between the Shadow Indexing reads and normal reads Shadow Indexing needs to start retrieving data from the object store to the cache in order to be able to serve fetch requests Also Shadow Indexing doesn t use record batch cache Because of that it s more suitable for batch workloads Implementing Shadow Indexing in RedpandaThe development of the Shadow Indexing subsystem started with the write path We developed our own S client using Seastar However Seastar didn t allow us to use the existing object store client efficiently and the framework didn t have an http client that could be used to access the object store API In order to overcome this challenge we developed our own http client using Seastar The next step was development of the scheduler service This service schedules individual uploads from different partitions This sounds easy on paper but the task is actually quite challenging Firstly the scheduler needs to provide a fairness guarantee to prevent a situation in which one of the partitions doesn t receive enough upload bandwidth and lags behind The situation would be dangerous because it could cause a disk space leak by preventing the retention policy from doing its job Secondly every replica of the partition may have different segment alignment and the segments may begin and end on different offsets despite having the same data inside Because of this we may have a situation when after the leadership transfer only part of the segment needs to be uploaded The newly elected leader must be able to see what offset range is already uploaded for the partition and has to compute a starting point for the next upload inside one of the segments Once all bits and pieces for the write path were in place we started to work on topic recovery which allows Redpanda to restore a topic using data from the object store However it s not possible to just download the data In order for Redpanda to be able to use data we need to create a proper log and bootstrap a Raft group There is a lot of bookkeeping used to manage Raft state outside the log itself and this needs to be taken care of The recovery process should create entries in the internal KV store create a Raft snapshot archival metadata snapshot etc Also the log itself needs to be pre processed upon download This is because the log contains different non data messages that Raft and other subsystems are using However these messages can break the newly created Raft group So to remove them the downloaded log has to be patched on the fly The offsets have to be updated and the checksums recalculated Next we developed the components in the read path These are the cache service the archival metadata stm and the remote partition The cache service is a tricky affair because it is global per node but Seastar likes everything to be shared per CPU But if we shard the cache then one hot partition could theoretically cause a lot of downloads from the object store with other shards underutilized With one global cache service this isn t a problem though it does mean we have to consider other issues For instance cache eviction Cache eviction has to be done globally which requires coordination between the shards The remote partition is interesting because it has to track all uploaded segments at once In order to be able to serve fetch requests it should be able to locate individual segments download them to the cache directory and create readers the cursor like things The problem here is that the bucket may contain much more data than the Redpanda node can handle think how much data Redpanda can send to the object store over several years Because of that the remote partition can t just create a remote segment object for every log segment that it tracks Instead it creates the remote segment instances on demand and destroys them when they re idle long enough Because of this the remote segment has to be a complex state machine that has a bit of internal state for every uploaded segment The process of developing Shadow Indexing had its fair share of challenges but in the end we dare say the system came together nicely ConclusionThe new Shadow Indexing feature allows for infinite data retention with good performance at a low cost It offers application developers ease of use and flexibility when designing applications For operators it allows cluster infrastructure to scale optimally according to live load provides additional tools for data recovery and helps improve MTTR by reducing the amount of data that needs to be replicated when a broker fails This is just the start We have further enhancements planned for Redpanda in the future including Full cluster recovery Redpanda will support a complete restore from object store based on data that has been uploaded to the archive Faster data balancing When adding new brokers to the cluster Redpanda supports automatic balancing by replicating partition data to the new brokers using Raft This process can be more resource efficient by allowing the new brokers to fetch only parts of the log that haven t been archived and serving the rest from object store using Shadow Indexing Analytical clusters Some workloads are analytical in nature and involve reading large chunks of historical data to perform analysis These workloads tend to be ad hoc disruptive and have an adverse effect on operational workloads with strict SLAs Redpanda will provide a way to deploy analytical clusters that have read only access to archived data in the object store This will allow for true elasticity as multiple read only clusters can be deployed and decommissioned according to need It will also provide true isolation of real time operational workloads from analytical workloads To learn more about Shadow Indexing and how to use it please view our documentation here or join our Slack community if you have specific questions feedback or ideas for enhancements	2022-04-20 22:04:54
Linux	Linux Journal	Geek Guide: Purpose-Built Linux for Embedded Solutions	https://www.linuxjournal.com/content/geek-guide-purpose-built-linux-embedded-solutions	Geek Guide Purpose Built Linux for Embedded Solutions by Webmaster The explosive growth of the Internet of Things IoT is just one of several trends that is fueling the demand for intelligent devices at the edge Increasingly embedded devices use Linux to leverage libraries and code as well as Linux OS expertise to deliver functionality faster simplify ongoing maintenance and provide the most flexibility and performance for embedded device developers This e book looks at the various approaches to providing both Linux and a build environment for embedded devices and offers best practices on how organizations can accelerate development while reducing overall project cost throughout the entire device lifecycle Download PDF Go to Full Article	2022-04-20 22:51:29
金融	金融総合：経済レポート一覧	FX Daily（4月19日）～ドル円、129円台に上昇	http://www3.keizaireport.com/report.php/RID/493153/?rss	fxdaily	2022-04-21 00:00:00
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金融	金融総合：経済レポート一覧	1ドル130円は通過点。市場機能を損ねる日銀金融政策の弊害が急激な円安を招く：木内登英のGlobal Economy ＆ Policy Insight	http://www3.keizaireport.com/report.php/RID/493167/?rss	lobaleconomypolicyinsight	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	第22回情報セキュリティ・シンポジウム「スマートフォンの利用にかかるセキュリティ」の模様	http://www3.keizaireport.com/report.php/RID/493170/?rss	情報セキュリティ	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	暗号資産の保有に係る会計上の取扱いに関する考察：会計マネー・ツリーを用いたアプローチから	http://www3.keizaireport.com/report.php/RID/493171/?rss	日本銀行金融研究所	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	1960年代末における国際収支に対する認識と金融政策：金融政策の転換前後における日本銀行の視点を中心に	http://www3.keizaireport.com/report.php/RID/493172/?rss	日本銀行	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	Credible Forward Guidance	http://www3.keizaireport.com/report.php/RID/493173/?rss	credibleforwardguidance	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	深層中国 第9回「中国におけるフィンテックの生成と大手プラットフォーマーの課題」	http://www3.keizaireport.com/report.php/RID/493187/?rss	東京財団	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	IPOマーケットレポート（4/4～4/15）～東証スタンダードに1社、東証グロースに2社の合計3社の新規上場が行われました。	http://www3.keizaireport.com/report.php/RID/493189/?rss	新規上場	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	東証プライム、TCFD提言とエンゲージメント：日本株運用者の視点	http://www3.keizaireport.com/report.php/RID/493200/?rss	運用	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	国内キャッシュレス決済市場に関する調査を実施(2021年)【概要】～国内キャッシュレス決済市場は2025年度には約153兆円までの拡大を予測。コンタクトレス決済の拡大とモバイル化の進展	http://www3.keizaireport.com/report.php/RID/493201/?rss	矢野経済研究所	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	金融市場NOW：デンマーク・カバード債券の足元の状況について～欧州(ドイツ)長期金利は上昇	http://www3.keizaireport.com/report.php/RID/493215/?rss	金融市場	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	KAMIYAMA Seconds!：J-REITが割安とみる理由	http://www3.keizaireport.com/report.php/RID/493218/?rss	kamiyamasecondsjreit	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	海外勢の日本株投資動向：市川レポート	http://www3.keizaireport.com/report.php/RID/493219/?rss	三井住友	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	つみたてNISAの手続き・活用法に関する解説動画	http://www3.keizaireport.com/report.php/RID/493221/?rss	解説動画	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	第2回 若年層への情報提供に向けて押さえたい家計管理の基本とポイント：金融教育の時代に必須の取組！若年層取引につなげる情報提供	http://www3.keizaireport.com/report.php/RID/493222/?rss	情報提供	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	人民元週間レポート【貿易統計－輸入は前年同月比マイナス－】2022年4月15日	http://www3.keizaireport.com/report.php/RID/493235/?rss	貿易統計	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】チャイナリスク	http://search.keizaireport.com/search.php/-/keyword=チャイナリスク/?rss	検索キーワード	2022-04-21 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】5秒でチェック、すぐに使える! 2行でわかるサクサク仕事ノート	https://www.amazon.co.jp/exec/obidos/ASIN/4046053631/keizaireport-22/	結集	2022-04-21 00:00:00
ニュース	BBC News - Home	Partygate: PM seeks to delay Commons vote on probe	https://www.bbc.co.uk/news/uk-politics-61170379?at_medium=RSS&at_campaign=KARANGA	commons	2022-04-20 22:10:15
ニュース	BBC News - Home	French election: Macron and Le Pen clash in TV presidential debate	https://www.bbc.co.uk/news/world-europe-61166601?at_medium=RSS&at_campaign=KARANGA	debateemmanuel	2022-04-20 22:08:33
ニュース	BBC News - Home	Alec Baldwin: Rust film producers were indifferent to gun safety - report	https://www.bbc.co.uk/news/entertainment-arts-61169495?at_medium=RSS&at_campaign=KARANGA	firearm	2022-04-20 22:24:52
ニュース	BBC News - Home	The Papers: 'Palace shock' at Harry interview, and Queen is 96	https://www.bbc.co.uk/news/blogs-the-papers-61170793?at_medium=RSS&at_campaign=KARANGA	front	2022-04-20 22:51:13
ニュース	BBC News - Home	Chelsea 2-4 Arsenal: Mikel Arteta vindicated as Eddie Nketiah inspires stunning Gunners win	https://www.bbc.co.uk/sport/football/61167084?at_medium=RSS&at_campaign=KARANGA	Chelsea Arsenal Mikel Arteta vindicated as Eddie Nketiah inspires stunning Gunners winArsenal stunned Chelsea with a win at Stamford Bridge to put themselves back on track in the hunt for a Champions League spot after three straight defeats	2022-04-20 22:33:44
ニュース	BBC News - Home	World Snooker Championship 2022: John Higgins goes through, Ding Junhui out	https://www.bbc.co.uk/sport/snooker/61164857?at_medium=RSS&at_campaign=KARANGA	World Snooker Championship John Higgins goes through Ding Junhui outFour time champion John Higgins is made to work hard for his first round win over Thepchaiya Un Nooh at the World Championship in Sheffield	2022-04-20 22:19:30
ニュース	BBC News - Home	Everton 1-1 Leicester City: Richarlison scores late to earn hosts precious point	https://www.bbc.co.uk/sport/football/59625599?at_medium=RSS&at_campaign=KARANGA	Everton Leicester City Richarlison scores late to earn hosts precious pointEverton manager Frank Lampard says he hopes Dele Alli s role in the late draw with Leicester can be a big starting point for him after his January move from Tottenham	2022-04-20 22:46:16
ニュース	BBC News - Home	Match of the Day analysis: How Man City leapfrogged Liverpool with win over Brighton	https://www.bbc.co.uk/sport/av/football/61171299?at_medium=RSS&at_campaign=KARANGA	Match of the Day analysis How Man City leapfrogged Liverpool with win over BrightonMatch of the Day pundits Gary Lineker Alan Shearer and Micah Richards discuss Manchester City s victory over Brighton to put them back top of the Premier League above Liverpool	2022-04-20 22:52:55
ビジネス	ダイヤモンド・オンライン - 新着記事	ディズニー特区廃止法案、フロリダ州上院が可決 - WSJ発	https://diamond.jp/articles/-/302085	特区	2022-04-21 07:10:00
北海道	北海道新聞	ＮＹ円、１２７円後半　米長期金利の上昇一服	https://www.hokkaido-np.co.jp/article/672123/	上昇一服	2022-04-21 07:03:00
ニュース	THE BRIDGE	韓国のメタバーススタートアップDoubleMe、2,500万米ドルをシリーズA調達——サムスン電子らが出資	https://thebridge.jp/2022/04/doubleme-series-a-funding	韓国のメタバーススタートアップDoubleMe、万米ドルをシリーズA調達ーサムスン電子らが出資TechinAsiaでは、有料購読サービスを提供。	2022-04-20 22:45:00
ニュース	THE BRIDGE	Animoca Brands、豪マーケティング会社Be Mediaを買収へ——デジタル所有権のNFT化を支援	https://thebridge.jp/2022/04/animoca-brands-acquire-be-media	AnimocaBrands、豪マーケティング会社BeMediaを買収へーデジタル所有権のNFT化を支援TechinAsiaでは、有料購読サービスを提供。	2022-04-20 22:30:30
ニュース	THE BRIDGE	【Web3起業家シリーズインタビュー】Proved／和組DAO で注目の連続起業家・小林清剛氏が見るWeb3	https://thebridge.jp/2022/04/knot-kobayashi-mugenlabo-magazine	【Web起業家シリーズインタビュー】Proved和組DAOで注目の連続起業家・小林清剛氏が見るWeb本稿はKDDIが運営するサイト「MUGENLABOMagazine」に掲載された記事からの転載MUGENLABOMagazineでは、ブロックチェーン技術をもとにしたNFTや仮想通貨をはじめとする、いわゆるWebビジネスの起業家にシリーズで話を伺います。	2022-04-20 22:15:14