投稿時間:2021-06-11 16:47:41 RSSフィード2021-06-11 16:00 分まとめ(60件)

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TECH	Engadget Japanese	Androidのデフォルト検索オプション、検索企業による入札方式を撤廃へ	https://japanese.engadget.com/google-android-search-engine-064048427.html	android	2021-06-11 06:40:48
ROBOT	ロボスタ	お天気アプリ 「ウェザーニュース」の新CM 6月12日から放送開始 計82パターンを天気やエリアに合わせて最適放送	https://robotstart.info/2021/06/11/weathernews-new-cm.html	放送開始	2021-06-11 06:25:29
IT	ITmedia 総合記事一覧	[ITmedia News] NetflixがECサイト「Netflix.shop」を米国でオープン　新進アーティストとアニメやドラマのコラボ商品を展開	https://www.itmedia.co.jp/news/articles/2106/11/news119.html	itmedianewsnetflix	2021-06-11 15:36:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] 1人残らず最適な教育環境を――岐阜県教委、慶應大学SFC研究所、日本マイクロソフトが連携協定　岐阜県立学校のICT化を推進	https://www.itmedia.co.jp/pcuser/articles/2106/11/news110.html	ITmediaPCUSER人残らず最適な教育環境をー岐阜県教委、慶應大学SFC研究所、日本マイクロソフトが連携協定岐阜県立学校のICT化を推進県立学校におけるICT化を進めるべく、岐阜県教育委員会が慶應義塾大学SFC研究所、日本マイクロソフトと連携協定を締結した。	2021-06-11 15:30:00
IT	ITmedia 総合記事一覧	[ITmedia News] ユニクロのセルフレジ特許権侵害訴訟を現状整理する　知財高裁で勝っても戦況が明るくない理由	https://www.itmedia.co.jp/news/articles/2106/11/news117.html	itmedia	2021-06-11 15:23:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] AOC、240Hz駆動に対応したフルHD対応の27型/31.5型ゲーミング液晶ディスプレイ	https://www.itmedia.co.jp/pcuser/articles/2106/11/news116.html	itmediapcuseraoc	2021-06-11 15:14:00
IT	MOONGIFT	Spearmint - React/Redux/アクセシビリティのテストを実行管理	http://feedproxy.google.com/~r/moongift/~3/dTFsZT4Giq0/	ユニットテストならまだしも、Webブラウザを使ったようなテストだと、プロジェクトごとに環境を整えるのが大変です。	2021-06-11 17:00:00
TECH	Techable（テッカブル）	友だちと集まって遊べる通話SNS「パラレル」、グローバル展開を本格化	https://techable.jp/archives/156318	react	2021-06-11 06:00:11
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	負の値をbyte変換したい	https://teratail.com/questions/343472?rss=all	error	2021-06-11 15:59:02
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Flask内関数をインタープリタで確認しながらスクリプティング/デバッグを行いたい	https://teratail.com/questions/343471?rss=all	Flask内関数をインタープリタで確認しながらスクリプティングデバッグを行いたい前提・実現したいことFlaskを使って、機械学習による推論結果を返すWebアプリを作りたいと思っています機械学習モデルは別スクリプトで作成済この時、Flaskの各関数内の処理を、インタプリタで実行結果を確認しながらスクリプトを書いたり、デバッグをしたいのですが、どのように実施したらいいでしょうかIDEはPyCharmを利用していますが、IDE非依存の方法でも助かります。	2021-06-11 15:56:51
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	python　リストから選択	https://teratail.com/questions/343470?rss=all	pythonリストから選択前提・実現したいこと歴か月です。	2021-06-11 15:56:14
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	MySQLのibdata1がコピーできない	https://teratail.com/questions/343469?rss=all	MySQLのmyininbspに設定しているdatadir内のファイルをコピーすると、ibdataがコピーできませんでした。	2021-06-11 15:49:29
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	戻るボタンで前のページに戻れずそのページの最上部が表示されてしまう問題	https://teratail.com/questions/343468?rss=all	発生している問題・エラーメッセージ下層ページに移動し、下にスクロールして、ブラウザの戻るボタンで前のページに戻ろうとした際に前のページに戻らず、そのページの最上部が表示されてしまいます。	2021-06-11 15:48:59
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	ゼロから作るDeep Leaningのクラスのコンストラクタの挙動	https://teratail.com/questions/343467?rss=all	ゼロから作るDeepLeaningのクラスのコンストラクタの挙動※全く同じ質問をしてる人がいるが、その答えだと全く理解できなかったためもう一度質問をさせていただいた。	2021-06-11 15:47:30
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	VScodeで文字化けを直す方法	https://teratail.com/questions/343466?rss=all	VScodeで文字化けを直す方法前提・実現したいことVScodeで、Javaを実行できる環境を作りたいです。	2021-06-11 15:39:38
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	SQLITEのDBを参照しデータがなければ書込みしたい	https://teratail.com/questions/343465?rss=all	SQLITEのDBを参照しデータがなければ書込みしたいHPのフォームから送られたデータをPYTHONで受け取り、DB内にそのデータがなければ情報を追記しようとしております。	2021-06-11 15:36:40
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	rsyncで増分のみでバックアップをしたい	https://teratail.com/questions/343464?rss=all	rsyncで増分のみでバックアップをしたい前提・実現したいことOSCentosnbsprsyncで増分のみをバックアップし世代管理をしたいと思っています。	2021-06-11 15:34:36
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	modelをjsonにシリアライズする際に出力カラムを指定できない。	https://teratail.com/questions/343463?rss=all	modelをjsonにシリアライズする際に出力カラムを指定できない。	2021-06-11 15:23:18
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Wordpressのwp_mailでメールを送信しているのですが、selectboxの値のみ反映されないのは何故でしょうか？	https://teratail.com/questions/343462?rss=all	Wordpressのwpmailでメールを送信しているのですが、selectboxの値のみ反映されないのは何故でしょうか前提・実現したいことinputの値は上手くPHPの変数に格納出来ているのですが、selectboxのみ上手く格納されず空になってしまいます。	2021-06-11 15:09:54
Ruby	Rubyタグが付けられた新着投稿 - Qiita	FontAwesomeの<svg>にjsで処理をする時の注意	https://qiita.com/texpandafter/items/5840440d47435bcf281a	FontAwesomeのltsvggtにjsで処理をする時の注意実行環境macOSHRubypRailsturbolinksFontAwesomeFontAwesomeの仕組みhearthtmllticlassfasfaheartgtltigtこのように特定のclassを持ったiタグをhtmlに記述すると次のようにクラス名に対応するアイコンがsvgで描画されます。	2021-06-11 15:46:12
Ruby	Rubyタグが付けられた新着投稿 - Qiita	閲覧数を管理する足跡モデルの設計 その2 設計の変更とその実装	https://qiita.com/Ub_Iwerks/items/c722fe3eb6c7aeb5f665	課題点の解決策の閲覧数にの差が発生してしまう問題に関しては、根本的な解決策にはなっていない気もするが、以下のようにして対応する。	2021-06-11 15:08:54
Docker	dockerタグが付けられた新着投稿 - Qiita	Docker CentOS8にnginxを入れてSSL化してみた	https://qiita.com/A-Kira/items/92e95169879ba375274d	indexhtmlltDOCTYPEhtmlgtlthtmllangjagtltheadgtltmetacharsetUTFgtlttitlegtテストlttitlegtltheadgtltbodygtltpgttestltpgtltbodygtlthtmlgtgitignore蛇足ですが、ローカル用の証明書と秘密鍵をgitにあげなくていいように設定します。	2021-06-11 15:42:28
Azure	Azureタグが付けられた新着投稿 - Qiita	AzureでRHEL8のVMをアップグレードする方法	https://qiita.com/johanburati/items/d2dbb8d198b1845a5d7c	catetcyumvarsreleasever上記の場合、リリースでロックされています。	2021-06-11 15:01:26
Ruby	Railsタグが付けられた新着投稿 - Qiita	FontAwesomeの<svg>にjsで処理をする時の注意	https://qiita.com/texpandafter/items/5840440d47435bcf281a	FontAwesomeのltsvggtにjsで処理をする時の注意実行環境macOSHRubypRailsturbolinksFontAwesomeFontAwesomeの仕組みhearthtmllticlassfasfaheartgtltigtこのように特定のclassを持ったiタグをhtmlに記述すると次のようにクラス名に対応するアイコンがsvgで描画されます。	2021-06-11 15:46:12
Ruby	Railsタグが付けられた新着投稿 - Qiita	閲覧数を管理する足跡モデルの設計 その2 設計の変更とその実装	https://qiita.com/Ub_Iwerks/items/c722fe3eb6c7aeb5f665	課題点の解決策の閲覧数にの差が発生してしまう問題に関しては、根本的な解決策にはなっていない気もするが、以下のようにして対応する。	2021-06-11 15:08:54
技術ブログ	Developers.IO	システム開発プロジェクトにおけるIAMポリシー権限はどうしたらいいですか	https://dev.classmethod.jp/articles/iam-role-base-permission/	ohumanlaborisnohumanerror	2021-06-11 06:12:08
海外TECH	DEV Community	An Introduction to Reinforcement Learning With OpenAI Gym’s ‘Taxi’	https://dev.to/joooyz/an-introduction-to-reinforcement-learning-with-openai-gym-s-taxi-258c	An Introduction to Reinforcement Learning With OpenAI Gym s Taxi In this introductory tutorial we ll apply reinforcement learning RL to train an agent to solve the Taxi environment from OpenAI Gym We ll cover A basic introduction to RL Setting up OpenAI Gym amp Taxi Step by step tutorial on how to train a Taxi agent in Python using RL Before we start what s  Taxi Taxi is one of many environments available on OpenAI Gym These environments are used to develop and train reinforcement learning agents The goal of Taxi is to pick up passengers and drop them off at the destination in the least amount of moves In this tutorial you ll start with an agent that plays randomly …and successfully apply reinforcement learning to train an agent to solve the game An introduction to Reinforcement LearningThink about how you might teach a dog a new trick like telling it to sit If it performs the trick correctly it sits you ll reward it with a treat positive feedback ️ If it doesn t sit correctly it doesn t get a treat negative feedback By continuing to do things that lead to positive outcomes the dog will learn to sit when it hears the command in order to get its treat Reinforcement learning is a subdomain of machine learning which involves training an agent the dog to learn the correct sequences of actions to take sitting on its environment in response to the command sit in order to maximise its reward getting a treat This can be illustrated more formally as Source Sutton amp  Barto ️Installing OpenAI Gym and TaxiWe ll be using the Taxi v environment for this tutorial You ll need to install OpenAI Gym pip install gym NumPy pip install numpyThe following snippet will import the necessary packages and create the Taxi environment import numpy as npimport gymimport random create Taxi environmentenv gym make Taxi v Random AgentWe ll start by implementing an agent that doesn t learn at all Instead it will select actions at random This will be our baseline The first step is to give our agent the initial state A state tells our agent what the current environment looks like In Taxi a state defines the current positions of the taxi passenger and pick up and drop off locations Below are examples of three different states for Taxi Note Yellow taxi Blue letter pickup location Purple letter drop off destinationTo get the initial state create a new instance of taxi and get the initial statestate env reset Next we ll run a for loop to cycle through the game At each iteration our agent will Make a random action from the action space    south    north    east    west    pick up    drop off Receive the new stateHere s our random agent script import gymimport numpy as npimport random create Taxi environmentenv gym make Taxi v create a new instance of taxi and get the initial statestate env reset num steps for s in range num steps print f step s out of num steps sample a random action from the list of available actions action env action space sample perform this action on the environment env step action print the new state env render end this instance of the taxi environmentenv close You can run this and watch your agent make random moves Not super exciting but hopefully this helped you get familiar with the OpenAI Gym toolkit Next we ll implement the key algorithm that will enable our agent to learn from the environment in order to solve Taxi Q Learning AgentQ learning is a reinforcement learning algorithm that seeks to find the best possible next action given its current state in order to maximise the reward it receives the Q in Q learning stands for quality   i e how valuable an action is Let s take the following starting state Which action up down left right pick up or drop off should it take in order to maximise its reward Note blue pick up location and purple drop off destination First let s take a look at how our agent is rewarded for its actions Remember in reinforcement learning we want our agent to take actions that will maximise the possible rewards it receives from its environment Taxi reward systemAccording to the Taxi documentation …you receive points for a successful drop off and lose point for every timestep it takes There is also a point penalty for illegal pick up and drop off actions Looking back at our original state the possible actions it can take and the corresponding rewards it will receive are shown below In the image above the agent loses point per timestep it takes It will also lose points if it uses the pick up or drop off action here We want our agent to go North towards the pick up location denoted by a blue R but how will it know which action to take if they are all equally punishing ExplorationOur agent currently has no way of knowing which action will lead it closest to the blue R This is where trial and error comes in   we ll have our agent take random actions and observe what rewards it gets i e our agent will do some exploration of the environment This is what our random agent was doing earlier Over many iterations our agent will have observed that certain sequences of actions will be more rewarding than others Along the way our agent will need to keep track of which actions led to what rewards Introducing…Q tablesA Q table is simply a look up table storing values representing the maximum expected future rewards our agent can expect for a certain action in a certain state known as Q values It will tell our agent that when it encounters a certain state some actions are more likely than others to lead to higher rewards It becomes a cheatsheet telling our agent what the best action to take is The image below illustrates what our Q table will look like Each row corresponds to a unique state in the Taxi environment Each column corresponds to an action our agent can take Each cell corresponds to the Q value for that state action pair   a higher Q value means a higher maximum reward our agent can expect to get if it takes that action in that state Before we begin training our agent we ll need to initialize our Q table as so state size env observation space n total number of states S action size env action space n total number of actions A initialize a qtable with s for all Q valuesqtable np zeros state size action size As our agent explores it will update the Q table with the Q values it finds To calculate our Q values we ll introduce the Q learning algorithm Q Learning AlgorithmThe Q learning algorithm is given below We won t go into details but you can read more about it in Ch of Sutton amp Barto The Q learning algorithm will help our agent update the current Q value Q St At with its observations after taking an action I e increase Q if it encountered a positive reward or decrease Q if it encountered a negative one Note that in Taxi our agent doesn t receive a positive reward until it successfully drops off a passenger points Hence even if our agent is heading in the correct direction there will be a delay in the positive reward it should receive The following term in the Q learning equation addresses this This term adjusts our current Q value to include a portion of the rewards it may receive sometime in the future St The a term refers to all the possible actions available for that state The equation also contains two hyperparameters which we can specify Learning rate α how easily the agent should accept new information over previously learnt informationDiscount factor γ how much the agent should take into consideration the rewards it could receive in the future versus its immediate rewardHere s our code for implementing the Q learning algorithm hyperparameters to tunelearning rate discount rate dummy variablesreward R t state env observation space sample S taction env action space sample A tnew state env observation space sample S t Qlearning algorithm Q s a Q s a learning rate reward discount rate max Q s a Q s a qtable state action learning rate reward discount rate np max qtable new state qtable state action Exploration vs Exploitation Trade offEarlier we let our agent explore the environment to update our Q table As our agent learns more about the environment we can let it use this knowledge to take more optimal actions   known as exploitation During exploitation our agent will look at its Q table and select the action with the highest Q value instead of a random action Over time our agent will need to explore less and start exploiting what it knows instead There are many ways to implement an exploration exploitation strategy Here s just one example dummy variablesepisode random randint qtable np random randn env observation space sample env action space sample exploration exploitation tradeoffepsilon probability that our agent will exploredecay rate of epsilonif random uniform lt epsilon explore action action env action space sample else exploit action np argmax qtable state epsilon decreases exponentially gt our agent will explore less and lessepsilon np exp decay rate episode In the example above we set some value epsilon between and If epsilon is there is a chance that on this step our agent will explore instead of exploit We ve set epsilon to exponentially decay with each step so that our agent explores less and less over time Bringing it all togetherWe re done with all the building blocks needed for our reinforcement learning agent The process for training our agent will look like Initialising our Q table with s for all Q valuesLet our agent play Taxi over a large number of gamesContinuously update the Q table using the Q learning algorithm and an exploration exploitation strategyHere s the full implementation import numpy as npimport gymimport randomdef main create Taxi environment env gym make Taxi v initialize q table state size env observation space n action size env action space n qtable np zeros state size action size hyperparameters learning rate discount rate epsilon decay rate training variables num episodes max steps per episode training for episode in range num episodes reset the environment state env reset done False for s in range max steps exploration exploitation tradeoff if random uniform lt epsilon explore action env action space sample else exploit action np argmax qtable state take action and observe reward new state reward done info env step action Q learning algorithm qtable state action qtable state action learning rate reward discount rate np max qtable new state qtable state action Update to our new state state new state if done finish episode if done True break Decrease epsilon epsilon np exp decay rate episode print f Training completed over num episodes episodes input Press Enter to watch trained agent watch trained agent state env reset done False rewards for s in range max steps print f TRAINED AGENT print Step format s action np argmax qtable state new state reward done info env step action rewards reward env render print f score rewards state new state if done True break env close if name main main What s next There are many other environments available on OpenAI Gym for you to try e g Frozen Lake You can also try optimising the implementation above to solve Taxi in fewer episodes Some other useful resources include A good article on RL and its real world applications Deep Reinforcement Learning Series by Jonathan Hui AlphaGo full documentary on Youtube Reinforcement Learning by Sutton and Barto	2021-06-11 06:54:28
海外TECH	DEV Community	250+ FREE Tutorials on Web Development, Machine Learning and more | Subscribe now 🔥	https://dev.to/thenerdydev/250-free-tutorials-on-web-development-machine-learning-and-more-subscribe-now-40n	FREE Tutorials on Web Development Machine Learning and more Subscribe now Hey guys Check out more than tutorial videos on technologies like Machine Learning Web Development and more Here is a quick glance over all the videos that I have on my channel So loads of awesome videos to get you started and more Subscribe now here PS I am working on a BRAND new FREE Web Developer Full Course on YouTube check the below article to know more about the topics that we cover in this course Web Developer Full Course HTML CSS JavaScript Node js and MongoDB The Nerdy Dev・Apr ・ min read html css node javascript Follow me on Twitter Follow me on Twitter YouTube See you on the other side	2021-06-11 06:43:09
海外TECH	DEV Community	Wireshark	https://dev.to/vishwasnarayan5/wireshark-3jg	Wireshark Wireshark introductionWireshark is a network analyzer that allows you to see what is going on with your network Wireshark allows you to dissect network packets at a microscopic level including detailed information on individual packets Wireshark was first made available in Back then it was considered Ethereal Wireshark is compatible with all major operating systems Most businesses and government agencies also use Wireshark as a primary network analyzer Wireshark is now fully open source thanks to the global network engineering ecosystem While most security systems are command line based Wireshark has an excellent user interface OSI ModelThe Open Systems Interconnection OSI paradigm standardises the manner in which two or more computers communicate with one another The OSI Model classifies network architecture into seven layers Application Presentation Session Transport Network Datalink and Physical Here is what each layer does OSI Model LayersIf you want to read more about the OSI model check out this comprehensive essay PacketsNow that you understand the OSI model let s look at network packets When data is transmitted from one device to another it is divided into smaller units known as packets When you download a file from the internet the data is transmitted as packets from the server Your machine reassembles these packets to send you the original file IPV PacketA packet can contain the following data source and destination IP addressesprotocolsource and destination portsDataLength flags TTL etc protocolEach packet includes important information about the devices involved in the packet transfer Thousands if not millions of these data packets are transmitted between the source and destination devices for each data connection You can now understand the significance of Wireshark Wireshark allows you to catch and search each of these packets for details A network engineer s equivalent of a biologist s microscope is Wireshark Wireshark allows you to listen to a live network after connecting to it record and inspect packets on the move You may use Wireshark as a network engineer or ethical hacker to debug and protect the networks As a bad guy which I do not recommend you can sniff network packets and grab information such as credit card purchases This is why connecting to a public network such as Starbucks and doing financial transfers or accessing private data is risky Even though HTTPS sites can encrypt the packets they are still readable across the network If someone is determined enough they will be able to break it Wireshark FundamentalsLet s take a look at how you can use Wireshark Wireshark can be downloaded and installed from this page Unlike other penetration testing software Wireshark provides an excellent graphical user interface This is how Wireshark appears when you launch it Wireshark displays a list of the networks to which you are linked and you can choose one of them to begin listening to the network Wireshark UIThere are three panes in Wireshark Packet List PaneThe listing of packages in Wireshark is by default displayed using the following columns Package NumberTimeSource IP Your device IP when sending packages see the following tutorial if you re unfamiliar with IP s Destination IP Your device IP when receiving Packages Network Protocol used Typically TCP or UDP see the following video if you re in doubt of the difference between these Package LengthInfo Information not listed in one of the above columns Packet List PaneThis window shows the collected packets Each line represents a separate packet which you can click on and examine in greater depth using the other two panes Packet Details PaneSelecting a packet allows you to examine the packet information in greater depth using the Packet Details pane It shows information such as IP addresses ports and other data from the packet Packet Bytes PaneThis pane displays the raw data of the chosen packet in bytes The data is presented as a hex dump which is binary data in hexadecimal format FiltersFilters in Wireshark assist you in narrowing down the kind of data you are searching for Filters are classified into two types capture filters and display filters Traffic FilteringWireshark supports filters based on a broad range of criteria to reduce the amount of information shown at the start The filters can be applied directly in the search bar of the Wireshark programme as seen below with a TCP protocol filter Capture FilterUntil beginning to evaluate a network you should apply a catch filter When a catch filter is set it only catches packets that fit the capture filter For eg if you only need to listen to the packets being sent and received from an IP address you can set a capture filter as follows host Once you set a capture filter you cannot change it till the current capture session is completed Display FiltersTo grab packets display filters are used For eg if you just want to see requests coming from a certain IP address you can do so you can apply a display filter as follows ip src Show filters can be modified on the fly when they are added to collected data In a nutshell capture filters allow you to filter the traffic while view filters add certain filters to the captured packets Wireshark is good for debugging because it can catch hundreds of packets on a busy network Wireshark s Main FeaturesNow that you ve mastered the fundamentals of Wireshark let s take a look at some main features You can do it with Wireshark Recognize network security risks and malicious activitiesDebug dynamic networks by observing network traffic Filter traffic according to protocols ports and other criteria Capture packets and store them in a Pcap file for later review To improve research apply coloring rules to the packet list Captured data can be exported to an XML CSV or plain text format ConclusionEvery year Wireshark is ranked in the top ten network security software Wireshark is simple to understand and use thanks to its simple but efficient user interface It is an important weapon in the arsenal of any penetration tester	2021-06-11 06:41:14
海外TECH	DEV Community	Fre-2.1 has been pulished	https://dev.to/132/fre-2-1-and-react-18-have-been-pulished-what-s-the-difference-between-them-22o8	Fre has been pulishedI announce that fre is officially released which is a major breakthrough version Offscreen renderingThe bigist breakthrough is offscreen rendering a core algorithm refactor before after Offscreen rendering is an algorithm level optimization it traverses vdom in reverse order from bottom to top from right to left to ensure that the front DOM pointer is in memory and finally it is drawn to the screen at one time With off screen rendering fre has become the best performance frameworks in vdom world not one of Just as react also released alpha version fre also tried to be compatible with them CreateRootimport render useState from fre function App const count setCount useState return lt gt lt h gt count lt h gt lt button onClick gt setCount count gt lt button gt lt gt createRoot document body render lt App gt hereThis API is more ergonomic and for the callback you can do this function App callback return lt div ref callback gt lt h gt Hello World lt h gt lt div gt createRoot document body render lt App callback gt console log renderered gt startTransitonThis is an API for lowering priority which is very useful so I decide to build it in function App const count setCount useState console log count const update gt startTransition gt setCount setCount return lt gt lt h gt count lt h gt lt button onClick update gt lt button gt lt gt It works can be understood as setTimeout cb but the callback function is executed synchronously and the update is delayed asynchronously auto updatesFre has always been supportive Suspense SSRThis is the only breakthrough of react I like it very much but fre doesn t support it now I need to spend some time to study it SummaryFre has also been released If you are interested in the front end framework you can jump to GitHub It has all the advanced features of react but only lines of code and its performance is much better than react	2021-06-11 06:10:36
海外TECH	Engadget	DOJ charges security exec for hacking a Georgia healthcare company in 2018	https://www.engadget.com/doj-charges-security-exec-hacking-georgia-healthcare-company-053011517.html?src=rss_b2c	DOJ charges security exec for hacking a Georgia healthcare company in A security company executive has been charged for hacking into the Gwinnett Medical Center s network on or around September th	2021-06-11 06:02:11
海外TECH	CodeProject Latest Articles	Simple Software for Optimal Control	https://www.codeproject.com/Articles/863257/Simple-Software-for-Optimal-Control	control	2021-06-11 06:42:00
海外TECH	CodeProject Latest Articles	Popup Menu	https://www.codeproject.com/Tips/1194456/Popup-Menu	basic	2021-06-11 06:41:00
医療系	医療介護 CBnews	医療機関・高齢者施設等へ抗原簡易キット配布を周知- 14日までに都道府県から厚労省へ申し込み	https://www.cbnews.jp/news/entry/20210611154106	医療機関	2021-06-11 15:55:00
医療系	医療介護 CBnews	1日50回超の接種、病院に1日当たり10万円交付-診療所にも手厚く支援、厚労省	https://www.cbnews.jp/news/entry/20210611152629	医療機関	2021-06-11 15:40:00
金融	JPX マーケットニュース	[東証]改訂コーポレートガバナンス・コードの公表	https://www.jpx.co.jp/news/1020/20210611-01.html	東証	2021-06-11 15:30:00
金融	JPX マーケットニュース	[東証]新規上場の承認（マザーズ）：（株）ラキール	https://www.jpx.co.jp/listing/stocks/new/index.html	新規上場	2021-06-11 15:30:00
金融	JPX マーケットニュース	[東証]ＪＡＳＤＡＱスタンダードから市場第一部への変更：（株）メイコー	https://www.jpx.co.jp/listing/stocks/transfers/02.html	東証	2021-06-11 15:30:00
金融	JPX マーケットニュース	[OSE]長期国債先物取引に係る中心限月取引の変更	https://www.jpx.co.jp/news/2020/20210611-01.html	先物取引	2021-06-11 15:15:00
金融	JPX マーケットニュース	[OSE]特別清算数値（2021年6月限）：日経225、TOPIX等	https://www.jpx.co.jp/markets/derivatives/special-quotation/index.html	topix	2021-06-11 15:15:00
金融	ニッセイ基礎研究所	欧州経済見通し－景況感急改善で夏以降の回復期待が高まる	https://www.nli-research.co.jp/topics_detail1/id=67999?site=nli	目次欧州経済概況・振り返りこれまでのコロナ禍の状況・振り返り月期は前期比、四半期連続のマイナス・現状月期にはようやく持ち直し・振り返りこれまでのコロナ禍の状況財政月以降は復興基金の資金調達も開始欧州経済の見通し・見通しバカンスシーズン以降の急回復に期待・見通しポイント物価・金融政策・長期金利の見通し・見通しインフレ率は高いが一時的・見通し金融政策も正常化を模索振り返りこれまでのコロナ禍の状況昨年春に新型コロナウイルスの感染が拡大第波してから年以上が経過した。	2021-06-11 15:21:30
ニュース	ジェトロ ビジネスニュース（通商弘報）	英国政府、北アイルランド議定書の実施状況に関する評価を発表	https://www.jetro.go.jp/biznews/2021/06/00e065e00dbf2d32.html	北アイルランド	2021-06-11 06:20:00
海外ニュース	Japan Times latest articles	Diet enacts revision to Japan’s law on referendums for constitutional reform	https://www.japantimes.co.jp/news/2021/06/11/national/politics-diplomacy/diet-referendum-law-revision-enacted/	Diet enacts revision to Japan s law on referendums for constitutional reformThe change to the law coincides with calls for an emergency clause that would give broad authority to the Cabinet and limit citizens rights under	2021-06-11 15:42:34
ニュース	BBC News - Home	G7: UK and US have an 'indestructible relationship', PM says	https://www.bbc.co.uk/news/uk-politics-57436035	special	2021-06-11 06:13:47
ニュース	BBC News - Home	UK economy gets boost in April as shops reopen	https://www.bbc.co.uk/news/business-57438437	coronavirus	2021-06-11 06:39:42
北海道	北海道新聞	全産業、増収増益の見通し　２１年度、ワクチン接種に期待	https://www.hokkaido-np.co.jp/article/554393/	増収増益	2021-06-11 15:14:00
北海道	北海道新聞	米国のスタバ、品切れ商品が続出　供給網混乱で、ファン悲鳴	https://www.hokkaido-np.co.jp/article/554389/	続出	2021-06-11 15:11:00
北海道	北海道新聞	ゴールデンナイツ準決勝へ　ＮＨＬプレーオフ４強そろう	https://www.hokkaido-np.co.jp/article/554388/	準決勝	2021-06-11 15:04:00
北海道	北海道新聞	東京都庁に接種センター開設へ　１８日から、五輪関係者対象	https://www.hokkaido-np.co.jp/article/554386/	定例記者会見	2021-06-11 15:02:00
ビジネス	東洋経済オンライン	豊臣秀吉が｢大坂城より力入れて造った城｣の正体 ｢本能寺の変｣後に歴史に残る城を多く築いた | リーダーシップ・教養・資格・スキル | 東洋経済オンライン	https://toyokeizai.net/articles/-/431601?utm_source=rss&utm_medium=http&utm_campaign=link_back	本能寺の変	2021-06-11 16:00:00
ニュース	Newsweek	NASAが40年ぶりの金星ミッションへ　気候変動で何が起きるのかを探る	https://www.newsweekjapan.jp/stories/world/2021/06/nasa40.php	打ち上げから年後、大型機に搭載した直径メートルほどの小型探査機を放出し、小型機は約時間の下降を経て金星の地表に着陸する。	2021-06-11 15:00:59
IT	週刊アスキー	『モンスターハンターライズ』特別な称号が手に入るイベントクエスト「称号・シノビの心」が配信開始！	https://weekly.ascii.jp/elem/000/004/058/4058762/	nintendo	2021-06-11 15:55:00
IT	週刊アスキー	アクアの洗剤自動導入洗濯機「Prette」＆超音波部分洗い洗濯機「Prette plus」に新モデル10機種が登場	https://weekly.ascii.jp/elem/000/004/058/4058752/	prette	2021-06-11 15:50:00
IT	週刊アスキー	布にまつわる“手仕事”の世界がすごい　横浜市歴史博物館の企画展「布 うつくしき日本の手仕事」7月17日から	https://weekly.ascii.jp/elem/000/004/058/4058756/	日本常民文化研究所	2021-06-11 15:50:00
IT	週刊アスキー	『ブルーアーカイブ』で★3「コハル（CV：赤尾ひかるさん）」の期間限定ピックアップ募集を開催中！	https://weekly.ascii.jp/elem/000/004/058/4058760/	archive	2021-06-11 15:45:00
IT	週刊アスキー	横浜の人気スポットで叶えたい「願い事」を抽選で実現、横浜マリンタワー	https://weekly.ascii.jp/elem/000/004/058/4058754/	横浜マリンタワー	2021-06-11 15:40:00
IT	週刊アスキー	ウナギに見えるけど蒲鉾です。「すごーく長いうな次郎」山椒付きで販売中	https://weekly.ascii.jp/elem/000/004/058/4058723/	一正蒲鉾	2021-06-11 15:30:00
IT	週刊アスキー	懐かしの市電を見よう　横浜都市発展記念館、横浜市営交通100年の歩みを紹介するコーナー展を開催中	https://weekly.ascii.jp/elem/000/004/058/4058749/	横浜都市発展記念館	2021-06-11 15:20:00
IT	週刊アスキー	『オクトパストラベラー 大陸の覇者』にて「闘技大会 -ガートルード杯-」が開催！	https://weekly.ascii.jp/elem/000/004/058/4058758/	octopathtraveler	2021-06-11 15:20:00
マーケティング	AdverTimes	ダイキン×SNSクリエイター・ゆな＆せりしゅん コラボWebCM「ぜんぶ、湿度のせい。」公開	https://www.advertimes.com/20210611/article354179/	youtube	2021-06-11 06:30:59