投稿時間:2021-10-20 06:20:02 RSSフィード2021-10-20 06:00 分まとめ(26件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Engadget Japanese	2000年10月20日、小さなボディにハイスペックを詰め込んだコンデジ「DSC-P1」が発売されました：今日は何の日？	https://japanese.engadget.com/today-203012448.html	発売	2021-10-19 20:30:12
TECH	Engadget Japanese	Pixel 6ではセキュリティも強化、「セキュリティハブ」と「プライバシーダッシュボード」を新搭載	https://japanese.engadget.com/google-security-privacy-202852271.html	google	2021-10-19 20:28:52
TECH	Engadget Japanese	Pixel 6は1/1.3インチの大判センサー採用　光の取り込み量が最大150%に向上	https://japanese.engadget.com/pixel-6-camera-202038230.html	google	2021-10-19 20:20:38
TECH	Engadget Japanese	Pixel 6のSoC「Tensor」の詳細公開　高性能コア2基搭載し80%性能向上	https://japanese.engadget.com/pixel-tensor-201206201.html	google	2021-10-19 20:12:06
IT	ITmedia 総合記事一覧	[ITmedia News] 自動翻訳、日本語書き起こしもできる「Pixel 6」登場　Proも登場しハイエンド復帰の内容は？	https://www.itmedia.co.jp/news/articles/2110/20/news033.html	google	2021-10-20 05:36:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【縦棒グラフの実装】アニメーション	https://teratail.com/questions/365261?rss=all	【縦棒グラフの実装】アニメーション前提・実現したいこと【縦棒グラフの実装】ページ上に表示された時に、本のうち本だけ下から上に動くアニメーションを実装したいです。	2021-10-20 05:07:15
海外TECH	MakeUseOf	3 Tips for Managing Your Trello Boards	https://www.makeuseof.com/tips-managing-trello-boards/	trello	2021-10-19 20:45:46
海外TECH	MakeUseOf	How to Edit the Date and Time of Content in Google Photos	https://www.makeuseof.com/how-to-edit-date-time-google-photos/	photos	2021-10-19 20:30:11
海外TECH	MakeUseOf	8 Excellent Alternative Desktops for Fedora and How to Install Them	https://www.makeuseof.com/desktop-environments-fedora/	Excellent Alternative Desktops for Fedora and How to Install ThemWant to give your Fedora distro a new look Here s how to install a new Linux desktop environment on Fedora and which ones to try	2021-10-19 20:16:21
海外TECH	DEV Community	How to make comical visualizations in Python: Explained using Netflix Movie and TV Show dataset	https://dev.to/paridhi/how-to-make-comical-visualizations-in-python-explained-using-netflix-movie-and-tv-show-dataset-4418	How to make comical visualizations in Python Explained using Netflix Movie and TV Show datasetAfter you re done watching a brilliant show or movie on Netflix does it ever occur to you just how awesome Netflix is for giving you access to this amazing plethora of content Surely I m not alone in this am I One thought leads to another and before you know it you ve made up your mind to do an exploratory data analysis to find out more about who the most popular actors are and which country prefers which genre Now I ve spent my fair share of time making regular bar plots and pie plots using Python and while they do a perfect job in conveying the results I wanted to add a little fun element to this project I recently learned that you can create XKCD like plots in Matplotlib Python s most popular data viz library and decided that I should comify all my plots in this project just to make things a little more interesting Let s take a look at what the data has to say The dataI used this dataset that s available on Kaggle It contains movie and TV show titles available on Netflix as of To start off I installed the required libraries and read the CSV file import pandas as pdimport matplotlib pyplot as pltplt rcParams figure dpi df pd read csv input netflix shows netflix titles csv df head I also added new features to the dataset that will be used later on in the project df date added pd to datetime df date added df year added df date added dt year astype Int df month added df date added dt monthdf season count df apply lambda x x duration split if Season in x duration else axis df duration df apply lambda x x duration split if Season not in x duration else axis df head Now we can get to the interesting stuff Let me also add that to XKCDify plots in matplotlib you just need to engulf all your plotting code within the following block and you ll be all set with plt xkcd all your regular visualization code goes in here Netflix through the yearsFirst I thought it would be worth looking at a timeline that depicts the evolution of Netflix over the years from datetime import datetime these go on the numbers belowtl dates nFounded nMail Service nGoes Public nStreaming service nGoes Global nNetflix amp Chill tl x the numbers go on thesetl sub x tl sub times tl text Netflix com launched Starts nPersonal nRecommendations Billionth DVD Delivery Canadian nLaunch UK Launch with plt xkcd Set figure amp Axes fig ax plt subplots figsize constrained layout True ax set ylim ax set xlim Timeline line ax axhline xmin xmax c deeppink zorder Timeline Date Points ax scatter tl x np zeros len tl x s c palevioletred zorder ax scatter tl x np zeros len tl x s c darkmagenta zorder Timeline Time Points ax scatter tl sub x np zeros len tl sub x s c darkmagenta zorder Date Text for x date in zip tl x tl dates ax text x date ha center fontfamily serif fontweight bold color royalblue fontsize Stemplot vertical line levels np zeros len tl sub x levels levels markerline stemline baseline ax stem tl sub x levels use line collection True plt setp baseline zorder plt setp markerline marker color darkmagenta plt setp stemline color darkmagenta Text for idx x time txt in zip range len tl sub x tl sub x tl sub times tl text ax text x idx time ha center fontfamily serif fontweight bold color royalblue fontsize ax text x idx txt va top ha center fontfamily serif color royalblue Spine for spine in left top right bottom ax spines spine set visible False Ticks ax set xticks ax set yticks Title ax set title Netflix through the years fontweight bold fontfamily serif fontsize color royalblue ax text From DVD rentals to a global audience of over m people is it time for Netflix to Chill fontfamily serif fontsize color mediumblue plt show This plot paints a pretty decent picture of Netflix s journey Also the plot looks hand drawn because of the plt xkcd function Wicked stuff Movies vs TV ShowsNext I decided to take a look at the ratio of Movies vs TV Shows col type grouped df col value counts reset index grouped grouped rename columns col count index col with plt xkcd explode only explode the nd slice i e TV Show fig ax plt subplots figsize dpi ax pie grouped count explode explode labels grouped type autopct f shadow True startangle ax axis equal Equal aspect ratio ensures that pie is drawn as a circle plt show The number of TV shows on the platform is less than a third of the total content So probably both you and I have better chances of finding a relatively good movie than a TV Show on Netflix sighs Countries with the most contentFor my third visualization I wanted to make a horizontal bar graph that represented the top countries with the most content The country column in the dataframe had a few rows that contained more than country separated by commas To handle this I split the data in the country column with “ as the separator and then put all the countries into a list called categories  from collections import Countercol country categories join df col fillna split counter list Counter categories most common counter list for in counter list if labels for in counter list values for in counter list with plt xkcd fig ax plt subplots figsize dpi y pos np arange len labels ax barh y pos values align center ax set yticks y pos ax set yticklabels labels ax invert yaxis labels read top to bottom ax set xlabel Content ax set title Countries with most content plt show Some overall thoughts after looking at the plot above The vast majority of content on Netflix is from the United States quite obvious Even though Netflix launched quite late in India in it s already in the second position right after the US So India is a big market for Netflix I m going to look for content from Thailand on Netflix now that I know that it s there on the platform brb Popular directors and actorsTo take a look at the popular directors and actors I decided to plot a figure each with six subplots from the top six countries with the most content and make horizontal bar charts for each subplot Take a look at the plots below and read that first line again a Popular directors from collections import Counterfrom matplotlib pyplot import figureimport mathcolours orangered mediumseagreen darkturquoise mediumpurple deeppink indianred countries list United States India United Kingdom Japan France Canada col director with plt xkcd figure num None figsize x for country in countries list country df df df country country categories join country df col fillna split counter list Counter categories most common counter list for in counter list if labels for in counter list values for in counter list if max values lt values int range math ceil max values else values int range math ceil max values plt subplot x plt barh labels values color colours x plt xticks values int plt title country x plt suptitle Popular Directors with the most content plt tight layout plt show b Popular actors col cast with plt xkcd figure num None figsize x for country in countries list df from country df country fillna apply lambda x if country lower in x lower else small df df from country cast join small cast fillna split tags Counter cast most common tags for in tags if labels values for in tags for in tags if max values lt values int range math ceil max values elif max values gt and max values lt values int range math ceil max values else values int range math ceil max values plt subplot x plt barh labels values color colours x plt xticks values int plt title country x plt suptitle Popular Actors with the most content plt tight layout plt show Some of the oldest movies and TV showsI thought it would be quite interesting to look at the oldest movies and TV shows that are available on Netflix and how long back they re dated a Oldest movies small df sort values release year ascending True small duration stores empty values if the content type is TV Show small small small duration reset index small title release year b Oldest TV shows small df sort values release year ascending True small season count stores empty values if the content type is Movie small small small season count reset index small small title release year smallWoah Netflix has some realllyyy old movies and TV shows some even released more than years ago Have you watched any of these Fun fact When he began implementing Python Guido van Rossum was also reading the published scripts from “Monty Python s Flying Circus a BBC comedy series from the s that was added on Netflix in Van Rossum thought he needed a name that was short unique and slightly mysterious so he decided to call the language Python Does Netflix have the latest content Yes Netflix is cool and all for having content from a century ago but does it also have the latest movies and TV shows To find this out first I calculated the difference between the date on which the content was added on Netflix and the release year of that content df year diff df year added df release year Then I created a scatter plot with x axis as the year difference and y axis as the number of movies TV shows col year diff only movies df df duration only shows df df season count grouped only movies col value counts reset index grouped grouped rename columns col count index col grouped grouped dropna grouped grouped head grouped only shows col value counts reset index grouped grouped rename columns col count index col grouped grouped dropna grouped grouped head with plt xkcd figure num None figsize plt scatter grouped col grouped count color hotpink plt scatter grouped col grouped count color c values int range math ceil max grouped col plt xticks values int plt xlabel Difference between the year when the content has been n added on Netflix and the realease year plt ylabel Number of Movies TV Shows plt legend Movies TV Shows plt tight layout plt show As you can see the majority of the content on Netflix has been added within a year of its release date So Netflix does have the latest content most of the time If you re still here here s an xkcd comic for you you re welcome What kind of content is Netflix focusing upon I also wanted to explore the rating column and compare the amount of content that Netflix has been producing for kids teens and adults and if their focus has shifted from one group to the other over the years To achieve this first I took a look at the unique ratings in the dataframe print df rating unique Output TV MA R PG TV TV PG NR TV G TV Y nan TV Y PG G NC TV Y FV UR Then I classified the ratings according to the groups namely ー Little Kids Older Kids Teens and Mature they fall into and changed their values in the rating column to their group names ratings group list Little Kids Older Kids Teens Mature ratings dict TV G Little Kids TV Y Little Kids G Little Kids TV PG Older Kids TV Y Older Kids PG Older Kids TV Y FV Older Kids PG Teens TV Teens TV MA Mature R Mature NC Mature for rating val rating group in ratings dict items df loc df rating rating val rating rating groupFinally I made line plots with year on the x axis and content count on the y axis df rating val x labels kinda nless not so nbad holy shit nthat s too nmany with plt xkcd for r in ratings group list grouped df df rating r year df grouped groupby year added sum year df reset index level inplace True plt plot year df year added year df rating val color colours x marker o values int range math ceil max year df year added plt yticks labels plt xticks values int plt title Count of shows and movies that Netflix n has been producing for different audiences fontsize plt xlabel Year fontsize plt ylabel Content Count fontsize x plt legend ratings group list plt tight layout plt show Okay so the content count for mature audiences on Netflix is way more than the other groups Another interesting observation is that there was a surge in the count of content produced for Little Kids from whereas the content for Older Kids Teens and Mature Audiences decreased during that time period Top Genres Countrywise col listed in colours violet cornflowerblue darkseagreen mediumvioletred blue mediumseagreen darkmagenta darkslateblue seagreen countries list United States India United Kingdom Japan France Canada Spain South Korea Germany with plt xkcd figure num None figsize x for country in countries list df from country df country fillna apply lambda x if country lower in x lower else small df df from country genre join small listed in fillna split tags Counter genre most common tags for in tags if labels values for in tags for in tags if max values gt values int range math ceil max values elif max values gt and max values lt values int range math ceil max values else values int range math ceil max values plt subplot x plt barh labels values color colours x plt xticks values int plt title country x plt suptitle Top Genres plt tight layout plt show Key takeaways from this plot Dramas and Comedies are the most popular genres in almost every country Japan watches a LOT of anime Romantic TV Shows and TV Dramas are big in South Korea I m addicted to K Dramas too btw Children and Family Movies are the third most popular genre in Canada WordcloudsI finally ended the project with two word clouds ー first a word cloud for the description column and a second one for the title column a Wordcloud for Description from wordcloud import WordCloudimport randomfrom PIL import Imageimport matplotlib Custom colour map based on Netflix palettecmap matplotlib colors LinearSegmentedColormap from list ff b text str list df description replace replace replace replace replace mask np array Image open input finallogo New Note png wordcloud WordCloud background color white width height colormap cmap max words mask mask generate text plt figure figsize plt imshow wordcloud interpolation bilinear plt axis off plt tight layout pad plt show Live love life friend family world and find are some of the most frequent words to appear in the descriptions of movies and shows Another interesting thing is that the words ー one two three and four ー all appear in the word cloud b Wordcloud for Title cmap matplotlib colors LinearSegmentedColormap from list ff b text str list df title replace replace replace replace replace mask np array Image open input finallogo New Note png wordcloud WordCloud background color white width height colormap cmap max words mask mask generate text plt figure figsize plt imshow wordcloud interpolation bilinear plt axis off plt tight layout pad plt show Do you see Christmas right at the center of this word cloud Seems like there is an abundance of Christmas movies on Netflix Other popular words are ー Love World Man Life Story Live Secret Girl Boy American Game Night Last Time and Day And that s it Working on projects like these is what makes Data Science fun If you want to add unique projects like this to your resume join Build To Learn Club I m building it to help aspiring Data professionals build a “dangerously good resume It s for Python enthusiasts who are tired of doing online courses If you have any questions feedback or would just like to chat you can reach out to me on Twitter or LinkedIn	2021-10-19 20:18:12
海外TECH	DEV Community	Watching the Requests Go By: Reconstructing an API Spec with APIClarity	https://dev.to/mbogan/watching-the-requests-go-by-reconstructing-an-api-spec-with-apiclarity-31h8	Watching the Requests Go By Reconstructing an API Spec with APIClarity Reconstructing an OpenAPI Specification through ObservationAPIs are ubiquitous in modern microservice architectures They make it easy to consume data from external apps and reduce the amount of code developers need to write The general result is easier delivery of useful software products However the prevalence of APIs means they represent a large attack surface In fact Gartner predicts that by API attacks will be the most common attack vector for enterprise web applications Similarly an IBM report found that two thirds of data breaches could be traced to misconfigured APIs Clearly enterprises need to take a proactive approach to ensure their use of APIs is secure Unfortunately with the complexity of modern apps third party code dependencies and a lack of documentation API observability is a huge challenge Often enterprises simply don t have any API specifications for their production apps As a result security related misconfigurations go undetected and apps use a variety of deprecated “zombie APIs and undocumented “shadow APIs in production The fundamental first step to solving this problem is to create an API spec and use it to audit and document the APIs your apps use Ideally we would create an API spec simply by observing API traffic in real world applications In the past there was no simple scalable and open source tooling capable of doing this Now we have APIClarityーan open source API traffic visibility tool for Kubernetes Ks clusters It s purpose built to address the gap and enable API reconstruction through observation In this post we re going to look at what API reconstruction is and how APIClarity solves the API observability problem Then we ll walk through a practical example of using APIClarity with a microservices based app running on Ks The Importance of API ReconstructionPut simply API reconstruction is the building of an API specification simply by observing traffic to and from that API Done right API reconstruction gives you visibility into the APIs your microservices use and enables you to assess your API security risks Once the spec is built the same tooling can compare runtime traffic against the specification to detect deviations Key components of an API specification include Parameter detection paths header parameters query parameters request body parameters and cookies Object referencesFile transferSecurity definitionsIdeally an API reconstruction tool needs to quantify these components in an OpenAPI Specification OAS compliant format without introducing unnecessary overhead or complexity to an app Before APIClarity there were several tools that partially addressed API reconstruction use cases but there were no comprehensive open source solutions Some of these other tools for API visibility include Opticーan extensible language agnostic and open source tool It s useful for documenting reviewing and approving API prior to deployment SwaggerHubーa popular tool for converting API traffic to OAS CloudVector API Sharkーcan monitor multi service environments and generate an OAS specification from runtime traffic Imvisionーa robust API visibility and documentation tool for multi service environments Optic wasn t built for monitoring multi service environments and SwaggerHub doesn t integrate with runtime environments Neither API Shark nor Imvision are open source None of the above tools fully met the needs for API reconstruction How APIClarity Solves the API Reconstruction and Visibility ChallengeAPIClarity fills the gaps left by other tools and provides a robust open source and scalable multi service API visibility and reconstruction solution It easily integrates into existing environments using a service mesh framework With APIClarity developers can import an API spec or reconstruct one based on observation Developers can also monitor all API traffic in real time with no code or workload changes required So how does it work Image sourceAPIClarity is deployed in existing Ks clustersAPI traffic is mirrored from pods in the cluster to APIClarity s OpenAPI Spec EngineThe spec engine monitors internal and external traffic and records API eventsAPIClarity learns specifications based on API traffic and builds an API specUsers review edit and approve specifications APIClarity alerts users to security issues or if there is any deviation between an observed API and the approved API spec APIClarity in Action A WalkthroughNow that we know what APIClarity is let s dive into our tutorial to see it in action with a Ks cluster and microservices based application Here we will Deploy the Sock Shop app in our Ks cluster While we ll use Sock Shop as our example application you can deploy your own app to your cluster and still follow along Deploy APIClarity in our Ks cluster and configure monitoring Observe API traffic on the APIClarity dashboard Review and create an API specification and view the generated OpenAPI spec in Swagger format Identify deviations from an API spec along with usage of shadow and zombie APIs View and filter API events PrerequisitesTo follow along you ll need Kubernetes cluster with a default StorageClass definedIstio or above installed on the clusterYour Ks cluster can be deployed on any platform you prefer including minikube While APIClarity supports multiple integrations for proxying API traffic you need to download and install Istio Deploy the Sock Shop app in your Ks clusterWe ll use the popular Sock Shop microservices application as our test app With different microservices and an interactive front end it is a great way to test API traffic in a Ks cluster Create the sock shop namespace kubectl create namespace sock shop Enable Istio injection for the sock shop namespace kubectl label namespaces sock shop istio injection enabled Deploy the Sock Shop demo app in your cluster kubectl apply f Get the NodePort for the front end service kubectl describe svc front end n sock shop grep NodePort The output should look something like this NodePort lt unset gt TCP Connect to http lt node IP gt lt NodePort gt in your browser Using our example above if our node s IP is browse to If you don t know your node s IP you can verify with kubectl get nodes o yaml or minikube ip If everything is working the Sock Shop demo app should load Deploy APIClarity in our Ks cluster and configure monitoringFirst we need to deploy APIClarity in our cluster We ll start by cloning the GitHub repository to our home directory cd git clone Next navigate to the apiclarity directory cd apiclarity Use kubectl to deploy APIClarity Using the default apiclarity yaml the namespace will be apiclarity kubectl apply f deployment apiclarity yaml Confirm the pods are running kubectl get pods n apiclarityThe output should look something like this NAME READY STATUS RESTARTS AGEapiclarity b xpb Running mapiclarity postgresql Running m Initialize and update the wasm filters submodule git submodule init wasm filtersgit submodule update wasm filters Navigate to the wasm filters folder cd wasm filters Run the wasm deploy sh script so the Envoy Wasm filter can capture traffic from our Sock Shop The script accepts multiple namespaces as input parameters e g deploy sh lt namespace one gt lt namespace two gt lt namespace three gt but for this demo we only need to specify the sock shop namespace deploy sh sock shop Configure port forwarding for the APIClarity kubectl port forward n apiclarity svc apiclarity Use a web browser to connect to the APIClarity GUI at http localhost Observe API traffic on the APIClarity dashboardNow it s time to generate traffic Start by clicking through the different buttons and menus in the Sock Shop app Pro tip The more API traffic the better More traffic more observations deeper visibility For this portion of our demo we just need a little bit of traffic but keep this principle in mind for production After you ve generated some API traffic in Sock Shop head back to the APIClarity dashboard You ll notice that APIClarity has recorded all the different API calls that have been made In our example below we can see calls to the catalogue endpoint eight calls to carts and three calls to user We can also see how APIClarity begins to graph API usage Those graphs will get even more interestingーand usefulーafter we generate more traffic and create our API specifications Review and create an API specification and view docs in SwaggerNow let s create an API specification based on the relatively small amount of traffic we have Click on one of the “Most used APIs I ll use catalogue Click the Reconstructed tab and then click “Review Here we can review API paths add parameters and merge entries I ll add an example param and review and approve the paths Feel free to experiment with your choices here Now we have an OAS API specification We can view the API docs in Swagger directly from the APIClarity GUI Identify deviations from an API specNow that we have an API specification as a baseline APIClarity can flag deviations from the spec to help detect security issues and shadow APIs To see how that works go back to the Sock Shop GUI and experiment some more Click on some features or filters you did not use last time If you created an order delete it The key here is performing some actions that are not in the spec Those will be identified as “diffs For example here I made multiple calls to the catalogue endpoint that don t match my spec We can drill down by clicking a specific diff and view exactly what is different from the spec Here we can see the deviation was detected because my API call was missing some parameters That s an example of a documented API call with different parameters than the specification But what if the API call isn t documented in the specification at all In that case APIClarity will flag it as a shadow API That s exactly what happened for this API call to the carts path At the time we created the spec we only observed a GET and a POST so that is what was documented Therefore a DELETE call was outside of the spec and flagged as a shadow API As you might expect this is a legitimate API call that I should have documented This scenario provides us with a practical example of why it s useful to let APIClarity capture a large amount of traffic before creating your API specification View and filter API eventsWe can also view and filter API events with APIClarity To view events click on the events icon Here you will see a detailed list of all the API events over a given period of time “Last day is the default We can also drill down to review individual events as we did from the dashboard Additionally you can apply advanced filters to search for specific API events For example we can create a list of all the shadow API calls APIClarity has observed by applying the Spec of type is shadowfilter Detection of zombie APIs is similar We would modify the filter to look for Spec of type is zombie If one of our calls was to a deprecated API from our spec we d then see it here You can mix and match filters and sort results to achieve a variety of different views That way you can take deep dives on API events in your app Final thoughtsAs an open source project APIClarity continues to evolve and receive contributions from the developer community I hope you enjoyed this walkthrough We ve only scratched the surface here and there are several interesting use cases for API reconstruction and traffic monitoring with APIClarity In addition to improving API visibility and security it enables use cases like fuzzing tests client server code generation and improving internal and user facing documentation	2021-10-19 20:02:44
Apple	AppleInsider - Frontpage News	Compared: Google Pixel 6 Pro versus Apple iPhone 13 Pro Max	https://appleinsider.com/articles/21/10/19/compared-google-pixel-6-pro-versus-apple-iphone-13-pro-max?utm_medium=rss	Compared Google Pixel Pro versus Apple iPhone Pro MaxGoogle has launched a new lineup of high end Pixel devices equipped with chips of its own design Here s how the highest tier model the Pixel Pro stacks up against Apple s iPhone Pro Max Credit AppleInsiderWhile the Google Pixel Pro comes in at a lower price point of the device packs some premium features that bring it into competition with Apple s iPhone Pro Max which retails for Here s what the two devices have in common and how they differ as far as features cameras performance and more Read more	2021-10-19 20:49:54
海外TECH	Engadget	Facebook settles with Justice Department over H-1B hiring practices	https://www.engadget.com/facebook-doj-worker-settlement-201346028.html?src=rss	Facebook settles with Justice Department over H B hiring practicesFacebook has reached separate settlements with the Department of Justice and Department of Labor over its hiring practices related to foreign workers The settlements stem from allegations the Trump administration brought against Facebook in late At the time the DoJ said the company had “inadequately advertised at least positions between and that were eventually filled by workers on H B visas The company allegedly employed a recruitment process that was intentionally designed to dissuade US workers from applying for positions it had set aside for temporary visa holders Under the DoJ settlement Facebook will pay million to the federal government and up to million to eligible victims The fines while a drop in the ocean for a company like Facebook represent the largest such penalties the Department of Justice has enforced as part of the Immigration and Nationality Act More significantly they re another piece of bad news for a company that has been mired in it in recent weeks At the start of October whistleblower Frances Haugen testified before Congress how Facebook s algorithms have hampered its efforts to slow misinformation on its platforms The company has also faced increasing scrutiny over its efforts to downplay internal research that shows its platforms can be harmful to some young users We ve reached out to Facebook for comment	2021-10-19 20:13:46
海外科学	NYT > Science	If China Tested a New Orbital Weapon, It’s Not Much of a Surprise	https://www.nytimes.com/2021/10/19/science/china-orbital-weapon.html	russia	2021-10-19 20:44:12
ビジネス	ダイヤモンド・オンライン - 新着記事	脱炭素の目玉「2兆円基金」の内訳とは？半導体、水素、鉄鋼…予算争奪戦が勃発 - 脱炭素地獄	https://diamond.jp/articles/-/284754	脱炭素の目玉「兆円基金」の内訳とは半導体、水素、鉄鋼…予算争奪戦が勃発脱炭素地獄日本政府が目標とする年のカーボンニュートラルを実現するための目玉対策としているのが、兆円の「グリーンイノベーション基金」だ。	2021-10-20 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	相続税が上昇した駅ランキング【2021年路線価・関西60駅】2位森ノ宮、1位は？ - 死後の手続き お金の準備	https://diamond.jp/articles/-/284106	関西地方	2021-10-20 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	脱炭素シフトに「ついて行けない」危険性が高い企業ランキング【排出量が多い12業種】ANA、三菱ケミは何位？ - 脱炭素地獄	https://diamond.jp/articles/-/284753		2021-10-20 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	少額短期保険の保険料収入が高い会社ランキング【主要3分野別】総合力はあのネット金融 - 少額短期保険 111社の大乱戦	https://diamond.jp/articles/-/284680	少額短期保険	2021-10-20 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	農協のドンの「JAしゃぶり尽くし術」、恐怖政治と利益分配で幹部・職員を隷属支配 - 農協の大悪党 野中広務を倒した男	https://diamond.jp/articles/-/285083		2021-10-20 05:05:00
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北海道	北海道新聞	八村、渡辺ともＮＢＡ開幕戦欠場　日本人対決持ち越し	https://www.hokkaido-np.co.jp/article/601995/	持ち越し	2021-10-20 05:16:00
北海道	北海道新聞	会社選び　居心地の良さで判断	https://www.hokkaido-np.co.jp/article/601871/	選び	2021-10-20 05:01:00
北海道	北海道新聞	新生銀とＳＢＩ　公的資金返済の道筋を	https://www.hokkaido-np.co.jp/article/601950/	新生銀行	2021-10-20 05:01:00
北海道	北海道新聞	＃コロナ下２年　＃つながり求め　課外活動、手探り続く	https://www.hokkaido-np.co.jp/article/601869/	課外活動	2021-10-20 05:02:00
北海道	北海道新聞	バイクの男性衝突死　札幌・西区	https://www.hokkaido-np.co.jp/article/601705/	札幌市西区	2021-10-20 05:01:09
ビジネス	東洋経済オンライン	地下鉄新線｢豊住線｣東京メトロにもう一つの利点 利用者の利便性だけでなく運行､保守にも貢献 | 通勤電車 | 東洋経済オンライン	https://toyokeizai.net/articles/-/461579?utm_source=rss&utm_medium=http&utm_campaign=link_back	半蔵門線	2021-10-20 05:30:00