投稿時間:2021-11-13 09:35:31 RSSフィード2021-11-13 09:00 分まとめ(37件)

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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 不況下で人材を吸収する不動産業界　社会のセーフティーネット的な役割	https://www.itmedia.co.jp/business/articles/2111/13/news037.html	itmedia	2021-11-13 08:50:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 渋谷で「5000円乗り放題」を始めて、どんなことが分かってきたのか	https://www.itmedia.co.jp/business/articles/2111/13/news002.html	itmedia	2021-11-13 08:10:00
js	JavaScriptタグが付けられた新着投稿 - Qiita	[Nuxt.js, Vue] v-onでv-bindと同じように条件分岐する。	https://qiita.com/higuchimmy/items/32a83efc2f01da287bb1	vbindで三項演算子を使い条件分岐する方法はたくさん紹介されていたのですが、vonで三項演算子を使った例が見つけられず、半信半疑で書いてみたら普通に使えたので、その備忘録。	2021-11-13 08:00:25
海外TECH	MakeUseOf	What Is the Filter Bubble Transparency Act and How Will It Affect You	https://www.makeuseof.com/filter-bubble-transparency-act-explained/	What Is the Filter Bubble Transparency Act and How Will It Affect YouCongress is targeting major social media algorithms like Facebook and Instagram through a new Filter Bubble bill Here s how it might impact you	2021-11-12 23:29:49
Apple	AppleInsider - Frontpage News	Snap sued for misrepresenting impact of Apple privacy changes	https://appleinsider.com/articles/21/11/12/snap-sued-for-misrepresenting-impact-of-apple-privacy-changes?utm_medium=rss	Snap sued for misrepresenting impact of Apple privacy changesSocial media company Snap this week was slapped with a class action lawsuit claiming executives misrepresented the threat Apple privacy changes posed to the company s revenue stream Lodged with the U S District Court for the Northern District of California the suit from Snap investor Kellie Black claims company executives made misleading statements in regulatory filings and to the media about the impact Apple s privacy changes would have on Snap s advertising business Specifically Snap allegedly failed to disclose or made false statements about the material impact newly released iOS features would have and were having on the company s bottom line Further Snap overstated its ability to adapt to the changes downplayed the risks associated with Apple s operating system changes and exaggerated its commitment to privacy plaintiffs assert Read more	2021-11-12 23:21:33
海外TECH	CodeProject Latest Articles	Building Microsoft Teams Tabs Apps with Java Part 1: Creating a Personal Tab App with SSO	https://www.codeproject.com/Articles/5317287/Building-Microsoft-Teams-Tabs-Apps-with-Java-Part	article	2021-11-12 23:36:00
海外科学	NYT > Science	Glasgow Climate Talks Are Down to the Wire on Money, Ambition and Fossil Fuels	https://www.nytimes.com/2021/11/12/climate/glasgow-climate-cop26.html	Glasgow Climate Talks Are Down to the Wire on Money Ambition and Fossil FuelsAt COP negotiators from about countries worked overnight hashing out differences in the quest for a new global climate agreement	2021-11-12 23:34:33
金融	金融総合：経済レポート一覧	FX Daily(11月11日)～ドル円、114円ちょうどを挟んで推移	http://www3.keizaireport.com/report.php/RID/474858/?rss	fxdaily	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	Fundmark Report 2021年11月号 投信評価レポート	http://www3.keizaireport.com/report.php/RID/474860/?rss	fundmarkreport	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	【国内債券】SDGs債対象銘柄一覧（2021年10月末時点）	http://www3.keizaireport.com/report.php/RID/474861/?rss	野村総合研究所	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	工作機械受注が教えてくれる景況感～サイクルは後半へ：Market Flash	http://www3.keizaireport.com/report.php/RID/474862/?rss	marketflash	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	信用リスク・アセットの算出手法の見直し～2023年3月期から適用。内部モデルを用いない国内行は1年延期可：金融規制（バーゼル規制その他）	http://www3.keizaireport.com/report.php/RID/474866/?rss	信用リスク	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	投資してよかった？８割以上で運用益～2021年３月末時点での投資信託の運用状況：研究員の眼	http://www3.keizaireport.com/report.php/RID/474876/?rss	投資信託	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	日本調理機（東証二部）～集団給食施設向けが主力の業務用総合厨房機器の老舗メーカー：アナリストレポート	http://www3.keizaireport.com/report.php/RID/474883/?rss	厨房機器	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	保険業界における事業投資管理の高度化	http://www3.keizaireport.com/report.php/RID/474887/?rss	pwcjapan	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	金融機関における事業投資管理の高度化	http://www3.keizaireport.com/report.php/RID/474888/?rss	pwcjapan	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	金融機関の中小企業支援についての調査・分析事業 報告書	http://www3.keizaireport.com/report.php/RID/474892/?rss	中小企業	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	ヨハナ・カークランド：コロナ罹患を経て学んだこと：プロの視点	http://www3.keizaireport.com/report.php/RID/474895/?rss	視点	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	史上最高値圏で推移する世界リート(除く日本)～ワクチンの接種普及等を背景に業績の先行きに対する懸念が後退：REITレポート	http://www3.keizaireport.com/report.php/RID/474896/?rss	史上最高値	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	メキシコ金融政策（2021年11月）～4会合連続で0.25％ポイントの利上げ：マーケットレター	http://www3.keizaireport.com/report.php/RID/474897/?rss	投資信託	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	ノーベル賞受賞で弾みがついた地球温暖化対策～地球温暖化と異常気象との関係について科学的根拠の信憑性が向上...：マーケットレター	http://www3.keizaireport.com/report.php/RID/474898/?rss	地球温暖化	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	米国の7月以降の景気と長期金利の動向：夜明け前が一番暗い？：Economic Data Watch	http://www3.keizaireport.com/report.php/RID/474899/?rss	economicdatawatch	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	メキシコ中銀が4会合連続で利上げ～インフレ高止まりの長期化を警戒	http://www3.keizaireport.com/report.php/RID/474900/?rss	三井住友	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	FX Weekly（2021年11月12日号）～来週の為替相場見通し：ドル円：鮮度が低下した「インフレ＝円安」	http://www3.keizaireport.com/report.php/RID/474901/?rss	fxweekly	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	働く30代の老後不安（中国）：基礎研レポート	http://www3.keizaireport.com/report.php/RID/474904/?rss	老後	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	Weekly金融市場 2021年11月12日号～来週の注目材料、経済指標。バラマキ政策のツケは誰が負うのか...	http://www3.keizaireport.com/report.php/RID/474909/?rss	weekly	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	ポストコロナへのマーケットの論点～川口有一郎教授に聞く：不動産マーケットリサーチレポート	http://www3.keizaireport.com/report.php/RID/474922/?rss	三菱ufj信託銀行	2021-11-13 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】ステーブルコイン	http://search.keizaireport.com/search.php/-/keyword=ステーブルコイン/?rss	検索キーワード	2021-11-13 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】なぜウチの会社は変われないんだ! と悩んだら読む 大企業ハック大全	https://www.amazon.co.jp/exec/obidos/ASIN/4478114196/keizaireport-22/	選抜	2021-11-13 00:00:00
ニュース	BBC News - Home	Britney Spears released from 13-year conservatorship	https://www.bbc.co.uk/news/entertainment-arts-59217825?at_medium=RSS&at_campaign=KARANGA	father	2021-11-12 23:24:08
ニュース	BBC News - Home	Steve Bannon charged with contempt of Congress	https://www.bbc.co.uk/news/world-us-canada-59270291?at_medium=RSS&at_campaign=KARANGA	capitol	2021-11-12 23:19:23
ビジネス	ダイヤモンド・オンライン - 新着記事	トランプ氏元側近バノン氏、刑事訴追　議会侮辱罪で - WSJ発	https://diamond.jp/articles/-/287588	刑事訴追	2021-11-13 08:19:00
LifeHuck	ライフハッカー［日本版］	空気中のほこりを減らしてくれる観葉植物	https://www.lifehacker.jp/2021/11/245411what-you-need-is-a-dust-reducing-houseplant.html	観葉植物	2021-11-13 08:30:00
北海道	北海道新聞	「原子力安全、強化された」　ＩＡＥＡ、東電事故から１０年で	https://www.hokkaido-np.co.jp/article/611210/	国際原子力機関	2021-11-13 08:12:00
北海道	北海道新聞	漁業共済未加入者も支援　赤潮被害拡大で水産庁方針	https://www.hokkaido-np.co.jp/article/611177/	道東	2021-11-13 08:12:03
ビジネス	東洋経済オンライン	｢医療従事者にワクチン義務化｣の英国に潜む懸念 ｢コロナ再拡大の震源地にいる｣欧州の苦悩 | 新型コロナ、長期戦の混沌 | 東洋経済オンライン	https://toyokeizai.net/articles/-/468144?utm_source=rss&utm_medium=http&utm_campaign=link_back	医療従事者	2021-11-13 08:30:00
GCP	Cloud Blog	How spam detection taught us better tech support	https://cloud.google.com/blog/topics/developers-practitioners/how-spam-detection-taught-us-better-tech-support/	How spam detection taught us better tech supportInformation Technology teams especially in help desk and support need a way to track what problems people are having Ideally they also can know how those problems change over time especially when technology or policy shifts Imagine you are in charge of sending a newspaper delivery team to different neighborhoods Each person has a bicycle so you give them a route and they leave the papers at the right doors But the roads change Every day they change It s chaos What do you do when routes are changing constantly  How do you provide the information needed when the context is shifting all the time In an IT context we run into similar challenges with traditional problem management frameworks such as ITIL which tend to always assume a fixed well defined catalog of services That way every issue that the IT folks solve is tracked and accounted for That connection back to the catalog allows insight into what s causing issues or where outages or incidents may be impacting a large group of employees  At Google we don t have that In part because we focus on putting the user first and so we focus on getting people back to a productive state as job Also because products services and issues are always shifting just like the roads the route is never the same even if the goal remains consistent That means users come into our IT service desk with new problem types everyday Our tech support team called Techstop acts as the one stop shop for all IT issues and supports people across chat email and video channels They need to remain adaptable to new problems Googlers experience and new products they use In order to track what problems might be on the rise the Techstop team needs a way to catalog what tools applications and services are in use at Google  Thinking back to the newspaper delivery routes we used a rough approximate map rather than a very detailed one giving us a taxonomy of services that was “good enough for most of our use cases We got some useful data out of it but it didn t give us very granular insight Need for innovationCovid put a new focus on scalable problem understanding specifically for everyday employee IT issues With so much of the workforce moved to a work from home model we really needed to know where employees were experiencing technology pain It s as if whole new neighborhoods popped into existence overnight but our newspaper delivery crew was the same More ground to cover with totally novel street maps Furthermore products used everyday for productivity such as Google Meet began to see exponential growth in usage causing scaling issues and outages These product teams looked to the Techstop organization to help them prioritize the ever increasing list of feature requests and bugs being filled every day Ultimately the “good enough problem taxonomy failed to produce truly helpful insights We could find out which products were being affected the most but not what issues people were having with those products Even worse new issues that were unique to the work from home model were being hidden by the fact that the catalog could not update in time to catch the rapidly changing problem space underneath it Borrowing spam techTaking a look around other efforts at Google the Techstop team found examples of solving a similar problem detecting new patterns quickly in rapidly changing data Gmail handles spam filtering for over a billion people Those engineers had thought through “how do we detect a new spam campaign quickly  Spammers rapidly send bulk messages with slight variations in content noise misspellings etc Most classification attempts would become a game of cat and mouse since it takes classifiers some time to learn about new patterns Invoking a trend identification engine using unsupervised density clustering on unstructured text unlocked the ability for Gmail to detect ephemeral spam campaigns more quickly The Techstop problem had a similar shape to it Issues caused by rapidly changing products caused highly dynamic user journeys for both employees and the IT professionals troubleshooting these issues The tickets filed ーlike the spam emails ーwere similar with slight differences in spelling and word choice Density clusteringIn contrast to more rigid approaches such as centroid based algorithms like k means density based clustering is better suited to large and highly heterogeneous data sets which may contain clusters of drastically variant size This flexibility helps us tackle the task of problem identification across the entire scope of the company which requires the ability to detect and distinguish small but significant perturbations in the presence of large but stable patterns Our implementation uses ClustOn an in house technology with a hybrid approach that incorporates density based clustering But a more time tested algorithm such as DBSCAN ーan open source implementation of which is available via scikit learn s clustering module ーcould be leveraged to similar effect Middle of the road solution using MLPiggy backing off of what Gmail was able to do using density clustering techniques the Techstop team built a robust solution to tracking problems in a way that solved the rigid taxonomy problem With density clustering the taxonomy buckets are redefined as trending clusters and provide an index of issues happening in real time within the company Importantly these buckets emerge naturally rather than being defined ahead of time by the Engineering or Tech Support teams By using the technology built for billions of email accounts we knew we could handle the scale of Google s support requests And the solutions would be more flexible than a tightly defined taxonomy without compromising on relevance or granularity   The team took it one step further by modeling cluster behavior using Poisson regression and implemented anomaly detection measures to alert operations teams in real time about ongoing outages or poorly executed changes With a lightweight operations team and this new technology Techstop was able to find granular insights that would have taken an entire dedicated team to manually comb through and aggregate each incident The combination of ML and Operations transformed Techstop data into a valuable reference for product managers and engineering teams looking to understand the issues users face with their products in an enterprise environment  How it worksTo bring it all together we built a ML pipeline that we call Support Insights so we could automatically distill summary data from the many interactions and tickets we received The Support Insights Pipeline combines machine learning human validation and probabilistic analysis together in a single systems dynamics approach  As data moves through this pipeline they are Extracted Uses the BigQuery API to store and extract train and load support data To ingest the M amount of IT related support data Processed Part of speech tagging PII Redaction and TF IDF transformations to model support data for our clustering algorithmsClustered Centroid based clustering runs in timed batches with persistent snaphotting of previous run states to maintain cluster ids and track behavior of clusters over time Scored Uses Poisson Regression to model both long term and short term behavior of cluster trends and calculates the difference between the two to measure deviation This deviation score is used to detect anomalous behavior within a trend Operationalized Trends with an anomalous score over a certain threshold trigger an IssueTracker API bug This bug is then picked up by operations teams for relevant deep dive and incident tracking Resampled Uses statistical methods to estimate proportions of customer user journeys CUJs within trendsCategorized mapped We work with the Operations teams to map trend proportions to User Journey SegmentsIn our next post we ll detail what technologies and methods we used for these seven steps and walk through how you could use a similar pipeline yourself To get started start by loading your data into BigQuery and use BigQuery ML to cluster your support data Related ArticleHow to provide better search results with AI rankingAutomatic scoring and natural language models can make internal search results much more relevant and timely especially when new trends Read Article	2021-11-12 23:30:00