投稿時間:2022-04-15 08:39:48 RSSフィード2022-04-15 08:00 分まとめ(42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
IT	気になる、記になる…	｢Windows 11 Build 22000.651｣と｢Windows 10 21H2 Build 19044.1679｣がRelease Previewチャネル向けにリリース	https://taisy0.com/2022/04/15/155813.html	microsoft	2022-04-14 22:39:48
IT	気になる、記になる…	｢Google Pixel 7｣シリーズのリアカメラは｢Pixel 6｣シリーズと同じイメージセンサーを搭載か	https://taisy0.com/2022/04/15/155809.html	google	2022-04-14 22:31:16
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] デジタル重点計画6月改定　安全保障対応など盛り込む方針	https://mag.executive.itmedia.co.jp/executive/articles/2204/15/news064.html	itmedia	2022-04-15 07:46:00
IT	ITmedia 総合記事一覧	[ITmedia News] 曖昧な「ウェルビーイング」への向き合い方　従業員の“幸福度”を上げる施策の実践方法とは？	https://www.itmedia.co.jp/news/articles/2204/15/news050.html	itmedia	2022-04-15 07:30:00
AWS	AWS Media Blog	Introducing Autodesk Flame on AWS	https://aws.amazon.com/blogs/media/introducing-autodesk-flame-on-aws/	Introducing Autodesk Flame on AWSMarking a major milestone for an application celebrating it s th anniversary that was once only available on dedicated hardware Autodesk has enabled Flame to run on Amazon Web Services AWS Autodesk Flame provides high end D visual effects VFX finishing and color grading software used across advertising and episodic and feature film VFX …	2022-04-14 22:17:08
python	Pythonタグが付けられた新着投稿 - Qiita	AtCoder Beginner Contest 245 参戦記	https://qiita.com/c-yan/items/801717939940a66e6bfa	abcdmapintinput	2022-04-15 07:25:11
Ruby	Rubyタグが付けられた新着投稿 - Qiita	WHERE A and (B or C)をActiveRecordで記述する方法	https://qiita.com/yokoo-an209/items/616e4fc742dbb7d2d1fa	activerecord	2022-04-15 07:08:02
Ruby	Railsタグが付けられた新着投稿 - Qiita	WHERE A and (B or C)をActiveRecordで記述する方法	https://qiita.com/yokoo-an209/items/616e4fc742dbb7d2d1fa	activerecord	2022-04-15 07:08:02
技術ブログ	Developers.IO	【SwiftUI】Listの並び替えを実装する	https://dev.classmethod.jp/articles/swiftui-list-rearrange/	editbutto	2022-04-14 22:49:04
海外TECH	Ars Technica	Explaining why gamers are adopting Windows 11 more slowly than Windows 10	https://arstechnica.com/?p=1848161	windows	2022-04-14 22:09:30
海外TECH	DEV Community	Introduction to Sentence-BERT (SBERT)	https://dev.to/colinmcdermott/introduction-to-sentence-bert-sbert-nd2	Introduction to Sentence BERT SBERT An introduction to Sentence BERT by Ozan Yılmaz an NLP engineer from Germany This article was originally published on Search Candy In October Google announced a new AI based technology called BERT to further improve their search results BERT stands for Bidirectional Encoder Representations from Transformers and is a language representation model which was trained with M English words The huge difference between BERT and earlier versions of language models is that BERT understands the context in which a word is used To make this a bit clearer let s look at the following two example sentences   Bob is running a marathon Bob is running a company It is easy for a human reader to understand that running has a completely different meaning in these examples but for machines this task is far from trivial Early models which were widely used until would have put both cases of running into the same semantic box of walking fast because it would be the most common usage BERT on the other hand looks at the context of words before encoding their meaning into ones and zeros This makes it a much more accurate and powerful algorithm for encoding natural languages For BERT it is clear that running in example implies that Bob leads a company and has nothing to do with the act of walking After understanding what makes BERT so special it is easy to see the value behind this technology for Google s search algorithms Not only does it improve the understanding of words in user inputs but it also helps Google understand more natural user queries In you don t have to explain your grandparents keyword based search anymore With the help of BERT Google learns to understand whole sequences and their semantic connections Nowadays you can ask a question like What is the name of the movie where the little boy sees ghosts and get The Sixth Sense as the first result which is a perfectly fitting and in this case correct answer Sentence BERT SBERT As good as it sounds BERT is still not a solution for every kind of semantic search problem Although it is a superb way to encode the meanings of words in a query it doesn t perform well when it comes to comparing similarities of whole sentences Reimers and his colleague Gurevych the authors of the Sentence BERT paper realized this early on and explain the problem with the following statement Finding the most similar pair in a collection of sentences requires about million inference computations hours with BERT The construction of BERT makes it unsuitable for semantic similarity A few modifications and some more fine tuning for semantic textual similarity help Sentence BERT cut those hours to about  seconds which is an incredible performance boost The difference to regular BERT is the idea to go one abstraction level further and encode the semantic meaning of the whole sentence instead of only encoding the individual words The system s core still builds on the standard pre trained BERT algorithm and derives its semantic power from it So in a scenario where you want to find the most similar headlines to a given one you are better off utilizing the SBERT algorithm At first glance this information might not seem valuable in the scope of SEO but with new algorithms on the rise like SMITH which works similar to BERT but looks at longer contexts on document scale getting a feel for SBERT might be a good thing to do Fortunately I conducted an experiment with SBERT to give you some insights and shed some light on the capacities of BERT powered architectures Using SBERT to find similar asked questionsGiven that we now know SBERT is much faster when it comes to comparing sentences we will use this knowledge to identify questions from a database which are similar to a new question asked by the user To do this I downloaded a huge data dump from reddit s well known subreddit Explain Like I m Five ELI using a script by Facebook On ELI users can ask questions about all kinds of things and get layman friendly answers I filtered out the questions and encoded them with SBERT In the next step I built an algorithm which utilizes the commonly used vector similarity measure cosine similarity to compute questions from my data dump that are most similar to my input question To have a Baseline for comparison I did the same experiment with an encoding strategy called TF IDF TF IDF encodings reflect how important a word is to a document in a collection or corpus It is a quite naïve statistical method which operates on word level and doesn t take semantics into account In the following section I will discuss the results for two questions I asked and explain the underlying reasoning process Example Is there racism in animals Resulting similar questions TF IDFSBERTWhy is there racism Do animals express discrimination racism based on the colour of fur skin Why is there racism Do animals get embarrassed or feel shame Why is racism still a thing Why is incest breeding bad for animals Why is racism so common Are there murderers psychopaths or other behavioral deviations commonly associated with human beings among animals The top similar question for SBERT gives the user exactly what he asked for although the wording of the question is different Here we can observe the power of SBERT The sentences are compared using a deeper level of semantics which helps the system understand the query in a much more advanced way which is not possible for a simple TF IDF model The naïve system gets very focused on the word racism and provides two top answers containing is there racism It is obvious that the TF IDF search mechanism drops the important phrase in animals in order to maximize the similarity between the sequences This makes it clear that the baseline representation focuses on a word level encoding structure and doesn t take the context into account Further we can see that SBERT even understands that racism has a negative connotation This is the reason why it also lists other question results like Do animals get embarrassed or feel ashamed This behavior is out of scope for a simple statistical model which was fitted on short questions Example Why didn t we already fly to Mars Resulting similar questions TF IDFSBERTCould we see someone on Mars Why haven t we been able to land on Mars Why not mars Why haven t we put a man on Mars yet How high can a fly fly Why are we making our expedition to mars a one way trip Why does it sometimes cost more to fly from A →B than it does to fly from A →B →C Why do we need to go to Mars Once again the baseline model concentrates on the simple word level concepts of flying and mars while SBERT gets the intention of the question and provides the user with fitting results regarding the topic travel of mankind to mars It s interesting to observe the interchangeable usage of already and yet between the second SBERT provided question and the user query This again shows the semantic power of the BERT architecture As we can see SBERT has the capacity to encode even granular semantic information on the sentence level and provides meaningful results when used for sentence similarity tasks Given the fact that algorithms like SMITH are currently being developed it could be possible that a sentence level language model like SBERT could be implemented in Google s algorithm as well But what does this power of BERT and BERT like technologies mean for SEO Cutting Edge Language Technologies and SEONatural Language Processing made a huge forward leap with the introduction of BERT and there is no sign of slowing down New papers are being released on a regular basis and the field is getting pushed more than ever by companies like Google Facebook and Amazon With this in mind the challenge of SEO will change from caring about backlinks keyword optimization meta descriptions etc to actually just generating quality content for users People want to find precise and compact information when they search for something and you need to be able to deliver exactly that The times where we optimized for machines slowly comes to an end In the future machines will optimize for us Sources Google BERT announcement BERT Paper Sentence BERT Paper SMITH Algorithm Paper Reddit ELI Subreddit Facebook ELI Project Cosine Similarity TF IDF	2022-04-14 22:05:02
海外TECH	DEV Community	tsParticles 2.0.5 is out. Breaking changes ahead, keeping the 1.x support.	https://dev.to/matteobruni/tsparticles-205-is-out-breaking-changes-ahead-keeping-the-1x-support-2n63	tsParticles is out Breaking changes ahead keeping the x support tsParticles Changelog Breaking ChangesStarting from version tsparticles won t be a single package anymore Its growth makes me think a lot about splitting the project in more packages The new structure will be a common engine and lot of packages with single features that can be installed and loaded so everyone can install only the features they need and for the lazy ones there are bundles and presets ready to be used For example if you want to stick with the tsparticles library you can still install it and use the bundle file with the CDN You can easily set it up when using import or require since you have to add few lines of code to the v configuration import tsParticles from tsparticles engine this is the new common packageimport loadFull from tsparticles this function loads all the features contained in v package async gt await loadFull tsParticles this is needed to load all the features and can be done everywhere before using tsParticles load await tsParticles load tsparticles options this must be done after loadFull PROSmaller output you can import only the features you need without a lot of unused code Better performance since a lot of features are not imported they are not running reducing general performance More features more calculations needed CONSAll features needs to be installed which result in a long package json file that s why presets will be more important now Previous code won t work anymore without importing the right packages this is a needed breaking change New FeaturesAdded outside and inside values to particles move direction optionsAdded outside and inside values to particles move out modes options How to migrate from v to v Version x is still the latest tag on npm but the next version has a version which is something I need to release to the public to find issues and receive some feedbacks Migration Steps Vanilla JS HTML usageJust change the tsparticles file from tsparticles min js to tsparticles bundle min js if the slim version is used there s a bundle also there but it s a different package now called tsparticles slim ModulesInstall the package tsparticles engine using the next tag like this npm install tsparticles engine nextReplace all your tsparticles imports to tsparticles engine Add import loadFull from tsparticles in the imports or its RequireJS version This requires the new x version you can install it using npm install tsparticles nextCall loadFullIf using a React Vue Angular Svelte or other kind of component in particlesInit init property passing the same parameter coming from the init function to loadFullIf not just call loadFull tsParticles before any tsParticles usage AlternativeUsing the bundled version of the tsparticles package is not optimal it s easier to implement but it could load a lot of unnecessary stuff I want to take the following code as an example it s the core of tsparticles slim package import type Engine from tsparticles engine import loadAngleUpdater from tsparticles updater angle import loadBaseMover from tsparticles move base import loadCircleShape from tsparticles shape circle import loadColorUpdater from tsparticles updater color import loadExternalAttractInteraction from tsparticles interaction external attract import loadExternalBounceInteraction from tsparticles interaction external bounce import loadExternalBubbleInteraction from tsparticles interaction external bubble import loadExternalConnectInteraction from tsparticles interaction external connect import loadExternalGrabInteraction from tsparticles interaction external grab import loadExternalPauseInteraction from tsparticles interaction external pause import loadExternalPushInteraction from tsparticles interaction external push import loadExternalRemoveInteraction from tsparticles interaction external remove import loadExternalRepulseInteraction from tsparticles interaction external repulse import loadImageShape from tsparticles shape image import loadLifeUpdater from tsparticles updater life import loadLineShape from tsparticles shape line import loadOpacityUpdater from tsparticles updater opacity import loadOutModesUpdater from tsparticles updater out modes import loadParallaxMover from tsparticles move parallax import loadParticlesAttractInteraction from tsparticles interaction particles attract import loadParticlesCollisionsInteraction from tsparticles interaction particles collisions import loadParticlesLinksInteraction from tsparticles interaction particles links import loadPolygonShape from tsparticles shape polygon import loadSizeUpdater from tsparticles updater size import loadSquareShape from tsparticles shape square import loadStarShape from tsparticles shape star import loadStrokeColorUpdater from tsparticles updater stroke color import loadTextShape from tsparticles shape text export async function loadSlim engine Engine Promise lt void gt await loadBaseMover engine await loadParallaxMover engine await loadExternalAttractInteraction engine await loadExternalBounceInteraction engine await loadExternalBubbleInteraction engine await loadExternalConnectInteraction engine await loadExternalGrabInteraction engine await loadExternalPauseInteraction engine await loadExternalPushInteraction engine await loadExternalRemoveInteraction engine await loadExternalRepulseInteraction engine await loadParticlesAttractInteraction engine await loadParticlesCollisionsInteraction engine await loadParticlesLinksInteraction engine await loadCircleShape engine await loadImageShape engine await loadLineShape engine await loadPolygonShape engine await loadSquareShape engine await loadStarShape engine await loadTextShape engine await loadLifeUpdater engine await loadOpacityUpdater engine await loadSizeUpdater engine await loadAngleUpdater engine await loadColorUpdater engine await loadStrokeColorUpdater engine await loadOutModesUpdater engine Vanilla JS HTML UsageSplitting things can be a long activity using lt script gt tags but nothing impossible From the example above every package needs its own lt script gt tag and every load function needs to be called using tsParticles as a parameter then use the tsParticles object as always The tsparticles engine must be always present if there are no bundles tsparticles slim tsparticles or any bundled preset Every other package is required only if you want to use that feature Let s see an example As you can see in the JS options there are the needed scripts and before using tsParticles load their functions are called to load everything correctly Every load function is async so it s a Promise that can be awaited it s not always necessary like in this case but it s recommended ModulesIn this case importing modules is easier since every module can be installed easily using npm yarn or pnpm Once installed the required packages import them and the code used for Vanilla JS HTML Usage works also here The module sample can be found here Components React Vue Angular Svelte Every component has a init or particlesInit checkout the documentation until everything has the same attribute that is the place to load all the components that function has an engine attribute which is the tsParticles instance used by the component React SampleVue js x SampleVue js x SampleAngular Sample	2022-04-14 22:04:34
金融	金融総合：経済レポート一覧	65歳以上の給与所得者の増加と在職老齢年金の見直し	http://www3.keizaireport.com/report.php/RID/492444/?rss	給与所得	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	３億人の‘新市民’市場と保険サービス（中国）：基礎研レター	http://www3.keizaireport.com/report.php/RID/492445/?rss	研究所	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	シンガポール通貨庁、半年で３度目の引き締め決定、引き締めペースも強化～シンガポールは財政、金融政策の両面で引き締めが進むなど、ポスト・コロナを目指す動きが前進：Asia Trends	http://www3.keizaireport.com/report.php/RID/492446/?rss	asiatrends	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	受注好調でも物足りなさを覚える局面：Market Flash	http://www3.keizaireport.com/report.php/RID/492447/?rss	marketflash	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	日本銀行が中銀デジタル通貨（CBDC）第1段階実証実験の報告書を公表：木内登英のGlobal Economy ＆ Policy Insight	http://www3.keizaireport.com/report.php/RID/492450/?rss	lobaleconomypolicyinsight	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	FX Daily（4月13日）～ドル円、2002年5月以来の高値圏	http://www3.keizaireport.com/report.php/RID/492451/?rss	fxdaily	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	ドル円は約20年ぶりの円安水準に～今後の相場展開を考える：市川レポート	http://www3.keizaireport.com/report.php/RID/492487/?rss	三井住友	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	カナダ金融政策（2022年4月）～利上げペース加速とQT開始を決定：マーケットレター	http://www3.keizaireport.com/report.php/RID/492488/?rss	投資信託	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	インド準備銀行11会合連続政策金利据え置き～インフレ対策に重点を置く姿勢を示す：新興国レポート	http://www3.keizaireport.com/report.php/RID/492489/?rss	据え置き	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	投資のヒント「排出権市場が機能マヒ、今後の展開は？～ESGニュース　気になるトピック（4月）」	http://www3.keizaireport.com/report.php/RID/492490/?rss	三井住友トラスト	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	【ACI】ヘルスケア・マンスリー・レポート（2022年3月）～ヘルスケア株は絶対値で大幅に上昇...	http://www3.keizaireport.com/report.php/RID/492491/?rss	野村アセットマネジメント	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	暗号資産への投資に対する警告文書の発出（欧州）～欧州金融監督当局から公表された文書の紹介：基礎研レター	http://www3.keizaireport.com/report.php/RID/492492/?rss	Detail Nothing	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	不動産はインフレヘッジになるか：不動産マーケットリサーチレポート	http://www3.keizaireport.com/report.php/RID/492504/?rss	三菱ufj信託銀行	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	国民健康保険における予防・健康づくりに関する調査分析事業 報告書	http://www3.keizaireport.com/report.php/RID/492526/?rss	事業報告書	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	韓国中銀、次期総裁就任前も、物価対応を理由に追加利上げを決定～政策委員の「タカ派」傾斜が確認されるなか、次期体制下でも追加利上げに動く可能性は高い：Asia Trends	http://www3.keizaireport.com/report.php/RID/492529/?rss	asiatrends	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	足元の債券利回りの上昇が確定給付企業年金に与える影響について～退職給付会計と非継続基準の観点から	http://www3.keizaireport.com/report.php/RID/492556/?rss	確定給付企業年金	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	マテリアリティの開示が株主資本コストに及ぼす影響について	http://www3.keizaireport.com/report.php/RID/492564/?rss	日本政策投資銀行	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	ながさき暮らしのデータBOX：キャッシュレス決済、今後は？～6割近くが、「キャッシュレス決済が現金より多い」	http://www3.keizaireport.com/report.php/RID/492573/?rss	長崎	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	最近のウクライナ情勢とESG投資について～ロシアのウクライナ侵攻後の論説のサーベイと長期投資の観点からの考察	http://www3.keizaireport.com/report.php/RID/492584/?rss	長期	2022-04-15 00:00:00
金融	ニュース - 保険市場TIMES	第一生命、市民ランナー応援プロジェクトの2022年度協賛大会を決定	https://www.hokende.com/news/blog/entry/2022/04/15/080000	第一生命、市民ランナー応援プロジェクトの年度協賛大会を決定「RunwithYou」協賛大会決定第一生命保険株式会社以下、第一生命は、市民ランナー応援プロジェクト「RunwithYou」の年度協賛大会を決定したと年月日に発表した。	2022-04-15 08:00:00
ニュース	@日本経済新聞 電子版	18歳成人狙う悪質商法・バイト時給・ロシア外交官追放 【編集者が選ぶ3本】 https://t.co/nF98SNGHok	https://twitter.com/nikkei/statuses/1514740360328667145	悪質商法	2022-04-14 23:00:19
ニュース	@日本経済新聞 電子版	黒海艦隊旗艦の巡洋艦「モスクワ」が沈没したとロシア国防省が発表。米国防総省高官は大規模な火災があったと分析、ウクライナ軍がミサイル攻撃を実行した可能性を排除しないとしました。 https://t.co/THUXe3Z96K	https://twitter.com/nikkei/statuses/1514735273292312578	黒海艦隊旗艦の巡洋艦「モスクワ」が沈没したとロシア国防省が発表。	2022-04-14 22:40:06
ニュース	@日本経済新聞 電子版	SNSを通じた接触や美容に関する「憧れ」に落とし穴。18～19歳が新たに成人となり、相次ぐのは若者を狙った悪質商法被害。成人が結んだ契約は取り消しが難しく、消費者被害の増加が懸念されます。 https://t.co/lTlsqAcsOR	https://twitter.com/nikkei/statuses/1514730246632783874		2022-04-14 22:20:07
ニュース	@日本経済新聞 電子版	新型コロナウイルスの感染拡大による中国・上海市の都市封鎖（ロックダウン）で、中国経済の下押し圧力が強まっています。経済にどんな打撃があるのでしょうか。4月15日、日本経済新聞朝刊のポイントをお届けします。 #朝刊1面を読もう https://t.co/ZAmAa6v1LS	https://twitter.com/nikkei/statuses/1514728986584174599	新型コロナウイルスの感染拡大による中国・上海市の都市封鎖ロックダウンで、中国経済の下押し圧力が強まっています。	2022-04-14 22:15:07
ニュース	BBC News - Home	Behind the scenes in Zelensky's modern HQ	https://www.bbc.co.uk/news/world-europe-61113079?at_medium=RSS&at_campaign=KARANGA	invasion	2022-04-14 22:17:11
ビジネス	不景気.com	松竹の22年2月期は40億円の営業赤字、コロナ影響続く - 不景気.com	https://www.fukeiki.com/2022/04/shochiku-2022-loss.html	決算短信	2022-04-14 22:43:18
ビジネス	東洋経済オンライン	安倍､二階も窮地？衆院｢10増10減｣に自民党激震 自分たちで決めたルールなのにちゃぶ台返しも | 国内政治 | 東洋経済オンライン	https://toyokeizai.net/articles/-/581978?utm_source=rss&utm_medium=http&utm_campaign=link_back	国内政治	2022-04-15 07:30:00
仮想通貨	BITPRESS（ビットプレス）	[FT] 暗号資産にすがる英政府　新たな「金脈」規制後回し 暗号資産（仮想通貨）	https://bitpress.jp/count2/3_9_13168	金脈	2022-04-15 07:19:39
ニュース	THE BRIDGE	海外投資家による、国内スタートアップ市場への投資状況をチェック	https://thebridge.jp/2022/04/how-foreign-investors-bet-on-japanese-startups	海外投資家による、国内スタートアップ市場への投資状況をチェック本稿は独立系ベンチャーキャピタルSTRIVEによるものを一部要約して転載させていただいた。	2022-04-14 22:15:36
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