投稿時間:2022-09-13 19:35:40 RSSフィード2022-09-13 19:00 分まとめ(47件)

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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] イラスト作画AI、作家の敵か味方か　画風学習し新たな絵　盗用を恐れ炎上	https://www.itmedia.co.jp/business/articles/2209/13/news190.html	ITmediaビジネスオンラインイラスト作画AI、作家の敵か味方か画風学習し新たな絵盗用を恐れ炎上指定した言葉やイラストを学習して新しい絵を作り出す人工知能AI技術が国内外で相次いで提供され、話題となっている。	2022-09-13 18:34:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 寝具の西川、“お尻のまくら”を発売　睡眠化学を座り心地に活用	https://www.itmedia.co.jp/business/articles/2209/13/news181.html	itmedia	2022-09-13 18:32:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] ミズノが「ドライビングシューズ」に本格参入　運転と日常履きを両立、9月20日に新商品発売	https://www.itmedia.co.jp/business/articles/2209/13/news188.html	今月日からは、運転操作のサポート機能を搭載したドライビングシューズ「BARECLUTCHベアクラッチ」の発売を開始。	2022-09-13 18:31:00
IT	ITmedia 総合記事一覧	[ITmedia News] iOS 16の「被写体切り抜き機能」が意外と楽しい　誰でも“クソコラ職人”に	https://www.itmedia.co.jp/news/articles/2209/13/news180.html	iphone	2022-09-13 18:30:00
IT	ITmedia 総合記事一覧	[ITmedia Mobile] iOS 16の新「ロック画面」を試す　何がどう便利になった？	https://www.itmedia.co.jp/mobile/articles/2209/13/news186.html	apple	2022-09-13 18:21:00
IT	ITmedia 総合記事一覧	[ITmedia News] 任天堂、SIE、カプコン……　東京ゲームショウを前に新作発表会が続々	https://www.itmedia.co.jp/news/articles/2209/13/news175.html	itmedia	2022-09-13 18:15:00
IT	ITmedia 総合記事一覧	[ITmedia Mobile] OCN モバイル ONE、ドコモでんきのdポイント還元率が最大10％に	https://www.itmedia.co.jp/mobile/articles/2209/13/news168.html	itmediamobileocn	2022-09-13 18:05:00
python	Pythonタグが付けられた新着投稿 - Qiita	コピペでスクレイピング② クラウドワークスの新着情報を収集	https://qiita.com/Gen2423/items/6a300adb5867970c9f77	間隔	2022-09-13 18:41:43
python	Pythonタグが付けられた新着投稿 - Qiita	データレコードが文字列として１つにまとめられていて、なおかつデータ間の区切りスペース数が行ごとに異なる汚いcsv.ファイルをPythonで直してみた	https://qiita.com/Mii4a_Shota/items/5feace8cfb5ce6c44d0e	csvpdf	2022-09-13 18:40:30
js	JavaScriptタグが付けられた新着投稿 - Qiita	paizaラーニング レベルアップ問題集 ソートメニュー応用編 JavaScript スケジューリング	https://qiita.com/ZampieriIsa/items/2c94c0263930d183c922	javascript	2022-09-13 18:08:26
Ruby	Rubyタグが付けられた新着投稿 - Qiita	ActionController::RoutingErrorの凡ミス	https://qiita.com/Ittetsu_Watanabe/items/aacde131cf3a92045568	actioncontroller	2022-09-13 18:58:46
Ruby	Rubyタグが付けられた新着投稿 - Qiita	mimemagicのbundle installに手間取った件	https://qiita.com/lyd-ryotaro/items/fdccd88fb4b647f1d3bd	gemfilesw	2022-09-13 18:38:29
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【初心者向け】moduleの使い分け	https://qiita.com/gaia0683/items/66d7e010a69d307aec8c	module	2022-09-13 18:23:59
AWS	AWSタグが付けられた新着投稿 - Qiita	SPRESENSE SDKに独自の拡張機能を追加する【External Library】	https://qiita.com/ixy-shino/items/9c778243365cad50a443	externallibrary	2022-09-13 18:05:31
Ruby	Railsタグが付けられた新着投稿 - Qiita	mimemagicのbundle installに手間取った件	https://qiita.com/lyd-ryotaro/items/fdccd88fb4b647f1d3bd	gemfilesw	2022-09-13 18:38:29
Ruby	Railsタグが付けられた新着投稿 - Qiita	【初心者向け】moduleの使い分け	https://qiita.com/gaia0683/items/66d7e010a69d307aec8c	module	2022-09-13 18:23:59
Ruby	Railsタグが付けられた新着投稿 - Qiita	ActiveHashの使用例（備忘録）	https://qiita.com/shota13138380/items/96b5b3293273a1a759f8	activehash	2022-09-13 18:13:54
技術ブログ	Developers.IO	# [書評] 「VS Code Meetup Book」を読んでみました	https://dev.classmethod.jp/articles/book-review1-vs-code-meetup-book/	vscodemeetupbook	2022-09-13 09:48:35
海外TECH	DEV Community	Understanding How to Evaluate Textual Problems	https://dev.to/neurotech_africa/understanding-how-to-evaluate-textual-problems-32md	Understanding How to Evaluate Textual ProblemsAs a data professional building models is a common topic what differs is just what that model is for models should solve certain challenges then after we consider measuring the quality and performance of these models using evaluation metrics and these are essential to confirm something concerning built models Evaluation metrics are used to measure the quality of the statistical or machine learning model This article was originally published on the Neurotech Africa blog Need for evaluation The aim of building AI solutions is to apply them to real world challenges Mind you our real world is complicated so how do we decide which model to use and when that is when their metrics come into application A failure to know how to justify why your choosing a certain model instead of others or why a certain model is good or not indicates you are not aware of what your solving or the model you built When you can measure what you are speaking of and express it in numbers you know that on which you are discussing But when you cannot measure it and express it in numbers your knowledge is of a very meager and unsatisfactory kind Lord KelvinToday let s have a sense of what are the metrics used in Natural Language Processing challenges Textual Evaluation MetricsIn the Natural Language Processing NLP field it is difficult to measure the performance of models for different tasks challenge with labels is easier to evaluate but in the case of NLP task the ground truth or result can be varied We have lots of downstream tasks such as text or sentiment analysis language generation question answering text summarization text recognition and translation It is possible that biases creep into models based on the dataset or evaluation criteria Therefore it is necessary to make Standard Performance Benchmarks to evaluate the performance of models for NLP tasks These Performance metrics give us an indication of which model is better for which task Let s jump right in to discuss some of the textual evaluation metrics  Accuracy  common metric in sentiment analysis and classification not the best one but denotes the fraction of times the model makes a correct prediction as compared to the total predictions it makes Best used when the output variable is categorical or discrete For example how often a sentiment classification algorithm is correct Confusion Matrix  also used in classification challenges It provides a clear report on the prediction of models in different categories from the primary objective visualization of the model the following questions can be answered What percentage of the positive class is actually positive Precision What percentage of the positive class gets captured by the model Recall What percentage of predictions are correct Accuracy Also we can consider Precision and Recall are complementary metrics that have an inverse relationship If both are of interest to us then we d use the F score to combine precision and recall into a single metric Perplexity  is a great probabilistic measure used to evaluate exactly how confused our model is It s typically used to evaluate language models but it can be used in dialog generation tasks The language model refers to how machine generated text is similar to humans write it In other words given w previous word and the correct score of generating w token The lower you get the perplexity the better model you have Find this article about the perplexity evaluation metric and take your time to explore Perplexity in Language Models Bits per character BPC and bits per word   are other metrics often used for language models evaluations tasks It measures exactly the quantity that it is named after the average number of bits needed to encode on character “if the language is translated into binary digits or in the most efficient way the entropy is the average number of binary digits required per letter of the original language ShannonEntropy is the average number of BPC The reason that some language models report both cross entropy loss and BPC is purely technical In practice if everyone uses a different base it is hard to compare results across models For the sake of consistency when we report entropy or cross entropy we report the values in bits Mind you BPC is specific to character level language models When we have word level language models the quantity is called bits per word BPW the average number of bits required to encode a wordGeneral Language Understanding Evaluation GLUE  this is a multi task benchmark based on different types of tasks rather than evaluating a single task As language models are increasingly being used for the purposes of transfer learning to other NLP tasks the intrinsic evaluation of a language model is less important than its performance on downstream tasks Super General Language Understanding Evaluation superGLUE  methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks This is the better or modified version of the GLUE benchmark with a new set of more difficult language understanding tasks and improved resources after a GLUE benchmark performance comes close to the level of non expert humans It comprised new ways to test creative approaches on a range of difficult NLP tasks including sample efficient transfer multitask and self supervised learningBiLingual Evaluation Understudy BLEU  commonly used in Machine translation and Caption Generation Since manual labeling for professional translation is very expensive the metric used in comparing a candidate translation by machine to one or more reference translations by a human being And the output lies in the range of where a score closer to indicates good quality translations The calculation of BLEU involves the concept of n gram precision and sentence brevity penalty This metric has some drawbacks such as It doesn t consider the meaning It doesn t directly consider sentence structure and It doesn t handle morphologically rich languages Rachael Tatman wrote an amazing article about BLEU just take your time to read it here Self BLEU  this  is a smart use of the traditional BLEU metric for capturing and quantifying diversity in the generated text The lower the value of the self bleu score the higher the diversity in the generated text Long text generation tasks like story generation news generation etc could be a good fit to keep an eye on such metrics helping evaluate the redundancy and monotonicity in the model This metric can be complemented with other text generation evaluation metrics that account for the goodness and relevance of the generated text Metric for Evaluation of Translation with Explicit ORdering METEOR  Precision based metric to measure the quality of the generated text Sort of a more robust BLEU Allows synonyms and stemmed words to be matched with the reference word Mainly used in machine translation METEOR solved two BLEU drawbacks of not taking recall into account and only allowing exact 𝑛 gram matching Instead METEOR first performs exact word mapping followed by stemmed word matching and finally synonym and paraphrase matching then computes the F score using this relaxed matching strategy METEOR only considers unigram matches as opposed to 𝑛 gram matches it seeks to reward longer contiguous matches using a penalty term known as fragmentation penalty BERTScore  this is an automatic evaluation metric used for testing the goodness of text generation systems Unlike existing popular methods that compute token level syntactical similarity BERTScore focuses on computing semantic similarity between tokens of reference and hypothesis Bidirectional Encoder Representations from Transformers compute the cosine similarity of each hypothesis token 𝑗with each token 𝑖in the reference sentence using contextualized embeddings They use a greedy matching approach instead of a time consuming best case matching approach and then compute the F measure BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics Character Error Rate CER  this is a common metric of the performance of an automatic speech recognition system This value indicates the percentage of characters that were incorrectly predicted The lower the value the better the performance of the ASR system with a CER of being a perfect score Possible tasks  CER can be applied to measure the performance are Speech Recognition Optical Character Recognition OCR and Handwriting Recognition Word Error Rate WER  this is a common performance metric mainly used for speech recognition optical character recognition OCR and handwriting recognition When recognizing speech and transcribing it into text some words may be left out or misinterpreted WER compares the predicted output and the reference transcript word by word to figure out the number of differences between them There are three types of errors considered when computing WER Insertions when the predicted output contains additional words that are not present in the transcript for example  SAT becomes essay tea Substitutions  when the predicted output contains some misinterpreted words that replace words in the transcript for example  noose is transcribed as moose Deletions  when the predicted output doesn t contain words that are present in the transcript for example  turn it around becomes turn around For understanding let s consider the following reference transcript and predicted output Reference transcript “Understanding textual evaluation metrics is awesome for a data professional Predicted output “Understanding textual metrics is great for a data professiona l In this case the predicted output has one deletion the word “textual disappears and one substitution “awesome  becomes “great So what is the Word Error Rate of this translation Basically WER is the number of errors divided by the number of words in the reference transcript WER num inserted num deleted num substituted num words in the referenceThus in our example WER Lower WER often indicates that the Automated Speech Recognition ASR software is more accurate in recognizing speech A higher WER then often indicates lower ASR accuracy The drawback is that it assumes the impact of different errors is the same Sometimes insertion error may have a bigger impact than deletion Another limitation is that this metric cannot distinguish a substitution error from combing deletion and insertion error Recall Oriented Understudy for Gisting Evaluation  ROUGE  this is Recall based unlike BLEU which is Precision based ROUGE metric includes a set of variants ROUGE N ROUGE L ROUGE W and ROUGE S ROUGE N is similar to BLEU N in counting the 𝑛 gram matches between the hypothesis and reference This is a set of metrics used for evaluating automatic summarization and machine translation software in natural language processing The metrics compare an automatically produced summary or translation against a reference or a set of references or a human produced summary or translation Mind you in summarization tasks where it s important to evaluate how many words a model can recall recall of true positives versus both true and false positives Feel free to check out the python package here Final Thoughts Understanding which performance measure to use and the best one for the problem at hand help to validate the right solution to meet the needs of the particular challenge The challenge with NLP solutions is on measuring their performance for various tasks Speaking of other Machine learning tasks it is easier to measure the performance because the cost function or evaluation criteria are well defined and have a clear picture of what is to be evaluated One more reason for this is that labels are well defined in other tasks but in the NLP task the ground result can vary a lot Coming up with the best model depends on various factors but evaluation metrics are an essential factor to consider depending on the nature of the task you are solving References Evaluation Metrics for Language ModelingEvaluating Text Output in NLP BLEU at your own riskEvaluation of Text Generation A SurveyEvaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment oriented TextEvaluating Text Generation with BERTAutomated metrics for evaluating the quality of text generation	2022-09-13 09:47:52
海外TECH	DEV Community	🎨Sass: the style you want, the code you need	https://dev.to/akram_ak/sass-the-style-you-want-the-code-you-need-5188	Sass the style you want the code you needIf you are a developer or web designer chances are you have heard about Sass at one time or another Sass is just as easy to use as CSS but includes some additional features that make it even more powerful It s time to start the Sass series In this first article of the series we will learn what Sass is and why you need to use it for more efficient web development Sass is an extension of the CSS language that adds power and flexibility without getting too complicated It provides variables mixins inheritance and other handy tools for making CSS more maintainable Sass also helps with more advanced features like code organization partials data driven styles looks and responsive design In this series we ll be going over the basics of SASS SCSS We ll be covering a lot of ground from basic syntax to more advanced features So if you re new to SASS SCSS or want to brush up on some old skills this is the series for you So let us start with this question Should you be using Sass or CSS That s a question often asked and one we can answer with a simple “Yes It all comes down to stylesheets vs preprocessors ーand it s important to get things right SASS and its more powerful cousin SCSS are writing systems that let you write faster more flexible and more maintainable code by way of variables mixins and nesting SASS was designed by Hampton Catlin and Chris Eppstein The main goal of SASS is to make it easier for developers to maintain their stylesheets and also to help them avoid some common coding problems This code for instance creates a set of classes twice once using CSS and once with SCSS Write less and do more with Sass That s all about this post for today in the next article we will learn how to use sass in your project See You Please feel free to reach out to me at Linkedin if you have any questions	2022-09-13 09:23:46
海外TECH	DEV Community	GitHub as a database	https://dev.to/begoon/github-as-a-database-2g0m	GitHub as a databasegit is indeed a database GitHub is a remote database powered by git I needed a way to keep information about certain important events in my code nicely saved for later analysis What can be better than committing them to a VCS Timestamps commit descriptions etc I used local git first and then switched to GitHub GitHub provides API for all its functionality The little code below demonstrates how this approach works It needs two things to be set GITHUB TOKEN which can be generated in your GitHub account and the repo variable with the repository name It upserts a new file to create a file Then it upserts it again to modify it Then it deletes the file The repository log nicely keeps all these actions in the commit history Note The PyGithub package needs to be installed first pip instal pygithubCode import osfrom typing import Optional Unionimport githubfrom github Repository import Repositorydef get repo repo str gt Repository assert repo repository name is missing g github Github os environ GITHUB TOKEN return g get repo repo def upsert file name str body str message Optional str None repo Optional Union Repository str None branch Optional str main verbose Optional bool False r repo if isinstance repo Repository else get repo repo try description message or f Update name current r get contents name ref branch current r update file current path description body current sha branch branch if verbose print current except github GithubException message message or f Create name created r create file name message body branch branch if verbose print created def delete file name str message str None repo Optional Union Repository str None branch str main verbose Optional bool False r repo if isinstance repo Repository else get repo repo message message or f Delete name current r get contents name ref branch deleted r delete file current path message current sha branch branch if verbose print deleted assert os getenv GITHUB TOKEN Set GITHUB TOKEN variable repo lt YOUR GITHUB NAME gt lt REPO NAME gt upsert file README md NEW BODY repo repo verbose True upsert file README md UPDATED BODY repo repo verbose True delete file README md repo repo verbose True Execute it by python main pyIt prints something like content ContentFile path README md commit Commit sha acfecbbeacbbdddefbbcbf commit Commit sha effaafdabdebdeeef content ContentFile path README md commit Commit sha fefbeefcadeaf content NotSet Go to your GitHub repository and check the commits	2022-09-13 09:00:36
Apple	AppleInsider - Frontpage News	Battery percentage won't show on all iPhones with iOS 16	https://appleinsider.com/articles/22/09/13/battery-percentage-wont-show-on-all-iphones-with-ios-16?utm_medium=rss	Battery percentage won x t show on all iPhones with iOS Although Apple brought back the option for a battery percentage icon in iOS it has now confirmed that several iPhones will not be able to display it The battery percentage returned in iOS for some iPhonesEvery iPhone until s iPhone X had the ability to show a percentage battery life figure at the top of the screen It was removed because Apple had to conserve space in order to add Face ID and other sensors in the radical redesign that came with the iPhone X Read more	2022-09-13 09:45:56
医療系	医療介護 CBnews	熱中症救急搬送1,481人、6週連続で減少-総務省消防庁が5－11日の1週間の速報値公表	https://www.cbnews.jp/news/entry/20220913180154	救急搬送	2022-09-13 18:10:00
金融	金融庁ホームページ	入札公告等を更新しました。	https://www.fsa.go.jp/choutatu/choutatu_j/nyusatu_menu.html	公告	2022-09-13 11:00:00
金融	金融庁ホームページ	鈴木財務大臣兼内閣府特命担当大臣閣議後記者会見の概要（令和４年９月９日）を掲載しました。	https://www.fsa.go.jp/common/conference/minister/2022b/20220909-1.html	内閣府特命担当大臣	2022-09-13 10:00:00
ニュース	BBC News - Home	Public urged to queue now to see Queen's coffin in Edinburgh	https://www.bbc.co.uk/news/uk-scotland-62887444?at_medium=RSS&at_campaign=KARANGA	progresses	2022-09-13 09:32:45
ニュース	BBC News - Home	Charles to visit Northern Ireland for first time as King	https://www.bbc.co.uk/news/uk-northern-ireland-62878272?at_medium=RSS&at_campaign=KARANGA	leaders	2022-09-13 09:10:38
ニュース	BBC News - Home	Jean-Luc Godard: Legendary French film director dies at 91	https://www.bbc.co.uk/news/entertainment-arts-62886470?at_medium=RSS&at_campaign=KARANGA	scorsese	2022-09-13 09:41:49
ニュース	BBC News - Home	Aldi becomes Britain’s fourth-largest supermarket	https://www.bbc.co.uk/news/business-62887477?at_medium=RSS&at_campaign=KARANGA	research	2022-09-13 09:50:20
ニュース	BBC News - Home	Queen's lying-in-state: What we know of the plans	https://www.bbc.co.uk/news/uk-62878294?at_medium=RSS&at_campaign=KARANGA	coffin	2022-09-13 09:02:13
ニュース	BBC News - Home	What time is the Queen's state funeral? Will shops and schools close? And other questions	https://www.bbc.co.uk/news/uk-62844663?at_medium=RSS&at_campaign=KARANGA	daily	2022-09-13 09:36:43
ニュース	BBC News - Home	Will Jamaica now seek to 'move on' from royals as a republic?	https://www.bbc.co.uk/news/world-latin-america-62846653?at_medium=RSS&at_campaign=KARANGA	jamaicans	2022-09-13 09:21:15
ニュース	BBC News - Home	Ryan Fraser back for Scotland as Andy Robertson misses Nations League matches	https://www.bbc.co.uk/sport/football/62887094?at_medium=RSS&at_campaign=KARANGA	Ryan Fraser back for Scotland as Andy Robertson misses Nations League matchesRyan Fraser returns to the Scotland squad for September s Nations League matches but captain Andy Robertson misses out through injury	2022-09-13 09:05:41
ニュース	BBC News - Home	Mourners queue all night to see Queen's coffin in Edinburgh	https://www.bbc.co.uk/news/uk-scotland-62879795?at_medium=RSS&at_campaign=KARANGA	giles	2022-09-13 09:54:15
北海道	北海道新聞	３５０人に計１５６万円超過付与　マイナポイント不具合	https://www.hokkaido-np.co.jp/article/730377/	超過	2022-09-13 18:27:00
北海道	北海道新聞	映画監督ゴダールさん死去　ヌーベルバーグ、最後の巨匠	https://www.hokkaido-np.co.jp/article/730376/	映画監督	2022-09-13 18:26:00
北海道	北海道新聞	演歌歌手の角川さん、京太郎さん熱唱　富良野でコンサート	https://www.hokkaido-np.co.jp/article/730370/	日本舞踊	2022-09-13 18:20:00
北海道	北海道新聞	ウクライナ侵攻に風刺で抗議　京都で現地漫画家の作品展	https://www.hokkaido-np.co.jp/article/730369/	風刺漫画	2022-09-13 18:20:00
北海道	北海道新聞	松前の陸上養殖ニジマス上々　町内の建設会社が参入　豊洲で高評、今秋から加工品も	https://www.hokkaido-np.co.jp/article/730368/	建設会社	2022-09-13 18:20:00
北海道	北海道新聞	公明、石井幹事長の続投調整　山口代表、８選確実受け	https://www.hokkaido-np.co.jp/article/730367/	山口那津男	2022-09-13 18:20:00
北海道	北海道新聞	ポニー、ばん馬疾走　全道から４０頭参加　富良野でフェス	https://www.hokkaido-np.co.jp/article/730366/	疾走	2022-09-13 18:19:00
北海道	北海道新聞	東京円、１４２円台前半	https://www.hokkaido-np.co.jp/article/730361/	東京外国為替市場	2022-09-13 18:17:00
北海道	北海道新聞	台風１２号、先島諸島離れる　引き続き高波に注意	https://www.hokkaido-np.co.jp/article/730357/	先島諸島	2022-09-13 18:13:00
北海道	北海道新聞	セブンと万代が提携解消　戦略不一致、出資も見送り	https://www.hokkaido-np.co.jp/article/730356/	食品	2022-09-13 18:04:00
ニュース	Newsweek	領土奪還に沸くウクライナ、秀逸動画でロシアを嘲笑う	https://www.newsweekjapan.jp/stories/world/2022/09/post-99615.php		2022-09-13 18:45:41
IT	週刊アスキー	「ル ビアン 新宿小田急店」がお引越し！　新しい小田急百貨店 新宿店地下2階（新宿西口ハルク）にて営業開始	https://weekly.ascii.jp/elem/000/004/105/4105364/	小田急百貨店	2022-09-13 18:30:00
IT	週刊アスキー	ドラマティックモードも一部体験可能！『ソルクレスタ』の無料体験版『Trial Version』が配信開始	https://weekly.ascii.jp/elem/000/004/105/4105384/	nintendo	2022-09-13 18:15:00