投稿時間:2023-07-19 15:27:38 RSSフィード2023-07-19 15:00 分まとめ(34件)

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IT	SNSマーケティングの情報ならソーシャルメディアラボ【Gaiax】	インフルエンサーマーケティングとは？メリットや事例を徹底解説	https://gaiax-socialmedialab.jp/post-144366/	近年	2023-07-19 05:45:51
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	エクサウィザーズ、DX人材に必要なスキルと素養をWeb上で診断するツール「DIA3.0」 | IT Leaders	https://it.impress.co.jp/articles/-/25112	エクサウィザーズ、DX人材に必要なスキルと素養をWeb上で診断するツール「DIA」ITLeadersエクサウィザーズは年月日、DXアセスメントツール新版「DIA」デジタルイノベーターアセスメントを発表した。	2023-07-19 14:47:00
python	Pythonタグが付けられた新着投稿 - Qiita	Snowflake Python Connectorのための環境を direnv + pip-tools で構築してみる！	https://qiita.com/se_yataro/items/b90a9feca1a232ccb226	direnvpiptools	2023-07-19 14:35:40
python	Pythonタグが付けられた新着投稿 - Qiita	pythonライブラリのzoneinfoとは	https://qiita.com/AiNopo/items/65025a34c21e6c3df9e3	datetime	2023-07-19 14:20:06
python	Pythonタグが付けられた新着投稿 - Qiita	venv仮想環境がインタープリタとして認識されない	https://qiita.com/AugusTaro/items/720e6dbcc187d24f23a7	vscode	2023-07-19 14:05:17
Ruby	Rubyタグが付けられた新着投稿 - Qiita	erbで関数的にコードの再利用を行う	https://qiita.com/nazomikan/items/681673a188f2314a82f3	jinja	2023-07-19 14:10:21
AWS	AWSタグが付けられた新着投稿 - Qiita	マルチ AZ な RDS における監査ログの対応状況について	https://qiita.com/takumats/items/c1363a845a481cd1d457	mariadbpostgresqlaurora	2023-07-19 14:38:05
Git	Gitタグが付けられた新着投稿 - Qiita	Gitの基本	https://qiita.com/kanakanho/items/ce40e242545ffc9387b7	配置	2023-07-19 14:19:43
技術ブログ	Developers.IO	DevelopersIO 2023 大阪 – AWS質問ブースのご紹介	https://dev.classmethod.jp/articles/developersio-2023-osaka-aws-booth/	developersio	2023-07-19 05:58:18
技術ブログ	Developers.IO	[アップデート] Amazon QuickSight に、分析上の様々な操作を管理するための「分析メニュー」が実装されました	https://dev.classmethod.jp/articles/quicksight-analysis-menu/	amazonquicksight	2023-07-19 05:13:36
海外TECH	DEV Community	Natural Language Processing (NLP) in JavaScript (series 2)	https://dev.to/scofieldidehen/natural-language-processing-nlp-in-javascript-series-2-1h8f	Natural Language Processing NLP in JavaScript series Natural Language Processing NLP is a field of artificial intelligence that focuses on enabling computers to understand interpret and generate human language It plays a critical role in modern applications ranging from language translation and sentiment analysis to chatbots and search engines NLP techniques empower developers to extract insights from vast amounts of textual data making it a powerful tool for data analysis and decision making In the first part of this article we learned how to set up an environment and gather data strip them of irrelevant words while preparing the data to be used as samples In the following session we would see how to use the data Find the previous session here Part of Speech POS Tagging in JavaScriptIn Natural Language Processing NLP Part of Speech POS tagging is a crucial task that involves assigning specific parts of speech such as nouns verbs adjectives and more to each word in a sentence Understanding Part of Speech TaggingPOS tagging plays a vital role in NLP as it helps in understanding the grammatical structure of a sentence which is essential for various language related tasks The process involves analyzing each word in a sentence and assigning them their corresponding part of speech For example in the sentence The quick brown fox jumps over the lazy dog the words would be tagged as follows The gt Determiner DT quick gt Adjective JJ brown gt Adjective JJ fox gt Noun NN jumps gt Verb VBZ over gt Preposition IN the gt Determiner DT lazy gt Adjective JJ dog gt Noun NN Working with POS Tagging in JavaScriptWe ll use the natural NLP library follow the previous session to understand how to set up your environment To perform POS tagging in JavaScript which supports various NLP tasks including tokenization POS tagging and more Implementing POS TaggingTo implement this step you must set up the natural library and the steps are outlined in the first session of this two part series Let s implement POS tagging in JavaScript Import the natural libraryconst natural require natural Create a tokenizer and POS tagger instanceconst tokenizer new natural WordTokenizer const posTagger new natural BrillPOSTagger natural BrillPOSTagger defaultRules natural BrillPOSTagger defaultLexicon Sample sentence for POS taggingconst sentence The quick brown fox jumps over the lazy dog Tokenize the sentence into wordsconst words tokenizer tokenize sentence Perform POS taggingconst taggedWords posTagger tag words Print the tagged wordstaggedWords forEach word amp gt console log word word Explanation We import the natural library and create instances for tokenization and POS tagging using natural WordTokenizer and natural BrillPOSTagger We define a sample sentence we want to tag with parts of speech The sentence is tokenized into individual words using the tokenizer tokenize function The posTagger tag function performs POS tagging on the tokenized words Finally we iterate through the tagged words and print them along with their respective parts of speech Topic Modeling in JavaScriptTopic modeling is an unsupervised learning technique used to discover underlying themes or topics within a collection of text documents  We ll use a sample corpus of documents to extract meaningful topics Understanding Topic ModelingTopic modeling is a statistical approach aiming to uncover latent topics in a large set of text documents It enables us to identify the main themes or subjects without any prior labeling or human supervision One of the popular algorithms for topic modeling is Latent Dirichlet Allocation LDA LDA assumes that each document in the corpus is a mixture of various topics and a distribution of words represents each topic The algorithm then works iteratively to assign words to different topics and determine the probability of each topic s presence in a given document By the end of the process we get a list of topics and the words that contribute most to each topic Working with Topic Modeling in JavaScriptWe will utilize the natural NLP library to perform topic modeling in JavaScript We ll use a sample corpus of documents to demonstrate the process Implementing Topic Modeling Import the natural libraryconst natural require natural Create a new LDA instanceconst lda new natural LdaSandbox Sample corpus of documentsconst documents Machine learning is an exciting field in computer science JavaScript is a versatile programming language used for web development Data science involves extracting insights from data using various techniques Node js is a popular runtime environment for server side JavaScript applications Topic modeling helps in discovering latent themes from text documents Tokenize the documentsconst tokenizer new natural WordTokenizer const tokenizedDocs documents map doc amp gt tokenizer tokenize doc Perform topic modelingconst numTopics Set the number of topics to discoverconst numIterations Number of iterations for the algorithmlda train tokenizedDocs numTopics numIterations Print the extracted topicsconst topics lda getTopics console log Extracted Topics topics forEach topic index amp gt console log Topic index topic words join Explanation We import the natural library allowing us to work with topic modeling A sample corpus of documents is defined representing the collection of text we want to analyze and extract topics from The documents are tokenized into individual words using the natural WordTokenizer We set the number of topics numTopics we want the algorithm to discover and the number of iterations numIterations for the LDA algorithm to converge The lda train function performs topic modeling on the tokenized documents Finally we retrieve and print the extracted topics and the most representative words for each topic using the lda getTopics function Session Text Classification with NLPText classification is a vital Natural Language Processing NLP task that involves categorizing textual data into predefined classes or categories Understanding Text ClassificationText classification is crucial in various real world applications including sentiment analysis spam detection language identification and content categorization The goal is automatically assigning a label or category to a given text document based on its content To achieve text classification we can leverage machine learning algorithms that learn patterns and relationships between the textual data and their corresponding classes One commonly used text classification algorithm is the Naive Bayes classifier which is simple yet effective for many NLP tasks Implementing Text Classification Import the natural libraryconst natural require natural Create a new Naive Bayes classifier instanceconst classifier new natural BayesClassifier Training data for text classificationconst trainingData text I love this product It s fantastic category positive text This movie was boring and disappointing category negative text The weather is lovely today category positive text The service at this restaurant was terrible category negative text The new software update works perfectly category positive Training the classifier with the datatrainingData forEach data amp gt classifier addDocument data text data category classifier train Test data for text classificationconst testText The hotel stay was wonderful I had a great time Classify the test dataconst predictedCategory classifier classify testText Print the predicted categoryconsole log Predicted Category predictedCategory Explanation We import the natural library which provides the necessary tools for text classification We create a new instance of the Naive Bayes classifier using natural BayesClassifier The training data contains labeled examples of text and corresponding categories positive or negative in this case The classifier is trained on the training data using the classifier addDocument and classifier train functions We define a test text for which we want to predict the category The classifier classify function is used to classify the test text into a specific category The predicted category is printed on the console Language Translation with NLP in JavaScriptLanguage translation is a crucial NLP application that enables communication and understanding across different languages This session focuses on language translation techniques and demonstrates how to perform language translation in JavaScript using NLP libraries Language Translation TechniquesLanguage translation can be achieved using different techniques including rule based approaches statistical machine translation and neural machine translation In this session we ll utilize the power of NLP libraries to perform language translation Implementing Language Translation in JavaScriptTo perform language translation in JavaScript we can leverage NLP libraries such as translate google and translate to access translation services Example Translating Text Using translate google Library Import the translate google libraryconst translate require translate google Text to be translatedconst text Hello how are you Source and target languagesconst sourceLanguage en const targetLanguage es Translate the texttranslate text from sourceLanguage to targetLanguage then translation amp gt console log Translated Text translation catch error amp gt console error Translation Error error Example Translating Text Using translate Library Import the translate libraryconst translate require translate Configure the library with the translation servicetranslate engine google translate from en translate to fr Text to be translatedconst text Good morning how are you today Translate the texttranslate text then translation amp gt console log Translated Text translation catch error amp gt console error Translation Error error Explanation We import the required NLP libraries for language translation translate google or translate We define the text that needs to be translated We specify the source language sourceLanguage and the target language targetLanguage for translation The text is translated using the translate function provided by the respective library The translated text is printed to the console In the final session of this series we would look at the use case and future trend of NLP and how its implication in Javascript has the potential to transform learning Follow us to see how we build the final project as this is the first session of a three part series If you find this post exciting find more exciting posts on Learnhub Blog we write everything tech from Cloud computing to Frontend Dev Cybersecurity AI and Blockchain Resource Getting Started with Programming Javascript Email with Nodemailer How to send an email in node js using nodemailer Javascript Array method Best React JavaScript Frameworks For	2023-07-19 05:56:16
海外TECH	DEV Community	Real-time Data Processing Pipeline With MongoDB, Kafka, Debezium And RisingWave	https://dev.to/bobur/real-time-data-processing-pipeline-with-mongodb-kafka-debezium-and-risingwave-58kk	Real time Data Processing Pipeline With MongoDB Kafka Debezium And RisingWaveToday the demand for real time data processing and analytics is higher than ever before The modern data ecosystem requires tools and technologies that can not only capture store and process vast amounts of data but also it should deliver insights in real time This article will cover the powerful combination of MongoDB Kafka Debezium and RisingWave to analyze real time data how they work together and the benefits of using these open source technologies Understanding MongoDB Kafka Debezium and RisingWaveBefore we dive into the implementation details it s important to understand what these tools are and what they do Debezium is an open source distributed platform for change data capture CDC CDC is a technique to track data changes written to a source database and automatically sync target databases For example Debezium s MongoDB Connector can monitor for document changes in databases and collections as they occur in real time recording those changes as events in Kafka topics RisingWave is a distributed open source SQL database for stream processing Its main goal is to make it easier and cheaper to construct applications that operate in real time As it takes in streaming data RisingWave performs on the fly computations with each new piece of data and promptly updates the outcomes For example RisingWave accepts data from sources like Kafka constructs materialized views for complex data and you can query them using SQL Analyzing Real Time Data The PipelineOnce we have knowledge about each tool let s discuss how MongoDB Kafka Debezium and RisingWave can work together to create an efficient real time data analysis pipeline These technologies are free to use and easy to integrate with each other Data Generation and Storage in MongoDB Our data pipeline starts with the generation and storage of data in MongoDB Given MongoDB s flexible data model it is possible to store data in multiple formats making it suitable for diverse data sources Data Capture with Debezium The next step in the pipeline is the capture of changes all of the inserts updates and deletes in MongoDB using Debezium Debezium provides a CDC connector for MongoDB that can capture row level changes in the database Once the changes are captured they are sent to Kafka for processing Data Streaming with Kafka Kafka receives the data from Debezium and then takes care of streaming it to the consumers In our case we consume data with RisingWave Data Processing with RisingWave Finally the streamed data is received and processed by RisingWave RisingWave provides a high level SQL interface for complex event processing and streaming analytics The processed data can be passed to BI and Data analytics platforms or used for real time decision making anomaly detection predictive analytics and much more This pipeline s key strengths are its ability to handle large volumes of data process events in real time and produce insights with minimal latency For example this solution can be used for building a global hotel search platform to get real time updates on hotel rates and availability When rates or availability change in one of the platform s primary databases Debezium captures this change and streams it to Kafka and RisingWave can do trend analysis This ensures that users always see the most current information when they search for hotels How to integrate quickstartThis guide shows you how to configure technically the MongoDB Debezium Connector to send data from MongoDB to Kafka topics and ingest data into RisingWave After completing this guide you should understand how to use these tools to create a real time data processing pipeline and create a data source and materialized view in RisingWave to analyze data with SQL queries To complete the steps in this guide you must download clone and work on an existing sample project on GitHub The project uses Docker for convenience and consistency It provides a containerized development environment that includes the services you need to build the sample data pipeline Before You BeginTo run the project in your local environment you need the following GitEnsure you have Docker installed in your environment Ensure that the PostgreSQL interactive terminal psql is installed in your environment For detailed instructions see Download PostgreSQL Start the projectThe docker compose file starts the following services in Docker containers RisingWave Database MongoDB configured as a replica set Debezium Python app to generate random data for MongoDB Redpanda with the MongoDB Debezium Connector installed We use Redpanda as a Kafka broker Kafka Connect UI to manage and monitor our connectors To start the project run simply the following command from the tutorial directorydocker compose upWhen you start the project Docker downloads any images it needs to run You can see the full list of services in docker compose yaml file Data flowApp py generates random user data name address and email and inserts them into MongoDB users collection Because we configured the Debezium MongoDB connector to point to the MongoDB database and the collection we want to monitor it captures data in real time and sinks them to Redpanda into a Kafka topic called dbserver random data users Next steps we consume Kafka events and create a materialized view using RisingWave Create a data sourceTo consume the Kafka topic with RisingWave we first need to set up a data source In the demo project Kafka should be defined as the data source Open a new terminal window and run to connect to RisingWave psql h localhost p d dev U rootAs RisingWave is a database you can directly create a table for the Kafka topic CREATE TABLE users id JSONB PRIMARY KEY payload JSONB WITH connector kafka kafka topic dbserver random data users kafka brokers message queue kafka scan startup mode earliest ROW FORMAT DEBEZIUM MONGO JSON Normalize data with materialized viewsTo normalize user s data we create a materialized view in RisingWave CREATE MATERIALIZED VIEW normalized users ASSELECT payload gt gt name as name payload gt gt email as email payload gt gt address as addressFROM users The main benefit of materialized views is that they save the computation needed to perform complex joins aggregations or calculations Instead of running these operations each time data is queried the results are calculated in advance and stored Query dataUse the SELECT command to query data in the materialized view Let s see the latest results of the normalized users materialized view SELECT FROM normalized usersLIMIT In response to your query a possible result set with random data might look like idnameemailaddressJohn Doemailto john doe example com Elm St Anytown USAJane Smithmailto jane smith example com Oak St Anytown USABob Johnsonmailto bob johnson example com Pine St Anytown USAAlice Williamsmailto alice williams example com Maple St Anytown USACharlie Brownmailto charlie brown example com Cedar St Anytown USAEmily Davismailto emily davis example com Birch St Anytown USAFrank Millermailto frank miller example com Spruce St Anytown USAGrace Wilsonmailto grace wilson example com Ash St Anytown USAHenry Mooremailto henry moore example com Alder St Anytown USAIsabella Taylormailto isabella taylor example com Cherry St Anytown USA SummaryThis is a basic setup for using MongoDB Kafka Debezium and RisingWave for a real time data processing pipeline The setup can be adjusted based on your specific needs such as adding more Kafka topics tracking changes in multiple MongoDB collections implementing more complex data processing logic or combining multiple streams in RisingWave Related resourcesDebezium connector for MongoDBMongoDB Kafka Connector Recommended contentQuery Real Time Data in Kafka Using SQL How Streaming database differs from a Traditional database Community Join the Risingwave Community About the authorVisit my blog  www iambobur comFollow me on Twitter  BoburUmurzokov	2023-07-19 05:45:50
海外TECH	DEV Community	Understanding TypeScript Types: Primitives, Objects, and Type Manipulations	https://dev.to/rajrathod/understanding-typescript-types-primitives-objects-and-type-manipulations-bjj	Understanding TypeScript Types Primitives Objects and Type Manipulations TypeScript TypesTypeScript is a strongly typed programming language that builds on JavaScript giving you better tooling at any scale TypeScript types are categorized into primitive types and object types The basic types in TypeScript include string boolean number array tuple and enum TypeScript s type system allows you to build new types out of existing ones using a large variety of operators There are several types that you can use to define variables Here are some commonly used types Primitives Types number Represents numeric values like integers floats etc string Represents textual data enclosed in single quotes or double quotes boolean Represents a logical value either true or false null Represents the absence of any object value undefined Represents an uninitialized variable or an object that lacks a value symbol Represents unique and immutable values that can be used as identifiers Object Types object Represents a non primitive type i e anything that is not a number string boolean null or undefined array Represents an ordered list of values of a specific type You can define an array using square brackets tuple Represents an array with a fixed number of elements where each element can have a different type Tuples allow you to specify the type of each element in a specific order enum Represents a set of named constants Enums allow you to define a collection of related values that can be assigned to a variable function Represents a function type You can define the parameter types and return type of a function using function types Other Types any Represents a dynamic type that can hold any value When a variable is of type any TypeScript doesn t perform type checking for that variable unknown Represents a type safe counterpart of any Variables of type unknown can hold any value but TypeScript enforces type checking before performing any operations on them void Represents the absence of any type Typically used as the return type of functions that don t return a value never Represents a type for values that never occur It is used for functions that don t have a reachable endpoint or for variables that cannot have a value literal types Represents a specific value that a variable can hold For example you can define a variable of type success which can only have the value success These are some of the commonly used types in TypeScript Understanding and utilizing these types can help you write more robust and type safe code Type AssertionType Assertion is used in TypeScript to override the type inferred by the compiler It is a mechanism that tells the compiler about the type of a variable In TypeScript type assertions can be performed using the angle bracket syntax or the as keyword Here s an example of Type Assertion in TypeScript let code any let employeeCode lt number gt code console log typeof employeeCode Output numberIn this example we have a variable code of type any We assign the value of this variable to another variable called employeeCode using type assertion The compiler is informed that code is of type number The typeof operator confirms that employeeCode is indeed of type number Non null AssertionThe non null assertion operator tells the TypeScript compiler that a value typed as optional cannot be null or undefined It is placed after the variable name and is used to assert that the variable is not null or undefined Here s an example of how to use the non null assertion operator in TypeScript let word string null null const num if num word Hello World console log word toLowerCase In this example the variable word is of type string or null The non null assertion operator is used to assert that word is not null Therefore we can safely call the toLowerCase method on word Type InferenceType inference is used in TypeScript to provide type information when there is no explicit type annotation It occurs when initializing variables setting parameter default values and determining function return types TypeScript uses the best common type algorithm to select the best candidate types that are compatible with all variables TypeScript also uses contextual typing to infer types of variables based on their usage in the code Here s an example of how type inference works in TypeScript let x In this example the type of the variable x is inferred to be number because it is initialized with a numeric value Type CompatibilityTypeScript uses a structural type system where a type is compatible with another type if it has at least the same members TypeScript s type compatibility is based on the structure and shape of types rather than their explicit declarations Here s an example of type compatibility in TypeScript interface Pet name string let pet Pet let dog name Lassie owner Rudd Weatherwax pet dog In this example the Pet interface defines a property name We declare a variable pet of type Pet and assign it an object dog with a name property TypeScript considers the object dog to be compatible with the Pet interface because it has at least the same members Combining TypesIn TypeScript combining types refers to the ability to create new types by combining existing types in various ways These combinations can be done using union types intersection types and conditional types Let s explore each of these combining types in TypeScript Union TypesUnion types allow you to combine multiple types using the pipe symbol A value of a union type can be of any of the constituent types For example type MyType string number let x MyType x hello valid x valid x true invalid as boolean is not in the unionUnion types are useful when a value can be one of multiple types Intersection Types Intersection types allow you to combine multiple types into a single type that has all the properties and methods of each constituent type It is denoted by the amp ampersand symbol For example type FirstType name string type SecondType age number type CombinedType FirstType amp SecondType let obj CombinedType name John age console log obj name John console log obj age Intersection types are useful when you want to create a type that has the combined features of multiple types Conditional Types Conditional types introduce type transformations based on a condition They are defined using the extends keyword and are denoted by the question mark symbol Conditional types are evaluated dynamically based on the given type parameter For example type NonNullable lt T gt T extends null undefined never T let value NonNullable lt string null gt value hello valid value null invalid let numberValue NonNullable lt number undefined gt numberValue valid numberValue undefined invalidIn this example the NonNullable conditional type transforms the given type parameter by excluding null and undefined types These combining types provide powerful ways to create complex types in TypeScript allowing you to express a wide range of scenarios and enforce type safety in your code Type Guards and Narrowing in TypeScriptType guards in TypeScript allow you to narrow down the type of a value within a conditional block based on a specific condition Type narrowing is the process of reducing the possible types of a value within a certain scope This helps you write more precise and type safe code Here s an example that demonstrates type guards and narrowing interface Circle kind circle radius number interface Square kind square sideLength number type Shape Circle Square function getArea shape Shape number if shape kind circle Type guard narrows down the type to Circle return Math PI shape radius shape radius else if shape kind square Type guard narrows down the type to Square return shape sideLength shape sideLength else This code is unreachable because Shape only allows Circle or Square throw new Error Invalid shape const circle Circle kind circle radius console log getArea circle Output const square Square kind square sideLength console log getArea square Output In this example we have defined two interfaces Circle and Square both of which have a kind property indicating the type of the shape The Shape type is a union of Circle and Square The getArea function takes a Shape parameter and uses type guards to narrow down the type within the conditional blocks When we call getArea with a Circle object the type guard shape kind circle allows TypeScript to infer that the shape parameter is of type Circle within that block Similarly when shape kind square TypeScript knows that the shape parameter is of type Square This narrowing enables us to access the specific properties of each shape without type errors Type guards are a powerful feature in TypeScript allowing you to write more reliable and type safe code by narrowing down the possible types of values based on conditions Thank You Thank you for taking the time to read my blog post I hope you found it helpful and informative Your support and engagement mean a lot to me If you have any questions or feedback please don t hesitate to reach out I appreciate your continued interest and look forward to sharing more valuable content in the future Thank you once again	2023-07-19 05:24:04
海外TECH	Engadget	TikTok expands its music streaming service test to Australia, Mexico and Singapore	https://www.engadget.com/tiktok-expands-its-music-streaming-service-test-to-australia-mexico-and-singapore-055121108.html?src=rss	TikTok expands its music streaming service test to Australia Mexico and SingaporeTikTok has started inviting users in Australia Mexico and Singapore to participate in a closed beta test for its new music streaming service according to TechCrunch and CNBC The short form video hosting app initially launched beta testing for its fledgling streaming service in Brazil and Indonesia in early July Now it s expanding the scope of its music service s experimental phase and giving invited users in those regions a free three month trial to be able to try it out nbsp TikTok Music is a completely separate app that testers will be able to download from the Apple App Store or the Google Play Store It does however connect to the main TikTok app so users can find the full versions of songs that go viral on the video sharing platform The music streaming app reportedly offers personalized song recommendations real time lyrics collaborative playlists and the ability to find songs through a lyrics search feature as well TechCrunch says it has a Shazam like feature which presumably means it can find songs by listening to it and will let users download tracks for offline listening nbsp The ByteDance owned app told TechCrunch that once the testers trial period is done it will cost them AUD US per month in Australia Mex US in Mexico and S US in Singapore to be able to keep using the service TikTok already has a music streaming service called Resso available in India Brazil and Indonesia but it s shutting the app down in the last two countries in September The company has yet to announce if and when its music app is also coming to the US but it did file a trademark application for TikTok Music in the country back in May nbsp This article originally appeared on Engadget at	2023-07-19 05:51:21
医療系	医療介護 CBnews	データ提出加算、39病院が8月の算定不可に-提出遅れなどで厚労省通知	https://www.cbnews.jp/news/entry/20230719142708	厚生労働省	2023-07-19 14:40:00
金融	ニッセイ基礎研究所	ポリアの壺問題の問題－一歩間違えると、受験生の精神力や忍耐力ばかりを問うことに !?	https://www.nli-research.co.jp/topics_detail1/id=75465?site=nli	ポリアの壺は証明問題として出題しても、問題があるということになるのだろうかポリアの壺の性質を知っていても、本当に実社会でなんの役にも立たないのだろうかnbsp次のようなギャンブルの問題を考えてみよう。	2023-07-19 14:58:39
金融	ニッセイ基礎研究所	「仙台オフィス市場」の現況と見通し（2023年）	https://www.nli-research.co.jp/topics_detail1/id=75464?site=nli	東京建物株式会社「「TPLUSティープラス仙台」着工中規模オフィス「TPLUS」シリーズ、全国展開本格化」年月日建設通信新聞「仙台中央三丁目開発鹿島が月着工」年月日日本経済新聞「「電力ビル」軸に一体開発へ東北電力系など、仙台中心部で過去最大音楽ホール・オフィス入居高層複合ビルに」年月日nbsp日本経済新聞「仙台市、電力ビル再開発を環境配慮ビル認定」年月日日本経済新聞「仙台・電力ビル、本館は年解体高さ東北一に並ぶ」年月日日本経済新聞「ヨドバシ、仙台に複合ビル開業家電量販店は東北最大級」年月日国土交通省「仙台駅東口地区に魅力や賑わいを創出する都市空間を形成仮称ヨドバシ仙台第ビル計画整備事業を国土交通大臣が認定」年月日河北新報「「青葉通広場化」検討着手仙台・あす協議会発足」年月日河北新報「Re杜のまち仙台仙台「都心」再生と進化仙台市中心部で、高度経済成長期の年前後に造られた大型の商業ビルや公共施設が、老朽化による建て替えなどの課題に直面している。	2023-07-19 14:58:28
金融	ニッセイ基礎研究所	米連邦学生ローンの大規模債務救済措置－連邦最高裁は同措置に対して違憲の判断を示す	https://www.nli-research.co.jp/topics_detail1/id=75466?site=nli	HEROES法は戦争その他の軍事行動、国家非常事態の結果、借り手が学生ローンに関して経済的に不利な状況に置かれないようにするため、HEA第IV編に基づく連邦学生ローンに適用されるいずれの法令も適用されないまたは修正することができる権限を教育省長官に与えることを規定している。	2023-07-19 14:01:01
海外ニュース	Japan Times latest articles	‘Deeply hurt’: Anti-trans hostility rises in Japan	https://www.japantimes.co.jp/news/2023/07/19/national/anti-transgender-hostility-japan-rise/	Deeply hurt Anti trans hostility rises in JapanDisinformation linking transgender women to sexual violence in public spaces is being disseminated extremely widely the Japan Alliance for LGBT Legislation warned earlier this year	2023-07-19 14:36:05
海外ニュース	Japan Times latest articles	Thai court suspends Pita as lawmaker as parliament votes on PM	https://www.japantimes.co.jp/news/2023/07/19/asia-pacific/politics-diplomacy-asia-pacific/pita-limjaroenrat-parliament-suspended/	Thai court suspends Pita as lawmaker as parliament votes on PMPita s Move Forward Party said the suspension should not affect Pita s nomination for the prime ministerial vote which rival politicians have been seeking to block	2023-07-19 14:31:56
海外ニュース	Japan Times latest articles	Rory McIlroy hopes Hoylake return can finally end major drought	https://www.japantimes.co.jp/sports/2023/07/19/more-sports/golf/mcilroy-british-open-major-drought/	ireland	2023-07-19 14:12:32
ニュース	BBC News - Home	Met Police apologise over Daniel Morgan murder probe	https://www.bbc.co.uk/news/uk-66241903?at_medium=RSS&at_campaign=KARANGA	detective	2023-07-19 05:54:23
ニュース	BBC News - Home	Newspaper review: 'Heatwave from hell' and Farage's finances	https://www.bbc.co.uk/news/blogs-the-papers-66240780?at_medium=RSS&at_campaign=KARANGA	europe	2023-07-19 05:53:03
ニュース	BBC News - Home	Why some celebrities are embracing Artificial Intelligence deepfakes	https://www.bbc.co.uk/news/business-65995089?at_medium=RSS&at_campaign=KARANGA	actors	2023-07-19 05:41:36
ニュース	BBC News - Home	Wham stars liken new chart joy to 'warm hugs'	https://www.bbc.co.uk/news/uk-england-beds-bucks-herts-66238894?at_medium=RSS&at_campaign=KARANGA	album	2023-07-19 05:13:18
ニュース	BBC News - Home	Southend Adventure Island says Pride 'not for us' over drag act	https://www.bbc.co.uk/news/uk-england-essex-66239091?at_medium=RSS&at_campaign=KARANGA	adventure	2023-07-19 05:08:12
ニュース	BBC News - Home	Ashes 2023: England players play 'agree or disagree' with Chris Hughes	https://www.bbc.co.uk/sport/av/cricket/65945826?at_medium=RSS&at_campaign=KARANGA	hughes	2023-07-19 05:53:55
ニュース	BBC News - Home	British and Irish Lions to play combined New Zealand-Australia XV on 2025 tour	https://www.bbc.co.uk/sport/rugby-union/66240886?at_medium=RSS&at_campaign=KARANGA	British and Irish Lions to play combined New Zealand Australia XV on tourThe British and Irish Lions will play a New Zealand and Australia invitational XV as part of their nine match tour down under in	2023-07-19 05:03:57
ニュース	BBC News - Home	Tahnee Seagrave: British downhill mountain biker on effects of concussion	https://www.bbc.co.uk/sport/cycling/66192293?at_medium=RSS&at_campaign=KARANGA	downhill	2023-07-19 05:09:57
ニュース	BBC News - Home	Europe heatwaves: Wildfires rage in Greece as temperatures soar	https://www.bbc.co.uk/news/science-environment-66237960?at_medium=RSS&at_campaign=KARANGA	extreme	2023-07-19 05:53:01
IT	週刊アスキー	メタ、ChatGPT（3.5）級のチャットAIモデル「Llama 2」オープンソースで公開	https://weekly.ascii.jp/elem/000/004/145/4145820/	chatgpt	2023-07-19 14:50:00
IT	週刊アスキー	西のグルメが集結！ そごう横浜店「夏の京都・大阪・神戸味めぐり」を開催	https://weekly.ascii.jp/elem/000/004/145/4145830/	集結	2023-07-19 14:45:00
IT	週刊アスキー	7月30日は「土用の丑の日」！ 小田急百貨店オンラインショッピングでうなぎ特集	https://weekly.ascii.jp/elem/000/004/145/4145851/	土用の丑の日	2023-07-19 14:30:00
マーケティング	AdverTimes	ミツカン、心の凹みに食事で寄り添う「凹メシ食堂」を期間限定オープン	https://www.advertimes.com/20230719/article427526/	期間限定	2023-07-19 05:34:30