投稿時間:2023-09-01 06:32:20 RSSフィード2023-09-01 06:00 分まとめ(32件)

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IT	InfoQ	How Spotify Improved its LLM Chatbot in Sidekick	https://www.infoq.com/news/2023/08/Spotify-sidekick-llm-improvement/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	How Spotify Improved its LLM Chatbot in SidekickWhile using a Large Language Model chatbot opens the door to innovative solutions crafting the user experience so it is as natural as possible requires some specific effort argues Spotify engineer Ates Goral to prevent rendering jank and to reduce latency By Sergio De Simone	2023-08-31 21:00:00
AWS	AWS Big Data Blog	Introducing Amazon MSK as a source for Amazon OpenSearch Ingestion	https://aws.amazon.com/blogs/big-data/introducing-amazon-msk-as-a-source-for-amazon-opensearch-ingestion/	Introducing Amazon MSK as a source for Amazon OpenSearch IngestionIngesting a high volume of streaming data has been a defining characteristic of operational analytics workloads with Amazon OpenSearch Service Many of these workloads involve either self managed Apache Kafka or Amazon Managed Streaming for Apache Kafka Amazon MSK to satisfy their data streaming needs Consuming data from Amazon MSK and writing to OpenSearch Service has …	2023-08-31 20:59:24
AWS	AWS Messaging and Targeting Blog	Improve email delivery rates with email delivery and engagement history for every email	https://aws.amazon.com/blogs/messaging-and-targeting/improve-email-delivery-rates-with-email-delivery-and-engagement-history-for-every-email/	Improve email delivery rates with email delivery and engagement history for every emailEmail is a ubiquitous way to reach customers whether to stay in touch offer new services or inform customers of product changes or transaction status Amazon Simple Email Service helps customers send hundreds of billions of emails each month and now offers more tools to improve email delivery rates and explore campaign success SES Virtual …	2023-08-31 20:28:41
js	JavaScriptタグが付けられた新着投稿 - Qiita	javascriptで生成したデータをダウンロードさせるコード。	https://qiita.com/taoka-toshiaki/items/3ffc18cc8514281379dd	javascript	2023-09-01 05:46:55
技術ブログ	Developers.IO	パートナー限定ラウンジに行ってきました # Goolge Cloud Next	https://dev.classmethod.jp/articles/go-to-partnerlounge-goolge-cloud-next23/	cloud	2023-08-31 20:52:24
海外TECH	Ars Technica	AI-powered hate speech detection will moderate voice chat in Call of Duty	https://arstechnica.com/?p=1964820	toxmod	2023-08-31 20:13:59
海外TECH	Ars Technica	30 years after Descent, developer Volition is suddenly no more	https://arstechnica.com/?p=1964856	embracer	2023-08-31 20:03:47
海外TECH	MakeUseOf	7 Content Creation Myths That Can Ruin Your Personal Brand	https://www.makeuseof.com/content-creation-myths-personal-brand/	brand	2023-08-31 20:00:25
海外TECH	MakeUseOf	How to Remove a Virus From Your Android Phone Without a Factory Reset	https://www.makeuseof.com/tag/remove-virus-android-without-factory-reset/	How to Remove a Virus From Your Android Phone Without a Factory ResetNeed to remove a virus from your Android phone We show you how to clean your phone from a malware infection without a factory reset	2023-08-31 20:00:25
海外TECH	DEV Community	Building a Sentiment Analysis Chatbot Using Neural Networks	https://dev.to/jaynwabueze/building-a-sentiment-analysis-chatbot-using-neural-networks-3623	Building a Sentiment Analysis Chatbot Using Neural NetworksUnderstanding and responding to user sentiments is crucial to building engaging and effective conversational systems in today s digital world Think of a friend who responds to your questions and adapts his tone and words based on your emotions This article will explore the fascinating intersection of sentiment analysis and chatbot development We ll explore building a sentiment analysis chatbot using neural networks and rule based patterns Problem StatementAs developers we often seek to create applications that provide accurate information and connect with users on a deeper level Traditional chatbots must improve at delivering empathetic and relevant responses mainly when user emotions come into play This project addresses the challenge of building a chatbot that understands the sentiments behind user messages and tailors its responses accordingly Let s say you have a friend to whom you can relate all that troubles you and you suddenly talk to them and they respond in a way that doesn t resonate with your emotions Of course you d feel some amount of disappointment Combining sentiment analysis and rule based response generation we aim to enhance the user experience and create a more engaging conversational environment In the following sections we ll discuss the steps involved in developing this sentiment analysis chatbot We ll explore the dataset used for training the neural network architecture powering the sentiment analysis model the integration of sentiment analysis into the chatbot s logic and the rule based approach for generating contextual responses By the end of this journey you ll have gained insights into both sentiment analysis and chatbot development and you ll be equipped to create your own intelligent and emotionally aware chatbots Dataset and PreprocessingBuilding a robust sentiment analysis model requires access to a suitable dataset that covers a wide range of emotions and expressions For this project we utilized the Topical Chat dataset sourced from Amazon This dataset comprises over conversations and a staggering messages making it a valuable resource for training our sentiment analysis model Dataset DescriptionThe Topical Chat dataset captures real world conversations each with an associated sentiment label representing the emotion expressed in the message The dataset covers many sentiments including happiness sadness curiosity and more Understanding user emotions is crucial for the chatbot s ability to generate empathetic and contextually relevant responses Preprocessing StepsBefore feeding the data into our model we performed preprocessing to ensure the data s quality and consistency The preprocessing pipeline included the following steps python IMPORT NECESSARY LIBRARIESimport tensorflow as tffrom tensorflow import kerasimport numpy as npimport pandas as pdimport nltkfrom nltk tokenize import word tokenizefrom nltk corpus import stopwordsimport string Load the data Load the datasetbot dataset pd read csv topical chat csv Download stopwords and punkt tokenizernltk download punkt nltk download stopwords Preprocessing functiondef preprocess text text Tokenize tokens word tokenize text Remove stopwords and punctuation tokens word lower for word in tokens if word isalnum and word lower not in stopwords words english return join tokens Apply preprocessing to the message columnbot dataset processed message bot dataset message apply preprocess text Tokenization Breaking down sentences into individual words or tokens facilitates analysis and model training Text Cleaning Removing special characters punctuation and unnecessary whitespace to make the text more uniformStopword Removal Eliminating common words that don t contribute much to sentiment analysisLabel Encoding Converting sentiment labels into numerical values for model trainingBy conducting these preprocessing steps we transformed raw conversational data into a format the neural network model could understand and learn Now let s delve into the architecture of the neural network model used for sentiment analysis and explore how it predicts emotions from text messages Model ArchitectureIn this project we ll use a neural network model to understand and predict user emotions from text messages The architecture of this model is a crucial component that enables the chatbot to discern sentiments and generate appropriate responses Neural Network LayersThe model architecture is structured as follows Embedding Layer The embedding layer converts words or tokens into numerical vectors Each word is represented by a dense vector that captures its semantic meaning LSTM Long Short Term Memory Layers LSTM layers process the embedded sequences capturing the sequential dependencies in the text LSTMs are well suited for tasks involving sequences and can capture context over long distances Dense Layer The final dense layer produces an output representing the predicted sentiment This output is then used to generate responses that match the user s emotional tone Activation Functions and ParametersThroughout the architecture activation functions such as ReLU Rectified Linear Unit are applied to introduce non linearity and enhance the model s ability to capture complex relationships in the data Additionally hyperparameters such as batch size learning rate and the number of LSTM units are tuned to optimize the model s performance pythonfrom tensorflow keras models import Sequentialfrom tensorflow keras layers import Embedding LSTM Dense Dropoutmodel Sequential Embedding input dim output dim input length LSTM return sequences True LSTM Dense activation relu Dropout Dense activation linear Print the model summarymodel summary The model summary function will print the outline of the model layers as seen in the picture below Training and OptimizationThe model is trained using the preprocessed Topical Chat dataset The model learns to map text sequences to sentiment labels during training through backpropagation and gradient descent optimization Loss functions such as categorical cross entropy guide the training process by quantifying the difference between predicted and actual sentiments Next we ll delve into the training process evaluate the model s performance and explore how sentiment analysis is integrated into the chatbot s logic Training and EvaluationTraining a sentiment analysis model involves exposing it to labeled data and allowing it to learn the patterns that link text sequences to specific emotions This section will look at training and evaluating the model s performance Model TrainingThe model is trained using the preprocessed Topical Chat dataset The training process includes the following steps Input Sequences Text sequences from conversations are fed into the model Each sequence represents a message along with its associated sentiment label Forward Pass The input sequences pass through the model s layers The embedding layer converts words into numerical vectors while the LSTM layers capture the sequential context Prediction and Loss The model generates predictions for the sentiment labels The categorical cross entropy loss quantifies the difference between predicted and actual labels Backpropagation Gradient descent and backpropagation adjust the model s parameters The model learns to minimize the loss by iteratively updating its weights TRAIN THE MODELmodel fit X train padded y train encoded epochs batch size validation split Model EvaluationAfter training the model s performance is evaluated using a separate data set often called the validation or test set The evaluation metrics include accuracy precision recall and F score These metrics provide insights into how well the model generalizes to unseen data Evaluate the modelloss accuracy model evaluate X test padded y test encoded print Test accuracy accuracy Hyperparameter TuningHyperparameters such as learning rate batch size and LSTM units significantly influence the model s performance So we iteratively experiment and validate to find the optimal set of hyperparameters that yield the best results As we progress we ll examine how sentiment predictions are integrated into the chatbot s logic We ll use the rule based approach for generating responses based on predicted sentiments Integration with ChatbotLet s explore how sentiment analysis seamlessly integrates into the chatbot s logic enabling it to generate contextually relevant and emotionally aware responses Sentiment Based Response GenerationThe key innovation of our sentiment analysis chatbot lies in its ability to tailor responses based on predicted sentiments When a user inputs a message the chatbot performs the following steps Sentiment Analysis The message is passed through the trained sentiment analysis model which predicts the sentiment label Response Generation Based on the predicted sentiment the chatbot generates a response that matches the emotional tone of the user s message For example a sad sentiment might trigger a comforting response while a happy sentiment might foster an enthusiastic reply def predict sentiment text processed text preprocess text text sequence tokenizer texts to sequences processed text padded sequence pad sequences sequence maxlen padding post truncating post sentiment probabilities model predict padded sequence predicted sentiment id np argmax sentiment probabilities predicted sentiment label encoder inverse transform predicted sentiment id return predicted sentimentuser input input Enter a message predicted sentiment predict sentiment user input print Predicted sentiment predicted sentiment By incorporating sentiment analysis into the chatbot s logic we elevate the conversational experience to a new level of empathy and understanding Users feel heard and acknowledged as the chatbot responds in ways that resonate with their emotions This empathetic connection enhances user engagement and fosters a more meaningful interaction Rule Based Approach for Response GenerationWhile sentiment analysis is a powerful tool for enhancing the chatbot s responses a rule based approach further enriches the diversity and appropriateness of the generated content Let s look at how we implement rule based patterns to provide contextually relevant and emotionally aligned responses def generate rule based response predicted sentiment if predicted sentiment Happy response I m glad to hear that you re feeling happy elif predicted sentiment Sad response I m sorry to hear that you re feeling sad Is there anything I can do to help else response I m here to chat with you How can I assist you today return responsedef generate rule based response chatbot user input Predict sentiment using your neural network model code you ve shared earlier predicted sentiment predict sentiment nn user input Generate response based on predicted sentiment using rule based approach response generate rule based response predicted sentiment return responsedef generate pattern response user input patterns hello Hello How can I assist you today how are you I m just a chatbot but I m here to help How can I assist you help Sure I d be happy to help What do you need assistance with bye Goodbye If you have more questions in the future feel free to ask Add more patterns and responses here Look for pattern matches and return the corresponding response for pattern response in patterns items if pattern in user input lower return response If no pattern matches use the rule based response based on sentiment return generate rule based response chatbot user input while True user input input You if user input lower exit print Bot Goodbye break bot response generate pattern response user input print Bot bot response Pattern MatchingRule based patterns involve creating predefined rules that trigger specific responses based on user input Keywords phrases or clear sentiment labels can start with these rules The chatbot generates responses that resonate with the conversation s context by anticipating user needs and emotions Let s illustrate with an example User Input I feel excited about this project Predicted Sentiment Happy Based on the predicted sentiment we implement the following rule Rule Based Response I m glad to hear that you re feeling excited In this way the chatbot provides contextually relevant and empathetic responses that align with user emotions The rule based approach allows the chatbot to generate responses that adhere to specific patterns quickly ConclusionThroughout this project we ve explored the details of combining sentiment analysis with chatbot development resulting in a system that understands user emotions and responds with empathy and relevance Building a sentiment analysis chatbot that connects with users emotionally is a remarkable achievement in AI While we ve achieved a functional sentiment analysis chatbot the journey doesn t end here There are several exciting avenues for further enhancing our sentiment analysis chatbot and pushing the boundaries of conversational AI You can visit my Repo on Github for reference Jaynwabueze Simple Chat bot A simple interactive chatbot built with neural networks Simple chatbotDescriptionA chatbot project that combines sentiment analysis with response generation using neural networks UsageClone the repository git clone Run the chatbot script python chatbot pyDatasetThe sentiment analysis model is trained using the Topical Chat dataset from Amazon This dataset consists of over conversations and over messages Each message has a sentiment label representing the emotion of the sender Model InformationThe sentiment analysis model is built using a neural network architecture It involves an embedding layer followed by LSTM layers for sequence processing The model is trained on the sentiment labeled messages from the dataset to predict emotions ChatbotThe chatbot component leverages the trained sentiment analysis model to generate contextually appropriate responses Based on the predicted sentiment of the user input the chatbot provides empathetic and relevant responses ContactFor questions or feedback please contact Judenwabueze View on GitHub	2023-08-31 20:38:39
海外TECH	DEV Community	Use just to manage Rust project commands	https://dev.to/nazmulidris/use-just-to-manage-rust-project-commands-56cp	Use just to manage Rust project commands rbl org rbl ansi color Rust crate to generate formatted ANSI bit and truecolor bit color output to stdout IntroductionIn this tutorial we will learn how to use just by example to manage project specific commands just is like make but it is written in Rust and it works with cargo Before we get started please take a look at the just project README Please star and fork clone the rbl ansi color repo We will use this repo as an example to learn how to use just to manage project specific commands PrerequisitesIn order for our just file to work we must first install the Rust toolchain and just and cargo watch Install the Rust toolchain using rustup by following the instructions here Install cargo watch using cargo install cargo watch Install just on your system using cargo install just It is available for Linux macOS and Windows If you want shell completions for just you can follow these instructions If you install just using cargo install just or brew install just you will not get shell completions without doing one extra configuration step So on Linux it is best to use sudo apt install y just if you want them Basic usageFor Rust projects typically we will have a build run test project specific commands Let s start with these simple ones first The benefit of just is that we can use it to run these commands on any platform Linux Mac Windows And we don t need to create OS or shell specific scripts to do this Let s start by creating a justfile in the root of our project The justfile is where we will define our project specific commands Here is what it looks like for the rbl ansi color repo build cargo buildclean cargo cleanrun cargo run example mainThese are pretty simple commands The syntax is pretty simple The first line is the command name And the second line is the command to run The command can be a single command or a series of commands Now in order to run this we can just run just list in the root of our project And it will show us the list of commands that we can run just listAvailable recipes build clean runThen to run a command we can just run just lt command name gt For example to run the build command we can run just build just build Advanced usageWe will demonstrate two advanced use cases Using a shell that is not the default shell This is useful for running just commands on Windows Passing arguments into just commands Run on WindowsCurrently our justfile will run on Linux and macOS To make it run on Windows we have to restructure our commands with attributes that specify which OS to run the command on all variant that only runs on Windows The only thing that is different is the shell enabling and disabling recipes windows all set windows shell powershell exe c just all cross platform all variant that only runs on Linux and macOS Using the default shell enabling and disabling recipes unix all just all cross platform all command that runs on Windows Linux and macOS all cross platform just build just test just clippy just docs just rustfmtOn Windows we have to use PowerShell to run the commands so we set the windows shell variable value in that block The is the assignment operator And the powershell exe c is an array of strings that is the value of this variable and will be used to run the commands On Linux and macOS we can use the default shell However in both cases we can run the same all cross platform command You can pass variables in from the command line or even just read environment variable values There are less reliable ways of setting the windows shell variable using if statements and os family function Here s a list of the available functions And in testing across different OSes the method shown above is the most reliable It also works nicely in CI CD environments Github Actions Run in CI CD environments Github Actions We can also use just in CI CD environments For example here is the rust yml file for this repo s Github Actions It runs just all in the build step The one thing to note is that we are installing just in the docker container before we run the just command We do this by pulling in the prebuilt binary for Ubuntu as shown here curl proto https tlsv sSf bash s to DESTjobs build runs on ubuntu latest steps uses actions checkout v Install just before running it below name Install just run curl proto https tlsv sSf bash s to usr local bin Simply run the just all command name all run just allUsing just all is relatively straightforward way to run all our build steps that would run in a CI CD environment on our local computer w out installing docker While ensuring that these same steps are carried out in the CI CD environment Pass arguments into commandsWe can also pass arguments into our commands Let s say that we have a command that we can use to run a single test We can pass the name of the test into the command Here is what it looks like watch one test test name More info on cargo test More info on cargo watch cargo watch x check x test test threads nocapture test name c qThere are a few things to note here The syntax to name the command is still the same as before However we have added another string after the command name which is the argument name test name If an argument is not passed in then just will display an error and print a message stating that an argument is required This argument is used just like a variable would in a justfile The and enclose a variable name Now we can run this command by passing in the name of the test that we want to run For example if we want to run the test ansi color test we can run just watch one test test ansi color just watch one test boldHere s an example of a justfile that has a lot more commands for you to look at rbl ansi color justfile Next stepsThe just project README has lots of information on how to use just It is best to have a specific thing you are looking for before you visit this page Here are some interesting links inside the README Command line arguments Support for env files Conditional expressions Setting variables Please star and fork clone therbl ansi color repo We will use this repo asan example to learn how to use just to manage project specific commands If you would like to get involved in an open source project and like Rust crates please feel free to contribute to the rbl ansi color repo There are a lot of small features that need to be added And they can be a nice stepping stone into the world of open source contribution rbl org rbl ansi color Rust crate to generate formatted ANSI bit and truecolor bit color output to stdout	2023-08-31 20:06:29
海外TECH	DEV Community	The Communication Shortcomings of Programmers	https://dev.to/bytebodger/the-communication-shortcomings-of-programmers-4ej0	The Communication Shortcomings of ProgrammersIn my previous article I railed about my assessment that most organizations have no real concept about what programmers actually do In that article I spelled out many depressing truths about the many ways in which devs efforts are often discounted So I m following that article up with some advice As frustrating as it can be to deal with people who don t really grok the process of software development I do honestly believe that we as programmers often bring this problem on ourselves You see the simple and painful truth is that most programmers are pretty piss poor communicators If we were great communicators we wouldn t have chosen a career field where vast portions of our day are spent silently clacking away on a keyboard And while we can t make everyone else see the light about software development we could do much more to ensure that we re effectively communicating with everyone outside our dev team Mimicking CommunicationThe first thing I want to point out is something that any salesperson knows very well One of the most effective ways to communicate with others is to mimic the style in which they already communicate No I m not talking about trying to copy someone s voice or mannerisms I m talking about carefully observing how others choose to communicate and then whenever possible communicating with them in their chosen style Devs can be really crappy at this because they tend to have mastered a given communication channel their preferred communication channel and then they blow off anyone who doesn t prefer that particular channel For example maybe you hate Slack Maybe you constantly set yourself as Away even when you re right there at your desk working on code And to be clear on occasion this can make perfect sense But sometimes there are those who simply refuse to communicate in any way other than Slack If you know someone like this especially if it s someone who has input on your work then you may want to consider getting off your Slack high horse and swapping a few chat messages with them You won t like it It may not feel comfortable But in the end it will keep that person from seeing you as a non communicative jerk In my last job I had a manager who wanted nearly everything to be written up in a Quip doc It didn t matter if I d put copious notes on all my commits and pull requests It didn t matter if I wrote a tome of detail in the ticket It didn t matter if I sent him Slack messages and emails He wanted things written up in an endless stream of Quip docs I could spend paragraphs griping about this guy But the simple fact is that if I really wanted to get through to him it wasn t going to happen unless I started putting data in the tool of his choice Most developers despise this I despised this But getting angry about it didn t do me a damn bit of good Avoiding Face TimeWhen I ve joined a new company and I find myself in a group video conference I can usually identify the programmers in the meeting even before they ve introduced themselves They re the ones who stubbornly refuse to to turn on their cameras To be clear I m not claiming that you always need to have your camera on But when you remove the video from video conferencing you undercut a huge portion of its utility There are soooo many visual cues that we re subconsciously trained to pick up that fly out the window when one of the key participants just can t be bothered to turn on his camera Because of this the inability to match a face to a voice fosters the mental image that some have of programmers as antisocial urchins If you think this has no impact on your social standing in the company you re probably deluding yourself TechnobabbleProgrammers are notorious at failing refusing to read a room If you re sitting in a dev meeting with nothing but other developers it s fine to debate various approaches to dependency injection or to talk about the possibility that a given bug may be caused by a race condition But if you re sitting in one of your normal business meetings and talking about these concepts you re almost certainly losing your audience You may even be guilty of being a jerk One of the reasons why the business can be so clueless about what we actually do is because when they ask us directly we revel in drowning them in technobabble When this happens we often feel smug in believing that we ve explained exactly what we re doing But in reality we ve only made the situation worse Have you ever been frustrated when an inferior programmers gets promoted or some other type of recognition while your efforts go unnoticed It s probably because that doofus can describe his work even if his work is subpar IN BUSINESS TERMS Voluntary IsolationLemme tell you something that some may see as underhanded I ve known plenty of devs in my career who simply hated talking to some or nearly all of the business types They didn t wanna be in meetings They didn t wanna have to present demo any of their work They didn t wanna participate in any broader company activities In some of those cases they would literally ask me to handle the communication overhead And you know what I did it Months later they d be looking for a new job Or even rage quitting Why were they so frustrated Because I had received a ton of recognition while their efforts went unnoticed Now to be clear I ve never claimed anyone else s work as my own And I ve always gone out of my way to try to attribute credit wherever it s due But when those business types come to see me as the voice of dev it s only inevitable that they eventually put great value in my position and they see others as being expendable It s fine to hunker down when you re up against a tight deadline But if you re going out of your way to avoid any contact with anyone outside the dev team don t go crying in your beer when those non dev folks fail to understand your contributions Combativeness DefensivenessShow me a dev who doesn t enjoy a good fight and I ll show you a purple squirrel And yes I ll freely admit that I ve too often been guilty of this myself I find that most devs have a decent survival instinct when it comes to avoiding fights outside the dev team But when it comes to matters that are code specific Matters that are only debated and adjudicated between software engineers Well let s just say that things can get messy It s easy to assume that the other devs are on your side It s even easy to assume that normal disagreements between devs are understood and tolerated as the typical process of building applications But I ve seen far too often that the guy burying his knife deeeeeeep into my back is the same guy who didn t appreciate my argument that we should be using Zustand instead of Redux And when someone outside the dev team asked him to comment on me he was all too quick to paint me as a malcontent Let s be honest here You can t build a ton of software applications without occasionally having a debate with someone about the relative merits of one technical approach over another But if you want the broader business to focus on your worth and not on the putative headaches that you bring to the organization then you need to be very careful about where and when to pick your battles Because once the rest of the organization hears that you re a troublemaker they ll turn a deaf ear to any other efforts to herald your work	2023-08-31 20:02:24
Apple	AppleInsider - Frontpage News	Apple provides detailed reasoning behind abandoning iPhone CSAM detection	https://appleinsider.com/articles/23/08/31/apple-provides-detailed-reasoning-behind-abandoning-iphone-csam-detection?utm_medium=rss	Apple provides detailed reasoning behind abandoning iPhone CSAM detectionA child safety group pushed Apple on why the announced CSAM detection feature was abandoned and the company has given its most detailed response yet as to why it backed off its plans Apple s scrapped CSAM detection toolChild Sexual Abuse Material is an ongoing severe concern Apple attempted to address with on device and iCloud detection tools These controversial tools were ultimately abandoned in December leaving more controversy in its wake Read more	2023-08-31 20:36:03
海外科学	NYT > Science	Humanity’s Ancestors Nearly Died Out, Genetic Study Suggests	https://www.nytimes.com/2023/08/31/science/human-survival-bottleneck.html	Humanity s Ancestors Nearly Died Out Genetic Study SuggestsThe population crashed following climate change about years ago scientists concluded Other experts aren t convinced by the analysis	2023-08-31 20:30:18
ニュース	BBC News - Home	Pupils face disruption over concrete building closures	https://www.bbc.co.uk/news/education-66673971?at_medium=RSS&at_campaign=KARANGA	england	2023-08-31 20:02:41
ニュース	BBC News - Home	Proud Boys leader Joe Biggs sentenced to 17 years for Capitol riot	https://www.bbc.co.uk/news/uk-66676581?at_medium=RSS&at_campaign=KARANGA	january	2023-08-31 20:37:48
ニュース	BBC News - Home	Luis Rubiales: Sarina Wiegman dedicates Uefa Women's Coach of the Year award to Spain players	https://www.bbc.co.uk/sport/football/66677614?at_medium=RSS&at_campaign=KARANGA	Luis Rubiales Sarina Wiegman dedicates Uefa Women x s Coach of the Year award to Spain playersEngland manager Sarina Wiegman dedicates her Uefa Women s Coach of the Year award to Spain as Fifa president Gianni Infantino says their success had been spoiled	2023-08-31 20:34:11
ニュース	BBC News - Home	What we know so far about school buildings closures	https://www.bbc.co.uk/news/education-66669239?at_medium=RSS&at_campaign=KARANGA	measures	2023-08-31 20:34:39
ニュース	BBC News - Home	Police officer death: Wife pays tribute to officer killed on railway	https://www.bbc.co.uk/news/uk-england-nottinghamshire-66672411?at_medium=RSS&at_campaign=KARANGA	sacrifice	2023-08-31 20:36:46
ニュース	BBC News - Home	Aberdeen 1-3 BK Hacken (agg 3-5): Premiership side in Conference League after defeat	https://www.bbc.co.uk/sport/football/66605616?at_medium=RSS&at_campaign=KARANGA	Aberdeen BK Hacken agg Premiership side in Conference League after defeatAberdeen must settle for the Europa Conference League group stage this season after a gut wrenching defeat by Swedish champions BK Hacken in the Europa League play offs	2023-08-31 20:45:10
ニュース	BBC News - Home	PAOK 4-0 Hearts (agg 6-1): Scots hammered by PAOK in European exit	https://www.bbc.co.uk/sport/football/66605650?at_medium=RSS&at_campaign=KARANGA	PAOK Hearts agg Scots hammered by PAOK in European exitHearts hopes of reaching the Europa Conference League group stage for the second season in a row are brutally extinguished by a rampant PAOK in a one sided play off second leg	2023-08-31 20:41:56
ビジネス	ダイヤモンド・オンライン - 新着記事	「そごう・西武売却」セブン＆アイの2大誤算…経営陣は「逃げ得」できるのか？ - 今週もナナメに考えた　鈴木貴博	https://diamond.jp/articles/-/328495	鈴木貴博	2023-09-01 05:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	トヨタグループ23社の瓦解！「2つの株式持ち合い」が解消に向かい“豊田家至上主義”に綻び - トヨタ 史上最強	https://diamond.jp/articles/-/328202	史上最強	2023-09-01 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	中国経済の停滞が長期化、「バランスシート不況」の不動産と見えない“成長モデル” - 政策・マーケットラボ	https://diamond.jp/articles/-/328505	中国経済の停滞が長期化、「バランスシート不況」の不動産と見えない“成長モデル政策・マーケットラボバブル崩壊後の日本と同じように、バランスシート不況が中国でも起きつつあるのではないかという議論が盛り上がっている。	2023-09-01 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	処理水巡る日中の出口なき対立で、中国には「二つの誤算」があった？ - 永田町ライヴ！	https://diamond.jp/articles/-/328475	外交問題	2023-09-01 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	MARCHダブル合格者の進学率、「立教よりも何となく青学」の正体とは？若者が青山学院大を選ぶ理由 - 有料記事限定公開	https://diamond.jp/articles/-/328518		2023-09-01 05:10:00
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