投稿時間:2023-01-12 00:24:37 RSSフィード2023-01-12 00:00 分まとめ(29件)

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AWS	AWS	AWS Supports You | Recapping Big Data & Analytics Announcements from re:Invent 2022	https://www.youtube.com/watch?v=oQ-RjS7knik	AWS Supports You Recapping Big Data amp Analytics Announcements from re Invent We would love to hear your feedback about our show Please take our survey here AWS Supports You Recapping Big Data Analytics Announcements from re Invent gives viewers on our twitch tv aws channel an overview of several announcements made at re Invent including Amazon DataZone AWS Clean Rooms AWS Glue Amazon Redshift Amazon Aurora Amazon Athena and Amazon QuickSight This episode originally aired on January th Introduction Amazon DataZone AWS Clean Rooms AWS Glue AWS Glue for Ray Amazon Redshift integration for Apache Spark Amazon Aurora zero ETL to Amazon Redshift Amazon Athena for Apache Spark Amazon QuickSight Paginated Reports Conclusion Helpful Links Amazon DataZone AWS Clean Rooms AWS Glue AWS Glue Ray Amazon Redshift Integration with Apache Spark Amazon Redshift Streaming ingestion Amazon Aurora zero ETL to Amazon Redshift Amazon Athena for Apache Spark Amazon QuickSight Paginated reports Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2023-01-11 14:45:39
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nextjs,React勉強記~とらハック氏の動画メモ~	https://qiita.com/kajiyai/items/3bc95bde7b08ccc37720	nextjsreact	2023-01-11 23:59:16
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS Certified Cloud Practitioner 受験記録	https://qiita.com/kiwsdiv/items/72c74c1bc6bf4e652af5	ertifiedcloudpractitioner	2023-01-11 23:15:42
技術ブログ	Developers.IO	GitHub ProjectsでTable LayoutとGrouping機能が便利だった	https://dev.classmethod.jp/articles/github-projects-table-view-group/	developersio	2023-01-11 14:54:08
海外TECH	MakeUseOf	ATX 3.0: Do You Need to Upgrade Your PSU For Nvidia's RTX 40-Series GPUs?	https://www.makeuseof.com/atx-3-do-you-need-upgrade-psu-nvidia-rtx-40-gpus/	nvidia	2023-01-11 14:20:01
海外TECH	MakeUseOf	How to Do a Word Count in Microsoft Word	https://www.makeuseof.com/word-count-in-microsoft-word/	learn	2023-01-11 14:15:16
海外TECH	MakeUseOf	uThrone Gaming Massage Chair Review: The Royal Upgrade Your Back Deserves	https://www.makeuseof.com/uthrone-gaming-massage-chair-review/	chair	2023-01-11 14:05:15
海外TECH	MakeUseOf	The Best Google Pixel 7 Cases to Protect Your Phone	https://www.makeuseof.com/best-pixel-7-cases/	smartphone	2023-01-11 14:01:15
海外TECH	MakeUseOf	How to Turn Your Photos Into Pencil Drawings Using Photoshop	https://www.makeuseof.com/photoshop-how-to-turn-photo-into-pencil-drawing/	photoshop	2023-01-11 14:01:15
海外TECH	MakeUseOf	What Is Cloud Malware? Types of Attacks and How to Defend Against Them	https://www.makeuseof.com/what-is-cloud-malware-types-of-attacks-and-how-to-defend-against-them/	What Is Cloud Malware Types of Attacks and How to Defend Against ThemThe cloud consists of countless servers and malware can target those computers just like any other How do we keep the machines in the cloud safe	2023-01-11 14:01:15
海外TECH	DEV Community	Building Personalised Music Recommendation System	https://dev.to/vhutov/building-personalised-music-recommendation-system-24lc	Building Personalised Music Recommendation SystemI was excited to find a playlist dataset from Spotify because I have always been unhappy with the recommendations from music streaming platforms I wanted to use this dataset as an opportunity to learn how to build a personalised recommendation system In this series I ll walk you through the process of building such a system using Python for data processing and Node js for recommendations retrieval In this first article we ll focus on the data processing flow If you re interested join me to see the final result Conceptually this is what I wanted to implement The DataThe dataset we ll be working with consists of JSON files and contains around million public playlists Here s an example of a single playlist from the JSON playlist content name Throwbacks collaborative false pid modified at num tracks num albums num followers tracks pos artist name Missy Elliott track uri spotify track UaMYEvWZiZqiDOoHUYI artist uri spotify artist wIVseowClTgoWTtk track name Lose Control feat Ciara amp Fat Man Scoop album uri spotify album vVUrXcfyQDwuQoIK duration ms album name The Cookbook pos artist name Boys Like Girls track uri spotify track GIrItMwEGwjCQjGChX artist uri spotify artist vWCyXMrrvMlCcepuOJaGI track name The Great Escape album uri spotify album WqgusSAgXkrjbXzqdBY duration ms album name Boys Like Girls num edits duration ms num artists Each playlist consists of a name a PID and a list of tracks along with some additional data When building this recommendation system I made the assumption that people are more likely to add similar songs in a playlist While this may not hold true for every playlist I believe that on a larger scale it s a reasonable assumption As a result I ll be treating each playlist identified by its PID as a unit of taste preferences and modeling item similarities based on them using various algorithms Talk is cheap Show me the code ーLinus Torvalds Preparing DataBefore diving into running different ML algorithms we need to prepare the data for ingestion and do some cleaning The goal is to parse the URIs select the necessary fields unfold the nested playlist tracks structure and keep only the top K artists Also converting the JSON files to CSV and parsing the fields will save time when experimenting While this section may be basic I ve included it for completeness If you re familiar with these concepts feel free to skip ahead to the next section Below are some common imports that I ll be using throughout the examples imports import collectionsimport functoolsimport itertoolsimport jsonimport mathfrom collections import Counter namedtuplefrom pathlib import Pathimport pandas as pdimport tqdmfrom fastai collab import from fastai tabular all import from sklearn neighbors import KNeighborsClassifier Reading JSON datasetWe ll need a few helper functions The first one is simply reading a file and parsing the JSON contained within it def read file path with open path r as f return json load f We ll also need a few field getters for the track entity Notice that the URI parsing is just a simple substring operation artist uri prefix len spotify artist track uri prefix len spotify track def artist name track return track artist name def artist uri track return track artist uri artist uri prefix def track name track return track track name def track uri track return track track uri track uri prefix The next function reads the JSON files parses the required fields and returns an iterator of processed rows This part might be confusing if you re not familiar with the concept of generators Since we don t need to store the intermediate JSON files or the unfolded structure in memory I m using generators to lazily process them root dir kaggle input spotify millions playlist spotify data p Path root dir paths list p iterdir def read files num files lookup path paths if num files is None else paths num files jsons read file path for path in lookup path return row pid artist name track artist uri track track name track track uri track for data in jsons for row in data playlists for track in row tracks example of a row next read files Marian Hill xHQOGJIWOXHxGBISYc Lovit AgLUfgpNymEMwDOqKdg Most popular artistsDespite having a million of playlists these data may not be enough to produce accurate predictions for every track and artist in the dataset As a result I want to limit the task to providing recommendations for the N most popular artists To do this I ll need to count the number of artist mentions in the playlists artist uri pos c collections Counter r artist uri pos for r in read files None After executing this code I have the following structure c most common TVXtAsRInumwjSr You can visit to see the most popular artist according to the data we have from Spotify I should have actually counted unique artist mentions in a playlist here but in this case it s not a problem since I m not planning to use all K Let s save the top artists in a CSV for later use df pd DataFrame data c most common columns artist uri count df to csv common artist uri csv index False Transforming JSON to CSVNow we want to convert the initial JSON files to processed CSV To do this we ll start by reading the most popular artists created in the previous step artists pd read csv kaggle input common spotify artists common artist uri csv artists artists set index artist uri I ll also modify the previously introduced parsing function by filtering out artists that are not in the allowlist and playlists that have too few tracks This will help ensure that the models will produce better predictions def read files artist allowlist num files lookup path paths if num files is None else paths num files jsons read file path for path in lookup path return row pid artist name track artist uri track track name track track uri track for data in jsons for row in data playlists if row num tracks gt added filter or playlist number of tracks for track in row tracks if artist uri track in artist allowlist and filter on popular artists Since we don t want to write everything in a single huge file I ll split the rows into batches of K Here s the code for writing the CSV files gen read files artists index None n i column names pid artist name artist uri track name track uri while True batch slice itertools islice gen n df pd DataFrame batch slice columns column names if df empty break df to csv f playlist i csv index False i This leaves us with GB of CSV files containing playlist track data Similar items In this section we will compute the similarity between items in the dataset such as artists or tracks To do this we will use three different algorithms two variants of Collaborative Filtering and one based on Co Occurrence Collaborative Filtering is a popular approach for recommendation systems as it relies on the ratings or preferences of users to make predictions about what items a given user might like In our case we will use the presence of an artist in a playlist as a proxy for a rating of indicating that the artist is relevant to the playlist The Co Occurrence based algorithm on the other hand will measure the similarity between items based on how often they appear in the same playlist Preparing training DatasetWe ll start by creating a training dataset The first function reads every CSV file and combines them into a single pandas DataFrame For artist similarity we are only interested in the presence of artists in a playlist Here s the code for reading the raw dataset def read for artists allowlist p Path kaggle input spotify playlists csv paths list p iterdir df pd concat pd read csv f usecols pid artist uri for f in paths ignore index True df df df artist uri isin allowlist return dfAs previously mentioned I ll further limit the number of artists By playing with the data I noticed that it s reasonable to keep around K of them corresponding to K playlist mentions Here s the code for preparing the artist allowlist N k leaves K artistsN k leaves artistsN k leaves artistsdef get common artists index mentions common artists pd read csv kaggle input common spotify artists common artist uri csv df common artists common artists count gt mentions df df set index artist uri return df indexTo remove noise I suggest to remove playlists that have too few or too many artists Having too many artists might mean that this is a playlist with random songs while having too few may risk losing signals and overfitting the model def clean by pid df p min p max pid counts df groupby pid pid count drop df pid counts lt p min pid counts gt p max drop index drop df drop df index df df df pid isin drop index return dfHaving more than one track from the same artist in a playlist might signal something but I didn t want to introduce the complexity of a value function so let s leave only unique mentions of artists in the training set def preprocess artists df df drop duplicates subset pid artist uri inplace True return dfAfter performing all necessary manipulations on the dataset we are left with relevant pid artist pairs The first two algorithms we will consider are variants of Collaborative Filtering Collaborative Filtering relies on the notion of ratings so for every pair of pid and artist we ll assign a rating of to indicate that the artist is relevant to the playlist Additionally it will be useful to index the dataset by the pid artist pair This will allow us to efficiently union dataset when we add negative data def prepare positive df item name df rating df df set index pid item name return dfNow let s move on to generating negative labels as promised To do this we will need to obtain the unique playlist IDs and unique artists in our dataset We can then randomly match them a specified number of times to create our negative examples def generate negative pairs N unique pids unique items items np random choice unique items size N pids np random choice unique pids size N df pd concat pids items axis df rating return dfdef generate negative artists N pids artists df generate negative pairs N pids artists df df set index pid artist uri return dfTo create a dataset we can generate two random arrays one for artists and one for playlists this process can duplicate items if necessary Then we can join the arrays into a single dataset by adding them as columns It is important to note that we will need to index this new dataset accordingly To finalize the creation of our positive and negative datasets we join them together and reset the indexed columns def join dfs df df res pd concat df df sort True copy False make sure to free the occupied memory df drop df index inplace True df drop df index inplace True return resdef reindex df df reset index inplace True return dfHere is a summary of the steps we have discussed for creating a training dataset for collaborative filtering Obtain the list of the most popular artists to use as an allowlist Read in the raw data Clean up the noisy playlists Generate the negative dataset by randomly generating pairs of playlist IDs and artists that do not already exist in the dataset Assign positive and negative labels to the data accordingly Join datasets together into a single dataset Here is a function that combines these steps into a single flow for creating the training dataset for collaborative filtering def artist training set n common pid min max negative ratio read most popular artists allowed artists get common artists index n common read and preprocess positive labels data df read for artists allowed artists df clean by pid df pid min max pid min max df preprocess artists df generate negative labels df generate negative artists math ceil negative ratio len df df pid drop duplicates allowed artists to series prepare negative labels df prepare positive df artist uri finilise resulting dataset df join dfs df df return reindex df If we execute this function the resulting data will look this way training set training df artist training set n common N k users min max negative ratio pid artist uri rating DMveApCUnCNPfPvlHSU fqKkggKUBHNkbKtXEls eNHdiiabvmbperwrg LExTjHddEcZwqJJ wYsveUspgSokKOiI BYFdaZbEKbeauJysI ZWfKeBYxbusRsdQVPb uqPttqEjjIHbzwcrXF RhgnQNCQoBlaUvTMEe NhdGzEDvFeUwudug Training ModelsNow that the training dataset is prepared we can experiment with various item similarity algorithms As I mentioned we will begin with Collaborative Filtering which has two variations Matrix Factorization and Collaborative Filtering with Multi Layer Perceptron MLP Both methods follow a similar process we create a vector representation for each artist treat this vector as a location in N dimensional space and find the K closest artists to a given artist The main difference lies in how these vectors are computed I have tried to make this article as practical as possible If you are interested in learning more about the underlying theory behind these algorithms I recommend the following resources The Netflix Tech Blog has an in depth article on the collaborative filtering algorithms used by Netflix This tutorial from the DataCamp blog provides a practical introduction to implementing collaborative filtering in Python This course from Coursera Recommendation Systems and Deep Learning in Python covers collaborative filtering in detail and includes hands on exercises Article with theory behind how Collaborative Filtering algorithms work We will be utilizing the fastai library so let s go ahead and create a Dataset Loader that retrieves data from the created DataFrame device torch device cuda if torch cuda is available else cpu dls CollabDataLoaders from df df item name artist uri rating name rating username pid bs device device Internally the Dataset Loader will load the data into a device encode URIs shuffle and batch the data and create a validation subset Let s make sure it has got the correct data data batch dls show batch pid artist uri rating lyPtntblHJvAFMMhiE iZtZyCzpLItcwwtPID YBxlxCUIAUkUeadQ EVpmkEwrLYEgjIsiPMIb irinBFsewNPLIetAw jefIIksOiEazgRTfWPk fGqyOPoCkotzzRwviBu yAwtBaoHLEDWAnWRhBT nvCScmbHyNAOcjg HrcKCfwqkyDFHwpKM Matrix Factorization Collaborative FilteringI would like to begin by using the Matrix Factorization Collaborative Filtering item similarity method Let s create and train the model learn collab learner dls n factors y range epochs learning rate e weight decay learn fit one cycle epochs learning rate wd weight decay Here are model metrics epochtrain lossvalid losstime One of the most important parameters when creating the model is the n factors argument This parameter specifies the size of the N dimensional vector A larger value allows the model to capture more concepts but it will also require more data to be trained effectively After completing the model fitting process we can check the embeddings of the items artists learn weight lyPtntblHJvAFMMhiE is item True tensor These vectors may not be interpretable to humans but we can use them to find similar items by calculating the cosine or some another distance between two vectors Now we can construct a similarity matrix using another popular algorithm a KNN classifier To do this we will use the sklearn library The process is as follows train the KNN model and then for each artist in the dataset retrieve the N most similar artists Here is how we train the model weights learn model i weight weight cpu detach numpy items dls classes artist uri itemsknn KNeighborsClassifier n neighbors algorithm kd tree knn fit weights items Here is how we can retrieve similar items item learn weight TVXtAsRInumwjSr is item True cpu numpy knn kneighbors item return distance False array As you can see the returned items are not URIs but indices so let s convert them to URIs using the DatasetLoader see similar items dls classes artist uri items i for i in neighb CCjtDhCKBvgWGa dZTsnKHgTEHoZ jkutDXcXoBMOhFuqg cGUmnvZMqdXYQGTX tqhsYvyBBdwANFNzHtcr kFfYUjNdNyaeUgLIEbIF TIYQjFPwQSRmorSezPxX HjYETXAvcLmzaKjAmHK NWbwDZYVkRqFafuQmwk bgfjzUoWpyeVatGDjnH mDUnMUJnOSYHkjzoqM nwjYsZQLlVNUHEYE TsHKUlWqnOPVikirn GkmDLgOeUyjKVBLzbu NIIxcxNHmOoyBxSfTCD LLpKhyESsyAXpclaKU mFvqQjKpjmdKIqcwmG HPaUgqeutzrjxaWyDV WMRPWKqSmrBGDBFSop As previously you can follow link to see artists i e Multi Layer Perceptron Collaborative FilteringNow I would like to try a different algorithm for computing embeddings the Multi Layer Perceptron The data preparation step is the same as well as instantiation of a DataLoader The main difference lies in the model definition with fastai we can do this learn collab learner dls use nn True layers y range In this case we will let the fastai library to decide on the size of the embeddings and only specify the NN layers Let s train the model using the same parameters learn fit one cycle e wd Model metrics epochtrain lossvalid losstime Training and using the KNN model is slightly different due to internal structure difference in fastai embeddings model weights learn model embeds weight cpu detach numpy items dls classes artist uri itemsknn KNeighborsClassifier n neighbors algorithm kd tree knn fit weights items And this is how we use it item idx dls classes artist uri oi TVXtAsRInumwjSr item learn model embeds weight item idx cpu detach numpy neighb knn kneighbors item return distance False array converted artist URIs XyouuXCZmMpatFPJ KWrqBFWDnANFQUkSx pKCCKEajJHZKAiaKH cmlxpTdSFRgMOXPh CajNmpbOovFoOoasHHaY YZyLoLNWbxBtNhZWg sIBHcqhZNyqHKZFOL QHgLlAIqAwHtDYldmP ubrtQOOCPljQFLKca AacqylxrFIXCZ GGBxAlTqCwzJGjLp lHvQsamXTsMTBrO fAVVWsXOYnASrzqfmYu WMRPWKqSmrBGDBFSop vWDOPvNqNYHIOWvm iZtZyCzpLItcwwtPID RyvyyTExzBZywiAwpi tKWjrPDmMAVKlm dkjvSzLTtiykXeh Storing similaritiesWe are now able to query similar items for a given item so the next step is to compute a similarity matrix and store it in a file which we will use in the second part of this tutorial I plan to use the following structure and write it in a plain text seed item similar similar similar seed item similar similar similar seed itemN similar N similar N similar N You may find this structure too simple but in the next part I ll explain why we need it this way The first step is to compute the similarity matrix To do this we will need a few helper functions the most notable of them is get similar which queries similar items for a given batch of items The other functions should be self explanatory def get emb learn items if isinstance learn model EmbeddingNN emb learn model embeds weight items else emb learn model i weight weight items return emb cpu detach numpy def get similar knn learn items emb get emb learn items return knn kneighbors emb return distance False def to uri dls i return dls classes artist uri items i def total items dls return len dls classes artist uri items We can now construct a generator that will compute the similarity matrix but won t store it in memory This generator operates in batches when querying the KNN model but later flattens the batch and iterates over a single row Querying the KNN model in batches hopefully increases the throughput as it can benefit from CPU caches and vector instructions def compute similarity learn dls knn bs items total items dls stream idx similar arr for start idx in range items bs for idx similar arr in zip range start idx start idx bs get similar knn learn slice start idx start idx bs return to uri dls idx to uri dls i for i in similar if i idx for idx similar in stream The final piece of the puzzle is a loop that goes through the generator and writes the rows to a file def store similarity pairs stream name with open name w as f for k v in stream f write k f write f write join v f write n With that we have completed the Collaborative Filtering part Let s move on to another similarity algorithm Co ocurrence CountingThe next similarity algorithm is called Co ocurrence Counting Its idea is very simple it counts the co occurrences of items within a given window of events Co occurrences that happen often more than the specified threshold can be considered similar In the case of Spotify dataset instead of windows we can count the co occurrences of artists within a playlist For this algorithm we are only interested in pairs of playlist ID and artist and we don t need negative data or ratings One good aspect is that we can reuse most of the data processing logic from the previous example This is how we read and process the data allowed artists get common artists index N k df read for artists allowed artists df clean by pid df df preprocess artists df pid artist uri DMveApCUnCNPfPvlHSU fqKkggKUBHNkbKtXEls eNHdiiabvmbperwrg LExTjHddEcZwqJJ wYsveUspgSokKOiI QMyHYMIHPQjFjlqg XHOcRUPCLOrjwpvsx vgTXgyPaAgogJzoQH YhUSmokLWldQVwJkLlP IYUhFvPQItjxySrBmZkdThe number of unique artist pairs can increase by several orders of magnitude from the initial dataset size In such case using a pair of strings string string is very inefficient and will have a large memory footprint However we can encode factorize the artist IDs into unique integers and store pairs of integers int int instead This will reduce the memory footprint by a factor of df artist idx ia df artist uri factorize pid artist uri artist idx DMveApCUnCNPfPvlHSU fqKkggKUBHNkbKtXEls eNHdiiabvmbperwrg aCikOXgbLUpfJfKp LExTjHddEcZwqJJ vgTXgyPaAgogJzoQH YhUSmokLWldQVwJkLlP crLEWPfVovyUtMpvC qFrwsWUITRlzZkZotF IYUhFvPQItjxySrBmZkd Now we can generate all present combinations of artist pairs from every playlist def coco pairs df groupped df groupby pid artist idx apply list return item item if item lt item else item item for arr in groupped items for item item in itertools combinations arr r The logic is as follows we group the dataset by playlist ID and aggregate the artists into a list Then for each list generate combinations ensuring that the pair is ordered so that A B is the same as B A This is because the order of the pair does not matter I am using a generator because storing every pair before aggregating them would almost certainly not fit in memory The output of this generator is a single artist co occurrence pair from a playlist We can now use Python s Counter class to count the co occurrences of pairs c Counter coco pairs df Let s check the most common co occurrences c most common And convert the encoded numerical IDs back to artist URIs using the previously saved index i ia i for i in TVXtAsRInumwjSr KWrqBFWDnANFQUkSx YZyLoLNWbxBtNhZWg We will create a DataFrame with three columns item item and count Then group the DataFrame by item and get the item values that are similar to it so the item order now matters Therefore we will need to duplicate the counter and store both the A B pair and the B A pair o i i c i i for i i in c o i i c i i for i i in c coco pd DataFrame itertools chain o o columns item item count item item count Let s apply a threshold and see what similarities the co occurrence counting model yields similarities res res count gt item item count And convert encoded ints for the first result ia ia dfeRHaWDbWqFHLkxsgd bcqnyjGBuRriQg This might be a final solution but we re overlooking an important aspect when we filter pairs in this way less popular artists will always have fewer co occurrences yet they may still be similar to each other To address this we can add a normalisation step First let s count the total number of mentions of each artist item counts res groupby item count sum item Then we can add a normalised count to each pair which is computed as pair count diveded by a sum of total counts of individual items pair count res count item total res item map item counts item total res item map item counts res normalised count pair count item total item total This normalised count penalises popular artists and boosts less popular item item count normalised count If we apply the threshold to the normalised count we can see how different pairs that were previously missing or present have been affected similarities res res normalised count gt item item count normalised count For example the last pair which has only co occurrences would have certainly been filtered out previously These artist URIs are shown below you can judge for yourself how similar they are ia ia sTFGCigAQIUiEywSSQNF YSAbyXALzoTsSTlB Storing similaritiesLet s now create the similarity matrix and write it to a file in the same format as before similar items similarities groupby item item apply list item One downside of this algorithm is that it doesn t provide a guaranteed number of recommendations For example item idx has only similar items while item idx has etc And finally we can write them to the text file fname coco txt with open fname w as f for k v in similar items f write ia k f write v ia i for i in v f write join v f write n Since this article is getting quite long I ll leave it as an exercise for you to model the track similarities This concludes the section on finding similar items We overviewed how to build similarity matrices using Collaborative Filtering and Co occurence counting algorithms You can find an example matrix files in this GitHub Gist You can also try other algorithms that produce embeddings such as WordVec or even possibly content based algorithms that take artist or track features into account Share your artist or track similarity results in the comments In the next chapter I ll cover how we can use similarities to retrieve recommendations If you don t want to miss it be sure to hit the follow button Thank you for reading this far I hope it was interesting	2023-01-11 14:31:41
海外TECH	DEV Community	LOGICAL OPERATOR	https://dev.to/charos1mm/logical-operator-4811	LOGICAL OPERATORBir yoki bir necha shartlarni solishtirish va ularni to g ri yoki noto ri ekanligini aniqlash YO Q amp amp VA YOKI Natija har doim True yoki False True False qaytardi dan boshqa har qanday qiymat True yani Rost deb hisoblanaldi include lt iostream gt using namespace std int main int a int b a int c a amp amp b cout lt lt c return include lt iostream gt using namespace std int main int a int b a int c a lt b lt cout lt lt c return Dasrligimiz kundan kunga qiyin va qiziqarli bolib bormoqda dawroun	2023-01-11 14:28:14
海外TECH	DEV Community	How to implement mobile share module in ReactJS quickly?	https://dev.to/rkencoresky/how-to-implement-mobile-share-module-in-reactjs-quickly-3a31	How to implement mobile share module in ReactJS quickly Hello Thanks for reaching here Many people require a module which can share data through the default share feature available in our mobile phones Android iOS as shown below I know it s very small things you can implement by using any rd party packages modules available on the internet I have one quick solution for you actually I spent time on that and prepare it for you all You just need to install this package react mobile sharenpm i react mobile share npmyarn add react mobile share yarnpnpm add react mobile share pnpmand use it like this lt button onClick gt shareOnMobile text Hey checkout our package react mobile share url title React Mobile Share gt Share lt button gt That s all it will send data to mobile and prompt a sharing popup with the app suggestion that can handle your data Why should you use thisLightweightShare text url and imagesWorks on Android and iOS You can also try a demo example with CodeSandboxThanks for reading this	2023-01-11 14:04:14
Apple	AppleInsider - Frontpage News	Daily Deals Jan. 11: $200 off M2 MacBook Air, $139 15-in-1 Thunderbolt 3 dock, 33% off Apple Watch & more	https://appleinsider.com/articles/23/01/11/daily-deals-jan-11-200-off-m2-macbook-air-139-15-in-1-thunderbolt-3-dock-33-off-apple-watch-more?utm_medium=rss	Daily Deals Jan off M MacBook Air in Thunderbolt dock off Apple Watch amp moreThe top deals we discovered today include up to off Apple s M MacBook Air off a Samsung Galaxy Smartwatch a deep discount of off an M inch MacBook Pro and savings on a Canon Multifunction Color Laser Printer Save on a MacBook Air The AppleInsider team reviews deals at online stores to curate a list of can t miss discounts on the best technology products including deals on Apple products TVs accessories and other gadgets We list the top deals in our Daily Deals list to help you save money Read more	2023-01-11 14:49:47
Apple	AppleInsider - Frontpage News	Razer Edge 5G gaming handheld -- hands on at CES	https://appleinsider.com/articles/23/01/11/razer-edge-5g-gaming-handheld----hands-on-at-ces?utm_medium=rss	Razer Edge G gaming handheld hands on at CESRazer Edge is a streaming centric gaming handheld launching later in ーand we got to try it before its public release Razer Edge gaming handheldPowered by Android Razer says that the Edge is the first G gaming console available on the market Between connectivity options a high refresh rate excellent colors and a reliable controller this device is tempting even for Apple users Read more	2023-01-11 14:40:49
Apple	AppleInsider - Frontpage News	Casetify debuts new line of cases with Mickey and Friends	https://appleinsider.com/articles/23/01/11/casetify-debuts-new-line-of-cases-with-mickey-and-friends?utm_medium=rss	Casetify debuts new line of cases with Mickey and FriendsCasetify s newest Disney centric line has been announced with cases bands and more accessories featuring the iconic Mickey and Friends New Mickey and Friends lineupMickey and Friends is a whole new lineup of accessories featuring Mickey Minnie Pluto Daisy Duck Donald Duck and Goofy Accessories include iPhone cases AirPods cases MagSafe wallets AirTag keychains MagSafe compatible chargers and Apple Watch bands Read more	2023-01-11 14:18:15
海外TECH	Engadget	OpenAI will soon test a paid version of its hit ChatGPT bot	https://www.engadget.com/openai-chatgpt-professional-paid-chatbot-143004442.html?src=rss	OpenAI will soon test a paid version of its hit ChatGPT botIf you re eager to use ChatGPT for work you might soon have the option OpenAI has shared a waitlist for a experimental ChatGPT Professional service that for a fee would effectively remove the limits on the popular chatbot The AI tool would always be available with no throttling and as many messages as necessary The startup hasn t said when the pilot program might launch and it s asking would be participants for feedback on pricing OpenAI isn t shy about the reasoning behind its pro offering As TechCrunchnotes the company said on its Discord server that it s starting to think about how it will make money from ChatGPT and keep the technology viable in the long term CEO Sam Altman recently pointed out that ChatGPT costs OpenAI a few cents for every chat making it impractical to keep the bot completely free While Reuterssources say OpenAI anticipates making million in revenue this year it also reportedly wants to earn billion in ーsubscriptions might play an important role in that growth Whether there s a sizable audience for ChatGPT Professional is unclear The existing version had more than one million users as of early last month but it s uncertain how many of those are serious users versus enthusiasts and curious onlookers Schools and even AI conferences have banned the bot Microsoft a major OpenAI backer is rumored to be integrating ChatGPT into its Bing search engine as soon as March however and investors have even tried using the technology as part of their workflows The pilot could be crucial for gauging real world demand not to mention setting the prices needed to turn a profit	2023-01-11 14:30:04
海外TECH	Engadget	The best streaming devices you can buy in 2023	https://www.engadget.com/best-streaming-devices-media-players-123021395.html?src=rss	The best streaming devices you can buy in If you re in the market for a new streaming device chances are you want to improve your home entertainment experience Maybe you ve been relying on your phone or tablet for binge watch sessions or perhaps your TV s built in operating system just isn t cutting it anymore Streaming dongles like the Amazon Fire TV Stick Lite and set top boxes like the Apple TV K are ubiquitous at this point but sussing out the differences between them can be challenging Plus they re not the only gadgets that can deliver your latest Netflix obsession to your TV screen Let us break down all of the options you have today and give you our picks for the best streaming device you can buy Who needs a streaming device It s worth pointing out that if you only use a couple of streaming services say Netflix and Amazon Prime Video you might not need a standalone streaming device Most modern televisions ship with a basic selection of apps that usually include the most popular streaming services Some TVs and soundbars run on built in Roku or Fire TV operating systems which offer a robust selection of apps without the need for a separate device But if your TV is on the older side adding a streaming stick is obviously much cheaper than shelling out for a new smart TV Also a dedicated streaming device typically has access to a lot more streaming services and apps while content is often presented more intuitively Some devices also offer better search features including voice control Cord cutters in particular will benefit from this sort of streaming hardware as live TV services like Sling TV and Hulu Live aren t always available in basic TV interfaces How to pick a streaming deviceThe most important things to keep in mind when choosing a media streaming device are platform price and what you already have in your home Currently the most popular streaming platforms are Roku Amazon s Fire TV Apple TV and Google TV which is an overlay on top of Android TV All of them offer a similar selection of streaming services with the primary differences being the user interface Roku for example has a basic grid layout while Fire TV and Google TV emphasize personalized recommendations The prices for streaming devices typically start at around and can go all the way up to like the GB Apple TV K for example Sticks or dongles are generally more affordable while set top boxes tend to be pricier The main reason to opt for a streaming box over a dongle is if you need Ethernet connectivity Some models also have additional features like Ethernet ports and faster wireless connections Roku s set top boxes for example have Dolby Vision while its sticks don t Another benefit of a set top box is that they re faster than older TV processors and are easier to upgrade over time There are some feature differences too Fire TV uses Alexa for voice commands for example while Google TV uses Google Assistant In fact when it comes to devices from Amazon Google and Apple it s generally best to pick one that belongs in a previously chosen ecosystem So if you re already a committed iTunes user an Apple TV would make more sense than the Fire TV This isn t a hard and fast rule of course If you depend more on a streaming service than on buying or renting shows the ecosystem question isn t quite as relevant We ll get more into the details of each system later in this guide Below are some recommendations for the best streaming sticks and other budget friendly options We also included suggestions for set top boxes and devices geared toward gamers Best streaming stick Roku Streaming Stick KThe Roku Streaming Stick K plus earlier iterations like the Streaming Stick has long been an Engadget favorite and for good reason It packs a lot of features into a small and affordable package It has a straightforward user interface along with the widest selection of streaming TV options All you need to do to set it up is plug it into your TV s HDMI input and attach the power cable to your TV s USB port After that you can finish the rest of the installation process via the TV interface and Roku remote As mentioned Roku has a large selection of content According to the company you can choose to stream from more than movies and TV episodes Another useful feature is Roku s universal search which can find shows across a variety of platforms without prioritizing one over another In contrast Amazon s streaming platform prioritizes search results from its own Prime video service The Roku Streaming Stick K also supports Apple AirPlay so it s great for those who want to stream from Macs or iOS devices Additionally the Roku Streaming Stick K supports K HDR streaming and Dolby Vision We especially like Roku s private listening feature that lets you listen to shows using headphones which is useful if you don t want to disturb other members of your household You can do this via the Roku TV app which is available on both iOS and Android The app also works as an alternative to the physical remote control This is especially handy for entering login and password information which is a lot easier to do with a smartphone keyboard than having to hunt and peck using the remote In addition to the remote the Roku Streaming Stick K also responds to voice commands which you can use to search for your favorite shows adjust the volume or enable closed captioning If you want an upgraded remote consider upgrading to the Roku Streaming Stick K It has all the features of the Streaming Stick K except it comes with the rechargeable Roku Voice Remote Pro as well You can find your lost remote by saying “Hey Roku where s my remote and it has a built in mm headphone jack so you can use the private listening feature without the app It is almost more than the Streaming Stick K however so unless you really need that fancy remote we recommend the Streaming Stick K instead Runner up Chromecast with Google TVFor years Google s Chromecast was a dongle without a remote It was simply used to stream shows from your phone or laptop to the TV The updated Chromecast with Google TV however is completely different Not only does it now have a dedicated remote control but it ushers in a brand new menu interface plus Google Assistant smarts While the Roku might be our overall pick the Google Chromecast is best for those who like a more personalized interface and robust voice controls The highlight of the new Chromecast is Google TV which is actually a revamped version of Android TV The home screen is personalized based on your watching habits There s a rotating carousel of trending shows curated top picks as well as category headers like “Continue watching and “Trending on Google We especially appreciate the “Continue Watching row as it offers a quick shortcut to catch up on programming you were ーyou guessed it ーalready watching There are also algorithm generated recommendations such as “Shows about aliens or “Shows about murder What s nice is that most of the suggested shows are from services you re already subscribed to If you re a YouTube TV subscriber you ll appreciate the Live TV tab which acts as a channel guide for the service Google has said however that the tab should eventually work with other live TV services such as Sling or Hulu Live We also like that Google TV gives a lot of information about a specific show or movie such as the Rotten Tomatoes rating and all of the different ways you can watch it You can also add it to your watchlist right from the show page regardless of what streaming service it s on Renting or buying the show can only be done from the Google Play Movies amp TV store however One of our favorite features of the new Chromecast is Google Assistant integration You can make general queries like asking for a five day weather forecast Best of all contextual queries like “Find movies with Bill Murray and “Show me true crime documentaries all turn up highly relevant results You can also control the TV entirely with voice commands including powering it on and off and adjusting the volume The Chromecast with Google TV supports K HDR as well as Dolby Vision However it doesn t support Apple s AirPlay protocol so it s not quite as compatible with Macs and iOS devices It also doesn t currently carry Apple TV or iTunes videos We should also note that unlike other streaming sticks the latest Google Chromecast can t be powered by a TV s USB port you ll have to use the included watt power adapter instead Best budget streaming device Amazon Fire TV Stick LiteIf price is of the utmost importance and you don t need K Amazon s Fire TV Stick Lite is a decent alternative At it s one of the cheapest streaming sticks on the market it s also frequently discounted too It supports FHD streaming with HDR and just like its higher end siblings comes with an Alexa voice remote One of the reasons this is considered “Lite is that this particular Fire TV Stick can t control your TV you still have to use your television remote to power it on and off or to adjust its volume That s not that big a deal especially if it helps save you a few bucks In comparison the standard Fire TV Stick typically retails for while the Fire TV Stick K costs Amazon s Fire TV supports nearly all of the popular streaming services including Netflix Hulu HBO Max YouTube YouTube TV and Hulu among others The Live page features Twitch out of the box While you can t use Alexa to control the TV the Fire TV Stick Lite does let you use Alexa to search for shows and ask general questions like the weather forecast or the latest scores for your favorite sports team Despite its low price the Fire TV Stick Lite is a decent streaming dongle It comes with the updated Fire TV interface that adds features such as user profiles a new main menu navigation bar with show recommendations plus a scrolling list of your favorite streaming apps The layout isn t quite as intuitive as Google TV s and is more complicated than Roku s but it s still easy enough to figure out That said the interface tends to prioritize Amazon Prime Video content and there are a lot more ads than on other streaming platforms It s not the best streaming device out there but it s a good inexpensive choice if you don t have a smart TV and want to update an older set while spending as little as possible For a little more money you could also upgrade to either the Fire TV Stick K or the Fire TV Stick K Max Both feature K HDR Dolby Vision and Dolby Atmos but the Max has WiFi support and a faster processor Another budget option Roku ExpressThe Roku Express has the same user interface as the Streaming Stick but it s housed in a compact set top box instead It doesn t support K or HDR and the remote control lacks a voice command button But if all you want is a capable HD streaming device the Express fits the bill If you insist on having K however consider the Roku Express K which retails for It s very similar to the Express except it carries support for K HDR and AirPlay and it comes with a voice remote as well That could well make it worth the extra money Best set top box Roku UltraThe Roku Ultra has the same features of the Streaming Stick and then some There s a wide selection of content a simple user interface and support for K HDR streaming AirPlay and voice commands On top of that the Ultra adds Dolby Vision support along with HDR USB connections a microSD slot for external media and Ethernet connectivity Like the Streaming Stick K the Ultra comes with Roku s Voice Remote Pro The remote also features a couple of programmable shortcut keys that you can map to specific commands like “Launch YouTube or “Play classical music Best of all the Ultra features a remote finder in case you lose it in between your couch cushions The Ultra is definitely the most capable Roku device on the market but you ll have to pay quite a bit more for it Best premium streamer Apple TV KFor those who want a slightly higher end option and have a lot of Apple devices the Apple TV K could be worth a splurge It can stream in K HDR and Dolby Vision plus it supports AirPlay for streaming from Mac and iOS devices The tvOS platform has an attractive and clean user interface that s also easy to use There s also a “One Home Screen feature that lets you sync apps and their layout across multiple Apple TVs and you can use Siri to search for your favorite shows And ever since Apple revamped the Siri remote included with the box it s been much easier to navigate between apps scroll through content and generally find what you re looking for quickly As you might expect the Apple TV is also the only set top box that works with the entire Apple ecosystem This means that you can use the Apple TV to buy and rent movies from iTunes access music and podcasts play games from Apple Arcade and run apps like Fitness Apple s line of workout classes iPhone owners in particular are likely to benefit from owning an Apple TV You can hold your handset up to the box during setup to transfer all of your settings and enter passwords directly through the Remote app which is itself easily accessible via iOS s control center The model is still available right now but we recommend springing for the version that was just announced so you can get double the base storage GB at the new lower starting price of Best for gamers NVIDIA Shield TV ProFor those who want an all in one device that lets you stream movies run a PLEX media server and play games consider NVIDIA s Shield TV Pro It currently runs Android TV which is a little outdated at this point but there s a possibility that it could be upgraded to Google TV in the future Thanks to its capable Tegra X processor the Shield TV Pro can stream in native K and it can also upscale p and p video to K with the company s AI neural network It also supports Dolby Vision and HDR has GB of RAM GB of storage and two USB C ports Additionally there s a gigabit Ethernet port an HDMI socket and a microSD card slot Since it runs on Android you can use it to play most games from the Google Play Store The main reason you d choose the NVIDIA Shield TV Pro over other machines is that it gives you access to NVIDIA s GeForce Now cloud gaming service As long as you have a relatively speedy internet connection you can play top tier PC games that are streamed online to your Shield TV Pro Best gaming console for streaming Xbox Series XEven though both Sony s PlayStation and the Xbox Series X can stream video it s the Xbox that we think is the best gaming console for the job It can play K Blu Rays and supports all of the usual streaming video apps including Apple TV and Disney However the PS can t stream Disney in K or Dolby Atmos which is disappointing if you ever want to watch The Mandalorian in all its cinematic glory Fortunately that s not the case with the Xbox Series X On top of that the Xbox Series X and S also support Dolby Vision for streaming video which is especially great for people with newer TVs Of course the Xbox is also a pretty great gaming machine and it offers access to Game Pass Microsoft s subscription service that has a large library of titles	2023-01-11 14:15:12
海外TECH	CodeProject Latest Articles	Insight.Database - .NET Micro ORM - Part 2 - Executing And Carrying Out SELECT SQL Commands	https://www.codeproject.com/Articles/5351650/Insight-Database-NET-Micro-ORM-Part-2-Executing-An	commands	2023-01-11 14:07:00
海外科学	NYT > Science	Russia to Launch Space Station Rescue Mission to Bring Astronauts Home	https://www.nytimes.com/2023/01/11/science/nasa-space-station-soyuz.html	soyuz	2023-01-11 14:08:29
海外科学	NYT > Science	Sync Your Calendar With the Solar System	https://www.nytimes.com/explain/2023/01/01/science/astronomy-space-calendar	event	2023-01-11 14:11:12
海外科学	NYT > Science	The New Soldiers in Propane’s Fight Against Climate Action: Television Stars	https://www.nytimes.com/2023/01/11/climate/climate-propane-influence-campaign.html	group	2023-01-11 14:31:10
海外TECH	WIRED	A Police App Exposed Secret Details About Raids and Suspects	https://www.wired.com/story/sweepwizard-police-raids-data-exposure/	A Police App Exposed Secret Details About Raids and SuspectsSweepWizard an app that law enforcement used to coordinate raids  left sensitive information about hundreds of police operations publicly accessible	2023-01-11 14:12:19
金融	RSS FILE - 日本証券業協会	新型コロナウイルス感染症への証券関係機関等・各証券会社の対応について（リンク集）	https://www.jsda.or.jp/shinchaku/coronavirus/link.html	新型コロナウイルス	2023-01-11 15:30:00
ニュース	BBC News - Home	Andrew Bridgen suspended as Tory MP over Covid vaccine comments	https://www.bbc.co.uk/news/uk-politics-64236687?at_medium=RSS&at_campaign=KARANGA	conservative	2023-01-11 14:13:21
ニュース	BBC News - Home	PMQs: Rishi Sunak says he is registered with NHS doctor	https://www.bbc.co.uk/news/uk-politics-64237830?at_medium=RSS&at_campaign=KARANGA	healthcare	2023-01-11 14:17:59
ニュース	BBC News - Home	Fees for England universities frozen at £9,250 for two years	https://www.bbc.co.uk/news/education-64236906?at_medium=RSS&at_campaign=KARANGA	frozen	2023-01-11 14:28:14
ニュース	BBC News - Home	'Number of defects' led to fatal fire at Cameron House hotel	https://www.bbc.co.uk/news/uk-scotland-64236252?at_medium=RSS&at_campaign=KARANGA	december	2023-01-11 14:54:40
ニュース	BBC News - Home	Chelsea transfer news: Joao Felix signs for Blues on loan from Atletico Madrid	https://www.bbc.co.uk/sport/football/64226705?at_medium=RSS&at_campaign=KARANGA	felix	2023-01-11 14:57:11