投稿時間:2022-12-21 05:23:07 RSSフィード2022-12-21 05:00 分まとめ(28件)

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python	Pythonタグが付けられた新着投稿 - Qiita	Pythonでクリップボードにファイルをコピーする方法 (Windows)	https://qiita.com/Rilm2525/items/5ef4b4e8552b71971ad4	oswindowsproh	2022-12-21 04:53:16
python	Pythonタグが付けられた新着投稿 - Qiita	TwilioとPythonで夜中でも必ず起こす電話アラートを30分で実装した話	https://qiita.com/kabuakantech/items/92302f8de896e5c091ff	twilio	2022-12-21 04:10:22
海外TECH	Ars Technica	19 states choke TikTok on gov’t devices; feds push for nationwide restrictions	https://arstechnica.com/?p=1905904	state	2022-12-20 19:04:37
海外TECH	MakeUseOf	How to Fix the Microsoft Store Error Code 0x80131500	https://www.makeuseof.com/microsoft-store-fix-error-code-0x80131500/	error	2022-12-20 19:45:15
海外TECH	MakeUseOf	Linux Mint 21.1 Released With Decluttered Desktop, Other Improvements	https://www.makeuseof.com/linux-mint-211-released-with-decluttered-desktop/	linux	2022-12-20 19:42:53
海外TECH	MakeUseOf	Make the Holiday Season Merrier With Amazon Alexa's Christmas Skills	https://www.makeuseof.com/amazon-alexa-best-christmas-skills/	alexa	2022-12-20 19:30:15
海外TECH	MakeUseOf	7 Ways to Christmas-ify Your Windows 11	https://www.makeuseof.com/windows-11-christmas-customizations/	windows	2022-12-20 19:15:15
海外TECH	DEV Community	Improve AI Face Recognition Accuracy Using Deep Learning	https://dev.to/dmitriykisil/improve-ai-face-recognition-accuracy-using-deep-learning-43b4	Improve AI Face Recognition Accuracy Using Deep LearningBiometric identification of a person by facial features is increasingly used to solve business and technical issues The development of relevant automated systems or the integration of such tools into advanced applications has become much easier First of all this is caused by the significant progress in AI face recognition In this article we will explain what the components are of a face recognition software and how to overcome the limitations and challenges of these technologies You will find out how AI namely Deep Learning can improve the accuracy and performance of face recognition software and how thanks to this it is possible to train an automated system to correctly identify even poorly lit and changed faces It will also become clear what techniques are used to train models for face detection and recognition Do you remember trying to unlock something or validate that it s you with the help of a selfie you have taken but lighting conditions didn t allow you to do that Do you wonder how to avoid the same problem when building your app with a face recognition feature How Deep Learning Upgrades Face Recognition SoftwareTraditional face recognition methods come from using eigenfaces to form a basic set of images They also use a low dimensional representation of images using algebraic calculations Then the creators of the algorithms moved in different ways Part of them focused on the distinctive features of the faces and their spatial location relative to each other Some experts have also researched how to break up the images to compare them with templates As a rule an automated face recognition algorithm tries to reproduce the way a person recognizes a face However human capabilities allow us to store all the necessary visual data in the brain and use it when needed In the case of a computer everything is much harder To identify a human face an automated system must have access to a fairly comprehensive database and query it for data to match what it sees The traditional approach has made it possible to develop face recognition software which has proven itself satisfactorily in many cases The strengths of the technology made it possible to accept even its lower accuracy compared to other methods of biometric identification using the iris and fingerprints Automated face recognition gained popularity due to the contactless and non invasive identification process Confirmation of the person s identity in this way is quick and inconspicuous and also causes relatively fewer complaints opposition and conflicts Among the strengths that should be noted are the speed of data processing compatibility and the possibility of importing data from most video systems At the same time the disadvantages and limitations of the traditional approach to facial recognition are also obvious LIMITATIONS OF THE TRADITIONAL APPROACH TO FACIAL RECOGNITIONFirst of all it is necessary to note the low accuracy in conditions of fast movement and poor lighting Unsuccessful cases with the recognition of twins as well as examples which revealed certain racial biases are perceived negatively by users The weak point was the preservation of data confidentiality Sometimes the lack of guaranteed privacy and observance of civil rights even became the reason for banning the use of such systems Vulnerability to presentation attacks PA is also a major concern The need arose both to increase the accuracy of biometric systems and to add to them the function of detection of digital or physical PAs However the traditional approach to face recognition has largely exhausted its potential It does not allow using very large sets of face data It also does not ensure training and tuning identification systems at an acceptable speed AI ENHANCED FACE RECOGNITIONModern researchers are focusing on artificial intelligence AI to overcome the weaknesses and limitations of traditional methods of face recognition Therefore in this article we consider certain aspects of AI face recognition The development of these technologies takes place through the application of advances in such subfields of AI as computer vision neural networks and machine learning ML A notable technological breakthrough is occurring in Deep Learning DL Deep Learning is part of ML and is based on the use of artificial neural networks The main difference between DL and other machine learning methods is representation learning Such learning does not require specialized algorithms for each specific task Deep Learning owes its progress to convolutional neural networks CNN Previously artificial neural networks needed enormous computing resources for learning and applying fully connected models with a large number of layers of artificial neurons With the appearance of CNN this drawback was overcome In addition there are many more hidden layers of neurons in neural networks used in deep learning Modern DL methods allow training and use of all layers Among the ways of improving neural networks for face recognition systems it is appropriate to mention the following Knowledge distillation A combination of two similar networks of different sizes where the larger one trains the smaller one As a result of training a smaller network gives the same result as a large one but it does it faster Transfer learning Focused on training the entire network or its specific layers on a specific set of training data This creates the possibility of eliminating bottlenecks For example we can improve accuracy by using a set of images of exactly the type that errors occur most often Quantization This approach aims to speed up processing by reducing the number of calculations and the amount of memory used Approximations of floating point numbers by low bit numbers help in this Depthwise separable convolutions From such layers developers create CNNs that have fewer parameters and require fewer calculations but provide good performance in image recognition and in particular faces Regarding the topic we are considering it is important to train a Deep convolutional neural network DCNN to extract from images of faces unique facial embeddings In addition it is crucial to provide DCNN with the ability to level the impact of displacements different angles and other distortions in the image on the result of its recognition Due to the data augmentation the images are modified in every way before training This helps mitigate the risks associated with different angles distortions etc The more variety of images used during training the better the model will generalize Let us remember the main challenge of face recognition software development This is the provision of fast and error free recognition by an automated system In many cases this requires training the system at optimal speed on very large data sets It is deep learning that helps to provide an appropriate answer to this challenge Highlights of AI face recognition system softwareAs we said above at the moment when deciding how to build a face recognition system it is worth focusing on Convolutional Neural Networks CNN In this area there are already well proven approaches to creating architecture In this context we can mention residual neural network ResNet which is a variant of a very deep feedforward neural network And for example such a solution as EfficientNet is not only the architecture of a convolutional neural network but also a scaling method It allows uniform scaling of the depth and width of the CNN as well as the resolution of the input image used for training and evaluation Periodically thanks to the efforts of researchers new architectures of neural networks are created As a general rule newer architectures use more and more layers of deep neural networks which reduces the probability of errors It is true that models with more parameters may perform better but slower This should be kept in mind When considering face recognition deep learning models the topics of the algorithms that are embedded in them and the data sets on which they are trained come to the fore In this regard it is appropriate to recall how face recognition works HOW FACE RECOGNITION WORKSThe face recognition system is based on the sequence of the following processes Face detection and capture i e identification of objects in images or video frames that can be classified as human faces capturing faces in a given format and sending them for processing by the system Normalization or alignment of images i e processing to prepare for comparison with data stored in a database Extraction of predefined unique facial embeddings Comparison and matching when the system calculates the distance between the same points on the images and then infers face recognition The creation of artificial neural networks and algorithms is aimed at learning automated systems training them on data and detecting and recognizing images including all of the above stages Building AI face recognition systems is possible in two ways Use of ready made pre trained face recognition deep learning models Models such as DeepFace FaceNet and others are specially designed for face recognition tasks Custom model development When starting the development of a new model it is necessary to define several more parameters First of all this concerns the inference time for which the optimal range is set You will also have to deal with the loss function With its help you can by calculating the difference between predicted and actual data evaluate how successfully the algorithm models the data set Triplet loss and AM Softmax are most often used for this purpose The triplet loss function requires two images anchor and positive of one person and one more image negative of another person The parameters of the network are studied in order to approximate the same faces in the functionality space and conversely to separate the faces of different people The standard softmax function uses particular regularization based on an additive margin AM Softmax is one of the advanced modifications of this function and allows you to increase the level of accuracy of the face recognition system thanks to better class separation For most projects the use of pre trained models is fully justified without requiring a large budget and duration Provided you have a project team of developers with the necessary level of technical expertise you can create your own face recognition deep learning model This approach will provide the desired parameters and functionality of the system based on which it will be possible to create a whole line of face recognition driven software products At the same time the significant cost and duration of such a project should be taken into account In addition it should be remembered how facial recognition AI is trained and that the formation of a training data set is often a stumbling block Next we will touch on one of the main potentials that rely on face recognition machine learning We will consider how accurate facial recognition is and how to improve it Face recognition accuracy and how to improve itWhat factors affect the accuracy of facial recognition These factors are first of all poor lighting fast and sharp movements poses and angles and facial expressions including those that reflect a person s emotional state It is quite easy to accurately recognize a frontal image that is evenly lit and also taken on a neutral background But not everything is so simple in real life situations The success of recognition can be complicated by any changes in appearance for example hairstyle and hair color the use of cosmetics and makeup and the consequences of plastic surgery The presence in images of such items as hats headbands etc also plays a role The key to correct recognition is an AI face recognition model that has an efficient architecture and must be trained on as large a dataset as possible This allows you to level the influence of extraneous factors on the results of image analysis Advanced automated systems can already correctly assess the appearance regardless of for instance the mood of the recognized person closed eyes hair color change etc Face recognition accuracy can be considered in two planes First of all we are talking about the embeddings matching level set for specific software which is sufficient for a conclusion about identification Secondly an indicator of the accuracy of AI face recognition systems is the probability of their obtaining a correct result Let s consider both aspects in turn We noted above that the comparison of images is based on checking the coincidence of facial embeddings A complete match is possible only when comparing exactly the same images In all other cases the calculation of the distance between the same points of the images allows for obtaining a similarity score The fact is that most automated face recognition systems are probabilistic and make predictions The essence of these predictions is to determine the level of probability that the two compared images belong to the same person The choice of the threshold is usually left to the software development customer A high threshold may be accompanied by certain inconveniences for users Lowering the similarity threshold will reduce the number of misunderstandings and delays but will increase the likelihood of a false conclusion The customer chooses according to priorities specifics of the industry and scenarios of using the automated system Let s move on to the accuracy of AI face recognition in terms of the proportion of correct and incorrect identifications First of all we should note that the results of many studies show that AI facial recognition technology copes with its tasks at least no worse and often better than a human does As for the level of recognition accuracy the National Institute of Standards and Technology provides convincing up to date data in the Face Recognition Vendor Test FRVT According to reports from this source face recognition accuracy can be over thus significantly exceeding the capabilities of an average person By the way current FRVT results also contain data to answer common questions about which algorithms are used and which algorithm is best for face recognition When familiarizing with examples of practical use of the technologies the client audience is often curious about whether face recognition can be fooled or hacked Of course every information system can have vulnerabilities that have to be eliminated At the moment in the areas of security and law enforcement where the life and health of people may depend on the accuracy of the conclusion about the identification of a person automated systems do not yet work completely autonomously without the participation of people The results of the automated image search and matching are used for the final analysis by specialists For example the International Criminal Police Organization INTERPOL uses the IFRS face recognition system Thanks to this software almost criminals and missing persons have already been identified At the same time INTERPOL notes that its officers always carry out a manual check of the conclusions of computer systems Either way the AI face recognition software helps a lot by quickly sampling images that potentially match what is being tested This facilitates the task of people who will assess the degree of identity of faces To minimize possible errors multifactor identification of persons is used in many fields where other parameters are evaluated in addition to the face In general in the world of technology there is always a kind of race between those who seek to exploit technological innovations illegally and those who oppose them by protecting people s data and assets For example the surge of spoofing attacks leads to the improvement of anti spoofing techniques and tools the development of which has already become a separate specialization Various tricks and devices have been invented recently for computer vision dazzle Sometimes such masking is done to protect privacy and ensure the psychological comfort of people and sometimes with malicious purposes However automated biometric identification through the face can undoubtedly overcome such obstacles The developers include in the algorithms methods of neutralization of common techniques of combating face recognition In this context it is useful to recall the relatively high accuracy of neural networks facial recognition for people wearing medical masks demonstrated during the recent COVID pandemic Such examples instill confidence in the reality of achieving high face recognition accuracy even under unfavorable circumstances The ways to increase the accuracy of facial recognition technology are through the enhancement of neural network architectures and the improvement of deep learning models due to their continuous training on new datasets which are often larger and of higher quality Significant challenges in the development of automated systems are also the need to reduce the recognition time and the number of system resources without losing accuracy At the moment the technical level of advanced applications already allows to analyze the image and compare it with millions of records within a few seconds An important role is played by the use of improved graphical interfaces Performing face recognition directly on peripheral devices is also promising because it allows you to do without servers and maintain user data security by not sending it over the Internet ConclusionSo we considered how facial recognition uses AI and in particular machine learning We have listed the main areas of development of these technologies Touching on the technical aspects of creating automated systems for neural networks facial recognition we identified common problems that arise in this process and promising ways to solve them From this article you learned how AI face recognition works and what components it consists of Also we did not overlook the topic of the accuracy of this process In particular we revealed how to improve face recognition accuracy You will be able to use the knowledge obtained from this article to implement your ideas in the research field	2022-12-20 19:48:32
海外TECH	DEV Community	What is Kubernetes and how should you monitor it?	https://dev.to/newrelic/what-is-kubernetes-and-how-should-you-monitor-it-bld	What is Kubernetes and how should you monitor it In this blog post you ll learn what Kubernetes is and what components you ll need for complete observability It s the first part in a Monitoring Kubernetes series What is Kubernetes Kubernetes often abbreviated as “Ks is an open source platform that has established itself as the de facto standard for container orchestration Usage of Kubernetes has risen globally particularly in large organizations with the CNCF in reporting that there are million developers using Kubernetes worldwide representing of all backend developers As a container orchestration system it automatically schedules scales and maintains the containers that make up the infrastructure of any modern application The project is the flagship project of the Cloud Native Computing Foundation CNCF It s backed by key players like Google AWS Microsoft IBM Intel Cisco and Red Hat What can Kubernetes do Kubernetes automates the mundane operational tasks of managing the containers that make up the necessary software to run an application With built in commands for deploying applications Kubernetes rolls out changes to your applications scales your applications up and down to fit changing needs monitors your applications and more Kubernetes orchestrates your containers wherever they run which facilitates multi cloud deployments and migrations between infrastructure platforms In short Kubernetes makes it easier to manage applications Automated health checksKubernetes continuously runs health checks against your services For cloud native apps this means consistent container management Using automated health checks Kubernetes restarts containers that fail or have stalled Automated operationsYou can automate mundane sysadmin tasks using Kubernetes since it comes with built in commands that take care of a lot of the labor intensive aspects of application management Kubernetes can ensure that your applications are always running as specified in your configuration Infrastructure abstractionKubernetes handles the compute networking and storage on behalf of your workloads This allows developers to focus on applications and not worry about the underlying environment How Kubernetes changes your monitoring strategyIf you ever meet someone who tells you Kubernetes is easy to understand most would agree they are lying to you Kubernetes requires a new approach to monitoring especially when you are migrating away from traditional hosts like VMs or on prem servers Containers can live for only a few minutes at a time since they get deployed and re deployed adjusting to usage demand How can you troubleshoot if they don t exist anymore These containers are also spread out across several hosts on physical servers worldwide It can be hard to connect a failing process to the affected application without the proper context for the metrics you are collecting To monitor a large number of short lived containers Kubernetes has built in tools and APIs that help you understand the performance of your applications A monitoring strategy that takes advantage of Kubernetes will give you a bird s eye view of your entire application s performance even if containers running your applications are continuously moving between hosts or being scaled up and down Increased monitoring responsibilitiesTo get full visibility into your stack you need to monitor your infrastructure Modern tech stacks have made the relationship between applications and their infrastructure a more complicated than in the past Traditional infrastructureIn a traditional infrastructure environment you only have two things to monitor your applications and the hosts servers or VMs running them The introduction of containersIn Docker introduced containerization to the world Containers are used to package and run an application along with its dependencies in an isolated predictable and repeatable way This adds a layer of abstraction between your infrastructure and your applications Containers are similar to traditional hosts in that they run workloads on behalf of the application KubernetesWith Kubernetes full visibility into your stack means collecting telemetry data on the containers that are constantly being automatically spun up and dying while also collecting telemetry data on Kubernetes itself Gone are the days of checking a few lights on the server sitting in your garage There are four distinct components that need to be monitored in a Kubernetes environment each with their specificities and challenges Infrastructure worker nodes ContainersApplications Kubernetes clusters control plane Correlating application metrics with infrastructure metrics with metadataWhile making it easier to build scalable applications Kubernetes has blurred the lines between application and infrastructure If you are a developer your primary focus is on the application and not the cluster s performance but the cluster s underlying components can have a direct effect on how well your application performs For example a bug in a Kubernetes application might be caused by an issue with the physical infrastructure but it could also result from a configuration mistake or coding problem While making it easier to build scalable applications Kubernetes has blurred the lines between application and infrastructure If you are a developer your primary focus is on the application and not the cluster s performance but the cluster s underlying components can have a direct effect on how well your application performs For example a bug in a Kubernetes application might be caused by an issue with the physical infrastructure but it could also result from a configuration mistake or coding problem When using Kubernetes monitoring your application isn t optional it s a necessity Most Application Performance Monitoring APM language agents don t care where an application is running It could be running on an ancient Linux server in a forgotten rack or on the latest Amazon Elastic Compute Cloud Amazon EC instance However when monitoring applications managed by an orchestration layer having context into infrastructure can be very useful for debugging or troubleshooting to be able to relate an application error trace for example to the container pod or host that it s running on Configuring labels in KubernetesKubernetes automates the creation and deletion of containers with varying lifespans This entire process needs to be monitored With so many moving pieces a clear organization wide labeling policy needs to be in place in order to match metrics to a corresponding application pod namespace node etc Consistent labelling of objects in your Ks clusterBy attaching consistent labels across different objects you can easily query your Kubernetes cluster for these objects For example suppose you get a call from your developers asking if the production environment is down If the production pods have a “prod label you can run the following kubectl command to get all their logs kubectl get pods l name prodNAME READY STATUS RESTARTS AGErouter worker db btm ErrImagePull dhrouter worker db fnz Running s router worker db rlb Running h router worker db btm Running h In this example you might spot that one of the prod pods has an issue with pulling an image and providing that information to your developers who use the prod pod If you didn t have labels you would have to manually grep the output of kubectl get pods Common labeling conventionsIn the example above you saw an instance in which pods are labeled “prod to identify their use by environment Every team operated differently but the following naming conventions can commonly be found regardless of the team you work on Labels by environmentYou can create entities for the environment they belong to For example env productionenv qaenv developmentenv staging Labels by teamCreating tags for team names can be helpful to understand which team group department or region was responsible for a change that led to a performance issue Team tagsteam backendteam frontendteam db Role tagsroles architectureroles devopsroles pm Region tagsregion emearegion americaregion asia Labels by Kubernetes recommended labelsKubernetes provides a list of recommended labels that allow a baseline grouping of resource objects The app kubernetes io prefix distinguishes between the labels recommended by Kubernetes and the custom labels that you may separately add using a company com prefix Some of the most popular recommended Kubernetes labels are listed below LabelsKey Descriptionapp kubernetes io name Name of application such as redis app kubernetes io instance Unique name for this specific instance of the application such as redis department a app kubernetes io component A descriptive identifier of what the component is for such as login cache app kubernetes io part of The higher level application using this resource such as company auth With all of your Kubernetes objects labeled you can query your observability data to get a bird s eye view of your infrastructure and applications You can examine every layer in your stack by filtering your metrics And you can drill into more granular details to find the root cause of an issue Therefore having a clear standardized strategy for creating easy to understand labels and selectors should be an important part of your monitoring and alerting strategy for Kubernetes Ultimately health and performance metrics can only be aggregated by labels that you set ConclusionSo far we ve covered what Kubernetes is what it can do why it requires monitoring and best practices on how to set up proper Kubernetes monitoring In part two of this multi part series we ll go through a deep dive into Kubernetes architecture To read this full blog post from New Relic click here	2022-12-20 19:13:30
Apple	AppleInsider - Frontpage News	Next Major League Soccer season starts Feb. 1 on Apple TV+	https://appleinsider.com/articles/22/12/20/next-major-league-soccer-season-starts-feb-1-on-apple-tv?utm_medium=rss	Next Major League Soccer season starts Feb on Apple TV On Tuesday Major League Soccer announced its season schedule for Apple TV and introduced the MLS Season Pass MLS Season PassFans can enjoy consistent matches on Saturdays and select Wednesdays at PM local time starting February MLS Season Pass will also offer a five hour live whip around show to capture the key moments from every match from the start of the Eastern Conference games through the final whistle of the Western Conference games Read more	2022-12-20 19:29:54
海外TECH	Engadget	NASA's InSight lander says goodbye from Mars	https://www.engadget.com/nasa-mars-insight-lander-rip-194514276.html?src=rss	NASA x s InSight lander says goodbye from MarsThis is likely the final photo that NASA s Mars InSight lander will ever send back to Earth The robot has been snapping pics and gathering data about the Martian environment since landing on the planet in November ーand it s been steadily accumulating dust on its solar panels that entire time As NASA predicted earlier this year the layer of debris has finally become too thick for the solar panels to operate The InSight Twitter account officially said goodbye on December th with a final image from the surface of Mars quot My power s really low so this may be the last image I can send quot the tweet reads quot Don t worry about me though my time here has been both productive and serene If I can keep talking to my mission team I will but I ll be signing off here soon Thanks for staying with me quot You re welcome metal astronaut My power s really low so this may be the last image I can send Don t worry about me though my time here has been both productive and serene If I can keep talking to my mission team I will but I ll be signing off here soon Thanks for staying with me pic twitter com wkYKwwkQーNASA InSight NASAInSight December InSight touched down on Mars on November th It set up a seismometer on the Martian surface and collected data about marsquakes which helped NASA scientists compile a clearer picture of the planet s interior structure Over the past four years InSight provided data on more than quakes and at least one meteoroid impact From these reports NASA researchers concluded Mars core is about half the size of Earth s and likely composed of lighter elements than previously thought NASA announced in May that InSight would likely go dark by the end of the summer due to the dust settling on the lander s solar panels InSight had recently celebrated its fourth anniversary on Mars when it stopped communicating with NASA In a blog update on December th the agency said the following quot On Dec NASA s InSight did not respond to communications from Earth The lander s power has been declining for months as expected and it s assumed InSight may have reached its end of operations It s unknown what prompted the change in its energy the last time the mission contacted the spacecraft was on Dec The mission will continue to try and contact InSight quot	2022-12-20 19:45:14
海外TECH	Engadget	An algorithm can use WiFi signal changes to help identify breathing issues	https://www.engadget.com/wifi-signal-breathing-algorithm-nist-191032955.html?src=rss	An algorithm can use WiFi signal changes to help identify breathing issuesNational Institute of Standards and Technology NIST researchers have developed a way to monitor breathing based on tiny changes in WiFi signals They say their BreatheSmart deep learning algorithm could help detect if someone in the household is having breathing issues WiFi signals are almost ubiquitous They bounce off of and pass through surfaces as they try to link devices with routers But any movement will alter the signal s path including how the body moves as we breathe which can change if we have any issues For instance your chest will move differently if you re coughing Other researchers have explored the use of WiFi signals to detect people and movements but their approaches required dedicated sensing devices and their studies provided limited data A few years ago a company called Origin Wireless developed an algorithm that works with a WiFi mesh network Similarly NIST says BreatheSmart works with routers and devices that are already available on the market It only requires a single router and connected device The scientists changed the firmware on a router so that it would check quot channel state information or CSI more frequently CSI refers to the signals that are sent from a device such as a phone or laptop to the router CSI signals are consistent and the router understands what they should look like but deviations in the environment such as the signal being affected by surfaces or movement modify the signals The researchers got the router to request these CSI signals up to times per second to gain a better sense of how the signal was being modified The team simulated several breathing conditions with a manikin and monitored changes in CSI signals with an off the shelf router and receiving device To make sense of the data they collected NIST research associate Susanna Mosleh developed the algorithm In a paper the researchers noted that BreatheSmart correctly identified the simulated breathing conditions percent of the time Mosleh and Jason Coder who heads up NIST s research in shared spectrum metrology hope developers will be able to use their research to create software that can remotely monitor a person s breathing with existing hardware “All the ways we re gathering the data is done on software on the access point in this case the router which could be done by an app on a phone Coder said “This work tries to lay out how somebody can develop and test their own algorithm This is a framework to help them get relevant information	2022-12-20 19:10:32
医療系	医療介護 CBnews	認知症ケア加算が入院料の要件になる未来-データで読み解く病院経営（165）	https://www.cbnews.jp/news/entry/20221219172818	代表取締役	2022-12-21 05:00:00
ニュース	BBC News - Home	Rishi Sunak refusing to budge on pay as strike action escalates	https://www.bbc.co.uk/news/uk-politics-64037292?at_medium=RSS&at_campaign=KARANGA	ambulance	2022-12-20 19:39:06
ビジネス	ダイヤモンド・オンライン - 新着記事	第一生命・T＆D2桁増収、かんぽは14四半期ぶり増収も「そろって大減益」の訳 - ダイヤモンド 決算報	https://diamond.jp/articles/-/314988		2022-12-21 04:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	JR東海「廃線の危険度が高い路線」ランキング…2位紀勢線、1位は？ - DIAMONDランキング＆データ	https://diamond.jp/articles/-/314913	diamond	2022-12-21 04:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	米利上げ減速で目立つFRBと市場の乖離、「金融危機後の10年」に回帰するのか - 経済分析の哲人が斬る！市場トピックの深層	https://diamond.jp/articles/-/314986	金融危機	2022-12-21 04:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	黒田緩和「微調整」任期中の変更はここまでか、金融市場の歪み放置限界に - 政策・マーケットラボ	https://diamond.jp/articles/-/315019	打ち止め	2022-12-21 04:39:00
ビジネス	ダイヤモンド・オンライン - 新着記事	日銀YCCの誘導目標幅拡大、「緩和修正第一歩」の見方否定できず - 政策・マーケットラボ	https://diamond.jp/articles/-/315018	引き上げ	2022-12-21 04:37:00
ビジネス	ダイヤモンド・オンライン - 新着記事	保険業界の国際規制の歴史はわずか25年、それでも順守される3つの理由 - きんざいOnline	https://diamond.jp/articles/-/314985	online	2022-12-21 04:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	アップルがiPhoneをインドで生産、それでも「中国の代わり」になれない理由 - ＤＯＬ特別レポート	https://diamond.jp/articles/-/314815	iphone	2022-12-21 04:30:00
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ビジネス	ダイヤモンド・オンライン - 新着記事	衝突後3秒で炎上…韓国・現代自動車「アイオニック5」の事故に見る、EVの怖さ - News&Analysis	https://diamond.jp/articles/-/314892	衝突後秒で炎上…韓国・現代自動車「アイオニック」の事故に見る、EVの怖さNewsampampAnalysis月日、韓国で現代自動車ヒョンデの最新電気自動車「アイオニック」が事故を起こし、運転手の男性が亡くなった。	2022-12-21 04:20:00
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