Monthly Archives: April 2020

Travel bailouts: Airlines, hotels and travel agents all got them. Shouldn’t the public? – USA TODAY

Posted: April 11, 2020 at 7:33 pm

Christopher Elliott, Special to USA TODAY Published 7:00 a.m. ET April 10, 2020

Reporters and experts from across the country and the USA TODAY Network help answer America's most urgent questions about the $2 trillion stimulus package. USA TODAY

Travelers are furious.

During the past month, they've watched the travel industry line up at the trough for government handouts. A $500 billion loan fund for hotels and $50 billion for airlines. Travel agents who book airline tickets can apply for$25 billion in loans and loan guarantees.

Yeah, that's billion with a "B."

And what did America's taxpayers get for it? Not much.

President Trump signs $2 trillion stimulus package:What's in it for travelers?

Travel companies didn't have to promise to fix their abusive policies. Airlines may continue charging outrageous fees and squeezing us into small seats. Tour operators are allowed to force us into ridiculous contracts when we book a vacation. And hotels can keep on charging "gotcha" resort fees.

In fact, many travel companies just turned around and retroactively changed their refund policies to allow them to keep even more of your money.

"Im really fuming," says John Kovacs, a retired consultant and frequent traveler based in Denver. "They get a bailout and continue to force us into hamster-size seats."

So what would make travelers less angry? Well, maybe we need a bailout. Not a financial bailout, but a helping hand from the government. Travel companies should obey their own rules and government regulations. Maybe we can't go back and impose conditions on that $2 trillion in government aid, but can't we at least attach a string or two to future assistance?

More bailout money for airlines? Trump says talks are underway

When it comes to customer service, travel is lightly regulated. But there are a few rules. One is the Department of Transportation's requirement that airlines fully refund a flight if they cancel it, regardless of the reason.

By the way, if a refund is due, the DOT says your airline must process it within seven business days if you paid by credit card, and 20 business days if you paid by cash or check.

Airline cancel your flight due to coronavirus crisis? You're still due a refund, DOT says

But airlines apparently believe this rule is negotiable in the coronavirus outbreak. Last week, United Airlinesbegan telling customers that it can offer only a ticket credit, even when it cancels a flight. Others quickly followed.

I checked with the Transportation Department, whichreaffirmed that the rule is very much in effect. Then it issued an enforcement notice reminding airlines that passengers should be refunded promptly when their scheduled flights are canceled or significantly delayed.

"Although the COVID-19 public health emergency has had an unprecedented impact on air travel, the airlines obligation to refund passengers for canceled or significantly delayed flights remains unchanged," the agency said in its April 3 order.

I got my airline to refund my ticket amid the pandemic: Here's how to get one if you're eligible

It's the height of corporate arrogance to ask their customers the American taxpayer for a bailout and then to take even more of their money. Yet that's exactly what is happening.

No wonder travelers are livid.

It gets worse. For years, travel companies pushed their customers into one-sided contracts that limited or completely eliminated their rights. If you wanted a full refund on a cruise, flight or resort stay, there was only one certain way you could get it: The company had to cancel. But in recent days, companies have reneged on that industry-standard practice, too, citing "extraordinary" circumstances.

Consider what happened to Kelly Kraft when Sandals canceled her coming vacation at Beaches Turks & Caicos, an all-inclusive property. A representative contacted her and told her that the $11,284 she'd paid for her vacation was "fully nonrefundable." Sandals offered her a credit valid for one year.

"They are trying to find a reason to justify keeping the money I paid for services I am not going to receive," says Kraft, a sales director from Diamondhead, Mississippi.

Interestingly, Kraft's cancellation isn't even addressed in her contract. Sandals has rules for when you cancel a vacation, but not when it cancels. A Sandals representative said the company has always addressed these rare instances on a case-by-case basis. But she added that reaction from customers to itsvoucher offer has been "overwhelmingly positive."

I'm not so sure about that. Many travelers are stuck at home and are facing sickness or unemployment as the coronavirus spreads. Is it asking too much for a travel company to refund a vacation it canceled?

Travel fees: These are the most ridiculous travel surcharges to look out for

USA TODAY's Christopher Elliott explains how to avoid additional resort fees.

Travel companies may ask for even more aid soon. if and when they do, legislators should attach a list of common-sense requirements to the next bailout. At a minimum, airlines, cruise lines and hotels must follow their contracts and obey all applicable rules and regulations.

But these companies should also promise to do better. They can't just pick up where they left off when the outbreak started. Their customers deserve to be treated with respect and dignity, now more than ever.

In an age of social distancing, airlines should offer all of their passengers a humane and safe amount of personal space on a plane. Hotels must stop charging surprise resort fees. And all travel companies should honor their agreements.

The travel industry should have been treating their customers better all along. But if they're going to take our money, they need to start behaving better now.

Do you have the right to recline your airline airplane seat? No, and here's why

There is no space to recline in airplanes anymore so here are ways to deal with the people who still do. USA TODAY

The airline industry is likely to ask for more aid in the coming weeks. Here's what tax-paying travelers want in return for bailing out the airlines:

Fair fees. Airlines should agree to stop charging fees that are unreasonable or disproportional to the costs they incur. That means no more $750 ticket change fees or $200 baggage fees.

Humane seat sizes. The government is under a congressional mandate to set minimum airline seat sizes. Until it does, airlines must promise to refrain from moving their seats even closer together.

Flexible refunds. Airlines must fully refund a ticket when a passenger has an infectious disease. And those change fees they waived for spring flights? Make them permanent, please.

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Letter: The boogeyman – Northwest Herald

Posted: at 7:33 pm

To the Editor:

The boogey man is a mythical creature never actually seen, but often used by storytellers to strike fear into children as the punisher for their misbehavior. Without a personal appearance, the boogey man has become an evil monster in the folklore of every world culture.

We still cant see him, but the boogey man is here now and he is scaring all of us to self-quarantine or be radically punished for our misbehavior. The invisible C19 virus comes to invade, sicken, and potentially destroy its victims. Like Medusa, the boogey man wont hurt you unless you are foolish and want to look directly into his eyes.

It is our American heritage to challenge and battle against competitors we can see. We have never fully won the battles with previous viruses. We have respected their deadliness enough to keep them at a safe distance until we learn how to coexist with them. Coexistence has been our only option.

As pioneers, we have constantly traveled into the unknowns of sailing the sea, exploring remote wildernesses and even the unnatural silence of space travel. These were experimental adventures where technology gave us a sense of control. At the moment the C19 virus is controlling us and that makes us angry. Angry because we do not have the knowledge to attack it just yet.

During this social intermission, time and distance are our best allies. Social distancing is the most effective therapeutic known to us at this time so we must take it in the maximum dosage possible.

I can feel the boogie man surrounding me and see him in the eyes of those he has infected. I dont want to meet him face to face so for at least the next 30 days I cant meet with you face to face either.

Rick Dime

Richmond

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Culture – The lockdown librarian – part 3 – Luxembourg Times

Posted: at 7:33 pm

PHOTO: Shutterstock

Thought you'd read it all? The Luxembourg Times asked its culture squad to each pick five favourite books. Merel Miedema weighed in with ways to look at literature in multiples while locked down in Amsterdam.

Book pairings to show off with once you can meet up with your friends again:

Circe by Madeline Miller, The Silence of the Girls by Pat Barker and The Penelopeiad by Margaret Atwood:

If you love Greek myth but are bored with the largely male-dominated narrative, these three books are perfect for you. In Circe, the eponymous heroine is exiled on an island of her own after her powers are found out by the Gods. From her island, she interacts with crucial points in Greek mythology and subverts their meaning. The Silence of the Girls deals with the Trojan War from a captured royals perspective and offers some heart-breaking insights into the lives of slaves in ancient times. Atwoods Penelopeiad shows an interesting and humorous side of Penelopes experience during her husbands absence and return. Whether read together or not, these three novels form fresh female perspectives on well-known classics.

Neil Gaimans The Sandman and Batman: Knightfall by DC Comics:

Perfect for when you have some (or a lot) of time on your hands, both of these chronicles make use of different visual artists in elaborate story lines with interesting and colourful casts. The Sandman tells the story of Dream, recently escaped from captivity, and his six siblings; Death, Desire, Despair, Delirium, Destruction and Destiny. Batman: Knightfall is a crucial story arc within the Batman world, dealing with the playboy-turned-vigilantes burnout and eventual change of strategy. Beautiful, violent and sometimes outright shocking, these stories will keep you captivated long after you finish reading.

The Collected Short Stories of Roald Dahl and Isaac Asimovs The Complete Stories:

To cleanse your palate and refresh your perspective in between epic tomes, try reading a short story from either one of these masters of the art form. Dahls stories often have a cruel streak and are always a delight. Whether he writes about spousal murder or neglect, accidentally turning your baby into a monster or exacting the perfect revenge, his stories are both entertaining and hilarious. Aasimovs stories, on the other hand, are not only beautiful insights into the time in which they were written (where space travel is possible but women are mostly still only wives or secretaries), but also make their readers think about more existential questions.

David Mitchells Cloud Atlas and Emile Zolas Germinal:

If bleak is what you are looking for, look no further than Zola. His novel on the lives and conditions of French mineworkers in 1860s France is an incredible journey of hopelessness and despair, but also of insight and beauty. It is paired here with another challenging but more future-oriented novel. Cloud Atlas is disorienting at first and might confuse at the start, but understanding will follow as you move further along the nested storyline. Both dystopian (with Germinal more rooted in reality than CloudAtlas) and simultaneously tentatively hopeful, these two novels belong together not so much because they share a genre or subject matter but more because they show the value of human connections throughout time and hardships.

A Little Life by Hanya Yanagihara and Lolita by Vladimir Nabokov:

Pairing these two novels together is an uneasy move, and reading them both will most likely leave you with an uneasy feeling, too. Yanagiharas novel is a stunning and excruciating tale of friendship, love, and trauma. Juxtaposed as it is here with Nabokovs more light-hearted and satirical treatment of a similar subject, it will hopefully raise some interesting questions not only about the subject matter of both works, but also about the nature of art and its different applications in the processing of human trauma and emotion. These novels, and especially the combination of the two, are not for the faint of heart and are anything but escapist, but anyone who can make it through both will have a lot to think about. Keep your tissues close by.

The Mysteries of Udolpho by Ann Radcliffe and Northanger Abbey by Jane Austen:

This is one for the die-hards. Described as the archetypal Goth novel, The Mysteries of Udolpho is long, hard to read and complicated. You will want to throw it out the window several times during reading. Dont, because your reward will be a far better understanding of Northanger Abbey (and the Gothic genre). Only once youve read the first novel will Catherine Morlands ill-informed jumps to conclusion in the second make real sense and be much funnier. Make a pot of tea (or open a bottle of wine), supply yourself with plenty of biscuits, and sit yourself down for a truly challenging and rewarding adventure.

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World cities turn their streets over to walkers and cyclists – The Guardian

Posted: at 7:33 pm

A growing number of cities around the world are temporarily reallocating road space from cars to people on foot and on cycles to keep key workers moving and residents in coronavirus lockdown healthy and active while socially distancing.

Limited urban park space and leisure trails are under increasing pressure, with many closed to prevent the spread of coronavirus, further limiting urban dwellers access to outdoor space. While traffic has dropped around the world, and with it nitrogen dioxide levels, there are widespread concerns over a rise in speeding drivers endangering those walking and cycling.

Evidence suggests air pollution, including from exhaust fumes, significantly harms the survival chances of those with Covid-19. With pedestrians crammed on to narrow pavements, and acres of empty asphalt on roads, lower speed limits, filtering residential streets to prevent rat-running, introducing emergency cycleways and expanding footpaths are among potential solutions.

Tabitha Combs, a lecturer at the University of North Carolina, is collating examples from around the world, adding to growing calls for more such measures.

No matter where a city is on the spectrum of supporting walking and bicycling, there are actions that are within their reach, and precedents of those actions being implemented in peer cities around the globe, she says.

In Philadelphia officials closed 4.7 miles of Martin Luther King Jr Drive, a wide riverside boulevard, to motor traffic on 20 March following an 1,100-strong petition, as leisure trails became overwhelmed by residents seeking their daily exercise.

Minneapolis has closed part of its riverfront parkways to motor vehicles. Denver has introduced pop-up cycling and walking lanes on 16th and 11th Avenues and roads around Sloan Lake to help people socially distance while exercising. On Thursday, Oakland officials said they were planning to close 74 miles of roads 10% of the citys total to motor vehicles.

In Canada, Vancouvers park board announced that Stanley Park is now cycling and walking only, as well as the linked eastbound lane of Beach Avenue, to relieve congestion and stop visitors arriving by car and parking dangerously, amid a 40% increase in park users. In Winnipeg, four streets are restricted to cycling and walking from 8am-8pm daily, and in Calgary traffic lanes have been reallocated to cycling.

Like many cities, Budapest has seen a drop in bus use by almost 90%, with a 50% decrease in road traffic. City officials have now planned a cycling network on main roads.

Sydney, Perth and Adelaide in Australia, Chapel Hill in the US and Calgary in Canada are among the cities that have made pedestrian crossings automatic in some districts so that people do not have to press a button.

In Berlin, a slew of streets have new, wide bike lanes in place of some motor vehicle lanes. Bogot has ambitiously replaced 35km of traffic lanes with new emergency bike lanes using temporary cones, mirroring the Colombian capitals TransMilenio bus rapid transit network, an alternative to people using public transport. Workers adjust the lane width depending on usage.

In late March the bicycle mayor of Mexico City proposed 130km of temporary bike lanes. In the meantime, a 1.7km temporary lane, running 8am-7pm, has been installed on a major thoroughfare.

In the UK, however, it is a very different picture. In London, where traffic has dropped by 63% on main roads, walking and cycling commissioner Will Norman says emergency bike routes on the citys arterial roads would not protect cyclists without complex junction improvements, which would require construction workers to travel during lockdown.

Cycling UKs policy director, Roger Geffen, has suggested junctions could be redesigned while roads are quieter, saying temporary cycling infrastructure provides a good experience to new commuters, while claiming that kerb space when its not under pressure and not as disruptive to make changes.

Hackney council in east London is the first UK local authority openly planning to temporarily filter its streets, using bollards and planters to prevent rat-running while maintaining access for emergency vehicles and residents. Councillor Jon Burke says it will decide which streets to filter on 20 April, before starting work.

Burke told the Guardian pedestrians stepping into the road to socially distance from one another are put at risk by speeding drivers, whose number appears to be increasing during the lockdown. He says construction workers can operate while socially distancing, and it is one sector with excess capacity during the pandemic.

We are running around making sure vulnerable people have enough food but we arent doing something about the 40,000 people that are dying each year because of air pollution, he says. We havent got weeks to deliver it, we need to deliver it now, because this crisis is happening now.

Dr Rachel Aldred, reader in transport at the University of Westminster, says the UK could learn from other countries. It feels like they are treating [cycling] like a proper mode of transport and we are just fumbling around. Theres no guidance from the government I think if they can manage it in Bogot, which is a very complicated megacity with a lot of issues, you could imagine London doing similar, she said, adding that much of the planning could be done remotely.

Transport engineer Brian Deegan says 20mph streets, bikes for key workers, and core corridor emergency cycle routes would help more essential staff cycle, while removing guard rails on pavements and extending pedestrian space using traffic cones would help those on foot. The London Cycling Campaign has also come up with short, medium and long-term proposals to improve active travel in the capital during the crisis.

Coronavirus and volunteering: how can I help in the UK?

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Automated Machine Learning is the Future of Data Science – Analytics Insight

Posted: at 7:32 pm

As the fuel that powers their progressing digital transformation endeavors, organizations wherever are searching for approaches to determine as much insight as could reasonably be expected from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, thus, prompted a call for more data scientists capable with the most recent artificial intelligence (AI) and machine learning (ML) tools.

However, such highly-skilled data scientists are costly and hard to find. Truth be told, theyre such a valuable asset, that the phenomenon of the citizen data scientist has of late emerged to help close the skills gap. A corresponding role, as opposed to an immediate substitution, citizen data scientists need explicit advanced data science expertise. However, they are fit for producing models utilizing best in class diagnostic and predictive analytics. Furthermore, this ability is incomplete because of the appearance of accessible new technologies, for example, automated machine learning (AutoML) that currently automate a significant number of the tasks once performed by data scientists.

The objective of autoML is to abbreviate the pattern of trial and error and experimentation. It burns through an enormous number of models and the hyperparameters used to design those models to decide the best model available for the data introduced. This is a dull and tedious activity for any human data scientist, regardless of whether the individual in question is exceptionally talented. AutoML platforms can play out this dreary task all the more rapidly and thoroughly to arrive at a solution faster and effectively.

A definitive estimation of the autoML tools isnt to supplant data scientists however to offload their routine work and streamline their procedure to free them and their teams to concentrate their energy and consideration on different parts of the procedure that require a more significant level of reasoning and creativity. As their needs change, it is significant for data scientists to comprehend the full life cycle so they can move their energy to higher-value tasks and sharpen their abilities to additionally hoist their value to their companies.

At Airbnb, they continually scan for approaches to improve their data science workflow. A decent amount of their data science ventures include machine learning and numerous pieces of this workflow are tedious. At Airbnb, they use machine learning to build customer lifetime value models (LTV) for guests and hosts. These models permit the company to improve its decision making and interactions with the community.

Likewise, they have seen AML tools as generally valuable for regression and classification problems involving tabular datasets, anyway, the condition of this area is rapidly progressing. In outline, it is accepted that in specific cases AML can immensely increase a data scientists productivity, often by an order of magnitude. They have used AML in many ways.

Unbiased presentation of challenger models: AML can rapidly introduce a plethora of challenger models utilizing a similar training set as your incumbent model. This can help the data scientist in picking the best model family. Identifying Target Leakage: In light of the fact that AML builds candidate models amazingly fast in an automated way, we can distinguish data leakage earlier in the modeling lifecycle. Diagnostics: As referenced prior, canonical diagnostics can be automatically created, for example, learning curves, partial dependence plots, feature importances, etc. Tasks like exploratory data analysis, pre-processing of data, hyper-parameter tuning, model selection and putting models into creation can be automated to some degree with an Automated Machine Learning system.

Companies have moved towards enhancing predictive power by coupling huge data with complex automated machine learning. AutoML, which uses machine learning to create better AI, is publicized as affording opportunities to democratise machine learning by permitting firms with constrained data science expertise to create analytical pipelines equipped for taking care of refined business issues.

Including a lot of algorithms that automate that writing of other ML algorithms, AutoML automates the end-to-end process of applying ML to real-world problems. By method for representation, a standard ML pipeline consists of the following: data pre-processing, feature extraction, feature selection, feature engineering, algorithm selection, and hyper-parameter tuning. In any case, the significant ability and time it takes to execute these strides imply theres a high barrier to entry.

In an article distributed on Forbes, Ryohei Fujimaki, the organizer and CEO of dotData contends that the discussion is lost if the emphasis on AutoML systems is on supplanting or decreasing the role of the data scientist. All things considered, the longest and most challenging part of a typical data science workflow revolves around feature engineering. This involves interfacing data sources against a rundown of wanted features that are assessed against different Machine Learning algorithms.

Success with feature engineering requires an elevated level of domain aptitude to recognize the ideal highlights through a tedious iterative procedure. Automation on this front permits even citizen data scientists to make streamlined use cases by utilizing their domain expertise. More or less, this democratization of the data science process makes the way for new classes of developers, offering organizations a competitive advantage with minimum investments.

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How Machine Learning Is Being Used To Eradicate Medication Errors – Analytics India Magazine

Posted: at 7:32 pm

People working in the healthcare sector take extra precautions to avoid mistakes and medication errors that can put the lives of patients at risk. Yet, despite this, 2% of patients face preventable medical-related incidents that could be life-threatening. Inadequate systems, tools, processes or working conditions are some of the reasons contributing to these medical mistakes.

In a bid to solve this problem, Google collaborated with UCSFs Bakar Computational Health Sciences Institute to publish Predicting Inpatient Medication Orders in Electronic Health Record Data in Clinical Pharmacology and Therapeutics. The published paper discusses how machine learning (ML) can be used to anticipate standard prescribing patterns by doctors as per the availability of electronic health records.

Google used clinical data of de-identified patients, which included vital signs, laboratory results, past medications, procedures, diagnoses, and more. Googles new model was designed to anticipate a physicians prescription decisions three-quarters of the time, after evaluating the patients current state and medical history.

To train the model, Google chose a dataset containing approximately three million medication orders from more than 1,00,000 hospitals. The company acquired the retrospective electronic health data through de-identification, by choosing random dates and removing all the identifying checkpoints of the record as per the HIPPA rules and guidelines. The company did not gather any identifying information such as names, addresses, contact details, record numbers, names of physicians, free-text notes, images, etc.

The research by the tech giant was done using the open-sourced Fast Healthcare Interoperability Resources (FHIR) format that the company claims was previously applied to improve healthcare data and make it more useful for machine learning. Google did not restrict the dataset to a particular disease, which made the ML activity more demanding. It also allowed the model to identify a wider variety of medical conditions.

Google approached two different ML models the long short-term recurrent neural network, and the regularized time-bucketed logistic model, which are often used in clinical research. Both models were put into comparison against a simple baseline, which was ranked as the most commonly ordered medication based on a patients hospital service, along with time spent since the admission in the hospital. The models ranked a list of 990 possible medications every time a medication was entered in the retrospective data. The team further assessed if the models assigned high probabilities to the medication that were provided by the doctors for each case.

Googles best performing model was the LSTM model, which is capable of handling sequential data, including text and language. The model has been designed to choose the recent events in data and their order, which makes it an excellent option to deal with this problem. Almost 93% of the top-10 list included at least one medication that a clinician would prescribe to a patient within the next day.

The model rightly forecasted the medications prescribed by a doctor as one of the top-10 most likely medications, which calculated to an accuracy amount of 55%. 75% of the ordered medication were ranked in top-25, whereas false-negative cases, where a doctors medication did not make it into the top-25 results, found itself to be in the same 42% of the time as ranked by the model.

These models are trained to mimic a physicians behavior as it appears in historical data, and did not learn the optimal prescribing pattern. Due to this, the models do not understand how the medications might work, or if they have any side effects or not. As per Google, the learning sequence will take time to show normal behavior in a bid to spot abnormal and potentially dangerous orders. In the next phase, the company will examine the models under different circumstances to understand which medication error can cause harm to patients.

The result of this work by Google is a small step towards testing the hypothesis that machine learning can be applied to build different systems which can prevent mistakes on the part of doctors and clinicians to keep patients safe. Google is looking forward to collaborating with doctors, pharmacists, clinicians and patients to continue the research for a better result.

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Infragistics Adds Predictive Analytics, Machine Learning and More – Patch.com

Posted: at 7:31 pm

Infragistics is excited to announce a major upgrade to its embedded data analytics software, Reveal. In addition to its fast, easy integration into any platform or deployment option, Reveal's newest features address the latest trends in data analytics: predictive and advanced analytics, machine learning, R and Python scripting, big data connectors, and much more. These enhancements allow businesses to quickly analyze and gain insights from internal and external data to sharpen decision-making.

Some of these advanced functions include:

Outliers DetectionEasily detect points in your data that are anomalies and differ from much of the data set.

Time Series ForecastingReveal will make visual predictions based on historical data and trends, useful in applications such as sales and revenue forecasting, inventory management, and others.

Linear RegressionReveal finds the relationship between two variables and creates a line that approximates the data, letting you easily see historical or future trends.

"Our new enhancements touch on the hottest topics and market trends, helping business users take actions based on predictive data," says Casey McGuigan, Reveal Product Manager. "And because Reveal is easy to use, everyday users get very sophisticated capabilities in a powerfully simple platform."

Machine Learning and Predictive Analytics

Reveal's new machine learning feature identifies and visually displays predictions from user data to enable more educated business-decision making. Reveal reads data from Microsoft Azure and Google BigQuery ML Platforms to render outputs in beautiful visualizations.

R and Python Scripting

R and Python are the leading programming languages focused on data analytics. With Reveal support, users such as citizen data scientists can leverage their knowledge around R and Python directly in Reveal to create more powerful visualizations and data stories. They only need to paste a URL to their R or Python scripts in Reveal or paste their code into the Reveal script editor.

Big Data Access

With support for Azure SQL, Azure Synapse, Goggle Big Query, Salesforce, and AWS data connectors, Reveal pulls in millions of records. And it creates visualizations fastReveal's been tested with 100 million records in Azure Synapse and it loads in a snap.

Additional connectors include those for Google Analytics and Microsoft SQL Server Reporting Services (SSRS). While Google Analytics offers reports and graphics, Reveal combines data from many sources, letting users build mashup-type dashboards with beautiful visualizations that tell a compelling story.

New Themes Match App's Look and Feel

The latest Reveal version includes two new themes that work in light and dark mode. They are fully customizable to match an app's look and feel when embedding Reveal into an application and provide control over colors, fonts, shapes and more.

More Information

For in-depth information about Reveal's newest features, visit the Reveal blog, Newest Reveal FeaturesPredictive Analytics, Big Data and More.

About Infragistics

Over the past 30 years, Infragistics has become the world leader in providing user interface development tools and multi-platform enterprise software products and services to accelerate application design and development, including building business solutions for BI and dashboarding. More than two million developers use Infragistics enterprise-ready UX and UI toolkits to rapidly prototype and build high-performing applications for the cloud, web, Windows, iOS and Android devices. The company offers expert UX services and award-winning support from its locations in the U.S., U.K., Japan, India, Bulgaria and Uruguay.

There's a fine art to this age-old cooking technique. Here's how to get perfect hard-boiled eggs this Easter.

By Megan VerHelst, Patch Staff

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Machine Learning and Artificial Intelligence in healthcare market: Poised to Garner Maximum Revenues by 2027 with major key players in the market…

Posted: at 7:31 pm

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Machine Learning and Artificial Intelligence in healthcare market: Poised to Garner Maximum Revenues by 2027 with major key players in the market...

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60% of Content Containing COVID-Related Keywords Is Brand Safe – AiThority

Posted: at 7:31 pm

New data from GumGums content analysis AI system reveals that keyword-based safety strategies are unduly denying brands access to vast viable ad inventories

GumGum, Inc., an artificial intelligence company specializing in solutions for advertising and media, released data indicating that a majority of online content containing keywords related to the ongoing novel coronavirus pandemic is actually safe for brand advertising. The findings come from analysis by Verity, the companys machine learning-based content analysis and brand safety engine. Between March 25th and April 6th, Verity identified 2.85 million unique pages containing COVID-related keywords across GumGums publisher network. Of those pages, the systems threat detection models classified 62% as Safe.

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All the concerns raised lately about coronavirus keyword blocking hurting publishers are valid, said GumGum CEOPhil Schraeder. But this data shows that keyword-based brand safety is also failing brands. Its effectively freezing advertisers out of a huge volume of safe trending content, limiting their reach at a time when it should actually be expanding, as more people than ever are consuming online content.

In that one week alone, brands relying on keyword-based systems for brand safety protection missed out on over 1.5 billion impressions across GumGums supply, Mr. Schraeder pointed out, adding that GumGums publisher network offers a representative sample of impressions available across the wider web. Brands would have been blocked from accessing those impressions because the pages on which the impressions appeared contained one or more instance of the words covid, covid19, covid-19, covid 19, coronavirus, corona virus, pandemic, or quarantine.

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Verity deemed them brand safe based on multi-model natural language processing and computer vision analysis, which integrates assessments from eight machine learning models trained to evaluate threat-levels across distinct threat categories. The systems threat sensitivity is adjustable, as is its confidence threshold for validating safety conclusions. The findings released today are based on Veritys nominal safety and confidence settingsconfigured to align with the threat sensitivity of an average Fortune 100 brand.

Even when we apply the most conservative settings, more than half the content is safe, said GumGum CTO, Ken Weiner. Coronavirus is touching every facet of society, so its hardly surprising that even the most innocuous content references it. Keyword blocking just goes way too far, which is why people are calling for whitelisting of specific websites. That mindset shows whats wrong with the way people think about brand safety these days. The idea that you have to choose between reach and safety is false. Our industry needs to wake up to whats technologically available.

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Mr. Weiner noted that GumGums analysis shows that the pages containing COVID-related keywords in certain popular IAB content categories are particularly safe.

Let me put it this way: If youre looking for a quick and easy brand safety solution right now rather than keyword blocking or whitelisting everything Id recommend simply advertising on content categories like technology, pop culture, and video gaming. Youll get plenty of reach and over 80% of their COVID-related content is safe.

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60% of Content Containing COVID-Related Keywords Is Brand Safe - AiThority

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How Will the Emergence of 5G Affect Federated Learning? – IoT For All

Posted: at 7:31 pm

As development teams race to build outAI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitlyaccessing raw user data.

Conceived byGoogle in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Googles on-device machine learning approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was eitherdelivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.

Generally speaking, with federated learning, the AI algorithm is trained without ever recognizing any individual users specific data; in fact, the raw data never leaves the device itself. Only aggregated model updates are sent back. These model updates are thendecrypted upon delivery to the central server. Test versions of the updated model are then sent back to select devices, and after this process is repeated thousands of times, the AI algorithm is significantly improvedall while never jeopardizing user privacy.

This technology is expected to make waves in the healthcare sector. For example, federated learning is currently being explored by medical start-up Owkin. Seeking to leverage patient data from several healthcare organizations, Owkin uses federated learning to build AI algorithms with data from various hospitals. This can have far-reaching effects, especially as its invaluable that hospitals are able to share disease progression data with each other while preserving the integrity of patient data and adhering to HIPAA regulations. By no means is healthcare the only sector employing this technology; federated learning will be increasingly used by autonomous car companies, smart cities, drones, and fintech organizations. Several other federated learning start-ups are coming to market, includingSnips,S20.ai, andXnor.ai, which was recently acquired by Apple.

Seeing as these AI algorithms are worth a great deal of money, its expected that these models will be a lucrative target for hackers. Nefarious actors will attempt to perform man-in-the-middle attacks. However, as mentioned earlier, by adding noise and aggregating data from various devices and then encrypting this aggregate data, companies can make things difficult for hackers.

Perhaps more concerning are attacks that poison the model itself. A hacker could conceivably compromise the model through his or her own device, or by taking over another users device on the network. Ironically, because federated learning aggregates the data from different devices and sends the encrypted summaries back to the central server, hackers who enter via a backdoor are given a degree of cover. Because of this, it is difficult, if not impossible, to identify where anomalies are located.

Althoughon-device machine learning effectively trains algorithms without exposing raw user data, it does require a ton of local power and memory. Companies attempt to circumvent this by only training their AI algorithms on the edge when devices are idle, charging, or connected to Wi-Fi; however, this is a perpetual challenge.

As 5G expands across the globe, edge devices will no longer be limited by bandwidth and processing speed constraints.According to a recentNokia report, 4G base stations can support 100,000 devices per square kilometer; whereas, the forthcoming 5G stations will support up to 1 million devices in the same area.Withenhanced mobile broadband and low latency, 5G will provide energy efficiency, while facilitating device-to-device communication (D2D). In fact, it is predicted that 5G will usher in a 10-100x increase in bandwidth and a 5-10x decrease in latency.

When 5G becomes more prevalent, well experience faster networks, more endpoints, and a larger attack surface, which may attract an influx of DDoS attacks. Also, 5G comes with a slicing feature, which allows slices (virtual networks) to be easily created, modified, and deleted based on the needs of users.According to aresearch manuscript on the disruptive force of 5G, it remains to be seen whether this network slicing component will allay security concerns or bring a host of new problems.

To summarize, there are new concerns from both a privacy and a security perspective; however, the fact remains: 5G is ultimately a boon for federated learning.

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How Will the Emergence of 5G Affect Federated Learning? - IoT For All

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