Translate

Pages

Pages

Pages

Intro Video

Monday, November 9, 2020

TikTokers share viral videos of their Trump voter hotline pranks

TikTok users are tying up the hotline that Trump has set up to allege voter fraud

Donald Trump has long since gained notoriety for using social media to bully his opponents. Now he appears to be getting a taste of his own medicine thanks to tenacious TikTok users who have made it their mission to get under the president’s skin.

According to NBC News, the young people in question have started a campaign of calling into a hotline for voter fraud started by Trump’s team and using it to make outlandish false claims that tie up the phone system and therefore prevent actual MAGA supporters from using it.

READ MORE: Questlove opens up about ‘Karen’ incident at his new home

Donald Trump thegrio.com
President Donald Trump is getting some pushback from social media giants Facebook and Twitter, who are labeling and obscuring his false election-related posts. (Photo by Chip Somodevilla/Getty Images)

In an attempt to undermine what has been widely accepted as a projected win for Joe Biden and Kamala Harris, POTUS created a voter fraud hotline for people to report any suspicious activities they noticed at their voting stations.

Critics say this is a blatant and thinly veiled attempt to normalize racial profiling and voter suppression but this move should come as no surprise given Trump had made false claims about voter fraud even before the ballot counting began.

READ MORE: Lil’ Kim celebrates Kamala Harris’ victory despite past comments

“Help stop voter suppression, irregularities and fraud,” read a Twitter post by Trump’s son Eric promoting the hotline. “Tell us what you’re seeing.” 

Unfortunately for Trump, TikTokers like Alex Hirsch, creator of the Disney Channel series Gravity Falls, saw the hotline more as an opportunity to mess with the campaign than anything else.

“I’m don’t want to tell anyone what to do, but if you felt like calling this number and, say, reporting the Hamburglar, I can confirm from personal experience that it would be very funny,” Hirsch coyly tweeted on Friday.

In subsequent tweets he posted videos of himself trolling the line as characters from his show, confessing in one, “I went in there and I had a big ol’ sack and I just started taking ballots out of the box, and, you know, I didn’t even try to hide it.”

Hirsch’s tweets have garnered over 500,000 views and sparked several others to follow in his footsteps.


Have you subscribed to 
theGrio’s podcast “Dear Culture”? Download our newest episodes now!

TheGrio is now on Apple TV, Amazon Fire, and Roku. Download theGrio today!

The post TikTokers share viral videos of their Trump voter hotline pranks appeared first on TheGrio.



from TheGrio https://ift.tt/3p72cXc
via Gabe's Musing's

This 12-Year-Old Is Set To Become One of The Youngest Composers For the New York Philharmonic Orchestra

Teaching music to children has been said to offer positive growth during their early development. For one Brooklyn girl, her love for music led her to continue her dream toward composing original work while achieving remarkable milestones.

Grace Moore is a young musician who is poised for greatness and achieved a huge milestone this week. WPIX 11 reported that the seventh-grader is one of the youngest composers to enter the New York Philharmonic. Moore is enrolled in the organization’s Very Young Composers program designed to teach participants as young as 8-years-old how to create original scores. The members of the program will also get to see their work performed by professional musicians in the orchestra.

The student body of Poly Prep in Brooklyn congratulated their fellow student on its Twitter page to celebrate the high accomplishment. “#PolyPrep is so proud of seventh-grader Grace Moore ’26” the school wrote in a tweet. “Hear her beautiful music composed as part of the NY Philharmonic Very Young Composers program.”

 

In October, Moore was able to make her debut with the orchestra in a live performance she created for the program. The music organization shared a video of the performance featuring Moore in attendance to hear her music come to life on its Instagram page.

 



from Black Enterprise https://ift.tt/3paZX58
via Gabe's Musing's

The Xbox Series X Delivers Dazzling Visuals—on the Right TV

The latest Xbox will spoil you for older consoles, but to get the most out of it you might need a new TV.

from Wired https://ift.tt/36hZ8z0
via Gabe's Musing's

‘Godzilla’ Wasp Swims—Then Its Young Erupt From Caterpillars

In non-election news, Microgaster godzilla dives to find a caterpillar, forces it to the surface, and injects it with a baby that eats the host from the inside out.

from Wired https://ift.tt/32s5Zox
via Gabe's Musing's

The Next Covid Dilemma: How to Make Buildings Breathe Better

Better indoor ventilation systems could make people safer and healthier—and not just because they’d slow down the coronavirus.

from Wired https://ift.tt/32oiNfh
via Gabe's Musing's

Joe Biden Will Be the Next President. Now What?

On this week’s Get WIRED podcast, three WIRED writers talk about what's next for election security, tech policy, and online conspiracies.

from Wired https://ift.tt/3kcsVhA
via Gabe's Musing's

One Big Challenge for Biden? China’s Push for Tech Supremacy

Trump’s aggressive policy scored only modest successes. Analysts say the US needs a more nuanced approach if it wants to out-compete Beijing.

from Wired https://ift.tt/3eHk1r2
via Gabe's Musing's

Africa's tenth richest man Patrice Motsepe announces Caf presidential bid

South African Patrice Motsepe, one of Africa's richest men, announces his intention to become the next president of the Confederation of African Football.

from BBC News - Africa https://ift.tt/3ld3Cgl
via Gabe's Musing's

Ivorian Anouma to run for Caf president

Jacques Anouma announces he will contest the presidency of the Confederation of African Football - seven years after his first attempt.

from BBC News - Africa https://ift.tt/38oUf9P
via Gabe's Musing's

Netflix on YouTube

Riding with Sugar | Official Trailer | Netflix
When his dreams crash into reality, a young Joshua needs to make a decision, one that will rewrite his wrongs or prove his fate right. Starring Hakeem Kae-Kazim and Charles Mnene. #RidingWithSugar arrives November 27 SUBSCRIBE: http://bit.ly/29qBUt7 About Netflix: Netflix is the world's leading streaming entertainment service with over 195 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments. Riding with Sugar | Official Trailer | Netflix https://youtube.com/Netflix As painful memories spin in his head, a young refugee pedals toward a new future under a charitable mentor when fate challenges his BMX racing hopes.


View on YouTube

Sunday, November 8, 2020

Setting Up Hadoop Pre-requisites and Security Hardening – Part 2

Hadoop Cluster Building is a step by step process where the process starts from purchasing the required servers, mounting into the rack, cabling, etc. and placing in Datacentre. Then we need to install the

The post Setting Up Hadoop Pre-requisites and Security Hardening - Part 2 first appeared on Tecmint: Linux Howtos, Tutorials & Guides.



from Tecmint: Linux Howtos, Tutorials & Guides https://ift.tt/3eReKNX
via Gabe's MusingsGabe's Musings

Six travel ideas that are socially distanced without even trying to be

An upsurge in Covid-19 cases is hampering travel to most of the world's most popular tourist destinations — but remote retreats are enjoying a resurgence.

from Wealth https://ift.tt/3n5R8YK
via Gabe's MusingsGabe's Musings

Aspiring physician explores the many levels of human health

It was her childhood peanut allergy that first sparked senior Ayesha Ng’s fascination with the human body. “To see this severe reaction happen to my body and not know what was happening — that made me a lot more curious about biology and living systems,” Ng says.

She didn’t exactly plan it this way. But in her three and a half years at MIT, Ng, a biology and cognitive and brain sciences double major from the Los Angeles, California area, has conducted research and taken classes examining just about every level of human health — from cellular to societal.

Most recently, her passion for medicine and health equity led her to the National Foundation for the Centers for Disease Control and Prevention (CDC), where, this summer, she worked to develop guidelines for addressing health disparities on state and local health jurisdictions’ Covid-19 data dashboards. Now, as an aspiring physician amidst the medical school application process, Ng has a sense of how microbiological, physiological, and social systems interact to affect a person’s health.

Starting small

Throughout her entire first year at MIT, Ng studied the biology of health at a cellular level. Specifically, she researched the effects of fasting and aging on regeneration of intestinal stem cells, which are located in the human intestinal crypts and continuously self-divide and reproduce. Understanding these metabolic mechanisms is crucial, as their deregulation can lead to age-associated diseases such as cancer.

“That experience allowed me to broaden my technical skills, just getting used to so many different types of molecular biological techniques right away, which I really appreciated,” Ng says of her time at the Whitehead Institute for Biomedical Research in Professor David Sabatini’s lab.

“After some time, I realized that I also wanted to also study sciences at a broader, more macro level, instead of only the microbiology and molecular biology that we were studying in Course 7,” Ng says of her biology major.

In addition to studying the biology of cancer, Ng had developed a curiosity about the human brain and how it functions. “I was really interested in that, because my grandpa has dementia,” Ng says.

Seeing her grandfather’s cognitive decline, she was inspired to become involved in MIT BrainTrust, a student organization that offers a social support network for individuals from around the Boston, Massachusetts area who have brain injuries. “We have these meetings, in which I serve as one of only one or two students there to facilitate a safe space where we can have all these individuals with brain injury gather,” Ng says of the peer-support aspect of the program. “They can really share their mutual challenges and experiences.”

Investigating the brain

To pursue her interest in brain research and the societal impact of brain injuries, Ng traveled to the University of Hong Kong the summer after her first year as an MIT International Science and Technology Initiatives (MISTI) China Fung Scholar. Working with Professor Raymond Chang, she began to examine neurodegenerative disease and used tissue-clearing techniques to visualize 3D mouse brain structures at cellular resolution. “That was personally meaningful for me, to research about that and learn more about dementia,” Ng says.

Returning to MIT her sophomore year, Ng was certain that she wanted to continue studying the brain. She began working on Alzheimer’s research at the MIT Picower Institute for Learning and Memory in the lab of Professor Li-Huei Tsai, the Picower Professor of Neuroscience at MIT. Much existing research into Alzheimer’s disease has been at the bulk-tissue level, focusing on the neurons’ role in neurodegeneration associated with aging.

Ng’s work with Tsai considers the complexity of alterations across genes and less-abundant cell types, such as microglia, astrocytes, and other supporting glial cells that become dysregulated in the brains of patients with Alzheimer’s. Considering the interplay between and within cell types during neurodegeneration is most intriguing to her. While some molecular processes are protective, other damaging ones simultaneously occur and can exist even within the same cell type. This intricacy has made the mechanistic basis behind Alzheimer’s progression elusive and the research that much more crucial.

“It’s really interesting to see how heterogeneous and complex the responses are in Alzheimer's brains,” Ng says of the research program with Tsai, a founding director of MIT’s Aging Brain Initiative. “I really think about these potential new drug targets to improve treatment for Alzheimer's in the future because I have seen, with my grandpa especially, how treatment is really lacking in the neurodegeneration field. There’s no treatment that's been able to stop or even slow the progression of Alzheimer's disease.”

Her research project in the Tsai Lab relies on a technology called single-nucleus RNA sequencing (snRNA-seq), which extracts the genomic information contained in individual cells. This is followed by computational dimension reduction and clustering algorithms to examine how Alzheimer’s disease differentially affects genes and specific cell types.

“With that project, we've been able to use single-nucleus RNA sequencing to really look at the brains of human Alzheimer's patients,” Ng says. “And with the single-cell technology, we're able to look at brain tissue at a much higher resolution, allowing us to see that there’s so much heterogeneity within the brain.”

After conducting more than a year of Alzheimer’s research and then taking a human physiology class in her third year, Ng decided to add a second major in brain and cognitive sciences to gain deeper insight specifically into how the nervous system within the body functions.

“That class really allowed me to realize that I really love organ systems and wanted to study by looking at more physiological mechanisms,” Ng says. “It has been really great to, at the end of my college career, really delve more into a very specific system.”

Medicine and society

Having gained perspective on cellular and microbiology, and human organ systems, Ng decided to zoom out further, interning this past summer at the National Foundation for the CDC. She found the opportunity through MIT’s PKG Center, applied as one of 60 candidates, and was selected for a team of four. There, as a member of the CDC Foundation’s Health Equity Strike Team, she examined how to increase transparency of publicly available Covid-19 data on health disparities and how the narrative tied to health equity can be modified in public health messages. This involved harnessing data about the demographics of those most affected during Covid-19 — including how infection and mortality rates differ starkly based on social factors including housing conditions, socioeconomic status, race, and ethnicity.

“Thinking about all these factors, we compiled a set of best practices for how to present data about Covid-19, what data should be collected, and tried to push those out to help jurisdictions as best-practice recommendations,” Ng says. “That did really increase my interest in health equity and made me realize how important public health is as well.”

Amidst the Covid-19 pandemic, Ng is spending the first semester of her senior year at home with her family in the Los Angeles area. “I really miss the people and not being able to interact with not only other students and peers, but also faculty as well,” she says. “I really wanted to enjoy time with friends, and just explore more of MIT, too, which I didn't always get the chance to do over the past few years.”

Still, she continues to participate in both BrainTrust and MIT’s Asian Dance team, remotely, through weekly practices on Zoom.

“I think dance is one of the biggest de-stressors for me; I had never done dance before going to college. Getting to meet this team and join this community allowed me not only to connect to my Asian cultural roots, but also just expose myself to this new art form where I could really learn how to express myself on stage,” Ng says. “And that really has been the source of relief for me to just liberate any worries that I have, and has increased my sense of self-awareness and self-confidence.”

Armed with the many experiences she has enjoyed at MIT, both in and out of the classroom, Ng plans to continue studying both medicine and public health. She’s excited to explore different potential specialties and is currently most intrigued by surgery. Whichever specialty she may choose, she is determined to include health equity and cultural sensitivity in her practice.

“Seeing surgeons, I personally think that being able to physically heal a patient with my own hands, that would be the most rewarding feeling,” Ng says. “I will strive to, as a physician, use whatever platform that I have to advocate for patients and really drive health-care systems to overcome disparities.”



from MIT News https://ift.tt/3eDCOn4
via Gabe's Musing's

Can Biden fix America’s racism problem?

‘When Barack Obama was President, I was marching for Trayvon Martin and Eric Garner,’ said one activist

Deadspin reporter Chuck Modi took to the streets of Washington D.C. to speak with individuals in light of the 2020 Presidential elections.

One woman he spoke with was candid with the realities of America despite president-elect Joe Biden defeating Donald Trump.

“Maybe we can get back to normal. What was your normal? What was normal for you?” the woman asked. “Because when Barack Obama was President, I was marching for Trayvon Martin and Eric Garner fresh out of college.”

Read More: Biden-Harris campaign releases ‘Agenda for African Diaspora’

Though many feel as though the “work is done” after getting Trump out of office, there is a hard truth, according to the woman. “The work was never finished,” she said.

Illhan Omar retweeted the video with the caption, “Painful honest truths.”

“Not one time when we were marching for George Floyd did those people on the news – mainstream news – say we had the right to be there,” she said.

In Biden’s Saturday victory speech, he acknowledged the pivotal role African Americans played in his success during the election cycle.

“The African American community stood up again for me. You’ve always had my back, and I’ll have yours,” Biden said to the crowd.

Biden vowed to ease the racial tension and division in the United States and during the September presidential debate, he called out Trump for being a “racist,” according to NPR. Though Biden has said some promising things, many have called out his past actions.

In the same September debate, Trump challenged Biden on his past surrounding race: “You did a crime bill, 1994, where you call them super predators — African-Americans are super predators — and they’ve never forgotten it.”

NPR disputed that statement, acknowledging that Hillary Clinton was the one who used the phrase “super predators” in the 1990s and later apologized for that statement.

Biden’s role in writing the 1994 crime bill when he was a Delaware Senator is widely criticized today for the hardships it caused people of color. In a 2019 CNN article, Biden pushed back on the idea that the bill lead to mass incarceration.

“Folks, let’s get something straight. This idea that the crime bill generated mass incarceration—it did not generate mass incarceration.”

In 1972, after his election to the Senate, Biden criticized desegregating schools through the busing system. According to NPR, judges ordered buses be a remedy to segregation by sending Black students to white-dominated schools. The idea experienced push back from white residents.

“It was a classic liberal position to say, ‘I’m in favor of school integration in Little Rock or Montgomery and Selma, but not so much in Boston, Chicago, New York or Wilmington,” said historian and author Matthew Delmont.

He followed that Biden was “right” to focus his intentions on desegregation but “you can’t say you’re in favor of housing integration and not also be fighting for school integration at the same time.”

“There is academic ferment against it. There are young Black and young white leaders against it. There is social unrest which highlights it,” Biden said in a 1975 NPR interview.

It was a point that Sen. Kamala Harris, his future running mate, made in the 2019 presidential debate, asking, “Do you agree today that you were wrong to oppose busing in America?”

Eventually, perceptions surrounding Biden shifted and he became a widely-adored Vice President to Barack Obama.

Just as the young D.C. woman emphasized, the racial injustices during Trump’s presidency, Obama’s presidency and before, will still prevail. We have to make the “system uncomfortable” in order to make real change, said the woman.

“We got a blatant bigot and racist out of the way, but now we still have to do the work to dismantle the system what constantly oppresses black, brown and Indigenous people, period,” she concluded.

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

TheGrio is now on Apple TV, Amazon Fire, and Roku. Download theGrio today!

The post Can Biden fix America’s racism problem? appeared first on TheGrio.



from TheGrio https://ift.tt/36ipyjX
via Gabe's Musing's

Rep. Jim Clyburn was instrumental to Joe Biden’s success

Clyburn, a South Carolina Democrat, is the highest-ranking African American member of Congress

In a conversation with CNN‘s Dana Bush on Saturday, Rep. Jim Clyburn revealed he privately urged President-elect Joe Biden to choose a Black woman as his running mate.

House Majority Whip Rep. Jim Clyburn speaks to rally goers during a drive-in rally for Democratic Senate candidate Jaime Harrison on October 17, 2020 in North Charleston, South Carolina. Harrison is running against incumbent Sen. Lindsey Graham (R-SC). (Photo by Cameron Pollack/Getty Images)

With Biden securing the win by surpassing 270 electoral votes after gaining the battleground state of Pennsylvania, Kamala Harris makes history as the first female and Black-South Asian vice president elect.

“Joe and I talked about it several times when he was trying to make his decision,” Clyburn told Bush. “He had said it would be a woman. And I don’t mind saying now, I said to him in private that I thought that a lot of the results would turn on whether that woman (would) be a Black woman.”

Clyburn, a South Carolina Democrat, is the highest-ranking African American member of Congress.

According to CNN exit polls, 91% of Black female voters voted in favor of Biden in comparison to 8% who voted for Trump.

Read More: Kamala Harris has a message for toddler niece who aspires to be president

Clyburn endorsed Biden in February prior to South Carolina’s Democratic primary. “My buddy Jim Clyburn, you brought me back,” Biden said of Clyburn in February.

Clyburn told Bush he was cautious to not give Biden advice publicly so as not to “diminish” him.

“I gave all my advice to him in private. But I’m very pleased that it was a Black woman selected — I think it cemented his relationship to the Black community,” Clyburn said.

In an Instagram post, journalist Ed Gordon acknowledged the pivotal role Clyburn played in the Biden-Harris campaign including rallying in South Carolina.

“Be clear, NO ONE has more to do with @joebiden winning the Presidency than @whipclyburn Biden’s candidacy was all but dead before the majority whip put his name behind the former VP,” Gordon said. “Clyburn then rallied his state of South Carolina and brought victory and momentum to Biden that ultimate lead to him becoming the Democratic nominee. A big salute to the Congressman, well done.”

In her victory speech, Harris acknowledged making history as the first female vice president-elect and encouraged young girls to follow in her footsteps.

“While I may be the first woman in this office, I will not be the last.” she said. “Because every little girl watching tonight sees that this is a country of possibilities.”

Clyburn echoed those sentiments, saying, “I’m the father of three daughters and I have two granddaughters, and to me this breaks a glass ceiling … for them and all other daughters and granddaughters in the world.”

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

TheGrio is now on Apple TV, Amazon Fire, and Roku. Download theGrio today!

The post Rep. Jim Clyburn was instrumental to Joe Biden’s success appeared first on TheGrio.



from TheGrio https://ift.tt/36evKK4
via Gabe's Musing's

AOC might quit politics after Democrats blame progressives for House loss

AOC was re-elected in 2020 after many believed her first victory was luck

In an exclusive interview with the New York Times, House Rep. Alexandria Ocasio-Cortez (D-NY), aka AOC, said she might quit politics because of resistance from fellow Democrats.

Despite remaining the majority, House Democrats lost some seats in this election. Many of them went on to blame progressive members like AOC, according to Business Insider.

Representative Alexandria Ocasio-Cortez (D-NY), is seen as U.S. Postal Service Postmaster General Louis DeJoy testifies during a hearing before the House Oversight and Reform Committee on August 24, 2020 on Capitol Hill in Washington, DC. The committee is holding a hearing on “Protecting the Timely Delivery of Mail, Medicine, and Mail-in Ballots.” (Photo by Tom Williams-Pool/Getty Images)

Read More: AOC responds to Lindsey Graham’s attack on her at debate

“I don’t even know if I want to be in politics,” she told The Times. “You know, for real, in the first six months of my term, I didn’t even know if I was going to run for re-election this year.”

“I chose to run for re-election because I felt like I had to prove that this is real. That this movement was real. That I wasn’t a fluke,” AOC told The Times. “That people really want guaranteed health care and that people really want the Democratic Party to fight for them.”

With her stance on Medicare for All and a strong alliance with the Black Lives Matter movement, AOC is seen as a nuisance among center-left Democrats who favor liberal ideas but like to compromise with the right.

AOC said that some of her colleagues would appear to be supportive of her progressive causes in front of the media, but inside the chambers, she is met with hostility.

“Externally, there’s been a ton of support,” she said, according to The Times. “But internally, it’s been extremely hostile to anything that even smells progressive.”

The Bronx native said that fellow House members should embrace progressiveness, stating that “they’re just setting up their own obsolescence.”

Read More: AOC calls for expanding court, slams Trump for ‘classist, disgusting’ comments

She also praised activists, many of whom share her progressive opinions, on Twitter.

AOC was re-elected in 2020 after defeating former CNBC contributor Michelle Caruso-Cabrera (MCC) in the Democratic primary in New York’s 14th congressional district.

Many of her opposers believed her 2018 victory over former Rep. Joseph Crowley was a fluke.

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

TheGrio is now on Apple TV, Amazon Fire, and Roku. Download theGrio today!

The post AOC might quit politics after Democrats blame progressives for House loss appeared first on TheGrio.



from TheGrio https://ift.tt/35aTVK1
via Gabe's Musing's

AI and Employee experience| How does it positively go together?

Do you know 62% of workforce believe that AI will carry a favourable impact on their jobs?

And 67% say it is necessary to develop the skills for working in coordination with intelligent machines.

As per the research studies, AI will be the driving force behind cultural and economic shifts that will make the workforce more productive and agile. As AI plays a major role in the organisation of actively listening and understanding the perspective of employees. It allows the company to determine what exactly an employee wants and provide suitable information, such as career path recommendations and coaching. With AI algorithms, businesses can now formulate HR strategies that adapt to the particular needs of employees rather being limited by the resource planning of the HR team.

Today’s workforce not only looks forward to have a better workplace, but also expects to have a better brand story that adds credibility to their future career. However, working with a reputed brand in the market adds to the glory of employee experience. With AI tools, the company will be giving a first-hand impression to its employees, a personalised upskilling and experience of training, and an exceptional employee involvement process. These factors enhance the company opinions which leads to an overall improvement of the company brand.

Moreover, AI is also responsible to give minute details of HR processes that can grab the attention of HR managers. This way, the scope of productivity gets improved wherever possible. Not only this, AI plays a predominant role in many other HR processes.

Let's find out how AI can prove to be beneficial for the organisational team:

  • AI offers valuable insights on better interpersonal relationship among team members
  • AI is diligently skilful in handling mental health issues at the workplace
  • AI acts as a good interface in the recruitment process
  • AI improves the onboarding experience of an employee from the first day itself
  • AI promotes tasks automation
  • AI cultivates training paradigms amongst employee's learning prowess

However, AI shows the red flag to signal the HR manager wherever any team member faces difficulty in learning. This will, in turn, enables the HR manager to impart training exercises for improving those skills.

To sum up, AI enables the organisation to comply with HR processes so as to promote the smooth flow of productivity in a systematic manner and also improve the employee experience throughout their journey in the organisation.

Botomation with Tryvium desk empowers the organization to reinforce the AI dream of self-service for employee experience improvement. It gives the large organizations a platform to collaborate and become more interactive by adding value to MS Teams and Skype for the Business platform which focuses on enhancing the employee experience. It also integrates with major ITSM tools, Customer Support systems and CRMs available in the market. With the Tryvium desk, organizations can enhance first call resolution, improve agent productivity and reduce call handling time which makes the employee and their customers experience more powerful.  Botomation is one of our expertly developed applications combining AI and intelligent automation technologies to help companies with digital transformation. Whether it is customer support or employee request, Botomation can always provide near human experience with superior communication.

Many organizations have introduced Botomation in their system to optimize AI in a significant manner that interprets the action relating to business processes and communicates smoothly within the organization. This, in turn, improves operational efficiency and brings down the overhead costs by superseding the routine tasks of employees. It allows the employees to concentrate on higher-value processes which improve productivity by 86%. Botomation also upgrades the HR processes and extend the benefits to employees in the areas of performance review, monthly payroll and travel expense management.

To know more, download the ebook https://botomation.ai/insights/ebooks/ai-and-employee-experience

Please feel free to contact our sales team at +1 732-283-0499.We will be more than happy to assist you. 



from Featured Blog Posts - Data Science Central https://ift.tt/3lbuoFX
via Gabe's MusingsGabe's Musings

How do I select SVM kernels?

This article was written by Sebastian Raschka.

Given an arbitrary dataset, you typically don't know which kernel may work best. I recommend starting with the simplest hypothesis space first -- given that you don't know much about your data -- and work your way up towards the more complex hypothesis spaces. So, the linear kernel works fine if your dataset if linearly separable; however, if your dataset isn't linearly separable, a linear kernel isn't going to cut it (almost in a literal sense).

For simplicity (and visualization purposes), let's assume our dataset consists of 2 dimensions only. Below, I plotted the decision regions of a linear SVM on 2 features of the iris dataset.

To read the rest of the article, click here.

 

DSC Ressources

Follow us: Twitter | Facebook  



from Featured Blog Posts - Data Science Central https://ift.tt/3p9zsgp
via Gabe's MusingsGabe's Musings

Multi-stage heterogeneous ensemble meta-learning with hands-off demo

In this blog, I will introduce a R package for Heterogeneous Ensemble Learning (Classification, Regression) that is fully automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

Before we dwell into the package details, let’s start with understanding a few basic concepts.

Why Ensemble Learning?

Generally, predictions become unreliable when the input sample is out of the training distribution, bias to data distribution or error prone to noise, and so on. Most approaches require changes to the network architecture, fine tuning, balanced data, increasing model size, etc. Further, the selection of the algorithm plays a vital role, while the scalability and learning ability decrease with the complex datasets. Combining multiple learners is an effective approach, and have been applied to many real-world problems. Ensemble learners combine a diverse collection of predictions from the individual base models to produce a composite predictive model that is more accurate and robust than its components. With meta ensemble learning one can minimize generalization error to some extent irrespective of the data distribution, number of classes, choice of algorithm, number of models, complexity of the datasets, etc. So, in summary, the predictive models will be able to generalize better.

How can we build models in more stable fashion while minimizing under-fitting/overfitting which is very critical to the overall outcome? The solution is ensemble meta-learning of a heterogeneous collection of base learners.

Common Ensemble Learning Techniques

The different popular ensemble techniques are referred to in the figure below. Stacked generalization is a general method of using a high-level model to combine lower- level models to achieve greater predictive accuracy. In the Bagging method, the independent base models are derived from the bootstrap samples of the original dataset. The Boosting method grows an ensemble in a dependent fashion iteratively, which adjusts the weight of an observation based on the past prediction. There are several extensions of bagging and boosting.

Image for post

Overview

metaEnsembleR is an R package for automated meta-learning (Classification, Regression). The functionalities provided includes simple user input based predictive modeling with the selection choice of the algorithms, train-validation-test split, model valuations, and easy guided unseen data prediction which can help the user’s to build stack ensembles on the go. The core aim of this package is to cater the larger audiences in general. metaEnsembleR significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

Using metaEnsembleR

The package consists of the following components:

  • Ensemble Classifiers Training and Prediction

All these functions are very intuitive, and their use is illustrated with examples below covering the Classification and Regression problem in general.

Getting Started

The package can be installed directly from CRAN

Install from Rconsole: install.packages(“metaEnsembleR”)

However, the latest stable version (if any) could be found on Github, and installed using devtools package.

Install from GitHub:

if(!require(devtools)) install.packages(“devtools”)

devtools::install_github(repo = ‘ajayarunachalam/metaEnsembleR’, ref = ‘main’)

Usage

library(“metaEnsembleR”)

set.seed(111)

  • Training the ensemble classification model is as simple as one-line call to the ensembler.classifier function, in the following ways either passing the csv file directly or the imported dataframe, that takes into account the arguments in the following order starting the Dataset, Outcome/Response Variable index, Base Learners, Final Learner, Train-Validation-Test split ratio, and the Unseen data

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, read.csv(‘./unseen_data.csv’))

                        OR

unseen_new_data_testing iris[130:150,]

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing)

The above function returns the following, i.e., test data with the predictions, prediction labels, model result, and finally the predictions on unseen data.

testpreddata ← data.frame(ensembler_return[1])

table(testpreddata$actual_label)

table(ensembler_return[2])

#### Performance comparison #####

modelresult ← ensembler_return[3]

modelresult

#### Unseen data ###

unseenpreddata ← data.frame(ensembler_return[4])

table(unseenpreddata$unseenpreddata)

  • Training the ensemble regression model is the same as one-line call to the ensembler.regression function, in the following ways either passing the csv file directly or the imported dataframe, that takes into account the arguments in the following order starting the Dataset, Outcome/Response Variable index, Base Learners, Final Learner, Train-Validation-Test split ratio, and the Unseen data

house_price ←read.csv(file = ‘./data/regression/house_price_data.csv’)

unseen_new_data_testing_house_price ←house_price[250:414,]

write.csv(unseen_new_data_testing_house_price, ‘unseen_house_price_regression.csv’, fileEncoding = ‘UTF-8’, row.names = F)

ensembler_return ← ensembler.regression(house_price[1:250,], 1, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, read.csv(‘./unseen_house_price_regression.csv’))

                      OR

ensembler_return ← ensembler.regression(house_price[1:250,], 1, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing_house_price )

The above function returns the following, i.e., test data with the predictions, prediction values, model result, and finally the unseen data with the predictions.

testpreddata ← data.frame(ensembler_return[1])

####  Performance comparison  #####

modelresult ← ensembler_return[3]

modelresult

write.csv(modelresult[[1]], “performance_chart.csv”)

#### Unseen data  ###

unseenpreddata ← data.frame(ensembler_return[4])

Examples

demo classification

library(“metaEnsembleR”)

attach(iris)

data(“iris”)

unseen_new_data_testing ← iris[130:150,]

write.csv(unseen_new_data_testing, ‘unseen_check.csv’, fileEncoding = ‘UTF-8’, row.names = F)

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing)

testpreddata ← data.frame(ensembler_return[1])

table(testpreddata$actual_label)

table(ensembler_return[2])

####Performance comparison#####

modelresult ← ensembler_return[3]

modelresult

act_mybar ← qplot(testpreddata$actual_label, geom= “bar”)

act_mybar

pred_mybar ← qplot(testpreddata$predictions, geom= ‘bar’)

pred_mybar

act_tbl ← tableGrob(t(summary(testpreddata$actual_label)))

pred_tbl ← tableGrob(t(summary(testpreddata$predictions)))

ggsave(“testdata_actual_vs_predicted_chart.pdf”,grid.arrange(act_tbl, pred_tbl))

ggsave(“testdata_actual_vs_predicted_plot.pdf”,grid.arrange(act_mybar, pred_mybar))

####unseen data###

unseenpreddata ← data.frame(ensembler_return[4])

table(unseenpreddata$unseenpreddata)

table(unseen_new_data_testing$Species)

demo regression

library(“metaEnsembleR”)

data(“rock”)

unseen_rock_data ← rock[30:48,]

ensembler_return ← ensembler.regression(rock[1:30,], 4,c(‘lm’), ‘rf’, 0.40, 0.30, 0.30, unseen_rock_data)

testpreddata ← data.frame(ensembler_return[1])

####Performance comparison#####

modelresult ← ensembler_return[3]

modelresult

write.csv(modelresult[[1]], “performance_chart.csv”)

####unseen data###

unseenpreddata ← data.frame(ensembler_return[4])

Comprehensive Examples

More demo examples can be found in the Demo.R file, to see the results run Rscript Demo.R in the terminal.

Contact

If there is some implementation you would like to see here or add in some examples feel free to do so. You can always reach me at ajay.aruanchalam08@gmail.com

Always Keep Learning & Sharing Knowledge!!!



from Featured Blog Posts - Data Science Central https://ift.tt/3eEFdxM
via Gabe's MusingsGabe's Musings

Recent Java enhancements for numeric calculations

In the past, slow evaluation of mathematical functions and large memory footprint were the most significant drawbacks of Java compared to C++/C for numeric computations and scientific data analysis. However, recent enhancements in the Java Virtual Machine (JVM) enabled faster and better numerical computing due to several enhancements in evaluating trigonometric functions.

In this article we will use the DataMelt (https://datamelt.org) for our benchmarks. Let us consider the following algorithm implemented in the Groovy dynamically-typed language shown below. It uses a large loop, repeatedly calling the sin() and cos() functions. Save these lines in a file with the extension "goovy" and run it in DataMelt:


import java.lang.Math
long then = System.nanoTime()
double x=0
for (int i = 0; i < 1e8; i++)
x=x+Math.sin(i)/Math.cos(i)
itime = ((System.nanoTime() - then)/1e9)
println "Time: " + itime+" (sec) result="+x

The execution of this Groovy code is typically a 10-20% faster than for the equivalent code implemented in Python:


import math,time
then = time.time()
x=0
for i in xrange(int(1e8)):
x=x+math.sin(i)/math.cos(i)
itime = time.time() - then
print("Time:",itime," (sec) result=",x)

Note that CPython2 (version 2.7.2) is a 20% faster than CPython3 (version 3.4.2), but both CPython interpreters are slower for this example than Groovy.

The same algorithm re-implemented in Java:


import java.lang.Math;
public class Example {
public static void main(String[] args) {
long then = System.nanoTime();
double x=0;
for (int i = 0; i < (int)1e8; i++)
x=x+Math.sin(i)/Math.cos(i);
double itime = ((System.nanoTime() - then)/1e9);
System.out.println("Time for calculations (sec): " + itime+"\n");
System.out.println("Pi = " + x +"\n");
}
}

and processed using DataMelt with OpenJDK13 further increases the execution speed by a factor 2 compared to the Groovy dynamic language.

Similar benchmarks of the Java code have been carried out by repeating the calculation using Java SE 8 ("JDK1.8") released in March 2014. The computation was about a factor 8 slower than for the OpenJDK13. This was due to less optimized code for evaluation of trigonometric functions in JDK1.8 (and earlier versions). This confirms significant improvements for numeric computations in the recent JVM compared to the previous releases.

The question of code profiling using different implementations is a complex problem, and we do not plan to explore all possible scenarios in this article. The main conclusion we want to draw in this section is that the processing speed of the code that implements mathematical functions for numeric calculations is substantially better for Groovy than for CPython2 (CPython3). The observed performance improvements in dynamically-typed languages implemented in Java are due to the recent enhancements in the modern JVMs, leading to a large factor in the speed of evaluations
of mathematical functions.

Sergei Chekanov



from Featured Blog Posts - Data Science Central https://ift.tt/32sWBRg
via Gabe's MusingsGabe's Musings

Fans react to the death of Alex Trebek

People took to Twitter to send their condolences and pay homage to Trebek

Alex Trebek, the long-running host of the beloved game show Jeopardy!, lost his battle with pancreatic cancer on Sunday. He was 80.

In March 2019, the Canadian-born host announced his cancer diagnosis to the public but assured fans that despite the prognosis, “I’m going to fight this.”

“Just like 50,000 other people in the United States each year, this week I was diagnosed with stage 4 pancreatic cancer,” Trebek said.

Read More: Alex Trebek, long-running ‘Jeopardy!’ host, dies at 80

Trebek passed away at his home surrounded by his family and friends.

Pat Sajak, game show host of Wheel of Fortune, commended Trebek for his “courage, grace and strength” and their friendship.

Since the news of his passing, people took to Twitter to send their condolences and pay homage.

One fan shared his favorite clip of The Simpsons where Trebek made an appearance.

Actress Rosario Dawson thanked the host for sharing his life with viewers.

Stephen Colbert and Jimmy Fallon wished their fellow host well.

Read More: Alex Trebek to Rep. John Lewis: Let’s survive cancer in 2020

Many news outlets including CNN and E! News, shared videos of former Jeopardy! contestant Burt Thakur. Thakur shared an emotional story about the revered host who he said taught him how to speak English.

“My grandfather who raised me – I’m going to get tears right now – I used to sit on his lap and watch your everyday,” Thakur said to Trebek during his appearance on the show. “So, it’s a pretty special moment for me, man. Thank you very much.”

Thakur would go on to win the game with a total of $20,400.

It’s not the first time a contestant has shown appreciation for Trebek. In November 2019, contestant Dhruv Gaur gambled away his $2,000 earnings in Final Jeopardy with a message that made the host choke up: “What is: We love you, Alex!”

The gesture went viral with the hashtag #WeLoveYouAlex resulted in Gaur being invited to The Ellen Show.

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

TheGrio is now on Apple TV, Amazon Fire, and Roku. Download theGrio today!

The post Fans react to the death of Alex Trebek appeared first on TheGrio.



from TheGrio https://ift.tt/2If1feH
via Gabe's Musing's