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WILLIE CLINTON HENDERSON

WILLIE CLINTON HENDERSON

YOUNGSTOWN Services will be held at 11 a.m. on June 26, 2017, at the Price Memorial A.M.E. Zion Church for Mr. Willie Clinton Henderson, 91, who departed this life Wednesday, June 21, 2017, at Briarfield Manor after a short illness. Mr. Henderson was born Feb. 7, 1926, in Ramer, Ala., a son of Claude Henderson and Ada Fannin. He attended Little Zion Public Schools in Ramer. He relocated to Youngstown in 1942, and enlisted into the U.S. Army in 1944.

He was employed for 24 years at the Lordstown Ordinance Department and spent more than 13 years as a security guard for Pinkerton Securities. Mr. Henderson was a faithful member of the Price Memorial A.M.E. Zion Church, where he served on the adult usher board, the Willing Worker s Club, and was an honorary member of the Women s Home and Overseas Missionary Society of the A.M.E. Zion Church. He found joy in traveling, gardening, walking through the mall, watching baseball games on television, and having early morning breakfast at McDonald s with his friends. His presence will be dearly missed. He leaves to cherish his beloved memory, Rose L. Basham Henderson of Youngstown; four daughters, Carolyn L. Jackson, with whom he made his home, and Doranna E. (Larry) Speed, both of Youngstown, Deborah J. Henderson of Casa Granda, Ariz., and Trucilla A. Henderson (Larry Ash) of District Heights, Md.; three sisters, Mabel Southerland of Austin, Texas, AdaBelle Jammer of Cincinnati, and Mary Cummings, whom he reared, of Youngstown; two brothers, Thomas Harris of Cleveland and Sylvester (Annie Mae) Harris of Orlando, Fla.; two grandsons, Alfred Jackson Jr. of Washington, D.C. and Andre (Tarrah) Jackson Sr. of Charlotte, N.C.; two granddaughters, Kimberly Speed of Youngstown and Rashaun Speed of Boardman; three great-grandchildren, J Maira Stevens of Boardman, Andre Jackson Jr. and Evan Jackson, both of Charlotte; and a host of other relatives and friends.

He was preceded in death by his parents; his stepmother, Tenna Henderson; and three brothers, Lonnie, John D., and James Harris. Special thank you to the physicians and staff of Mercy Health Youngstown MICU and the director of Nursing and staff of Briarfield Manor Unit 4. Friends may call Monday, June 26, 2017, from 10 to 11 a.m. at the church.

Funeral arrangements have been entrusted to the L.E. Black, Phillips & Holden Funeral Home.

The Kaggle data science community is competing to improve airport …

The Kaggle Data Science Community Is Competing To Improve Airport ...

Going through airport security is a universally painful experience. And despite being slow and invasive, the TSA doesn t have a great record at catching threats[1]. With the help of the Kaggle[2] data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year[3], regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. Today s competition to improve threat recognition algorithms[4] will be Kaggle s third launch this year featuring more than a million dollars in prize money. With a top prize of $500,000 and a total of $1.5 million at stake, competitors will have to accurately predict the location of threat objects on the body. The TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. Importantly, these will be staged images created by the TSA rather than real-world examples a necessary move to ensure privacy.

The outcome of the competition will be a good indicator for how well we can expect such systems to work, Reza Zadeh, founder and CEO of computer vision startup Matroid told me. At the very least, we should have such a system augmenting current security guards to ensure they don t miss dangerous items.

The Kaggle Data Science Community Is Competing To Improve Airport ...

Competitors will be competing to predict the likelihood that weapons are hidden in 17 body zones.

Of course, the problem the TSA faces isn t just a machine learning issue. Expensive physical machines are complicated to upgrade, and none feature the kinds of sophisticated GPUs found in modern data centers. Thankfully, Google, Facebook and others are heavily investing in lighter versions of machine learning frameworks, optimized to run locally, at the edge (without internet).

This means that it s possible that some submissions to this competition could wind up in use on actual scanning machines it s just a matter of training beforehand and optimizing for the constrained conditions. The DHS has promised to work closely with the winners to explore potential real-world applications.

This is a really hard problem, machines do not have crazy GPUs, Anthony Goldbloom, Kaggle s creator, told me in an interview. But one thing that gets lost is that doing inference doesn t necessarily need such heavy compute. Another concern that Kaggle and the TSA had to account for was the risk of bias influencing the automated threat detection process a potential nightmare for travelers that could be inappropriately segregated based on arbitrary factors. To mitigate this, the TSA put special effort into creating the data set of images that will ultimately be used to train the detectors.

The TSA did a nice job in setting this up, Goldbloom emphasized. They recruited volunteers but made sure that they had a decent amount of diversity so models don t fail on a certain type of person. Google plans to make GCP available to competitors in the near future. And though Google owns Kaggle, it is thankfully not forcing people to use TensorFlow, its own open-source framework. You can check out additional details here[5]; the competition will draw to a close in December.

Featured Image: Andrew Harrer/Bloomberg via Getty Images/Getty Images

References

  1. ^ the TSA doesn t have a great record at catching threats (www.huffingtonpost.com)
  2. ^ Kaggle (www.kaggle.com)
  3. ^ acquired by Google earlier this year (techcrunch.com)
  4. ^ Today s competition to improve threat recognition algorithms (www.kaggle.com)
  5. ^ here (www.kaggle.com)

The Kaggle data science community is competing to improve airport security with AI

The Kaggle Data Science Community Is Competing To Improve Airport Security With AI

Going through airport security is a universally painful experience. And despite being slow and invasive, the TSA doesn t have a great record at catching threats[1]. With the help of the Kaggle[2] data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year[3], regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. Today s competition to improve threat recognition algorithms[4] will be Kaggle s third launch this year featuring more than a million dollars in prize money. With a top prize of $500,000 and a total of $1.5 million at stake, competitors will have to accurately predict the location of threat objects on the body. The TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. Importantly, these will be staged images created by the TSA rather than real-world examples a necessary move to ensure privacy.

The Kaggle Data Science Community Is Competing To Improve Airport Security With AI

Competitors will be competing to predict the likelihood that weapons are hidden in 17 body zones. Of course, the problem the TSA faces isn t just a machine learning issue. Expensive physical machines are complicated to upgrade, and none feature the kinds of sophisticated GPUs found in modern data centers. Thankfully, Google, Facebook and others are heavily investing in lighter versions of machine learning frameworks, optimized to run locally, at the edge (without internet). This means that it s possible that some submissions to this competition could wind up in use on actual scanning machines it s just a matter of training beforehand and optimizing for the constrained conditions. The DHS has promised to work closely with the winners to explore potential real-world applications.

This is a really hard problem, machines do not have crazy GPUs, Anthony Goldbloom, Kaggle s creator, told me in an interview. But one thing that gets lost is that doing inference doesn t necessarily need such heavy compute.

Another concern that Kaggle and the TSA had to account for was the risk of bias influencing the automated threat detection process a potential nightmare for travelers that could be inappropriately segregated based on arbitrary factors. To mitigate this, the TSA put special effort into creating the data set of images that will ultimately be used to train the detectors.

The TSA did a nice job in setting this up, Goldbloom emphasized. They recruited volunteers but made sure that they had a decent amount of diversity so models don t fail on a certain type of person.

Google plans to make GCP available to competitors in the near future. And though Google owns Kaggle, it is thankfully not forcing people to use TensorFlow, its own open-source framework. You can check out additional details here[5]; the competition will draw to a close in December.

Featured Image: Andrew Harrer/Bloomberg via Getty Images/Getty Images

References

  1. ^ the TSA doesn t have a great record at catching threats (www.huffingtonpost.com)
  2. ^ Kaggle (www.kaggle.com)
  3. ^ acquired by Google earlier this year (techcrunch.com)
  4. ^ Today s competition to improve threat recognition algorithms (www.kaggle.com)
  5. ^ here (www.kaggle.com)