Leroy Stanford is seeking an electrical technician position. He has experience as a biomedical equipment technician in the U.S. Army Reserves where he troubleshot medical devices down to the component level. He also has experience safely using tools in landscaping and interacting with customers in food delivery. Stanford is a computer science major with a mathematics minor and a 3.7 GPA from Cheyney University. His areas of expertise include circuit theory, electrical and mechanical troubleshooting, schematics, and component installation.
In this paper, we show that by using a relatively simple neural network architecture and including edge (i.e., nonsensical) cases into a dataset we can more reliably identify factual claims than predecessor SVM models. Doping the dataset with these nonsensical example results in a more robust model overall that is resistant to being tricked into classifying sentences into a certain category based on easily met criteria. Furthermore, we show that the use of multiple word-embeddings makes little difference to the overall accuracy of the model, but particular embeddings perform differently on text that contains digits (i.e., 0-9) which can be leveraged by using multiple models to come to a conclusion on the score for a particular piece of text. Our results also show, that for our particular dataset trying to differentiate sentences into more than two categories might hurt the overall accuracy of the models, or at least not provide any substantial benefits compared to the binary classification scenario.
In this paper, we show that by using a relatively simple neural network architecture and including edge (i.e., nonsensical) cases into a dataset we can more reliably identify factual claims than predecessor SVM models. Doping the dataset with these nonsensical example results in a more robust model overall that is resistant to being tricked into classifying sentences into a certain category based on easily met criteria. Furthermore, we show that the use of multiple word-embeddings makes little difference to the overall accuracy of the model, but particular embeddings perform differently on text that contains digits (i.e., 0-9) which can be leveraged by using multiple models to come to a conclusion on the score for a particular piece of text. Our results also show, that for our particular dataset trying to differentiate sentences into more than two categories might hurt the overall accuracy of the models, or at least not provide any substantial benefits compared to the binary classification scenario.
How you can get the best out of your next survey questionnaireKeith Meadows
QuesTReviewTM (incorporating our proprietary QuestAnalyzerTM diagnostic test) benchmarks the questionnaire against key parameters of good questionnaire design e.g. wording, sensitivity, appropriate response options etc. We use this information to create detailed feedback for the developer to provide an optimal version of the questionnaire prior to going into the field.
How you can get the best out of your next survey questionnaireKeith Meadows
QuesTReviewTM (incorporating our proprietary QuestAnalyzerTM diagnostic test) benchmarks the questionnaire against key parameters of good questionnaire design e.g. wording, sensitivity, appropriate response options etc. We use this information to create detailed feedback for the developer to provide an optimal version of the questionnaire prior to going into the field.
1. Leroy Stanford
334 Bailey Street
Woodstown, NJ 08098
856.624.4011 | leroystanford1104@gmail.com
Objective:
To contribute my drive and dedication as well as my strong mathematical and electrical
background as an electrical technician to the overall success of a company .
Experience:
Biomedical Equipment Technician, U.S. Army Reserves June 2013 – Present
Interpreted complex multi-page schematics and diagrams
Troubleshot and diagnosed electrical, mechanical, and pnuematic medical devices
Worked alone and collaboratively to troubleshoot units down to component level
Installed and components on printed circuit boards using soldering techniques
Confidently referenced Manufacturers literature and trained on proper use of Test
Measurement and Diagnostic Equipment (TMDE).
Crew Member, Costello's Custom Landscapes June 2006 – August 2012
Safely used hand tools and power tools on a daily basis.
Learned critical thinking and problem solving to satisfy to desires of the customer
Effectively worked with a team to provide customers with truly custom landscape features
such as patios, driveways, etc.
Delivery Specialist, Domino's September 2014 – November 2013
Proficiently performed duties for 35+ hours a week while attending school full-time
Interacted face to face with customers multiple times a night on delivery's as well as at the
store counter/register.
Education:
Cheyney University, Cheyney, PA August 2012-Present
Keystone Honors Student
Computer Science Major, Mathematics Minor
3.7 Grade Point Average
Woodstown High School, Woodstown, NJ September 2008-June 2012
Challenged myself with multiple Advanced Placement (AP) and Honors courses
Successfully completed and excelled in courses including:
AP Calculus
AP Physics
AP Chemistry
Honors Biology and English
Areas of Expertise:
· Circuit Theory · Electrical Troubleshooting · AC Electrical Systems
· Mathematics · Mechanical Troubleshooting · DC Electrical Systems
· Schematics · Quality Assurance · Component Installation
References:
