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LTU CS student develops a new computational method to improve crowdsourcing data analysis

Working with researchers from LTU and MTSU, Derek Diamond developed a novel algorithm the can estimate data annotators in crowdsourcing projects. Crowdsourcing is a common tool in industry and academia to analyze complex large databases. Projects such as Zooniverse and services such as Amazon Mechanical Turk are based on crowdsouring to provide industry and academia with solutions to problems that require human analysis of large databases.

 

Some tasks that are simple for humans are still considered very difficult for computing machines. The concept of crowdsouring uses a large number of users, normally through the internet, to annotate and analyze pieces of information that cannot be analyzed by the existing computer methodology.

 

Derek Diamond and his collaborators used the idea that inconsistent annotations will be revealed by weaker automatic classification if a machine learning algorithm is trained with these data. That can be used to rank data annotators automatically by their consistency, which can be used to allocate the human resources more efficiently among samples.

 

The study was peer-reviewed and published in IEEE Transactions on Human-Machine Systems – one of the world’s leading and most competitive computer science scientific journals.

 

paper reference:

Shamir, L., Diamond, D., Wallin, J., Leveraging pattern recognition consistency estimation for crowdsourcing data analysis, IEEE Transactions on Human-Machine Systems, 46(3), pp. 474-480, 2016

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Math and Computer Science LTU Students develop a mathematical model of human aging

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LTU computer science and math double-major Erin Lixie and math major Jameson Edgeworth analyzed longitudinal medical and physiological data from over 5000 people and developed a quantitative model of the human aging. The model shows that the human aging does not progress in a continuous linear fashion, but instead exhibits several stages separated by short periods of rapid aging. It also discovered that the fastest aging occurs around the age of 55.

Human aging is explained by two competing paradigm: One is that aging is the process of stochastic accumulation of irreparable environmental damage, and the other is that aging is driven by biological pathways “programmed” in our body. The study done by the two undergraduate students shows strong evidence that aging is controlled by biological mechanisms, making a significant breakthrough in the field.

Their research was peer-reviewed by one of the world’s most renowned aging researchers, and was published in the journal Gerontology.

 

Paper reference:

Lixie, E., Edgeworth, J., Shamir, L., Comprehensive analysis of large sets of age-related physiological indicators reveals rapid aging around the age of 55, Gerontology, 61(6), 526-533, 2015

 

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LTU computer science receives NSF funding for big data research

LTU computer science received an award of $115,000 for three years from the National Science Foundation to develop novel algorithms to mine big astronomical databases.

Current and future observational astronomy is based on robotic telescopes collecting very large databases of billions of astronomical objects, making them the world’s largest public databases. LTU helps turning these large databases into knowledge and discoveries by developing state-of-the-art algorithms that can mine through very large databases of both image and numerical data.

Big data is one of the fastest growing fields in computer science in both academia and industry. LTU has developed unique expertise in the field of big data in the form of software tools that can analyze some of the most complex existing databases.