Difference between revisions of "Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest"

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(Publications)
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=Publications=
 
=Publications=
# Lamy, Francois R., Raminta Daniulaityte, Monica J. Barratt, Usha Lokala, Amit Sheth, and Robert G. Carlson. "Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket." Drug and Alcohol Dependence (2020): 108115.
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# Lamy, Francois R., Raminta Daniulaityte, Monica J. Barratt, [http://knoesis.org/researchers/lokala/ Usha Lokala], [http://aiisc.ai/amit Amit Sheth], and Robert G. Carlson. "Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket." Drug and Alcohol Dependence (2020): 108115.
# Kumar, Ramnath, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth. "eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection." In Proceedings of The Web Conference 2020, pp. 1955-1965. 2020.
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# Kumar, Ramnath, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [http://knoesis.org/researchers/lokala/ Usha Lokala], and [http://aiisc.ai/amit Amit Sheth]. "eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection." In Proceedings of The Web Conference 2020, pp. 1955-1965. 2020.
# Lokala, Usha, Francois R. Lamy, Raminta Daniulaityte, Amit Sheth, Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory 25, no. 1 (2019): 48-59.
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# [http://knoesis.org/researchers/lokala/ Usha Lokala], Francois R. Lamy, Raminta Daniulaityte, [http://aiisc.ai/amit Amit Sheth], Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory 25, no. 1 (2019): 48-59.
# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur],[http://knoesis.org/researchers/lokala/ Usha Lokala],[http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan],[http://knoesis.wright.edu/amit Amit Sheth] and I. Budak Arpinar. [http://knoesis.org/node/2891 "Predictive Analysis on Twitter: Techniques and Applications"]. Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
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# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur], [http://knoesis.org/researchers/lokala/ Usha Lokala], [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [http://aiisc.ai/amit Amit Sheth] and I. Budak Arpinar. [http://knoesis.org/node/2891 "Predictive Analysis on Twitter: Techniques and Applications"]. Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
# Kursuncu, Ugur, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.
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# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur], [http://knoesis.org/researchers/lokala/ Usha Lokala], Anurag Illendula, [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], Raminta Daniulaityte, [http://aiisc.ai/amit Amit Sheth], and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.
  
 
=Tutorials=
 
=Tutorials=

Revision as of 22:33, 13 September 2020

BD Spokes
Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
200px
Project Overview
Motto To help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology
Timeline September 1, 2018 - November 30, 2019
Project Funding
Funding Agency National Science Foundation
Award Amount $120,000.00
Award Number OAC 1761931

BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest is a NSF funded project involving a collaboration between AI Institute, University of South Carolina and Ohio State University.

Overview

Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.

The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.

People

Funding

Nsf.jpg
  • NSF Award#: OAC 1761931
  • BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
  • Timeline: September 1, 2018 - November 30, 2019
  • Award Amount: $120,000.00

Publications

  1. Lamy, Francois R., Raminta Daniulaityte, Monica J. Barratt, Usha Lokala, Amit Sheth, and Robert G. Carlson. "Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket." Drug and Alcohol Dependence (2020): 108115.
  2. Kumar, Ramnath, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth. "eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection." In Proceedings of The Web Conference 2020, pp. 1955-1965. 2020.
  3. Usha Lokala, Francois R. Lamy, Raminta Daniulaityte, Amit Sheth, Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory 25, no. 1 (2019): 48-59.
  4. Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth and I. Budak Arpinar. "Predictive Analysis on Twitter: Techniques and Applications". Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
  5. Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.

Tutorials


Related Projects

Concurrent Projects