Title: Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies
Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure for effectively integrating such data to generate more value and gain insights is still not well-addressed in the research literature. In this paper, we present our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites. We also introduce several methodologies and applications that we are developing on top of the cyberinfrastructure to support diverse community stakeholders in cities. more »« less
Barrow, Golda; Kuhlman, Chris J.; Machi, Dustin; Ravi, S. S.
(, 13th ACM Web Science Conference 2021)
null
(Ed.)
Networks are readily identifiable in many aspects of society: cellular telephone networks and social networks are two common examples. Networks are studied within many academic disciplines. Consequently, a large body of (open-source) software is being produced to perform computations on networks. A cyberinfrastructure for network science, called net.science, is being built to provide a computational platform and resource for both producers and consumers of networks and software tools. This tutorial is a hands-on demonstration of some of net.science’s features.
Generative Artificial Intelligence (GAI) has emerged in recent years as an innovative tool with promising potential for enhancing student learning across a broad spectrum of academic disciplines. GAI not only offers students personalized and adaptive learning experiences, but it is also playing an increasingly important role in various industries. As technologies evolve and society adapts to the growing AI revolution, it becomes necessary to train students of all disciplines to become proficient in using GAI. This work builds on studies that have established the effectiveness of intelligent tutoring systems, adaptive learning environments, and the use of virtual reality in education. This work-in-progress paper presents preliminary findings related to the relationship between university students’ area of study and the frequency at which they utilize GAI to aid their learning. Data for this study were collected using a survey distributed to students from eight different colleges at a large Western university as part of a larger ongoing project geared towards gaining insight into student perceptions and use of GAI in higher education. The goal of the overall project is to establish a foundational understanding of how disruptive technologies, like GAI, can promote learner agency. By exploring why and how students choose to engage with these technologies, the project seeks to find proactive approaches to integrate GAI technology into education, ultimately enhancing teaching and learning practices across various disciplines.
Abstract Meeting the United Nation’ Sustainable Development Goals (SDGs) calls for an integrative scientific approach, combining expertise, data, models and tools across many disciplines towards addressing sustainability challenges at various spatial and temporal scales. This holistic approach, while necessary, exacerbates the big data and computational challenges already faced by researchers. Many challenges in sustainability research can be tackled by harnessing the power of advanced cyberinfrastructure (CI). The objective of this paper is to highlight the key components and technologies of CI necessary for meeting the data and computational needs of the SDG research community. An overview of the CI ecosystem in the United States is provided with a specific focus on the investments made by academic institutions, government agencies and industry at national, regional, and local levels. Despite these investments, this paper identifies barriers to the adoption of CI in sustainability research that include, but are not limited to access to support structures; recruitment, retention and nurturing of an agile workforce; and lack of local infrastructure. Relevant CI components such as data, software, computational resources, and human-centered advances are discussed to explore how to resolve the barriers. The paper highlights multiple challenges in pursuing SDGs based on the outcomes of several expert meetings. These include multi-scale integration of data and domain-specific models, availability and usability of data, uncertainty quantification, mismatch between spatiotemporal scales at which decisions are made and the information generated from scientific analysis, and scientific reproducibility. We discuss ongoing and future research for bridging CI and SDGs to address these challenges.
We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales.
Tall, Anne; Zou, Cliff; Wang, Jun
(, Interservice/Industry Training, Simulation and Education Conference (I/ITSEC))
In today's mobile-first, cloud-enabled world, where simulation-enabled training is designed for use anywhere and from multiple different types of devices, new paradigms are needed to control access to sensitive data. Large, distributed data sets sourced from a wide-variety of sensors require advanced approaches to authorizations and access control (AC). Motivated by large-scale, publicized data breaches and data privacy laws, data protection policies and fine-grained AC mechanisms are an imperative in data intensive simulation systems. Although the public may suffer security incident fatigue, there are significant impacts to corporations and government organizations in the form of settlement fees and senior executive dismissal. This paper presents an analysis of the challenges to controlling access to big data sets. Implementation guidelines are provided based upon new attribute-based access control (ABAC) standards. Best practices start with AC for the security of large data sets processed by models and simulations (M&S). Currently widely supported eXtensible Access Control Markup Language (XACML) is the predominant framework for big data ABAC. The more recently developed Next Generation Access Control (NGAC) standard addresses additional areas in securing distributed, multi-owner big data sets. We present a comparison and evaluation of standards and technologies for different simulation data protection requirements. A concrete example is included to illustrate the differences. The example scenario is based upon synthetically generated very sensitive health care data combined with less sensitive data. This model data set is accessed by representative groups with a range of trust from highly-trusted roles to general users. The AC security challenges and approaches to mitigate risk are discussed.
Shams, Shayan, Goswami, Sayan, Lee, Kisung, Yang, Seungwon, and Park, Seung-Jong. Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies. Retrieved from https://par.nsf.gov/biblio/10074273. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (2018) . Web. doi:10.1109/ICDCS.2018.00127.
Shams, Shayan, Goswami, Sayan, Lee, Kisung, Yang, Seungwon, & Park, Seung-Jong. Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (2018), (). Retrieved from https://par.nsf.gov/biblio/10074273. https://doi.org/10.1109/ICDCS.2018.00127
Shams, Shayan, Goswami, Sayan, Lee, Kisung, Yang, Seungwon, and Park, Seung-Jong.
"Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies". 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (2018) (). Country unknown/Code not available. https://doi.org/10.1109/ICDCS.2018.00127.https://par.nsf.gov/biblio/10074273.
@article{osti_10074273,
place = {Country unknown/Code not available},
title = {Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies},
url = {https://par.nsf.gov/biblio/10074273},
DOI = {10.1109/ICDCS.2018.00127},
abstractNote = {Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure for effectively integrating such data to generate more value and gain insights is still not well-addressed in the research literature. In this paper, we present our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites. We also introduce several methodologies and applications that we are developing on top of the cyberinfrastructure to support diverse community stakeholders in cities.},
journal = {2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (2018)},
author = {Shams, Shayan and Goswami, Sayan and Lee, Kisung and Yang, Seungwon and Park, Seung-Jong},
}
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