Digital Teaching Research Based on the Intelligent Research and Training Platform: Citing the Practice of the Chinese Teaching and Research Group of Senior Secondary School Affiliated to Xingyi Normal University for Minorities as a Case Study

The Intelligent Research and Training Platform (IRTP) of the National Center for Educational Technology (NECT) is an application designed to integrate AI technology and teacher education in response to the “Artificial Intelligence + Teacher Education” strategy, in order to provide teacher professional development and power the advancement of basic education. In this study, a school teaching and research team conducted instructional research on the seventh-grade Chinese lesson Wisteria, utilizing analytical reports of classroom observation, teaching behavior, and teacher ability matrix generated by the Intelligent Research and Training Platform’s big data-based evaluation of classroom teaching for this lesson. Using data analytics and scale grading, this kind of practice helps teachers get better at doing research in their own fields

Turning Disaster Data into Knowledge: Field Reconnaissance, Damage Assessment, and Lessons Learned from Hurricane Sandy, Harvey, and Michael

The recent hurricanes in 2012, 2017, and 2018, and efforts of researchers to capture vast quantities of perishable data through support of the National Science Foundation and other agencies, have created enormous databases of hurricane impacts to coastal structures that can be used to extract fundamental knowledge as to why these structures perform as they do during hurricanes. But exploration of these large data sets, untangling the complex factors contributing to various hurricane damages, and forming a holistic understanding of damage mechanisms are challenging tasks, requiring convergent approaches in system modeling, data science, and cyberinfrastructure design. In this presentation, Dr. Gong will discuss three hurricane reconnaissance trips, the associated data collection and analysis, and the advances in analytics for damage modeling with new AI and cyberinfrastructure approaches. He will also explain the findings as to the dominant factors contributing to damages based on the synthesis of these hurricane events and their implications to the New Jersey coastal community

Energy-Efficient Antenna Selection and Power Allocation for Large-Scale Multiple Antenna Systems with Hybrid Energy Supply

The combination of energy harvesting and large-scale multiple antenna technologies provides a promising solution for improving the energy efficiency (EE) by exploiting renewable energy sources and reducing the transmission power per user and per antenna. However, the introduction of energy harvesting capabilities into large-scale multiple antenna systems poses many new challenges for energy-efficient system design due to the intermittent characteristics of renewable energy sources and limited battery capacity. Furthermore, the total manufacture cost and the sum power of a large number of radio frequency (RF) chains can not be ignored, and it would be impractical to use all the antennas for transmission. In this paper, we propose an energy-efficient antenna selection and power allocation algorithm to maximize the EE subject to the constraint of user's quality of service (QoS). An iterative offline optimization algorithm is proposed to solve the non-convex EE optimization problem by exploiting the properties of nonlinear fractional programming. The relationships among maximum EE, selected antenna number, battery capacity, and EE-SE tradeoff are analyzed and verified through computer simulations.Comment: IEEE Globecom 2014 Selected Areas in Communications Symposium-Green Communications and Computing Trac