Visualizing the Past: Tools and Techniques for Understanding Historical Processes

The University of Richmond requests a Level I Digital Humanities Start-Up grant to bring together experts for investigations about how to overcome limitations that prevent most humanities scholars from taking advantage of visualization techniques in their research. The grant will fund a two-day workshop where invited scholars will discuss current work on visualizing historical processes, and together consider: (1) How can we harness emerging cyber-infrastructure tools and interoperability standards to explore, visualize, and analyze spatial and temporal components of distributed digital archives to better understand historical events and processes? (2) How can user-friendly tools or web sites be created to allow scholars and researchers to animate spatial and temporal data housed on different systems across the Internet? The grant will also fund initial experiments toward creating new tools for overcoming obstacles to data visualization work. Results will be presented as a white paper

Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.Comment: To appear at AAAI 2019; 9 page

Categories, Concepts and Units : Representing energy demand in and through time

Preprin

Data mining reactor fuel grab load trace data to support nuclear core condition monitoring

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described