The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the inter-connectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environ-mental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause events such as mudslides and landslides. The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc., can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire or drought case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a "common-denominator-variable" in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). Insights from EIKG facilitate data analysis using both classical and Quantum Machine Learning (QML) techniques. The EIKG organizes heterogeneous datasets and integrates relationships to address extreme weather events. This study incorporates various datasets, including mobility data, socioeconomic data from the US Census Bureau, climate data from NASA, and critical infrastructure data.

Even when we believe the research that shows that active learning is particularly helpful to both engage and empower students--they learn better, they learn deeper and it addresses issues of diversity--it's often not clear to us how we can implement it while keeping the quality of our course high. At UAlbany, courses taught using TBL including: computer programming, data structures, databases, web programming, networking and others. TBL is a structured method that can help instructors improve student learning and energize their classrooms. Team Based Learning (TBL) is an approach to course design that takes advantage of research into what helps students learn. Students work in permanent teams and course meetings are organized around application exercises. Courses are divided into units, and students are held responsible for the core reading in a beginning-of-unit test taken both as individuals and as teams. Application exercises conform to core principles ("4S"): Teams all work on the SAME problem, teams make a SPECIFIC choice, the problem must be SIGNIFICANT, and the teams must make a SIMULTANEOUS report of answers. A variant of the "flipped classroom," the TBL course spends far less time "covering the material" in class, allowing course time to be spent on skill development and feedback on student work. In this hands-on workshop, participants will experience a model TBL learning sequence and learn active learning techniques that they can adopt in any classroom while learning the basics of the TBL method.

Many proteins are composed of multiple structural domains. These domains can have important structural or functional properties. When a protein's sequence, but not structure, is known, being able to predict the division of the sequence into its domains may ease structural or functional analysis of the protein. However, predicting domain boundaries from sequence is still an open problem. This research uses machine learning approaches to the prediction problem. It starts with several standard methods -naive Bayes, support vector machines, and artificial neural networks. The results of these methods are combined using a mixture of experts (MoE) approach that gives the final boundary predictions. We show both a simple MoE approach, and one using context in the form of a window of predictions from the machine learning methods. Both MoE methods, especially the windowed MoE, show greatly increased predictive performance relative to the individual machine learning predictors.

A novel idea for information security education created by the New York State Center for Information Forensics and Assurance (CIFA) is presented. This new approach incorporates a teaching hospital model originally developed for medical training. In this model, information security problems from industry and government are solved and abstracted into living-cases used for training and education of university students and public-sector employees. Such a model helps ensure that the curriculum stays current even as the field of information assurance continues to evolve. Solving industry problems hones research skills, while exposing students to living cases helps build context for concepts in information assurance. The success of this approach is contingent upon strong partnerships with government and private organizations that have real security issues as well as an active research program in information security that involves faculty and students. This article presents an implementation of this approach at CIFA. Development of the curriculum, observations gleaned through dissemination of the curriculum, and the infrastructure developed to support this concept are discussed. Evaluation of students has demonstrated the effectiveness of the “teaching hospital” concept and provided us with feedback to further refine its implementation.