Cybersecurity education has been emphasized by several national organizations in the United States, including the National Academy of Engineering, which recognizes securing cyberspace as one of the 14 Engineering Grand Challenges. To prepare students for such challenges and to enhance cybersecurity education opportunities at our large research university, we implemented an NSF-funded cybersecurity education project. This project is a collaborative effort between faculty and graduate students in the Engineering Education, Computer Science (CS) and Computer Engineering (CPE) departments at a major US research university. In this effort, we integrated cybersecurity learning modules into multiple existing core CS and CPE courses following Jerome Bruner's spiral-theory model, which has previously been used to reformulate several academic curricula. In this paper, we present our cybersecurity curriculum initiative, describe the spiral-theory based process we developed to implement the curriculum and provide an in-depth description of four reusable cybersecurity learning modules that we developed. A core tenet of spiral theory holds to revisit topics as students advance through their curriculum. This work applies this approach to Cybersecurity education by carefully designing the learning objectives of the modules and its contents. For evaluating these learning modules we implemented pre and post-tests to assess students' technical knowledge, their perceptions towards the modules' learning objectives, and how it influenced their motivation to learn cybersecurity. Our findings are overwhelmingly positive and the students' feedback has helped us improve these learning modules. Since its inception, our initiative has educated more than $2\,000$ students and is currently being used to revise the affected courses' syllabi.

The current emphasis on cybersecurity worldwide, demonstrates the importance of this topic. This poster describes a unique NSF funded project that aims to create cybersecurity education opportunities at Virginia Tech (VT). It is a collaborative effort among faculty and graduate students in the Engineering Education, Computer Science (CS), Electrical and Computer Engineering (includes two majors, Electrical Engineering (EE) and Computer Engineering (CPE)) Departments, and the Hume Center in the College of Engineering at VT. The goal is to integrate cybersecurity modules into eight required CS and CPE courses, from freshman to junior levels, utilizing Jeremy Bruner's spiral-theory-based1,2 curriculum model. A spiraling theme of "handling threats to software for securing information" is chosen that will be returned to repeatedly as learners advance in their knowledge and intellectual capacity. Cybersecurity goals of the Confidentiality, Integrity, Availability, Authenticity, Anonymity, Assurance (CIA/AAA) triad, as appropriate for various academic levels, are adopted to develop the cybersecurity modules. Each module engages students in an authentic activity that reinforces the cybersecurity concepts. The project includes an engineering education research component, which is focused on evaluating the effectiveness of the curriculum in enhancing students' knowledge, skills, and motivation in cybersecurity concepts. The first year of the project has been completed by introducing cybersecurity modules into four courses (CS: Introduction to Software Design, and Software Design and Data Structures, and CPE: Engineering Problem Solving with C++, and Data Structures and Algorithms) impacting ~1600 students. The details of curriculum development, implementation and, preliminary findings of the research will be presented.

Summary form only given. The tremendous amount of data generated by large-scale, parallel scientific and engineering simulations make the archive and analysis of this data difficult. To address this problem we, in previous work, developed an efficient archival scheme based on the functional representation of simulation data-this approximation scheme can reduce storage requirement significantly. However, common visualization tools such as the marching cubes algorithm for isosurface generation cannot be directly applied in this representation. Thus, we propose a new efficient isosurface visualization approach that takes full advantage of the functional approximation of simulation data. This method is fundamentally different from the marching cubes approach in that the visualization of isosurfaces is achieved through the solution of sets of ordinary differential equations. We present computational results detailing the effectiveness of this new approach for a simulation modeling the fluid dynamics of a turbulent reacting flow. The results demonstrate that the method is efficient in a parallel environment and represents a promising approach for the visualization of isosurfaces in simulation data from large-scale scientific applications.

Incomplete factorization has been shown to be a good preconditioner for the conjugate gradient method on a wide variety of problems. It is well known that allowing some fill-in during the incomplete factorization can significantly reduce the number of iterations needed for convergence. Allowing fill-in, however, increases the time for the factorization and for the triangular system solutions. Additionally, it is difficult to predict a priori how much fill-in to allow and how to allow it. The unpredictability of the required storage/work and the unknown benefits of the additional fill-in make such strategies impractical to use in many situations. In this article we motivate, and then present, two “black-box” strategies that significantly increase the effectiveness of incomplete Cholesky factorization as a preconditioner. These strategies require no parameters from the user and do not increase the cost of the triangular system solutions. Efficient implementations for these algorithms are described. These algorithms are shown to be successful for a variety of problems from the Harwell-Boeing sparse matrix collection.

Efficient and reliable code to compute incomplete Cholesky factors of sparse matrices for use as preconditioners in a conjugate gradient algorithm is described. This code implements two recently developed, improved incomplete factorization algorithms. An efficient implementation of the standard incomplete Cholesky factorization is also included.

A general-purpose parallel iterative solver for large, sparse systems of linear equations is presented. This solver is used in two applications, a piezoelectric crystal vibration problem and a superconductor model, which could be solved only on the largest available massively parallel machine. Results obtained on the Intel DELTA show computational rates of up to 3.25 Gflops for these applications.>