Computational thinking (CT), which encompasses strategies for problem analysis and solution design, is highly applicable in all STEM disciplines, including computer science, for learning concepts as well as solving problems. Yet, the exposure of K-12 students to CT in many schools is practically non-existent. For students to acquire this important skill, teachers require in-depth knowledge of the problem solving strategies that define CT, and strategies for integrating CT into their lesson plans. The proposed special session will be both a tutorial on CT and an interactive session for sharing the experience and results from a CT workshop held at Columbus State University for middle and high school STEM subject teachers. This workshop, offered as a part of the ACT4STEM (Applying Computational Thinking for STEM Disciplines) project, took place in the summer of 2015. It brought together teachers from the metropolitan Columbus area in Georgia with priority given to teachers from high-need schools (as determined by the GA Department of Education [1]). The workshop introduced the participants to CT concepts and demonstrated how CT strategies of pattern recognition, generalization, problem decomposition and algorithmic thinking can be used to improve student learning of STEM concepts. The proposed special session will present sample CT modules developed for the ACT4STEM workshop in the form of a tutorial and projects and lesson plans designed by participants during the workshop. It will include interactive, hands-on activities and discussions in addition to results from the teachers' actual experiences on applying these lesson plans in their classes. This special session will benefit K12 teachers of STEM subjects interested in improving their students' comprehension of discipline related concepts and their application. Moreover, computer science faculty interested in helping K-12 teachers by organizing training sessions will gain valuable insights from the first-hand accounts of the presenters of their own experience with such an effort.

Computational thinking is an essential skill for everyone in today's world. It is a problem solving skill that incorporates problem decomposition, pattern recognition, abstraction, logical and algorithmic thinking. Yet the exposure of students in K-12 curriculum to computational thinking is minimal in most schools. There are various opportunities for computational thinking to be included in almost all content areas in the curriculum. In order for this to be realized, teachers need introduction to the concept of computational thinking and they need to be equipped with tools to provide practices in this concept in the classrooms. Inspired by this idea, we organized a workshop in June 2014 to involve all teachers from all grade levels and all disciplines where we introduced them to the concept of computational thinking and taught them tools such as Scratch, Google Sites, HTML, and AppInventor. The workshop attracted 34 teachers mostly from the Muscogee County School District in Georgia. In this poster we present our recruitment strategy, methodology of our workshop, and results from our pre and post surveys. In particular, we present the influence of our workshop on teachers' views of computational thinking. We also share highlights from the projects that the teachers worked on during the workshop and presented at the end.

Stepping-stone is the most popular way used to attack other computers. Some insiders use stepping-stone to launch their attacks pretending to be outsiders. In this paper, we propose a novel algorithm to detect stepping-stone insider attacks through comparing outgoing and incoming connections. We modify the existing packet matching algorithm by introducing sliding window to make the algorithm more efficient and practicable. The algorithm to compute the similarity between two time-pair sequences through finding the longest common sub sequence is proposed. The stepping-stone insider attacks detection algorithm is easy to be implemented and to use since there is no threshold needed. The experimental results showed the effectiveness of the algorithm to detect stepping-stone insider attacks.

Computer Science and technology have been driving scientific innovations, expanding the Information Technology sector and increasing productivity in all economic sectors for the last few decades, thus contributing significantly to the economic output of the U.S. Each sector of this country, ranging from national security to everyday life, is enormously impacted by computing technology. Clearly, future economic growth and prosperity of this country heavily rely on the strength and skills of the IT and computing workforce that will be able to expand and maintain the nationwide IT infrastructure. Unfortunately, the decline in students interested in Computer Science over the last several decades may threaten the United States' capability to expand and maintain the nationwide IT infrastructure. One solution that may slow and possibly even reverse this decline is to increase the awareness of Computer Science in the secondary school system and to increase the knowledge of Computer Science, the ability to teach Computer Science, and the Computer Science professionalism of teachers in that system. In this paper, the authors describe one technique for realizing this solution-implementing a Computer Science endorsement program for secondary school teachers.

Standard embedded sensor network models emphasize energy efficiency and distributed decision-making by considering untethered and unattended sensors. To this we add two constraints - the possibility of sensor failure and the fact that each sensor must tradeoff its own resource consumption with overall network objectives. In this paper, we develop an analytical model of data-centric information routing in sensor networks under all the above constraints. Unlike existing techniques, we use game theory to model intelligent sensors thereby making our approach sensor-centric. Sensors behave as rational players in an N-player routing game, where they tradeoff individual communication and other costs with network wide benefits. The outcome of the sensor behavior is a sequence of communication link establishments, resulting in routing paths from reporting to querying sensors. We show that the optimal routing architecture is the Nash equilibrium of the N-player routing game and that computing the optimal paths (which maximizes payoffs of the individual sensors) is NP-hard with and without data-aggregation. We develop a game-theoretic metric called path weakness to measure the qualitative performance of different routing mechanisms. This sensor-centric concept which is based on the contribution of individual sensors to the overall routing objective is used to define the quality of routing (QoR) paths. Simulation results are used to compare the QoR of different routing paths derived using various energy-constrained routing algorithms.