Exploring the mathematics of motion through construction and collaboration

In this paper we give a detailed account of the design principles and construction of activities designed for learning about the relationships between position, velocity and acceleration, and corresponding kinematics graphs. Our approach is model-based, that is, it focuses attention on the idea that students constructed their own models – in the form of programs – to formalise and thus extend their existing knowledge. In these activities, students controlled the movement of objects in a programming environment, recording the motion data and plotting corresponding position-time and velocity-time graphs. They shared their findings on a specially-designed web-based collaboration system, and posted cross-site challenges to which others could react. We present learning episodes that provide evidence of students making discoveries about the relationships between different representations of motion. We conjecture that these discoveries arose from their activity in building models of motion and their participation in classroom and online communities

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

Naval Integration into Joint Data Strategies and Architectures in JADC2

NPS NRP Technical ReportAs Joint capabilities mature and shape into the Joint All Domain C2 Concept, Services, COCOMs and Coalition Partners will need to invest into efforts that would seamlessly integrate into Joint capabilities. The objective for the Navy is to study the options for Navy, including Naval Special Warfare Command under SOCOM, on how to integrate Navy's data strategy and architecture under the unifying JADC2 umbrella. The other objectives are to explore alternatives considered by the SOCOM and the Air Force, which are responsible for JADC2 Information Advantage and Digital Mission Command & Control. A major purpose of Joint, Services/COCOMs, agencies and Coalition Partners capabilities is to provide shared core of integrated canonical services for data, information, and knowledge with representations for vertical interoperability across all command levels and JADC2, lateral interoperability between Naval Service/COCOMs, and any combination of JADC2 constituents, agencies, and coalition partners. Our research plan is to explore available data strategy options by leveraging previous NRP work (NPS-20-N313-A). We will participate in emerging data strategy by Navy JADC2 project Overmatch. By working with MITRE our team will explore Air Force JADC2 data strategy implemented in ABMS DataOne component. Our goal is to find a seamless integration between Naval Data Strategy and data strategies behind JADC2 Information Advantage and Digital Mission Command & Control capabilities. Our plan includes studying Service-to-Service and Service-to-COCOM interoperability options required for Joint operations with a goal to minimize OODA's loop latency across sensing, situation discovery & monitoring, and knowledge understanding-for-planning, deciding, and acting. Our team realizes JADC2 requires virtual model allowing interoperability between subordinate C2 for services, agencies, and partner. Without such flexible 'joint' intersection organizational principal hierarchical structure it would be impossible to define necessary temporal and spatial fidelities for each level of organizational command required for implanting JADC2. Research deliverables will document the results of the exploration of Joint, COCOM, Agency and Partner Data Strategies approaches as JADC2 interoperability options to the emerging JADC2. We strive for standard JADC2 interface. Keywords: JADC2, ABMS, DataOne, Information Advantage, Digital Mission Command, IntegrationN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.