Attribute Based Access Control (ABAC) has become more broadly adopted by enterprises around the world to provide more dynamic controls to accessing data than Role Based Access Control (RBAC). The challenge is that RBAC is still widely used inside enterprises and ABAC has several limitations reducing its benefits and slowing adoption.

Volunteers who help with online crowdsourcing such as citizen science tasks typically make only a few contributions before exiting. We propose a computational approach for increasing users' engagement in such settings that is based on optimizing policies for displaying motivational messages to users. The approach, which we refer to as Trajectory Corrected Intervention (TCI), reasons about the tradeoff between the long-term influence of engagement messages on participants' contributions and the potential risk of disrupting their current work. We combine model-based reinforcement learning with off-line policy evaluation to generate intervention policies, without relying on a fixed representation of the domain. TCI works iteratively to learn the best representation from a set of random intervention trials and to generate candidate intervention policies. It is able to refine selected policies off-line by exploiting the fact that users can only be interrupted once per session.We implemented TCI in the wild with Galaxy Zoo, one of the largest citizen science platforms on the web. We found that TCI was able to outperform the state-of-the-art intervention policy for this domain, and significantly increased the contributions of thousands of users. This work demonstrates the benefit of combining traditional AI planning with off-line policy methods to generate intelligent intervention strategies.

We present a method that is suitable for clustering of vehicle trajectories obtained by an automated vision system. We combine ideas from two spectral clustering methods and propose a trajectory-similarity measure based on the Hausdorff distance, with modifications to improve its robustness and account for the fact that trajectories are ordered collections of points. We compare the proposed method with two well-known trajectory-clustering methods on a few real-world data sets.

Several federal agencies have announced plans to acquire large scale systems within the next few years which will have peak performance in the petascale range. In order to make sure that end users can take maximum advantage of these systems, Federal agencies, cooperating through the NITRD Program are following a multi-faceted approach which: 1) provides access to leadership class platforms at DOD, DOE, NASA and NSF, 2) determines how to handle data on the petabyte scale through their File Systems and I/O activities, and 3) aids the development of petascale applications through support of multi-disciplinary teams as in DOE/SC SciDAC Program, NSF science application programs, and DOE/NNSA ASC Academic Alliance Program for example. In addition, the DARPA HPCS Program is focused on the development of next generation architectures that will substantially improve the productivity, useability and application scope of multi-petaflop systems. This BOF, presented by the HEC IWG will review the implementation, status and future plans for these and other activities.