Role-Based Access Control (RBAC) has the potential both to simplify administration and improve an organization's security. But for non-trivial configurations, there is a conflict between defining fine-grained roles which adhere to the principle of least privilege, and coarse-grained roles which simplify administration by reducing configuration complexity. In this paper we propose OnPar, a multi-objective role mining approach which introduces minimization of unnecessary privilege as a role mining objective, along with an associated unnecessary privilege metric. These allow an RBAC configuration's level of adherence to the principle of least privilege to be reasoned about and traded off against other objectives, including minimization of configuration complexity. A key feature of our approach is the elimination of user tuning of global optimization weights. We show experimentally that this tuning typically leads to the evaluation of sub-optimal candidates, while still missing many optimal candidates. To avoid these issues we leverage Pareto optimality and introduce multi-stage Pareto filtering and the hypervolume indicator to role mining. Their use allows OnPar to efficiently select a small set of candidates for evaluation by the administrator, which are equal best and representative of the full range of trade-offs that were found. Our experimental results demonstrate the effectiveness of this approach across a wide range of input configurations.

Facial aging is a complicated process which usually affects the facial appearance (e.g., wrinkles). Variations of facial appearance pose a big challenge to the automatic face recognition problem. How to eliminate the influence of aging factors to the verification performance is a very challenging problem. Multi-task learning has provided a principled framework for jointly learning multiple related tasks to improve generalization performance. In this paper, we leverage this powerful technique to improve the task of cross-age face verification. We present an end-to-end learning framework for cross-age face verification by designing a multi-task deep neural network architecture that exploits the intrinsic low-dimensional representation shared between the tasks of face verification and age estimation. We show that the algorithm effectively balances feature sharing and feature exclusion between the two given tasks. We evaluate the proposed framework on two standard benchmarks. Experimental results demonstrate that our algorithm has significant improvement over the state-of-theart (2.2% EER on MORPH and 7.8% EER on FG-NET, by more than 50.0% and 59.70% performance gain respectively).

Objects in fine-grained categories always share a high degree of shape similarity, making both “localizing discriminative parts” and “learning appearance descriptors” extremely difficult. We propose a framework to leverage 2D+3D cues to handle above two challenges. Towards the goal of image alignment to localize discriminative parts, traditional methods rely on either manual part annotation or image segmentation. Instead, our framework leverages each image's 3D camera pose estimation to align images; Towards the goal of “learning appearance descriptors” confined with small training data and memory/computation cost, we propose an unsupervised Convolutional Sparse Coding (CSC) + manifold learning that significantly reduces model complexity, but still successfully produces highly diverse feature filters like deep neural network. Our experimental results attest the advocated framework's accuracy is comparable to a deep network, demonstrating its great potential on mobile devices.

Computer systems increasingly rely on heterogeneity to achieve greater performance, scalability and energy efficiency. Because heterogeneous systems typically comprise multiple execution contexts with different programming abstractions and runtimes, programming them remains extremely challenging.

We propose a new set of OS abstractions to support GPUs and other accelerator devices as first class computing resources. These new abstractions, collectively called the PTask API, support a dataflow programming model. Because a PTask graph consists of OS-managed objects, the kernel has sufficient visibility and control to provide system-wide guarantees like fairness and performance isolation, and can streamline data movement in ways that are impossible under current GPU programming models.

This paper addresses the problem of scheduling concurrent jobs on clusters where application data is stored on the computing nodes. This setting, in which scheduling computations close to their data is crucial for performance, is increasingly common and arises in systems such as MapReduce, Hadoop, and Dryad as well as many grid-computing environments. We argue that data-intensive computation benefits from a fine-grain resource sharing model that differs from the coarser semi-static resource allocations implemented by most existing cluster computing architectures. The problem of scheduling with locality and fairness constraints has not previously been extensively studied under this resource-sharing model.