http://hdl.handle.net/1903/2225 2013-05-19T03:57:21Z http://hdl.handle.net/1903/13700 Title: Memory Trace Oblivious Program Execution Authors: Liu, Chang; Hicks, Michael; Shi, Elaine Abstract: Cloud computing allows users to delegate data and computation to cloud service providers, at the cost of giving up physical control of their computing infrastructure. An attacker (e.g., insider) with physical access to the computing platform can perform various physical attacks, including probing memory buses and cold-boot style attacks. Previous work on secure (co-)processors provides hardware support for memory encryption and prevents direct leakage of sensitive data over the memory bus. However, an adversary snooping on the bus can still infer sensitive information from the memory access traces. Existing work on Oblivious RAM (ORAM) provides a solution for users to put all data in an ORAM; and accesses to an ORAM are obfuscated such that no information leaks through memory access traces. This method, however, incurs significant memory access overhead. In this work, we are among the first to leverage programming language techniques to offer efficient memory-trace oblivious program execution, while providing formal security guarantees. We first formally define the notion of memory-trace obliviousness, and provide a type system for verifying that a program satisfies this property. We then design a compiler that transforms a program into one that satisfies memory trace obliviousness. To achieve optimal efficiency, our compiler aims to minimize the usage of ORAM whenever possible, and would partition variables in smaller ORAM banks (which are faster to access than larger ORAM banks) without risking security. We use several example programs to demonstrate the efficiency gains our compiler achieves in comparison with the naive method of placing all variables in the same ORAM. 2013-02-06T00:00:00Z http://hdl.handle.net/1903/13688 Title: The compiler for the XMTC parallel language: Lessons for compiler developers and in-depth description Authors: Tzannes, Alexandros; Caragea, George C.; Vishkin, Uzi; Barua, Rajeev Abstract: In this technical report, we present information on the XMTC compiler and language. We start by presenting the XMTC Memory Model and the issues we encountered when using GCC, the popular GNU compiler for C and other sequential languages, as the basis for a compiler for XMTC, a parallel language. These topics, along with some information on XMT specific optimizations were presented in [10]. Then, we proceed to give some more details on how outer spawn statements (i.e., parallel loops) are compiled to take advantage of XMT’s unique hardware primitives for scheduling flat parallelism and how we incremented this basic compiler to support nested parallelism. 2011-02-18T00:00:00Z http://hdl.handle.net/1903/13339 Title: Learning to Detect Carried Objects with Minimal Supervision Authors: Dondera, Radu; Morariu, Vlad I.; Davis, Larry S. Abstract: We propose a learning-based method for detecting carried objects that generates candidate image regions from protrusion, color contrast and occlusion boundary cues, and uses a classifier to filter out the regions unlikely to be carried objects. The method achieves higher accuracy than state of the art, which can only detect protrusions from the human shape, and the discriminative model it builds for the silhouette context-based region features generalizes well. To reduce annotation effort, we investigate training the model in a Multiple Instance Learning framework where the only available supervision is "walk" and "carry" labels associated with intervals of human tracks, i.e., the spatial extent of carried objects is not annotated. We present an extension to the miSVM algorithm that uses knowledge of the fraction of positive instances in positive bags and that scales to training sets of hundreds of thousands of instances. 2012-12-21T00:00:00Z http://hdl.handle.net/1903/13333 Title: Clashes in the Infosphere, General Intelligence, and Metacognition: Final project report Authors: Perlis, Don; Cox, Michael. T. Abstract: Humans confront the unexpected every day, deal with it, and often learn from it. AI agents, on the other hand, are typically brittle—they tend to break down as soon as something happens for which their creators did not explicitly anticipate. The central focus of our research project is this problem of brittleness which may also be the single most important problem facing AI research. Our approach to brittleness is to model a common method that humans use to deal with the unexpected, namely to note occurrences of the unexpected (i.e., anomalies), to assess any problem signaled by the anomaly, and then to guide a response or solution that resolves it. The result is the Note-Assess-Guide procedure of what we call the Metacognitive Loop or MCL. To do this, we have implemented MCL-based systems that enable agents to help themselves; they must establish expectations and monitor them, note failed expectations, assess their causes, and then choose appropriate responses. Activities for this project have developed and refined a human-dialog agent and a robot navigation system to test the generality of this approach. 2012-12-12T00:00:00Z