Log-based recovery for middleware servers
We have developed new methods for log-based recovery for middleware servers which involve thread pooling, private in-memory states for clients, shared in-memory state and message interactions among middleware servers. Due to the observed rareness of crashes, relatively small size of shared state and infrequency of shared state read/write accesses, we are able to reduce the overhead of message logging and shared state logging while maintaining recovery independence. Checkpointing has a very small impact on ongoing activities while still reducing recovery time. Our recovery mechanism enables client private states to be recovered in parallel after a crash. On a commercial middleware server platform, we have implemented a recovery infrastructure prototype, which demonstrates the manageability of system complexity and shows promising performance results.

title: Online B-tree merging
abstract: Many scenarios involve merging of two B-tree indexes, both covering the same key range. Increasing demand for continuous availability and high performance requires that such merging be done online, with minimal interference to normal user transactions. In this paper we present an online B-tree merging method, in which the merging of leaf pages in two B-trees are piggybacked lazily with normal user transactions, thus making the merging I/O efficient and allowing user transactions to access only one index instead of both. The concurrency control mechanism is designed to interfere as little as possible with ongoing user transactions. Merging is made forward recoverable by following a conventional logging protocol, with a few extensions. Should a system failure occur, both indexes being merged can be recovered to a consistent state and no merging work is lost. Experiments and analysis show the I/O savings and the performance, and compare variations on the basic algorithm.

title: Precomputed Visibility Cuts for Interactive Relighting with Dynamic BRDFs
abstract: This paper presents a novel PRT-based method that uses precomputed visibility cuts for interactive relighting with all-frequency environment maps and arbitrary dynamic BRDFs. Our method is inspired by the recent Lightcuts approach  and we parameterize distant environment lighting onto uniformly distributed sample points over the sphere. Using a binary tree structure of the points, we precompute and approximate each vertex's visibility function into clusters that we call the precomputed visibility cuts. These cuts are iteratively selected with bounded approximation error and confined cluster size. At run-time, a GPU-based relighting algorithm quickly computes the view-dependent shading color by accessing a dynamically built light tree, the precomputed visibility cuts, and a direct sampling of an arbitrary BRDF using each visibility cluster's average direction and the dynamic view direction. Compared to existing PRT techniques, our method guarantees uniform sampling of the lighting, requires no precomputed BRDF data, and can be easily extended to handle one-bounce glossy indirect transfer effects in real-time.

title: Panalyst: Privacy-Aware Remote Error Analysis on Commodity Software
abstract: Remote error analysis aims at timely detection and remedy of software vulnerabilities through analyzing run-time errors that occur on the client. This objective can only be achieved by offering users effective protection of their private information and minimizing the performance impact of the analysis on their systems without undermining the amount of information the server can access for understanding errors. To this end, we propose in the paper a new technique for privacy-aware remote analysis, called Panalyst. Panalyst includes a client component and a server component. Once a runtime exception happens to an application, Panalyst client sends the server an initial error report that includes only public information regarding the error, such as the length of the packet that triggers the exception. Using an input built from the report, Panalyst server performs a taint analysis and symbolic execution on the application, and adjusts the input by querying the client about the information upon which the execution of the application depends. The client agrees to answer only when the reply does not give away too much user information. In this way, an input that reproduces the error can be gradually built on the server under the client's consent. Our experimental study of this technique demonstrates that it exposes a very small amount of user information, introduces negligible overheads to the client and enables the server to effectively analyze an error.

title: Fusion of Near Infrared Face and Iris Biometrics
abstract: In this paper, we present a method for fusing face and iris biometrics using single near infrared (NIR) image. Fusion of NIR face and iris modalities is a natural way of doing multi-model biometrics because they can be acquired in a single image. An NIR face image is taken using a high resolution NIR camera. Face and iris are segmented from the same NIR image. Face and iris features are then extracted from the segmented parts. Matching of face and iris is done using the respective features. The matching scores are fused using various rules. Experiments give promising results.

title: Distributive subband allocation, power and rate control for relay-assisted OFDMA cellular system with imperfect system state knowledge
abstract: In this paper, we consider distributive subband, power and rate allocation for a two-hop downlink transmission in an orthogonal frequency-division multiple-access (OFDMA) cellular system with fixed relays which operate in decode-and-forward strategy. We take into account of the penalty of packet errors due to imperfect CSIT and system fairness by considering weighted sum goodput as our optimization objective. Based on the cluster-based architecture, we obtain a fast-converging distributive solution with only local imperfect CSIT by using decomposition of the optimization problem. To further reduce the signaling overhead and computational complexity, we propose a reduced feedback distributive solution, which can achieve asymptotically optimal performance for large number of users with arbitrarily small feedback overhead per user.We also derive asymptotic average system throughput so as to obtain useful design insights.

title: Ontology-based Query Construction for GeoCLEF
abstract: This paper describes experiments with geographical information retrieval (GIR). Being different from the traditional information IR, we focus more on the query expansion instead of document ranking. We parse each topic into the event part and the geographic part and use different ontologies to expand both parts respectively. The results show promising results of our strategy for this task.