Automatic cascade training with perturbation bias
Face detection methods based on cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework, which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning, which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias, which leverages the statistical differences between target and non-target classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.

title: Terrain Synthesis from Digital Elevation Models
abstract: In this paper, we present an example-based system for terrain synthesis. In our approach, patches from a sample terrain (represented by a height field) are used to generate a new terrain. The synthesis is guided by a user-sketched feature map that specifies where terrain features occur in the resulting synthetic terrain. Our system emphasizes large-scale curvilinear features (ridges and valleys) because such features are the dominant visual elements in most terrains. Both the example height field and user's sketch map are analyzed using a technique from the field of geomorphology. The system finds patches from the example data that match the features found in the user's sketch. Patches are joined together using graph cuts and Poisson editing. The order in which patches are placed in the synthesized terrain is determined by breadth-first traversal of a feature tree and this generates improved results over standard raster-scan placement orders. Our technique supports user-controlled terrain synthesis in a wide variety of styles, based upon the visual richness of real-world terrain data.

title: Spatio-temporal join selectivity
abstract: Given two sets S1, S2 of moving objects, a future timestamp tq, and a distance threshold d, a spatio-temporal join retrieves all pairs of objects that are within distance d at tq. The selectivity of a join equals the number of retrieved pairs divided by the cardinality of the Cartesian product S1 × S2. This paper develops a model for spatio-temporal join selectivity estimation based on rigorous probabilistic analysis, and reveals the factors that affect the selectivity. Initially, we solve the problem for ID (point and rectangle) objects whose location and velocities distribute uniformly, and then extend the results to multi-dimensional spaces. Finally, we deal with non-uniform distributions using a specialized spatio-temporal histogram. Extensive experiments confirm that the proposed formulae are highly accurate (average error below 10%).

title: A practical system of keyphrase extraction for web pages
abstract: Keyphrases can be used to facilitate Web users grasping the main topic(s) of a Web page. We present a practical system of automatic keyphrase extraction for Web pages. In this system, a regression model was first trained based on a set of human-labeled documents. Then it was used to extract keyphrases from new pages automatically. This paper makes three contributions. First, the structure information in a Web page was investigated for keyphrase extraction task. Second, the query log data associated with a Web page collected by a search engine server were used to help keyphrase extraction. Third, a method was put forward in this paper in order to evaluate the similarity of phrases.

title: Formal specification-based online monitoring
abstract: With current trends towards more complex software system and use of higher level languages, a monitoring technique is of increasing importance for the areas such as performance enhancement, dependability, correctness checking and so on. In this paper, we present a formal specification-based online monitoring technique. The key idea of our technique is to build a linking system, which connects a specification animator and a program debugger. The required information about dynamic behaviors of the formal specification and concrete implementation of a target system is obtained from the animator and the debugger. Based on that information, the judgment on the consistency of the concrete implementation with the formal specification will be provided. Not embedding any instrumentation code into the target system, our monitoring technique will not alter the dynamic behavior of the target system. Animating the formal specification, rather than annotating the target system with extra formal specifications, our monitoring technique separates the implementation-dependent description of the monitored objects and the formal requirement specification of them

title: Neighborhood formation and anomaly detection in bipartite graphs
abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.

title: Drag-and-drop pasting
abstract: In this paper, we present a user-friendly system for seamless image composition, which we call drag-and-drop pasting. We observe that for Poisson image editing [Perez et al. 2003] to work well, the user must carefully draw a boundary on the source image to indicate the region of interest, such that salient structures in source and target images do not conflict with each other along the boundary. To make Poisson image editing more practical and easy to use, we propose a new objective function to compute an optimized boundary condition. A shortest closed-path algorithm is designed to search for the location of the boundary. Moreover, to faithfully preserve the object's fractional boundary, we construct a blended guidance field to incorporate the object's alpha matte. To use our system, the user needs only to simply outline a region of interest in the source image, and then drag and drop it onto the target image. Experimental results demonstrate the effectiveness of our "drag-and-drop pasting" system.