Automatic schema matching algorithms are typically only concerned with finding attribute correspondences. However, real world data integration problems often require matchings whose arguments span all three types of elements in relational databases: relation, attribute and data value. This paper introduces the definitions and semantics of three additional correspondence types concerning both schema and data values. These correspondences cover the higher-order mappings identified in a seminal paper by Krishnamurthy, Litwin, and Kent. It is shown that these correspondences can be automatically translated to tuple generating dependencies (tgds), and thus this research is compatible with data integration applications that leverage tgds.

We propose an approach to automatically generate photo-on-photo compositions using only a consumer's own image collection, without imposing a strong theme constraint dictated by typical background art designs. Our goal is to ensure adequate foreground and background photo sizing, avoid occlusion of important background photo content, and respect simple aesthetic rules in photo arrangement. We propose a novel image content redundancy measure to extract the informative zone, which is a rectangular region of the background photo that should not be occluded by foreground content. Background photo layout and foreground layout regions are then calculated based on a placement algorithm that deals with possible aspect ratio mismatch of the canvas and background photo, as well as cases of insufficient foreground photo layout area.

We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial document ranking, user interaction with the system produces relevance feedback (RF) which the system then uses to revise the ranking. This interactive process repeats until the user terminates the search. To maximize accuracy relative to user effort, we propose an active learning strategy. At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it. Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user. Evaluation on Robust04 under noisy feedback and on LETOR collections further demonstrate the effectiveness of active learning, as well as value of negative feedback in this task scenario.

Repetition detection is a fundamental issue for music thumbnailing and summarization. In this paper, we propose a new feature, called chroma histogram, which enables us to find out repetitive segments from popular songs accurately and quickly. The feature is robust to tempo variation, because sequential information is removed during the process. The low dimensional feature guarantees a very low computational cost, which is proved by theoretic analysis and experimental evaluation. The objective evaluation results demonstrate that our algorithm outperforms previous approaches in terms of both detecting accuracy and efficiency.