14212803
Merging Databases Under Constraints
The problem of integrating information from conflicting sources comes up in many current applications, such as cooperative information systems, heterogeneous databases, and multiagent systems. We model this by the operation of merging first-order theories. We propose a formal semantics for this operation and show that it has desirable properties, including abiding by majority rule in case of conflict and syntax independence. We apply our semantics to the special case when the theories to be merged represent relational databases under integrity constraints. We then present a way of merging databases that have different or conflicting schemas caused by problems such as synonyms, homonyms or type conflicts mentioned in the schema integration literature.
8985170
Consistent Belief Reasoning in the Presence of Inconsistency
Since everything is a consequence of an inconsistency, classical logics are not useful in modeling the reasoning of an agent who has inconsistent beliefs. In this paper, we differentiate consistent beliefs from inconsistent beliefs. We propose two belief operators Bc and B, standing for consistent belief and belief, respectively. Bc has the modus ponens property, by which the agent is able to reason with consistent beliefs as normal and draw consistent conclusions. B tolerates inconsistency, and by B the agent can reason about his inconsistent beliefs as well. The concept of consistent belief and our logical formalism for it are new, in that reasoning consistently about the information in an inconsistent knowledge base is possible. We also present a complete axiomatization for the logic and discuss the application of Bc and B in reasoning about implicit knowledge in a group of agents and eliminating inconsistency from a knowledge base.
1825025
The START Multimedia Information System: Current Technology and Future Directions
To address the problem of information overload in today’s world, we have developed Start, a natural language question answering system that provides users with high-precision multimedia information access through the use of natural language annotations. To address the difficulty of accessing large amounts of heterogeneous data, we have developed Omnibase, which assists Start by integrating structured and semistructured Web databases into a single, uniformly structured “virtual database.” Our ultimate goal is to develop a computer system that acts like a “smart reference librarian,” and we believe we have laid a firm foundation for achieving our goal. This paper describes our current implemented system and discusses future research directions.
3682795
Multibody grouping via orthogonal subspace decomposition
Multibody structure from motion could be solved by the factorization approach. However, the noise measurements would make the segmentation difficult when analyzing the shape interaction matrix. This paper presents an orthogonal subspace decomposition and grouping technique to approach such a problem. We decompose the object shape spaces into signal subspaces and noise subspaces. We show that the signal subspaces of the object shape spaces are orthogonal to each other. Instead of using the shape interaction matrix contaminated by noise, we introduce the shape signal subspace distance matrix for shape space grouping. Outliers could be easily identified by this approach. The robustness of the proposed approach lies in the fact that the shape space decomposition alleviates the influence of noise, and has been verified with extensive experiments.
1721157
Modeling the constraints of human hand motion
Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-efficiently when considering the motion constraints of a hand. Although some constraints can be represented as equalities or inequalities, there exist many constraints which cannot be explicitly represented. In this paper, we propose a learning approach to model the hand configuration space directly. The redundancy of the configuration space can be eliminated by finding a lower-dimensional subspace of the original space. Finger motion is modeled in this subspace based on the linear behavior observed in the real motion data collected by a CyberGlove. Employing the constrained motion model, we are able to efficiently capture finger motion from video inputs. Several experiments show that our proposed model is helpful for capturing articulated motion.
42438893
Some Decomposition Invariants of Crowns
8822889
Iterative Incremental Clustering of Time Series
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. The dilemma of choosing the initial centers is mitigated by initializing the centers at each approximation level, using the final centers returned by the coarser representations. In addition to casting the clustering algorithms as anytime algorithms, this approach has two other very desirable properties. By working at lower dimensionalities we can efficiently avoid local minima. Therefore, the quality of the clustering is usually better than the batch algorithm. In addition, even if the algorithm is run to completion, our approach is much faster than its batch counterpart. We explain, and empirically demonstrate these surprising and desirable properties with comprehensive experiments on several publicly available real data sets. We further demonstrate that our approach can be generalized to a framework of much broader range of algorithms or data mining problems.