41922638
Guarded recursive datatype constructors
34190440
Coercive subtyping for the calculus of constructions
6370123
Full integration of subtyping and if-expression
The combination of full subsumption and conditional expression is a challenging problem, because in such a system, a term might not have a type which is a representative of all types of the term. Therefore, the traditional type checking technique fails. Due to such a difficulty, Java typing rule for if-expression uses only a restricted form of subtyping. In this paper, we propose a type system which includes both of the above mentioned features and enjoys decidable type checking. We also show that the system has the subject reduction property. It is expected that this technique could be used to improve the type system of Java with full subsumption.
1048322
Soundness of Coercion in the Calculus of Constructions
A coercive subtyping system for the Calculus of Constructions is presented. The proposed system λC≤co is obtained essentially by adding coercions and η-conversion to λC≤, which is a subtyping extension to the Calculus of Constructions without coercions. We proved that a coercion c:A ≤ B is semantically sound. That is, (1) c is a term of type A → B; (2) c behaves like an identity function. This development follows the spirit of semantical interpretation of subtyping.
7031383
Image retrieval based on Sugeno fuzzy integral
In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.
2937871
Improving XML Querying with Maximal Frequent Query Patterns
Querying on XML data is a computational-expensive process due to the complex nature of both the XML data and the query. In this paper, we propose an approach to expedite XML query processing by caching the results of a specific class of queries, namely the maximal frequent queries. We mine the maximal frequent query patterns from user-issued queries and cache the results of such queries. We propose a recursive algorithm for query processing using the cached query results. Query rewriting is employed to deal with four kinds of similar queries namely exact matching, exact containment, semantic matching and semantic containment. We perform experiments on the XMARK datasets and show that the proposed methods are both effective and efficient in improving the performance of XML queries.
37291002
Subdivision Depth Computation for Extra-Ordinary Catmull-Clark Subdivision Surface Patches
A second order forward differences based subdivision depth computation technique for extra-ordinary Catmull-Clark subdivision surface (CCSS) patches is presented. The new technique improves a previous technique in that the computation of the subdivision depth is based on the patch's curvature distribution, instead of its dimension. Hence, with the new technique, no excessive subdivision is needed for extra-ordinary CCSS patches to meet the precision requirement and, consequently, one can make trimming, finite element mesh generation, boolean operations, and tessellation of CCSSs more efficient.
22227630
An Index Scheme for XML Documents Based on Relationship Joins
XML is rapidly emerging as a standard for information storage, representation and exchange on the Web. How to rapidly search and query XML documents efficiently has received many attentions in resent research. However, current querying schemes of XML documents typically involve in both node content and tree structural information, which may limit efficiency when facing the application that the tree structural information is more complicated than the tree node itself. In this paper, we propose the node relationships joins algorithms that utilize available indexes mainly on tree structural information. The relationships join algorithms work perfectly especially for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation highlight the correctness and efficiency of our solution
5928745
Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model
In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. “true” when the predicting label is right or “false” when the predicting label is wrong, this new kind of problems arouses much of researchers’ interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.