45863395
First Order Rough Logic-Revisited
Based on axiomatic rough set theory, first order rough logic was developed earlier. In this paper, a new model theory for that logic is introduced. With this new semantic, first order rough logic is shown to be equivalent to first order S5, and hence consistent and complete.
32477053
lambda-Level Rough Equality Relation and the Inference of Rough Paramodulation
In the paper, it defines a λ-level rough equality relation in rough logic and establishes a rough sets on real-valued information systems with it. We obtain some related properties and relative rough paramodulation inference rules. They will be used in the approximate reasoning and deductive resolutions of rough logic. Finally, it proves that λ-level rough paramodulant reasoning is sound.
14783314
Ontology Capture Based on Granular Computing
Ontology capture is a key topic in constructing an ontology, and it is a procedure of knowledge acquisition. In this paper, by applying the principle of granular computing, some concepts about information granules, including domain feature information granule, domain object information granule, were defined, the context of the domain ontology may be granulated. A domain granule and a domain granule lattice were presented. An algorithm of generating the domain granule lattice was proposed, and was illustrated by a real example. With the domain granule lattice, ontology capture can be fulfilled. Therefore, granular computing should be of great help in representing and constructing an ontology
8754175
Summarizing level-two topological relations in large spatial datasets
Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.
10184967
Tree-to-String Alignment Template for Statistical Machine Translation
We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. The model is linguistically syntax-based because TATs are extracted automatically from word-aligned, source side parsed parallel texts. To translate a source sentence, we first employ a parser to produce a source parse tree and then apply TATs to transform the tree into a target string. Our experiments show that the TAT-based model significantly outperforms Pharaoh, a state-of-the-art decoder for phrase-based models.
8945696
Synthesis and placement flow for gain-based programmable regular fabrics
In this paper we present the Gain-based Logic Block Array (GLA), a new via-programmable regular fabric. GLA is an array of Gain-based Logic Blocks (GLBs). The GLB is a semi-universal logic block designed based on logical effort theory[12]. Customization of the GLBs is provided by programmable vias. To achieve the best performance, appropriate fabric has to be selected from a family of GLAs with different performance-area trade-offs. We describe a synthesis and placement flow which, for a given design to be implemented, allows us to select the best GLA from the candidate family.
17071580
Pre-layout physical connectivity prediction with application in clustering-based placement
In this paper, we introduce a structural metric, logic contraction, for pre-layout physical connectivity prediction. For a given set of nodes forming a cluster in a netlist, we can predict their proximity in the final layout based on the logic contraction value of the cluster. We demonstrate a very good correlation of our pre-layout measure with the post-layout physical distances between those nodes. We show an application of the logic contraction to circuit clustering. We compare our seed-growth clustering algorithm with the existing efficient clustering techniques. Experimental results demonstrate the effectiveness of our new clustering method.
2785368
Tree Based Behavior Monitoring for Adaptive Fraud Detection
The general basis for anomaly detection and fraud detection is pattern recognition. An effective online fraud detection system should be able to discover both known and new attacks as early as possible. The detection process should be self-adjustable to allow the system to deal with the constantly changing nature of online attacks. In this paper, we present an anomaly detection technique based on behavior mining and monitoring that work at both the individual and system level. Frequent pattern tree is utilized to profile the normal behavior adaptively. A novel tree-based pattern matching algorithm is designed to discover individual level anomalies. An algorithm for computing tree similarity is proposed to solve the system level problems. Empirical evaluations of our technique on both synthetic and real-world data show that we can accurately differentiate anomalous behaviors from the profiled normal behavior