Partitioned formulation of internal and gravity waves interacting with flexible structures

This paper presents a partitioned modeling of internal and gravity fluid waves that interact with flexible structures. The governing interaction model consists of three completely partitioned entities: fluid model, structural model, and interface model that acts as an internal constraint on the fluid–structure interface boundary. Thus, the proposed partitioned multi-physics modeling can employ two completely modular fluid and structure software modules plus an interface solver, hence amenable to partitioned solution algorithms. The interface discretization can exploit the nonmatching interface algorithm previously developed via the method of localized Lagrange multipliers. Also noted is that the present fluid model can make use of widely available finite element software for standard Poisson-type problems.     

Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus

We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model.     

A Multidirectional Occlusion Shading Model for Direct Volume Rendering

In this paper, we present a novel technique which simulates directional light scattering for more realistic interactive visualization of volume data. Our method extends the recent directional occlusion shading model by enabling light source positioning with practically no performance penalty. Light transport is approximated using a tilted cone‐shaped function which leaves elliptic footprints in the opacity buffer during slice‐based volume rendering. We perform an incremental blurring operation on the opacity buffer for each slice in front‐to‐back order. This buffer is then used to define the degree of occlusion for the subsequent slice. Our method is capable of generating high‐quality soft shadowing effects, allows interactive modification of all illumination and rendering parameters, and requires no pre‐computation.     

Partitioning large networks without breaking communities

The identification of cohesive communities is a key process in social network analysis. However, the algorithms that are effective for finding communities do not scale well to very large problems, as their time complexity is worse than linear in the number of edges in the graph. This is an important issue for those interested in applying social network analysis techniques to very large networks, such as networks of mobile phone subscribers. In this respect, the contributions of this paper are twofold. First, we demonstrate these scaling issues using a prominent community-finding algorithm as a case study. Then, we show that a two-stage process, whereby the network is first decomposed into manageable subnetworks using a multilevel graph partitioning procedure, is effective in finding communities in networks with more than 10⁶ nodes.     

Ontological reasoning for improving the treatment of emotions in text

With the advent of affective computing, the task of adequately identifying, representing and processing the emotional connotations of text has acquired importance. Two problems facing this task are addressed in this paper: the composition of sentence emotion from word emotion, and a representation of emotion that allows easy conversion between existing computational representations. The emotion of a sentence of text should be derived by composition of the emotions of the words in the sentence, but no method has been proposed so far to model this compositionality. Of the various existing approaches for representing emotions, some are better suited for some problems and some for others, but there is no easy way of converting from one to another. This paper presents a system that addresses these two problems by reasoning with two ontologies implemented with Semantic Web technologies: one designed to represent word dependency relations within a sentence, and one designed to represent emotions. The ontology of word dependency relies on roles to represent the way emotional contributions project over word dependencies. By applying automated classification of mark-up results in terms of the emotion ontology the system can interpret unrestricted input in terms of a restricted set of concepts for which particular rules are provided. The rules applied at the end of the process provide configuration parameters for a system for emotional voice synthesis.     

Logic programming for combinatorial problems

Combinatorial problems appear in many areas in science, engineering, biomedicine, business, and operations research. This article presents a new intelligent computing approach for solving combinatorial problems, involving permutations and combinations, by incorporating logic programming. An overview of applied combinatorial problems in various domains is given. Such computationally hard and popular combinatorial problems as the traveling salesman problem are discussed to illustrate the usefulness of the logic programming approach. Detailed discussions of implementation of combinatorial problems with time complexity analyses are presented in Prolog, the standard language of logic programming. These programs can be easily integrated into other systems to implement logic programming in combinatorics.     

Left-forbidding cooperating distributed grammar systems

A left-forbidding grammar, introduced in this paper, is a context-free grammar, where a set of nonterminal symbols is attached to each context-free production. Such a production can rewrite a nonterminal provided that no symbol from the attached set occurs to the left of the rewritten nonterminal in the current sentential form. The present paper discusses cooperating distributed grammar systems with left-forbidding grammars as components and gives some new characterizations of language families of the Chomsky hierarchy. In addition, it also proves that twelve nonterminals are enough for cooperating distributed grammar systems working in the terminal derivation mode with two left-forbidding components (including erasing productions) to characterize the family of recursively enumerable languages.