Sharing models

Xem 1-20 trên 218 kết quả Sharing models
  • In this paper, we have presented a secure group data access control paradigm driven by Diffe Hellman ciphering exchanges. An interesting extension to this work is the implementation of a time driven self destructing cloud group data sharing model that is a sender friendly approach that can deal with the problem of revocation in an automated approach.

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  • SAS defines data mining as the process of uncovering hidden patterns in large amounts of data. Many industries use data mining to address business problems and opportunities such as fraud detection, risk and affinity analyses, database marketing, householding, customer churn, bankruptcy prediction, and portfolio analysis.The SAS data mining process is summarized in the acronym SEMMA, which stands for sampling, exploring, modifying, modeling, and assessing data.

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  • Around the world, many fisheries are shared fisheries in the sense that a common fish stock is accessed by both commercial and non-commercial fishers. Non-commercial fishers can include recreational fishers, or customary fishers, or both. A prevalent problem in these fisheries is competition between commercial and non-commercial fishers for access to a resource that is subject to increasing utilisation pressure.

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  • As I am finishing this book, Science magazine is running a special issue about the sequencing of the macaque genome. It turns out that macaques share about 93 percent of their genes with us, humans. Previously it has been already reported that chimpanzees share about 96 percent of their genes with us. Yes, the macaque is our common ancestor, and it might be expected that, together with the chimps, we continued with our natural selection some 23 million years ago until, some 6 million years ago, we departed from the chimps to continue our further search for better adaptation.

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  • This paper presents an enhanced model of plan-based dialogue understanding. Most plan-based dialogue understanding models derived from [Litman and Allen, 1987] assume that the dialogue speakers have access to the same domain plan library, and that the active domain plans are shared by the two speakers. We call these features shared domain plan constraints.

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  • This paper describes a structure-sharing method for the representation of complex phrase types in a parser for PATR-[I, a unification-based g r a m m a r formalism. In parsers for unification-based grammar formalisms, complex phrase types are derived by incremental refinement of rite phrase types defined in grammar rules and lexical entries. In a naive implementation, a new phrase type is built by copying older ones and then combining the copies according to the constraints stated in a grammar rule. ...

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  • The Context-Free backbone of some natural language analyzers produces all possible CF parses as some kind of shared forest, from which a single tree is to be chosen by a disambiguation process that may be based on the finer features of the language. We study the structure of these forests with respect to optimality of sharing, and in relation with the parsing schema used to produce them. In addition to a theoretical and experimental framework for studying these issues, the main results presented are: sophistication in chart parsing schemata (e.g.

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  • We study parsing of tree adjoining grammars with particular emphasis on the use of shared forests to represent all the parse trees deriving a well-formed string. We show that there are two distinct ways of representing the parse forest one of which involves the use of linear indexed grammars and the other the use of context-free grammars. The work presented in this paper is intended to give a general framework for studying tag parsing.

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  • This chapter presents the following content: Theories of investor preferences, signaling effects, residual model, dividend reinvestment plans, stock dividends and stock splits, stock repurchases.

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  • One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. ...

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  • Long-span features, such as syntax, can improve language models for tasks such as speech recognition and machine translation. However, these language models can be difficult to use in practice because of the time required to generate features for rescoring a large hypothesis set. In this work, we propose substructure sharing, which saves duplicate work in processing hypothesis sets with redundant hypothesis structures.

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  • Since we can ‘spin’ words and concepts to suit our affective needs, context is a major determinant of the perceived affect of a word or concept. We view this re-profiling as a selective emphasis or de-emphasis of the qualities that underpin our shared stereotype of a concept or a word meaning, and construct our model of the affective lexicon accordingly .

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  • In recent years, research in natural language processing has increasingly focused on normalizing SMS messages. Different well-defined approaches have been proposed, but the problem remains far from being solved: best systems achieve a 11% Word Error Rate. This paper presents a method that shares similarities with both spell checking and machine translation approaches. The normalization part of the system is entirely based on models trained from a corpus. Evaluated in French by 10-fold-cross validation, the system achieves a 9.3% Word Error Rate and a 0.83 BLEU score. ...

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  • In this paper we present a novel discriminative mixture model for statistical machine translation (SMT). We model the feature space with a log-linear combination of multiple mixture components. Each component contains a large set of features trained in a maximumentropy framework. All features within the same mixture component are tied and share the same mixture weights, where the mixture weights are trained discriminatively to maximize the translation performance.

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  • Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multiple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the translation hypergraphs they produce individually. Therefore, one model can share translations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint decoding achieves an absolute improvement of 1.

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  • We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitute a mixture of hidden topics; each word-pair follows a topic-specific bilingual translation model. Three BiTAM models are proposed to capture topic sharing at different levels of linguistic granularity (i.e., at the sentence or word levels).

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  • A constraint is proposed in the Centering approach to pronoun resolution in discourse. This "property-sharing" constraint requires that two pronominal expressions that retain the same Cb across adjacent utterances share a certain common grammatical property. This property is expressed along the dimension of the grammatical function SUBJECT for both Japanese and English discourses, where different pronominal forms are primarily used to realize the Cb. It is the zero pronominal in Japanese, and the (unstressed) overt pronoun in English.

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  • We describe a prototype SK~RED CmAt~eAR for the syntax of simple nominal expressions in Arabic, E~IL~lx, French, German, and Japanese implemented at MCC. In this Oamm~', a complex inheritance ian/cc of shared gr~mmAtlcal templates provides pans that each language can put together to form lansuug~specific gramm-ti~tl templates. We conclude that grammar shsrin8 is not only possible but also desirable. It forces us to reveal crossliuguistically invm'iant grammatie~ primitives that may otherwise r e m ~ conflamd with other primitives if we deal only with a single ~.nousge or l-n~uuge type. ...

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  • This paper describes the development of a rule-based computational model that describes how a feature-based representation of shared visual information combines with linguistic cues to enable effective reference resolution. This work explores a language-only model, a visualonly model, and an integrated model of reference resolution and applies them to a corpus of transcribed task-oriented spoken dialogues.

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  • We argue that the resource sharing that is commonly manifest in semantic accounts of coordination is instead appropriately handled in terms of structure-sharing in LFG f-structures. We provide an extension to the previous account of LFG semantics (Dalrymple et al., 1993a) according to which dependencies between f-structures are viewed as resources; as a result a one-to-one correspondence between uses of f-structures and meanings is maintained.

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