948 resultados para Strongly Semantic Information


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The research reported here concerns the principles used to automatically generate three-dimensional representations from line drawings of scenes. The computer programs involved look at scenes which consist of polyhedra and which may contain shadows and various kinds of coincidentally aligned scene features. Each generated description includes information about edge shape (convex, concave, occluding, shadow, etc.), about the type of illumination for each region (illuminated, projected shadow, or oriented away from the light source), and about the spacial orientation of regions. The methods used are based on the labeling schemes of Huffman and Clowes; this research provides a considerable extension to their work and also gives theoretical explanations to the heuristic scene analysis work of Guzman, Winston, and others.

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Two new notions of reduction for terms of the λ-calculus are introduced and the question of whether a λ-term is beta-strongly normalizing is reduced to the question of whether a λ-term is merely normalizing under one of the new notions of reduction. This leads to a new way to prove beta-strong normalization for typed λ-calculi. Instead of the usual semantic proof style based on Girard's "candidats de réductibilité'', termination can be proved using a decreasing metric over a well-founded ordering in a style more common in the field of term rewriting. This new proof method is applied to the simply-typed λ-calculus and the system of intersection types.

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This is an addendum to our technical report BUCS TR-94-014 of December 19, 1994. It clarifies some statements, adds information on some related research, includes a comparison with research be de Groote, and fixes two minor mistakes in a proof.

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Remembering past events - or episodic retrieval - consists of several components. There is evidence that mental imagery plays an important role in retrieval and that the brain regions supporting imagery overlap with those supporting retrieval. An open issue is to what extent these regions support successful vs. unsuccessful imagery and retrieval processes. Previous studies that examined regional overlap between imagery and retrieval used uncontrolled memory conditions, such as autobiographical memory tasks, that cannot distinguish between successful and unsuccessful retrieval. A second issue is that fMRI studies that compared imagery and retrieval have used modality-aspecific cues that are likely to activate auditory and visual processing regions simultaneously. Thus, it is not clear to what extent identified brain regions support modality-specific or modality-independent imagery and retrieval processes. In the current fMRI study, we addressed this issue by comparing imagery to retrieval under controlled memory conditions in both auditory and visual modalities. We also obtained subjective measures of imagery quality allowing us to dissociate regions contributing to successful vs. unsuccessful imagery. Results indicated that auditory and visual regions contribute both to imagery and retrieval in a modality-specific fashion. In addition, we identified four sets of brain regions with distinct patterns of activity that contributed to imagery and retrieval in a modality-independent fashion. The first set of regions, including hippocampus, posterior cingulate cortex, medial prefrontal cortex and angular gyrus, showed a pattern common to imagery/retrieval and consistent with successful performance regardless of task. The second set of regions, including dorsal precuneus, anterior cingulate and dorsolateral prefrontal cortex, also showed a pattern common to imagery and retrieval, but consistent with unsuccessful performance during both tasks. Third, left ventrolateral prefrontal cortex showed an interaction between task and performance and was associated with successful imagery but unsuccessful retrieval. Finally, the fourth set of regions, including ventral precuneus, midcingulate cortex and supramarginal gyrus, showed the opposite interaction, supporting unsuccessful imagery, but successful retrieval performance. Results are discussed in relation to reconstructive, attentional, semantic memory, and working memory processes. This is the first study to separate the neural correlates of successful and unsuccessful performance for both imagery and retrieval and for both auditory and visual modalities.

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The aim of this work is to improve retrieval and navigation services on bibliographic data held in digital libraries. This paper presents the design and implementation of OntoBib¸ an ontology-based bibliographic database system that adopts ontology-driven search in its retrieval. The presented work exemplifies how a digital library of bibliographic data can be managed using Semantic Web technologies and how utilizing the domain specific knowledge improves both search efficiency and navigation of web information and document retrieval.

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Kurzel(2004) points out that researchers in e-learning and educational technologists, in a quest to provide improved Learning Environments (LE) for students are focusing on personalising the experience through a Learning Management System (LMS) that attempts to tailor the LE to the individual (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez,2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman; 2003). According to Kurzel (2004) this tailoring can have an impact on content and how it’s accessed; the media forms used; method of instruction employed and the learning styles supported. This project is aiming to move personalisation forward to the next generation, by tackling the issue of Personalised e-Learning platforms as pre-requisites for building and generating individualised learning solutions. The proposed development is to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system’s processing engine. This paper will discuss some of our early work and ideas.

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With emergence of "Semantic Web" there has been much discussion about the impact of technologies such as XML and RDF on the way we use the Web for developing e-learning applications and perhaps more importantly on how we can personalise these applications. Personalisation of e-learning is viewed by many authors (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez, 2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman, 2003) as the key challenge for the learning technologists. According to Kurzel (2004) the tailoring of e-learning applications can have an impact on content and how it's accesses; the media forms used; method of instruction employed and the learning styles supported. This paper will report on a research project currently underway at the eCentre in University of Greenwich which is exploring different approaches and methodologies to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system's processing engine.

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A BSP (Bulk Synchronous Parallelism) computation is characterized by the generation of asynchronous messages in packages during independent execution of a number of processes and their subsequent delivery at synchronization points. Bundling messages together represents a significant departure from the traditional ‘one communication at a time’ approach. In this paper the semantic consequences of communication packaging are explored. In particular, the BSP communication structure is identified with a general form of substitution—predicate substitution. Predicate substitution provides a means of reasoning about the synchronized delivery of asynchronous communications when the immediate programming context does not explicitly refer to the variables that are to be updated (unlike traditional operations, such as the assignment $x := e$, where the names of the updated variables can be extracted from the context). Proofs of implementations of Newton's root finding method and prefix sum are used to illustrate the practical application of the proposed approach.

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Selection power is taken as the fundamental value for information retrieval systems. Selection power is regarded as produced by selection labor, which itself separates historically into description and search labor. As forms of mental labor, description and search labor participate in the conditions for labor and for mental labor. Concepts and distinctions applicable to physical and mental labor are indicated, introducing the necessity of labor for survival, the idea of technology as a human construction, and the possibility of the transfer of human labor to technology. Distinctions specific to mental labor, particular between semantic and syntactic labor, are introduced. Description labor is exemplified by cataloging, classification, and database description, can be more formally understood as the labor involved in the transformation of objects for description into searchable descriptions, and is also understood to include interpretation. The costs of description labor are discussed. Search labor is conceived as the labor expended in searching systems. For both description and search labor, there has been a progressive reduction in direct human labor, with its syntactic aspects transferred to technology, effectively compelled by the high relative costs of direct human labor compared to machine processes.

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Information retrieval in the age of Internet search engines has become part of ordinary discourse and everyday practice: "Google" is a verb in common usage. Thus far, more attention has been given to practical understanding of information retrieval than to a full theoretical account. In Human Information Retrieval, Julian Warner offers a comprehensive overview of information retrieval, synthesizing theories from different disciplines (information and computer science, librarianship and indexing, and information society discourse) and incorporating such disparate systems as WorldCat and Google into a single, robust theoretical framework. There is a need for such a theoretical treatment, he argues, one that reveals the structure and underlying patterns of this complex field while remaining congruent with everyday practice. Warner presents a labor theoretic approach to information retrieval, building on his previously formulated distinction between semantic and syntactic mental labor, arguing that the description and search labor of information retrieval can be understood as both semantic and syntactic in character. Warner's information science approach is rooted in the humanities and the social sciences but informed by an understanding of information technology and information theory. The chapters offer a progressive exposition of the topic, with illustrative examples to explain the concepts presented. Neither narrowly practical nor largely speculative, Human Information Retrieval meets the contemporary need for a broader treatment of information and information systems.

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Latent semantic indexing (LSI) is a technique used for intelligent information retrieval (IR). It can be used as an alternative to traditional keyword matching IR and is attractive in this respect because of its ability to overcome problems with synonymy and polysemy. This study investigates various aspects of LSI: the effect of the Haar wavelet transform (HWT) as a preprocessing step for the singular value decomposition (SVD) in the key stage of the LSI process; and the effect of different threshold types in the HWT on the search results. The developed method allows the visualisation and processing of the term document matrix, generated in the LSI process, using HWT. The results have shown that precision can be increased by applying the HWT as a preprocessing step, with better results for hard thresholding than soft thresholding, whereas standard SVD-based LSI remains the most effective way of searching in terms of recall value.

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This paper examines the occurrence and fragility of information cascades in two laboratory experiments. One group of low informed participants sequentially guess which of two states has been randomly chosen. In a matched pairs design, another group of high informed participants make similar guesses after having observed the guesses of the low informed participants. In the second experiment, participants' beliefs about the chosen state are elicited. In equilibrium, low informed players who observe an established pattern of identical guesses herd without regard to their private information whereas high informed players always guess according to their private information. Equilibrium behavior implies that information cascades emerge in the group of low informed participants, the belief based solely on cascade guesses is stationary, and information cascades are systematically broken by high informed participants endowed with private information contradicting the cascade guesses. Experimental results show that the behavior of low informed participants is qualitatively in line with the equilibrium prediction. Information cascades often emerge in our experiments. The tendency of low informed participants to engage in cascade behavior increases with the number of identical guesses. Our main finding is that information cascades are not fragile. The behavior of high informed participants differs markedly from the equilibrium prediction. Only one-third of laboratory cascades are broken by high informed participants endowed with private information contradicting the cascade guesses. The relative frequency of cascade breaks is 15% for the situations where five or more identical guesses are observed. Participants' elicited beliefs are strongly consistent with their own behavior and show that, unlike in equilibrium, the more cascade guesses participants observe the more they believe in the state favored by those guesses.

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Web databases are now pervasive. Such a database can be accessed via its query interface (usually HTML query form) only. Extracting Web query interfaces is a critical step in data integration across multiple Web databases, which creates a formal representation of a query form by extracting a set of query conditions in it. This paper presents a novel approach to extracting Web query interfaces. In this approach, a generic set of query condition rules are created to define query conditions that are semantically equivalent to SQL search conditions. Query condition rules represent the semantic roles that labels and form elements play in query conditions, and how they are hierarchically grouped into constructs of query conditions. To group labels and form elements in a query form, we explore both their structural proximity in the hierarchy of structures in the query form, which is captured by a tree of nested tags in the HTML codes of the form, and their semantic similarity, which is captured by various short texts used in labels, form elements and their properties. We have implemented the proposed approach and our experimental results show that the approach is highly effective.

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Many plain text information hiding techniques demand deep semantic processing, and so suffer in reliability. In contrast, syntactic processing is a more mature and reliable technology. Assuming a perfect parser, this paper evaluates a set of automated and reversible syntactic transforms that can hide information in plain text without changing the meaning or style of a document. A large representative collection of newspaper text is fed through a prototype system. In contrast to previous work, the output is subjected to human testing to verify that the text has not been significantly compromised by the information hiding procedure, yielding a success rate of 96% and bandwidth of 0.3 bits per sentence. © 2007 SPIE-IS&T.

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In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.