2,033 research outputs found

    A Complementary Approach for Adaptive and Adaptable Hypermedia: Intensional Hypertext

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    In this paper we describe a methodology and an authoring/publishing tool for adaptable Web documents (user-determined adaptable Web pages) as a complementary approach to Adaptive Hypermedia. Our approach is based on intensional logic, the logic of assertions and expressions, which vary over a collection of contexts or possible worlds. In our approach the contexts are sets of values for parameters which specify the current user profile as supplied by the current Web page URL, and the latest user input. The author produces generic (multi-version) source in the form of HTML with extra markup delimiting parts that are sensitive (in various ways) to the parameters. This source (in what we call Intensional Markup Language) is translated into a Perl-like language called ISE (Intensional Sequential Evaluator). To generate the appropriately adapted individual pages, the server runs the ISE program in the appropriate context. The program produces HTML that, when displayed in the user's browser, is rendered into the desired adaptation of the requested page. Although this intensional approach was originally designed to work without any explicit user model, we can extend it (and make the documents adaptive as well as adaptable) simply by incorporating a user model that monitors the user and computes some of the profile parameters

    Terminal semantics for codata types in intensional Martin-L\"of type theory

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    In this work, we study the notions of relative comonad and comodule over a relative comonad, and use these notions to give a terminal coalgebra semantics for the coinductive type families of streams and of infinite triangular matrices, respectively, in intensional Martin-L\"of type theory. Our results are mechanized in the proof assistant Coq.Comment: 14 pages, ancillary files contain formalized proof in the proof assistant Coq; v2: 20 pages, title and abstract changed, give a terminal semantics for streams as well as for matrices, Coq proof files updated accordingl

    kLog: A Language for Logical and Relational Learning with Kernels

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    We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials

    A platform for discovering and sharing confidential ballistic crime data.

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    Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases

    Ontology Population for Open-Source Intelligence

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    We present an approach based on GATE (General Architecture for Text Engineering) for the automatic population of ontologies from text documents. We describe some experimental results, which are encouraging in terms of extracted correct instances of the ontology. We then focus on a phase of our pipeline and discuss a variant thereof, which aims at reducing the manual effort needed to generate pre-defined dictionaries used in document annotation. Our additional experiments show promising results also in this case

    Non-wellfounded trees in Homotopy Type Theory

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    We prove a conjecture about the constructibility of coinductive types - in the principled form of indexed M-types - in Homotopy Type Theory. The conjecture says that in the presence of inductive types, coinductive types are derivable. Indeed, in this work, we construct coinductive types in a subsystem of Homotopy Type Theory; this subsystem is given by Intensional Martin-L\"of type theory with natural numbers and Voevodsky's Univalence Axiom. Our results are mechanized in the computer proof assistant Agda.Comment: 14 pages, to be published in proceedings of TLCA 2015; ancillary files contain Agda files with formalized proof
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