26,883 research outputs found
Reducing the Number of Annotations in a Verification-oriented Imperative Language
Automated software verification is a very active field of research which has
made enormous progress both in theoretical and practical aspects. Recently, an
important amount of research effort has been put into applying these techniques
on top of mainstream programming languages. These languages typically provide
powerful features such as reflection, aliasing and polymorphism which are handy
for practitioners but, in contrast, make verification a real challenge. In this
work we present Pest, a simple experimental, while-style, multiprocedural,
imperative programming language which was conceived with verifiability as one
of its main goals. This language forces developers to concurrently think about
both the statements needed to implement an algorithm and the assertions
required to prove its correctness. In order to aid programmers, we propose
several techniques to reduce the number and complexity of annotations required
to successfully verify their programs. In particular, we show that high-level
iteration constructs may alleviate the need for providing complex loop
annotations.Comment: 15 pages, 8 figure
Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications
Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications
MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum
In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application
The Validation of Speech Corpora
1.2 Intended audience........................
A machine learning approach for layout inference in spreadsheets
Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach deliver s very high accuracy bringing us a crucial step closer towards automatic table extraction.Peer ReviewedPostprint (published version
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