DepAnn - An Annotation Tool for Dependency Treebanks

DepAnn is an interactive annotation tool for dependency treebanks, providing both graphical and text-based annotation interfaces. The tool is aimed for semi-automatic creation of treebanks. It aids the manual inspection and correction of automatically created parses, making the annotation process faster and less error-prone. A novel feature of the tool is that it enables the user to view outputs from several parsers as the basis for creating the final tree to be saved to the treebank. DepAnn uses TIGER-XML, an XML-based general encoding format for both, representing the parser outputs and saving the annotated treebank. The tool includes an automatic consistency checker for sentence structures. In addition, the tool enables users to build structures manually, add comments on the annotations, modify the tagsets, and mark sentences for further revision

SWI-Prolog and the Web

Where Prolog is commonly seen as a component in a Web application that is either embedded or communicates using a proprietary protocol, we propose an architecture where Prolog communicates to other components in a Web application using the standard HTTP protocol. By avoiding embedding in external Web servers development and deployment become much easier. To support this architecture, in addition to the transfer protocol, we must also support parsing, representing and generating the key Web document types such as HTML, XML and RDF. This paper motivates the design decisions in the libraries and extensions to Prolog for handling Web documents and protocols. The design has been guided by the requirement to handle large documents efficiently. The described libraries support a wide range of Web applications ranging from HTML and XML documents to Semantic Web RDF processing. To appear in Theory and Practice of Logic Programming (TPLP)Comment: 31 pages, 24 figures and 2 tables. To appear in Theory and Practice of Logic Programming (TPLP

Recovering Grammar Relationships for the Java Language Specification

Grammar convergence is a method that helps discovering relationships between different grammars of the same language or different language versions. The key element of the method is the operational, transformation-based representation of those relationships. Given input grammars for convergence, they are transformed until they are structurally equal. The transformations are composed from primitive operators; properties of these operators and the composed chains provide quantitative and qualitative insight into the relationships between the grammars at hand. We describe a refined method for grammar convergence, and we use it in a major study, where we recover the relationships between all the grammars that occur in the different versions of the Java Language Specification (JLS). The relationships are represented as grammar transformation chains that capture all accidental or intended differences between the JLS grammars. This method is mechanized and driven by nominal and structural differences between pairs of grammars that are subject to asymmetric, binary convergence steps. We present the underlying operator suite for grammar transformation in detail, and we illustrate the suite with many examples of transformations on the JLS grammars. We also describe the extraction effort, which was needed to make the JLS grammars amenable to automated processing. We include substantial metadata about the convergence process for the JLS so that the effort becomes reproducible and transparent

XRound : A reversible template language and its application in model-based security analysis

Successful analysis of the models used in Model-Driven Development requires the ability to synthesise the results of analysis and automatically integrate these results with the models themselves. This paper presents a reversible template language called XRound which supports round-trip transformations between models and the logic used to encode system properties. A template processor that supports the language is described, and the use of the template language is illustrated by its application in an analysis workbench, designed to support analysis of security properties of UML and MOF-based models. As a result of using reversible templates, it is possible to seamlessly and automatically integrate the results of a security analysis with a model. (C) 2008 Elsevier B.V. All rights reserved

The LCG POOL Project, General Overview and Project Structure

The POOL project has been created to implement a common persistency framework for the LHC Computing Grid (LCG) application area. POOL is tasked to store experiment data and meta data in the multi Petabyte area in a distributed and grid enabled way. First production use of new framework is expected for summer 2003. The project follows a hybrid approach combining C++ Object streaming technology such as ROOT I/O for the bulk data with a transactionally safe relational database (RDBMS) store such as MySQL. POOL is based a strict component approach - as laid down in the LCG persistency and blue print RTAG documents - providing navigational access to distributed data without exposing details of the particular storage technology. This contribution describes the project breakdown into work packages, the high level interaction between the main pool components and summarizes current status and plans.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 5 pages. PSN MOKT00

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Developing Collaborative XML Editing Systems

In many areas the eXtensible Mark-up Language (XML) is becoming the standard exchange and data format. More and more applications not only support XML as an exchange format but also use it as their data model or default file format for graphic, text and database (such as spreadsheet) applications. Computer Supported Cooperative Work is an interdisciplinary field of research dealing with group work, cooperation and their supporting information and communication technologies. One part of it is Real-Time Collaborative Editing, which investigates the design of systems which allow several persons to work simultaneously in real-time on the same document, without the risk of inconsistencies. Existing collaborative editing research applications specialize in one or at best, only a small number of document types; for example graphic, text or spreadsheet documents. This research investigates the development of a software framework which allows collaborative editing of any XML document type in real-time. This presents a more versatile solution to the problems of real-time collaborative editing. This research contributes a new software framework model which will assist software engineers in the development of new collaborative XML editing applications. The devised framework is flexible in the sense that it is easily adaptable to different workflow requirements covering concurrency control, awareness mechanisms and optional locking of document parts. Additionally this thesis contributes a new framework integration strategy that enables enhancements of existing single-user editing applications with real-time collaborative editing features without changing their source code

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches