Document semantics: Two approaches

SGML introduced DTD idea to formally describe document syntax and structure. One of its main characteristics is the fact of being purely declarative and fully independent of the future document's processing (typesetting, formatting, translation/transformation). In this context, SGML has become the international standard to be followed. Sooner or later, a document has to be processed. In order to do that we need to associate semantics to the document's structure. In a compiler context, normally we separate semantics in two, static and dynamic. Establishing a parallelism with document processing, we can think of the document's decorated tree (as recognized by a SGML analyzer) as being the static semantics and document's tree transformation and/or reaction as dynamic semantics. Pursuing this idea, we will present and discuss a study of the relationship between SGML, DAST (Decorated Abstract Syntax Tree), and Algebraic Specification tools, in order to better understand how to formally process documents in general and how to specify and build generic document processing tools

Rutger's CAM2000 chip architecture

This report describes the architecture and instruction set of the Rutgers CAM2000 memory chip. The CAM2000 combines features of Associative Processing (AP), Content Addressable Memory (CAM), and Dynamic Random Access Memory (DRAM) in a single chip package that is not only DRAM compatible but capable of applying simple massively parallel operations to memory. This document reflects the current status of the CAM2000 architecture and is continually updated to reflect the current state of the architecture and instruction set

Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network

Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software entities elements to provide the system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of the software entities onto the hardware platform; (iv) the performance view that collects specific performance indices. In this paper we present a lightweight infrastructure that is able to detect performance antipatterns at runtime through monitoring. The proposed approach precalculates such predicates and identifies antipatterns whose static, dynamic and deployment sub-predicates are validated by the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043

Revisiting the Provision of Nanoscale Precision of Cutting on the Basis of Dynamic Characteristics Modeling of Processing Equipment

The article deals with the issues related to the development of the processing equipment providing na-noscale precision of cutting by means of turning and milling. Building of a machine dynamic model is car-ried out to solve of this task. This allows taking into account the dynamic characteristics of the existing or designed equipment and the errors of dynamic setting of the machine and this also allows providing pro-cessing precision in nanometer range. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3634