Unification of documents in pre-revolutionary Russia: historical and legal issues

The subject. The article reveals the main historical trends and legal problems concerning unification of documents used by Russian authorities during different historical periods. The purpose of the article is to identify the prerequisites for the origin of document unification, as well as to characterize the periods of development and main directions of document flow standardization in pre-revolutionary Russia. The methodology includes historical-legal method, formal-legal method, systematic approach, chronological method, analysis, synthesis. The main results of research. Scientific understanding of the historical and legal aspects of document flow standardization is closely related to the main stages of its development, and therefore the problem of periodization of document flow standardization in Russia for the purpose of systematization and scientific generalization of this field of knowledge comes to the fore. The chronological approach was chosen as the most appropriate criterion, which allows to trace the evolutionary development of document management standardization, link it with the general history of office work in Russia and state policy in this area. The research will help to determine ways to improve the current system of document management standardization in Russia. The research topic becomes especially relevant in connection with the activation of the processes of implementation of international standards, and the wide application of foreign practice in the field of documentation management over the past decade in Russia. Generalization and analysis of the historical experience of our country in this area makes it possible to identify the national specifics of document management and its standardization. It helps to determine the prospects for the implementation of international standards. Conclusions. The study of the history of documentation practice in Russia allows us to conclude that the issues of document flow rationalization were of great importance since the XVII century. Considerable experience was accumulated in the field of document unification in pre-revolutionary Russia. The beginnings of document unification arose at the dawn of the XVII century and developed gradually with the formation and complexity of the office system in Russia. At the first stage unification was manifested in the consolidation of spontaneously formed norms and rules for drawing up business papers, by the end of the XIX century it turned into an independent element in the field of document management. The gradual evolution of the form as well as the introduction of stamp paper led to the appearance of legally established forms of documents with permanent details in the XIX century, and the first unified documentation systems were created. The appearance of collections of business paper samples showed that government and Russian society understood the importance of using sustainable document models in order to streamline document flow.

Cross-Sectional Study of Vehicle Speeds on Rural Four-Lane Highway Curves

Research has consistently shown that horizontal curves are often associated with increased crash rates compared with similar tangent highway sections. These crashes are often related to speed and the difficulty of judging the severity of the curve. Curve speed models are used for a variety of applications, including assessing operational characteristics, evaluating design speed, conducting spot safety analyses, and setting curve advisory speeds. However, most of the documented curve speed models apply to rural two-lane highways, while relatively few models have been developed for rural multilane highways. These types of highways may exhibit different driver behavior in curves because of their more generous geometric design and higher traffic volumes. The objective of this paper is to document models that have been developed for several types of rural four-lane highways, including undivided highways, divided highways, and freeways. The authors developed models that account for geometric characteristics like curve radius, superelevation rate, and deflection angle, as well as operational characteristics like approach tangent (TN) speed. These models were calibrated using a database of about 46,000 vehicles across 29 horizontal curve sites in central Texas.

What is a diagram, really?

Models for XML documents often focus on text documents, but XML is used for many other kinds of data as well: databases, math, music, vector graphic images, and more. This paper examines how basic document models in the “text” world, do and do not fit a quite different kind of data: vector graphic images, and in particular their very common application for many kinds of diagrams.

Topic Modeling in Embedding Spaces

Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word’s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.

A Document Classification using NLP and Recurrent Neural Network

The classification technique is most important for supervised and semi supervised base machine learning task. Many classification algorithms has introduced already for existing systems. Class-label classification is an important machine learning task wherein one assigns a subset of candidate without label to an object. Classification of various document models based on short text, metadata, heading levels these are the existing techniques which are introduced in literature survey. Sometime whole data reading and processing might be take a much time for classification, so it increase the time complexity for entire system. We proposed a new document classification method based on deep learning using NLP and machine learning approach. In this work system has several attractive properties: it captures some metadata from entire abstract section and built the training set first. Once complete all document process, it deals with optimization algorithm. Recurrent Neural Network has used to categories the individual object according to their weights. And it provides final class label for entire test dataset. Based on the various experimental analysis system provides data classification accuracy as well as minimum time complexity than classical machine learning algorithms.