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    Π£ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ ΠΎΡ‚ ΡƒΠΏΠ»Π°Ρ‚Ρ‹ Π½Π°Π»ΠΎΠ³ΠΎΠ²: библиомСтричСский Π°Π½Π°Π»ΠΈΠ· Ρ‚ΠΎΡ‡Π΅ΠΊ зрСния власти, бизнСса ΠΈ Π½Π°ΡƒΠΊΠΈ

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    Π‘Ρ‚Π°Ρ‚ΡŒΡ посвящСна Π°Π½Π°Π»ΠΈΠ·Ρƒ ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ, ΠΊΠ°ΡΠ°ΡŽΡ‰ΠΈΡ…ΡΡ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ уклонСния ΠΎΡ‚ ΡƒΠΏΠ»Π°Ρ‚Ρ‹ Π½Π°Π»ΠΎΠ³ΠΎΠ². Π­Ρ‚Π° Ρ‚Π΅ΠΌΠ° ΠΏΡ€ΠΈΠ²Π»Π΅ΠΊΠ°Π΅Ρ‚ ΠΏΡ€ΠΈΡΡ‚Π°Π»ΡŒΠ½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π΅ Ρ‚ΠΎΠ»ΡŒΠΊΠΎ Π½Π°ΡƒΡ‡Π½ΠΎΠ³ΠΎ сообщСства. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ исслСдуСтся соотвСтствиС ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠΈ Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… Ρ€Π°Π±ΠΎΡ‚ ΠΏΠΎ ΡƒΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡŽ ΠΎΡ‚ ΡƒΠΏΠ»Π°Ρ‚Ρ‹ Π½Π°Π»ΠΎΠ³ΠΎΠ² практичСским вопросам, обсуТдаСмым заинтСрСсованными Π»ΠΈΡ†Π°ΠΌΠΈ. Π’ качСствС источника Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠ΅ использовалась элСктронная Π±Π°Π·Π° e-Library. Π’ ΠΊΡ€ΡƒΠ³ заинтСрСсованных Π»ΠΈΡ†, Π½Π°ΠΏΡ€ΡΠΌΡƒΡŽ зависящих ΠΎΡ‚ ΠΏΡ€Π°Π²ΠΈΠ» налогооблоТСния, входят бизнСссообщСство ΠΈ государствСнныС ΠΎΡ€Π³Π°Π½Ρ‹. Для Π½ΠΈΡ… источниками ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΠΎ исслСдуСмой Ρ‚Π΅ΠΌΠ΅ ΡΠ²Π»ΡΡŽΡ‚ΡΡ элСктронная Π±Π°Π·Π° ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ ΠΈΠ·Π΄Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠšΠΎΠΌΠΌΠ΅Ρ€ΡΠ°Π½Ρ‚ΡŠΒ» ΠΈ «Российская Π³Π°Π·Π΅Ρ‚Π°Β». Для Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΡ‚ΠΎΠ±Ρ€Π°Π½Π° 301 публикация Π·Π° 2013-2015 Π³Π³. Π˜Π·ΡƒΡ‡Π΅Π½ΠΈΠ΅ соотвСтствия ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠΈ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡŒ ΠΏΡƒΡ‚Π΅ΠΌ сравнСния ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ активности Π² Ρ€Π°Π·Ρ€Π΅Π·Π΅ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ. На ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС исслСдования Π±Ρ‹Π» Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ качСствСнный ΠΊΠΎΠ½Ρ‚Π΅Π½Ρ‚-Π°Π½Π°Π»ΠΈΠ· посрСдством выявлСния ΠΎΠ±Ρ‰ΠΈΡ… Ρ‚Π΅ΠΌ, обсуТдаСмых Π² публикациях. Π—Π°Ρ‚Π΅ΠΌ проводился количСствСнный Π°Π½Π°Π»ΠΈΠ· Ρ‡Π΅Ρ€Π΅Π· сравнСниС распрСдСлСния ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ ΠΏΠΎ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΉ Ρ‚Π΅ΠΌΠ΅ ΠΈΠ· ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ источника. Для количСствСнного Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Π²ΠΈΠ·ΡƒΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² использовались ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ библиомСтричСского Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ картирования. РасчСты ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΠΈΡΡŒ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Π° QDA Miner v.5.0 ΠΌΠΎΠ΄ΡƒΠ»ΡŒ WordStat v.7.1.7. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ исслСдования Π±Ρ‹Π»ΠΈ сдСланы Π²Ρ‹Π²ΠΎΠ΄Ρ‹, Ρ‡Ρ‚ΠΎ самыми популярными Ρ‚Π΅ΠΌΠ°ΠΌΠΈ, интСрСс ΠΊ ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΌ Π½Π΅ мСняСтся, ΡΠ²Π»ΡΡŽΡ‚ΡΡ: ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π°, законотворчСство ΠΈ усилСниС принуТдСния. Π’Π΅ΠΌΡ‹, ΠΊ ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΌ Π·Π° рассматриваСмый ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ снизился интСрСс, ΠΊΠ°ΡΠ°ΡŽΡ‚ΡΡ ΠΌΠ΅ΠΆΠ΄ΡƒΠ½Π°Ρ€ΠΎΠ΄Π½Ρ‹Ρ… аспСктов налогооблоТСния, Ρ‚Π΅Π½Π΅Π²ΠΎΠΉ Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ, собствСнности ΠΈ инвСстиций. ΠžΡ‚ΠΌΠ΅Ρ‡Π΅Π½ΠΎ Π²Ρ‹Ρ€Π°ΠΆΠ΅Π½Π½ΠΎΠ΅ возрастаниС интСрСса сообщСства ΠΊ Ρ„ΠΈΡ€ΠΌΠ°ΠΌ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠ°ΠΌ, руководству ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΊ вопросам ΡˆΡ‚Ρ€Π°Ρ„ΠΎΠ² ΠΈ ΠΏΠ΅Π½ΠΈ. ИсслСдованиС ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π²Ρ‹ΡΠ²ΠΈΡ‚ΡŒ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ нСсоотвСтствиС Ρ‚Π΅ΠΌ, обсуТдаСмых бизнСсом ΠΈ Π²Π»Π°ΡΡ‚ΡŒΡŽ, ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с Ρ‚Π΅ΠΌΠ°ΠΌΠΈ Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ. РаспространСнныС Π² Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… публикациях Ρ‚Π΅ΠΌΡ‹ (тСнСвая экономика, коррупция, Ρ„ΠΈΡ€ΠΌΡ‹-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠΈ, взносы Π½Π° ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠ΅ страхованиС), Π³ΠΎΡ€Π°Π·Π΄ΠΎ Ρ€Π΅ΠΆΠ΅ Π²ΡΡ‚Ρ€Π΅Ρ‡Π°ΡŽΡ‚ΡΡ Π½Π° рСсурсах ΠΈΠ·Π΄Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠšΠΎΠΌΠΌΠ΅Ρ€ΡΠ°Π½Ρ‚ΡŠΒ» ΠΈ Π² «Российской Π³Π°Π·Π΅Ρ‚Π΅Β», ΡΠΎΡΡ€Π΅Π΄ΠΎΡ‚Π°Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΡ… основноС Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π° вопросах законотворчСства ΠΈ обсуТдСния ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π΅. Анализ взаимосвязСй Π² тСкстах Π² соотвСтствии с источниками ΠΈ Π³ΠΎΠ΄ΠΎΠΌ ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», Ρ‡Ρ‚ΠΎ Ρ‚Π΅ΠΌΡ‹ Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… исслСдований ΡΠ±Π»ΠΈΠΆΠ°ΡŽΡ‚ΡΡ с ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ, рассматриваСмыми Π²Π»Π°ΡΡ‚ΡŒΡŽ, Π° бизнСс-сообщСство Π² большСй стСпСни вовлСкаСтся обсуТдСниС ΠΏΡ€Π°Π²ΠΎΠ²ΠΎΠΉ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠΈ, Ρ‚. Π΅. Ρ‚ΠΎΡ‡ΠΊΠ° зрСния власти Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ опрСдСляСт обсуТдСниС Ρ‚Π΅ΠΌΡ‹ уклонСния ΠΎΡ‚ Π½Π°Π»ΠΎΠ³ΠΎΠ² бизнСс-сообщСством, ΠΈ Π½Π°ΡƒΡ‡Π½Ρ‹ΠΌΠΈ ΠΊΡ€ΡƒΠ³Π°ΠΌΠΈ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ, библиомСтричСскиС ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π°Π½Π°Π»ΠΈΠ·Π° тСкстов ΠΌΠΎΠ³ΡƒΡ‚ ΠΏΡ€ΠΈΠΌΠ΅Π½ΡΡ‚ΡŒΡΡ для провСдСния Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… исслСдований, составлСния ΠΎΠ±Π·ΠΎΡ€ΠΎΠ² Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ ΠΈ тСматичСского поиска ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ.This article analyzes the publications relating to the problem of tax evasion. This topic is attractive not only for the academic community, but also for public at whole. The article explores to what extent the scientific publications on tax evasion correspond to practical issues discussed among the stakeholders. We used the electronic database of e-Library as a source of scientific publications on the subject. The principal stakeholders directly dependent on the taxation are the taxpayers and public authorities. We used the electronic database of publications Β«KommersantΒ» publishing house and the Β«Rossiyskaya GazetaΒ» to reflect issues discussed among the stakeholders. We selected for analyze 301 publications for the period of 2013-2015. The study was conducted by comparing the publication activity by types and period of publications. In the first stage of the study we have done the qualitative content analysis by identification the common themes discussed in hole sample of publications. Then, a quantitative analysis was conducted by comparing the distribution of publications on a particular topic from each source. We used bibliometric analysis method for the quantitative and bibliographic mapping method to visualize the results of research. Calculations were performed using the software QDA Miner v.5.0 module WordStat v.7.1.7. As a result, studies have concluded that the most popular topics of interest for which no changes are: changes in legislation, legislation and increased enforcement. Using the results of the conducted study, we can identify the main similarities and differences between the monitored sources. We can see the special attention to the: Legislation changes, Law enforcement, Entrepreneurship. Marked reduction of interest can be noted regarding to the following topics: International aspects of taxation, Shadow economy, Ownership, property, investment. The growth of interest can be noted in relation to the following topics: Directorship, Article of the Tax Code, Short-lived companies, Arrears and fines. The study revealed a certain disparity between the topics discussed among academic community and stakeholders. The topics discussed in the majority of scientific texts (shadow economy, corruption, the firm one-day, social security contributions), a much rarer can be found in the publication of Β«KommersantΒ» and Β«Rossiyskaya GazetaΒ» which focuses mainly on matters of legislation. Analysis of the relationships in the texts according to the source and year of publication showed that research topics converge with issues considered by the public authorities. The business community more involved in discussion the legal issues, because the government notion works upon the impression about tax evasion of the business community and academia. Thus, bibliometric text analysis techniques can be used for research, preparation of literature reviews and thematic information retrieval

    Aspect-Controlled Neural Argument Generation

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    We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model

    A Scalable Asynchronous Distributed Algorithm for Topic Modeling

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    Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands). Second, one needs a scalable and efficient way of distributing the computation across multiple machines. In this paper we present a novel algorithm F+Nomad LDA which simultaneously tackles both these problems. In order to handle large number of topics we use an appropriately modified Fenwick tree. This data structure allows us to sample from a multinomial distribution over TT items in O(log⁑T)O(\log T) time. Moreover, when topic counts change the data structure can be updated in O(log⁑T)O(\log T) time. In order to distribute the computation across multiple processor we present a novel asynchronous framework inspired by the Nomad algorithm of \cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform state-of-the-art on massive problems which involve millions of documents, billions of words, and thousands of topics

    Reading Habits in Different Communities

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    Reading is foundational to learning and the information acquisition upon which people make decisions. For centuries, the capacity to read has been a benchmark of literacy and involvement in community life. In the 21st Century, across all types of U.S. communities, reading is a common activity that is pursued in myriad ways. As technology and the digital world expand and offer new types of reading opportunities, residents of urban, suburban, and rural communities at times experience reading and e-reading differently. In the most meaningful ways, these differences are associated with the demographic composition of differentkinds of communities -- the age of the population, their overall level of educational attainment, and the general level of household income.Several surveys by the Pew Research Center's Internet & American Life Project reveal interesting variations among communities in the way their residents read and use reading-related technology and institutions

    1st INCF Workshop on Sustainability of Neuroscience Databases

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    The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability
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