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Ukrainian Civil Society from the Orange Revolution to Euromaidan: Striving for a New Social Contract
In: IFSH (ed.), OSCE Yearbook 2014, Baden-Baden 2015, pp. 219-235. Iryna Solonenko Ukrainian Civil Society from the Orange Revolution to Euromaidan: Striving for a New Social Contract This is the Maidan generation: too young to be burdened by the experi- ence of the Soviet Union, old enough to remember the failure of the Orange Revolution, they don’t want their children to be standing again on the Maidan 15 years from now. Sylvie Kauffmann, The New York Times, April 20141 Introduction Ukrainian civil society became a topic of major interest with the start of the Euromaidan protests in November 2013. It has acquired an additional dimen- sion since then, as civil society has pushed for reforms following the ap- pointment of the new government in February 2014, while also providing as- sistance to the army and voluntary battalions fighting in the east of the coun- try and to civilian victims of the war. In the face of the weakness of the Ukrainian state, which is still suffering from a lack of political will, poor governance, corruption, military weakness, and dysfunctional law enforce- ment – many of those being in part Viktor Yanukovych’s legacies – civil so- ciety and voluntary activism have become a driver of reform and an import- ant mobilization factor in the face of external aggression. This contribution examines the transformation of Ukrainian civil society during the period between the 2004 Orange Revolution and the present day. Why this period? The Orange Revolution and the Euromaidan protests are landmarks in Ukraine’s post-independence state-building and democratiza- tion process, and analysis of the transformation of Ukrainian civil society during this period offers interesting findings.2 Following a brief portrait of Ukrainian civil society and its evolution, the contribution examines the rela- tionships between civil society and three other actors: the state, the broader society, and external actors involved in supporting and developing civil soci- ety in Ukraine. -
Digital Rights Agenga for Ukraine
Digital Security Lab FreeNet Ukraine Coalition DIGITAL RIGHTS AGENGA FOR UKRAINE The document has been developed as part of the Digital Rights Agenda for Ukraine project funded by the United States Agency for International Develop- ment (USAID) through Counterpart International September 2019 Digital Rights Agenda for Ukraine / Vita Volodovska, Maksym Dvorovyi — Kyiv: NGO Digital Security Lab Ukraine, 2019. — 56 p. This publication was prepared and published as part of the Digital Rights Agenda for Ukraine proj- ect funded by the United States Agency for International Development (USAID) through Counterpart International. The views expressed in this publication -re flect the views of the authors and do not necessarily reflect those of USAID and Counterpart International. Kyiv, 2019 © NGO Digital Security Lab Ukraine 2 Contents Digital Rights are Human Rights ........................................................................ 5 General recommendations on the enhanced protection of online human rights .............................................................................................. 7 Right to Internet Access ..................................................................................... 8 Recommendations on Internet access ..................................................... 12 Freedom of Expression Online ........................................................................ 13 General recommendations ....................................................................... 17 Freedom of expression and national -
Neural Networks, Biometrics and the New Artificial Intelligence Mathias Fuchs & Ramón Reichert 5
ISBN: 2838 Digital Vol. 4, Issue 1/2018 Ramón Reichert, Mathias Fuchs, Pablo Abend, Annika Richterich, Karin Wenz (eds.) Rethinking AI: Neural Networks, Biometrics and the New Artifical Intelligence Ramón Reichert, Annika Richterich (eds.) Digital Material/ism The journal is edited by Annika Richterich, Karin Wenz, Pablo Abend, Mathias Fuchs, Ramón Reichert Editorial Board Maria Bakardjieva, Brian Beaton, David Berry, Jean Burgess, Mark Coté, Colin Cremin, Sean Cubitt, Mark Deuze, José van Dijck, Delia Dumitrica, Astrid Ensslin, Sonia Fizek, Federica Frabetti, Richard A. Grusin, Orit Halpern, Irina Kaldrack, Wendy Hui Kyong Chun, Denisa Kera, Lev Manovich, Janet H. Murray, Jussi Parikka, Lisa Parks, Christiane Paul, Dominic Pettman, Rita Raley, Richard Rogers, Julian Rohrhuber, Marie-Laure Ryan, Mirko Tobias Schäfer, Jens Schröter, Trebor Scholz, Tamar Sharon, Roberto Simanowski, Nathaniel Tkacz, Nanna Verhoeff, Geoffrey Winthrop-Young, Sally Wyatt Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche National bibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de © 2018 transcript Verlag, Bielefeld All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. -
Beyond Colours: Assets and Liabilities of ‘Post-Orange’ Ukraine
Beyond Colours: Assets and Liabilities of ‘Post-Orange’ Ukraine International Renaissance Foundation Kyiv 2010 Stefan Batory Foundation Warsaw 2010 Co-authors Grzegorz Gromadzki independent expert, Warsaw Veronika Movchan Institute for Economic Research and Policy Consulting, Kyiv Mykola Riabchuk Ukrainian Center for Cultural Studies, Kyiv Iryna Solonenko International Renaissance Foundation, Kyiv Susan Stewart Stiftung Wissenschaft und Politik, Berlin Oleksandr Sushko Institute for Euro-Atlantic Cooperation, Kyiv Kataryna Wolczuk University of Birmingham The authors of the publication would like to thank Roman Wolczuk for his valuable comments on the report. Beyond Colours: Assets and Liabilities of ‘Post-Orange’ Ukraine International Renaissance Foundation Sichovykh Striltsiv (Artema) 46 04053 Kyiv tel. |38 044| 486 25 96 fax |38 044| 486 76 29 [email protected] http://www.irf.ua Stefan Batory Foundation Sapieżyńska 10a 00-215 Warsaw tel. |48 22| 536 02 00 fax |48 22| 536 02 20 [email protected] http://www.batory.org.pl Review and language editing Roman Wolczuk Proof-reading Maurice O’Brien Cover design by Teresa Oleszczuk Typesetting by K.I.S. Ltd. The idea of this publication emerged in the context of the growing mutual disillusionment between the EU and Ukraine in the wake of the 5th anniversary of the Orange Revolu- tion and the 2010 presidential elections. The International Renaissance Foundation and the Stefan Batory Foundation invited a group of international experts to write the report that would present the vision of where Ukraine stands not only five years after the Orange Revo- lution, but also almost 20 years after its independence. This publication is the result of the collective effort of this team. -
Assessment of the Modeling Abilities of Neural Networks Can Be Formed
ASSESSMENT OF THE MODELING ABILITIES OF NEURAL NETWORKS by Alvin Ramsey Submitted to the Department of Mechanical Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology January 1994 © Massachusetts Institute of Technology 1994 All rights reserved Signature of Author .. lDpartment of Mechanical Engineering January 1994 /t / I I Certified by --- George Chryssolouris -~~ 1 I ,'4ro . essor Thesis Supervisor l l V~rto Accepted by - ............- ... -'-- Professor Ain A. Sonin i A)-:S1'!!3NSr Graduate Committee OFTFrHR A 5,,'RARIE DEDICATION To my mother ( o o3 ) and to my brother ( Bro, on my word, we shall go fishing someday! ). 2 ASSESSMENT OF THE MODELING ABILITIES OF NEURAL NETWORKS by Alvin Ramsey Submitted to the Department of Mechanical Engineering on January 1994 in partial fulfillment of the requirements for the Degree of Master of Science in Mechanical Engineering ABSTRACT The treatment of manufacturing problems, whether in process control, process optimization, or system design and planning, can be helped by input-output models, namely, relationships between input and output variables. Artificial neural networks present an opportunity to "learn" empirically established relationships and apply them subsequently in order to solve a particular problem. In light of the increasing amount of applications of neural networks, the objective of this thesis is to evaluate the ability of neural networks to generate accurate models for manufacturing applications. Various neural network models has been tested on a number of "test bed" problems which represent the problems typically encountered in manufacturing processes and systems to assess the reliability of neural network models and to determine the efficacy of their modeling capabilities. -
Artificial Intelligence & the Future of Data-Driven Advertising
Artificial Intelligence & The Future of Data-Driven Advertising Dr. Jochen Schlosser Chief Strategy Officer [email protected] 1 Dr. Jochen Schlosser Chief Strategy Officer Adform Owner of Adform’s strategy with dedicated responsibility for Product & Technology. • Broad Paid / Rented / Owned Media Experience • Trained Data and Computer Scientist with minor degree in psychology • Happy to have survived 10+ years in Advertising General Introduction What is Artificial Intelligence? A computer program which has the ability to think, solve problems, learn, set goals, and create. It is the science and engineering of making intelligent machines, especially intelligent computer programs. 4 - John McCarthy, 2002 Deep, Machine, Intelligence Learning? Deep Learning Machine Learning Artificial Intelligence Subarea of ML Subarea of AI Highly Simplified Where multi-layered Neural Algorithms that enable machines to A program that can “sense”, reason, Networks learn based on make decisions and improve by act, and adapt. high volumes of data learning over time. 5 Brief Timeline Deep Learning Machine Learning Artificial Intelligence 1965 1805-1957 Since 2000s - GAME CHANGER ”Birth”* of deep learning Algorithm groundwork and first self- Cost of computations and storage drop (Ukraine) learning algorithm * Principle of automatic models generation, inconclusive decisions, and selection by external criteria by Alexey Ivakhnenko 6 Keeping the Perspective That is what we mostly do Accomplishment of one or a very few goals NARROW AI 7 SUPERVISED Learning 8 UNSUPERVISED Learning 9 Some examples on how that works today What exactly is this about? What exactly is this about? It is about GO and MOVE 37 AI vs HI - Humans still are still able to win!!! A Major Challenge ExampleThe TRANSPARENCY on TRUST buildingProblem Even if the outcome is correct, the path to it might not necessarily be the one you expected. -
Abuse of Power – Corruption in the Office of the President Is His Most Recent Book
Contents 1. Preface 2. 1 “Evil has to be stopped” 3. 2 Marchuk, the arch-conspirator 4. 3 Kuchma fixes his re-election 5. 4 East & West celebrate Kuchma’s victory 6. 5 Kuchma and Putin share secrets 7. 6 Corruption 8. 7 Haunted by Lazarenko 9. 8 Bakai “the conman” 10. 9 “Yuliya must be destroyed” 11. 10 Prime minister’s wife “from the CIA”? 12. 11 Kidnapping Podolsky & killing Gongadze 13. 12 Covering up murder 14. 13 Marchuk’s “secret coordinating center” 15. 14 Kolchuga fails to oust Kuchma 16. 15 The Melnychenko-Kuchma pact 17. 16 “We can put anyone against the wall” 18. 17 Fixed election sparks Orange Revolution 19. 18 Yanukovych’s revenge 20. Bibliography 21. Acknowledgements 22. A note on the author 23. Books by JV Koshiw Artemia Press Ltd Published by Artemia Press Ltd, 2013 www.artemiabooks.com ISBN 978-0-9543764-3-7 Copyright © JV Koshiw, 2013 All rights reserved. Database right Artemia Press Ltd (maker) The photograph on the front cover It shows President Leonid Kuchma and Viktor Yushchenko clasping hands, while his rival Viktor Yanukovych looks on. Yushchenko’s pot marked face bears witness to the Dioxin poisoning inflicted on him a few weeks earlier during the 2004 presidential election campaign. Photo taken by Valeri Soloviov on Nov. 26, 2004, during the negotiations to end the Orange Revolution (Photo UNIAN). System of transliterations The study uses the Library of Congress system of transliteration for Ukrainian, with exceptions in order to make Ukrainian words easier to read in English. The letter є will be transcribed as ye and not ie. -
From Friends to Foes: National Identity and Collaboration in Diverse Teams
From Friends to Foes: National Identity and Collaboration in Diverse Teams Nadzeya Laurentsyeva (LMU Munich) Discussion Paper No. 226 December 18, 2019 Collaborative Research Center Transregio 190 | www.rationality-and-competition.de Ludwig-Maximilians-Universität München | Humboldt-Universität zu Berlin Spokesperson: Prof. Dr. Klaus M. Schmidt, University of Munich, 80539 Munich, Germany +49 (89) 2180 3405 | [email protected] From Friends to Foes: National Identity and Collaboration in Diverse Teams a Nadzeya Laurentsyevab December 15, 2019 This project studies collaboration in highly skilled, nationally diverse teams. An unexpected international political conflict makes national diversity more salient among existing and potential team members. I exploit this natural experiment to quantify the role of social, identity-driven, costs for performance and formation of diverse teams. Using microdata from GitHub, the world’s largest hosting platform for software projects, I estimate the causal impacts of a political conflict that burst out between Russia and Ukraine in 2014. I find that the conflict strongly reduced online cooperation between Russian and Ukrainian programmers. The conflict lowered the likelihood that Ukrainian and Russian programmers work in the same team and led to the performance decline of existing joint projects. I provide evidence that the observed e↵ects were not driven by economic considerations. Rather, the conflict activated national identities and shifted programmers’ taste for teammates and projects. My results highlight the role of identity- driven concerns that can distort existing and prevent future collaborations, otherwise profitable from an economic perspective. JEL classification: D22, D74, F23, F51, J71 Keywords: Teams, Diversity, Conflict, National identity, Open source aI thank Michele Battisti, Florian Englmaier, Thomas Fackler, Yvonne Giesing, participants of COPE, EEA, SIOE, Ohlstadt conferences, as well as several research seminars in Munich for helpful comments and suggestions. -
N0.86 SUMMER, 1992 Sooth ANNIVERSARY of THE
N0.86 SUMMER, 1992 SOOth ANNIVERSARY OF THE COSSACKS OF UKRAINE BARCELONA OLYMPICS 1st UKRAINIAN LIBRARIAN CONGRESS ZHULYNSKY ON fORUM A UKRAINIAN REVIEW N0.86 SUMMER, 1992 ANDREW G REGOROVIC H . .. Editor-in-Chief ADVISORY BOARD CONTENTS PAGE Iva n Oleksyn, Peter Salak Natalka G awdiak, George Kl apisc hak SOOth Anniversary of the Ukrainian Cossacks ..... 3'9 Special Shevchenko Issue Commemorating his Boyko Folk Architecture ............ .. .... 10,11 I 75th Anniversary still avail able U. S. - $5.00 Canada- $6.00 U.S. Funds 1st Ukrainian Librarians Congress, Libraries Strengthen Cultural Identity (IFLA) . 12, 17 Published Quarterl y in March, June, September and December, by the Barcelona Olympics ... ..... .... ... .... .. ..... 18 UKRAINIAN FRATERNAL ASSOCIATION 440 Wyoming Ave., Scranton, Pa. 1850 1-0350 Phone: Area Code 717-342-0937 Ukraine's Olympic Medalists 1992 ............... 19 YEARLY SUBSCRIPTIONS: $10.00 United States $10.00 Canada- U.S. Funds N a tiona! Medals Standing ... .. ...... ........ 19 Back Issues- $3.00 per copy Single copy- $2.50 $2 .50 Canada U. S. Fu nds Serhiy Bubka: Fallen Star ..... .. ............. 19 Mail Subscriptions to: FORUM SUBSCRIPTIONS De Ia Feuille's 1733 Map of Ukraine ..· .... ....... 20 440 Wyoming Ave., Scranton, Pa. 1850 1-0350 Mail Manuscripts and Letters to the Editor to: Bookmark: ANDREW GREGOROVICH 314 Oriole Park way, Toronto, Canada M5P 2H5 Ukrainian Nationalism (41 6) 480-2440 Byzantium and the Slavs, Readers are invited to send in contributio ns to Newsletter, Rudnyckiana FO RUM but query the Editor before writing a feature· length article. Local ac tiviti es ca nnot be reported but events Selected Poetry of Lina Kostenko of general interest will be acce pted. -
A Depth of Deep Learning for Big Data and Its Applications
Special Issue - 2020 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 ENCADEMS - 2020 Conference Proceedings A Depth of Deep Learning for Big Data and its Applications Abhay Narayan Tripathi, Bharti Sharma Research Scholar, DIT Univeristy, Assistant Professor, DIT University Dehradun, Uttarakhand Dehradun,Uttrakhand ABSTRACT:- Although Machine Learning (ML) has become computational power and advances in data science. DL’s inherent synonymous for Artificial Intelligence (AI); recently, Deep ability to discover correlations from vast quantities of data in an Learning (DL) is being used in place of machine learning unsupervised fashion has been the main drive for its wide adoption. persistently. While machine learning is busy in supervised and Deep Learning also enables dynamic discovery of features from unsupervised methods, deep learning continues its motivation for data, unlike traditional machine learning approaches, where feature replicating the human nervous system by incorporating advanced selection remains a challenge. Deep Learning has been applied to types of Neural Networks (NN).. If we apply Deep Learning to various domains such as speech recognition and image Big Data, we can find unknown and useful patterns that classification, nature language processing, and computer vision. were impossible so far. Deep Learning is applied in self Typical deep neural networks (DNN) require large amounts of data driving cars, visual recognition, healthcare, transportation to learn parameters (often reaching to millions), which is a etc. Nowadays, companies have started to realize the importance computationally intensive process requiring significant time to train of data availability in large amounts in order to make the correct a model. As the data size increases exponentially and the deep decision and support their strategies. -
Treasures of Ukrainian Folk Arts
Міністерство освіти і науки України Полтавський національний педагогічний університет імені В.Г.Короленка Факультет філології та журналістики Кафедра англійської та німецької філології Сосой Галина Станіславівна TREASURES OF UKRAINIAN FOLK ARTS Навчально-методичний посібник з англійської мови для студентів IІ-V курсів факультету філології та журналістики, спеціальність «Філологія. Мова та література (німецька)» Полтава – 2014 УДК 7. 011. 26 (477) (072) = 111 ББК 85. 12 (4Укр) С 66 Затверджено на засіданні вченої ради Полтавського національного педагогічного університету імені В.Г. Короленка. (Протокол № 11 від 29 травня 2014 року) Укладач – Cосой Г.С., ст. викл. кафедри англійської та німецької філології. Рецензенти: Вишня Н.Г., кандидат філологічних наук, доцент, зав. кафедри загального і слов’янського мовознавства та іноземних мов ПНПУ ім. В.Г. Короленка. Парій А.В., професор кафедри іноземних мов Полтавської державної аграрної академії. Сосой Г.С. «Treasures of Ukrainian Folk Arts» : навч.-метод. посіб. з англ. мови для студентів ІІ-V курсів факультету філології та журналістики, спеціальність «Філологія. Мова та література (німецька)» / Сосой Галина Станіславівна. – Полтава : ПНПУ імені В.Г. Короленка, 2014. – 99 с. Навчально-методичний посібник «Treasures of Ukrainian Folk Arts» являє собою збірник текстів та вправ, складений з урахуванням програми з англійської мови для студентів ІІ- V курсів факультету філології та журналістики, спеціальність «Філологія. Мова та література (німецька)». Мета навч.-метод. посібника – у цікавій, доступній формі допомогти студентам, які почали вивчати англійську мову як другу іноземну, розвивати навички усного мовлення, читання, літературного перекладу, збагатити словниковий запас в цілому, закріпити граматичний матеріал з англійської мови. Посібник складається з 23 оригінальних текстів, пов’язаних з життям, народними традиціями та ремеслами та системи тренувальних вправ на створення та закріплення навичок мовлення та усних вправ творчого характеру. -
The Ukrainian Weekly, 2019
INSIDE: l Canada set to recognize Tatar deportation as genocide – page 7 l Review: At The Ukrainian Museum’s film festival – page 9 l Ribbon-cutting highlights renovations at Bobriwka – page 17 THEPublished U by theKRAINIAN Ukrainian National Association, Inc., celebrating W its 125th anniversaryEEKLY Vol. LXXXVII No. 26-27 THE UKRAINIAN WEEKLY SUNDAY, JUNE 30 -JULY 7, 2019 $2.00 Ukrainian delegation bolts, Ukrainian Day advocacy event held in Washington Zelenskyy ‘disappointed’ as PACE reinstates Russia RFE/RL’s Ukrainian Service Ukraine’s delegation to the Parliamentary Assembly of the Council of Europe (PACE) has walked out in pro- test and President Volodymyr Zelenskyy has voiced his “disappointment” over Russia having its voting rights reinstalled at the body after a three-year hiatus. In a June 25 statement on his Facebook page, President Zelenskyy said he tried to convince French President Emmanuel Macron and German Chancellor Angela Merkel in separate meetings to not allow Russia back into Europe’s main human rights body until it meets PACE’s demands on adherence to princi- ples of rule of law and human rights. Ukrainian Day participants at the breakfast briefing session. “It’s a pity that our European partners didn’t hear us and acted differently,” Mr. Zelenskyy said of the lop- and a former co-chair of the Congressional Ukrainian sided vote from the Council of Europe’s 47 member On the agenda: Russia sanctions, Caucus, delivered observations from the perspective of states, where only 62 of the 190 delegates present energy security, occupation of Crimea, Congress. “Members of Congress highly value and appreci- ate the efforts of their constituents to visit Washington, opposed a report that made it possible for Russia to continued U.S.