Countering Terrorism Online with Artificial Intelligence an Overview for Law Enforcement and Counter-Terrorism Agencies in South Asia and South-East Asia
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Combating Islamic Extremist Terrorism 1
CGT 1/22/07 11:30 AM Page 1 Combating Islamic Extremist Terrorism 1 OVERALL GRADE D+ Al-Qaeda headquarters C+ Al-Qaeda affiliated groups C– Al-Qaeda seeded groups D+ Al-Qaeda inspired groups D Sympathizers D– 1 CGT 1/22/07 11:30 AM Page 2 2 COMBATING ISLAMIC EXTREMIST TERRORISM ive years after the September 11 attacks, is the United States win- ning or losing the global “war on terror”? Depending on the prism through which one views the conflict or the metrics used Fto gauge success, the answers to the question are starkly different. The fact that the American homeland has not suffered another attack since 9/11 certainly amounts to a major achievement. U.S. military and security forces have dealt al-Qaeda a severe blow, cap- turing or killing roughly three-quarters of its pre-9/11 leadership and denying the terrorist group uncontested sanctuary in Afghanistan. The United States and its allies have also thwarted numerous terror- ist plots around the world—most recently a plan by British Muslims to simultaneously blow up as many as ten jetliners bound for major American cities. Now adjust the prism. To date, al-Qaeda’s top leaders have sur- vived the superpower’s most punishing blows, adding to the near- mythical status they enjoy among Islamic extremists. The terrorism they inspire has continued apace in a deadly cadence of attacks, from Bali and Istanbul to Madrid, London, and Mumbai. Even discount- ing the violence in Iraq and Afghanistan, the tempo of terrorist attacks—the coin of the realm in the jihadi enterprise—is actually greater today than before 9/11. -
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
CS855 Pattern Recognition and Machine Learning Homework 3 A.Aziz Altowayan
CS855 Pattern Recognition and Machine Learning Homework 3 A.Aziz Altowayan Problem Find three recent (2010 or newer) journal articles of conference papers on pattern recognition applications using feed-forward neural networks with backpropagation learning that clearly describe the design of the neural network { number of layers and number of units in each layer { and the rationale for the design. For each paper, describe the neural network, the reasoning behind the design, and include images of the neural network when available. Answer 1 The theme of this ansewr is Deep Neural Network 2 (Deep Learning or multi-layer deep architecture). The reason is that in recent years, \Deep learning technology and related algorithms have dramatically broken landmark records for a broad range of learning problems in vision, speech, audio, and text processing." [1] Deep learning models are a class of machines that can learn a hierarchy of features by building high-level features from low-level ones, thereby automating the process of feature construction [2]. Following are three paper in this topic. Paper1: D. C. Ciresan, U. Meier, J. Schmidhuber. \Multi-column Deep Neural Networks for Image Classifica- tion". IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012. Feb 2012. arxiv \Work from Swiss AI Lab IDSIA" This method is the first to achieve near-human performance on MNIST handwriting dataset. It, also, outperforms humans by a factor of two on the traffic sign recognition benchmark. In this paper, the network model is Deep Convolutional Neural Networks. The layers in their NNs are comparable to the number of layers found between retina and visual cortex of \macaque monkeys". -
Attribution and Response to Cybercrime/Terrorism/Warfare Susan W
Journal of Criminal Law and Criminology Volume 97 Article 2 Issue 2 Winter Winter 2007 At Light Speed: Attribution and Response to Cybercrime/Terrorism/Warfare Susan W. Brenner Follow this and additional works at: https://scholarlycommons.law.northwestern.edu/jclc Part of the Criminal Law Commons, Criminology Commons, and the Criminology and Criminal Justice Commons Recommended Citation Susan W. Brenner, At Light Speed: Attribution and Response to Cybercrime/Terrorism/Warfare, 97 J. Crim. L. & Criminology 379 (2006-2007) This Symposium is brought to you for free and open access by Northwestern University School of Law Scholarly Commons. It has been accepted for inclusion in Journal of Criminal Law and Criminology by an authorized editor of Northwestern University School of Law Scholarly Commons. 0091-4169/07/9702-0379 THE JOURNALOF CRIMINAL LAW & CRIMINOLOGY Vol. 97. No. 2 Copyright 0 2007 by NorthwesternUniversity. Schoolof Low Printedin U.S.A. "AT LIGHT SPEED": ATTRIBUTION AND RESPONSE TO CYBERCRIME/TERRORISM/WARFARE SUSAN W. BRENNER* This Article explains why and how computer technology complicates the related processes of identifying internal (crime and terrorism) and external (war) threats to social order of respondingto those threats. First, it divides the process-attribution-intotwo categories: what-attribution (what kind of attack is this?) and who-attribution (who is responsiblefor this attack?). Then, it analyzes, in detail, how and why our adversaries' use of computer technology blurs the distinctions between what is now cybercrime, cyberterrorism, and cyberwarfare. The Article goes on to analyze how and why computer technology and the blurring of these distinctions erode our ability to mount an effective response to threats of either type. -
Annual Report 2016 22 March 2017 Table of Contents
Annual report 2016 22 March 2017 Table of contents Overview 5 Highlights 5 Message from the chairman 6 About Delta Lloyd 8 Our brands 8 Our strategy 9 Our environment 12 How we create value 14 Value creation model 14 Delta Lloyd’s contribution to the UN SDGs 16 Stakeholders and materiality 17 Materiality assessment 20 Delta Lloyd in 2016 23 Capital management 27 Financial and operational performance 29 Life Insurance 31 General Insurance 34 Asset Management 37 Bank 39 Corporate and other activities 41 Investor relations and share developments 41 Human capital 46 Risk management and compliance 50 Risk management 50 Risk management philosophy 50 Risk governance 51 Risk management responsibilities 52 Risk processes and systems 53 Risk culture 54 Risk taxonomy 55 Top five risks 58 Compliance 61 Fraud 62 Corporate governance 64 Executive Board and Supervisory Board 64 Executive Board 64 Supervisory Board 64 Supervisory Board committees 65 Report of the Supervisory Board 66 Role of the Supervisory Board 67 Strategy 67 Key issues in 2016 68 Other issues 69 Supervisory Board composition 70 Supervisory Board meetings 70 Supervisory Board committees 71 Financial statements and profit appropriation 75 A word of thanks 76 Remuneration report 2016 77 Remuneration policy 77 Governance of the remuneration policy 77 Remuneration received by Executive Board members 81 Remuneration of the Supervisory Board 93 Corporate governance 96 Corporate governance statement 104 EU directive on takeover bids 104 In control statement 106 Management statement under Financial -
Real Vs Fake Faces: Deepfakes and Face Morphing
Graduate Theses, Dissertations, and Problem Reports 2021 Real vs Fake Faces: DeepFakes and Face Morphing Jacob L. Dameron WVU, [email protected] Follow this and additional works at: https://researchrepository.wvu.edu/etd Part of the Signal Processing Commons Recommended Citation Dameron, Jacob L., "Real vs Fake Faces: DeepFakes and Face Morphing" (2021). Graduate Theses, Dissertations, and Problem Reports. 8059. https://researchrepository.wvu.edu/etd/8059 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Real vs Fake Faces: DeepFakes and Face Morphing Jacob Dameron Thesis submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Xin Li, Ph.D., Chair Natalia Schmid, Ph.D. Matthew Valenti, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2021 Keywords: DeepFakes, Face Morphing, Face Recognition, Facial Action Units, Generative Adversarial Networks, Image Processing, Classification. -
Deep Neural Network Models for Sequence Labeling and Coreference Tasks
Federal state autonomous educational institution for higher education ¾Moscow institute of physics and technology (national research university)¿ On the rights of a manuscript Le The Anh DEEP NEURAL NETWORK MODELS FOR SEQUENCE LABELING AND COREFERENCE TASKS Specialty 05.13.01 - ¾System analysis, control theory, and information processing (information and technical systems)¿ A dissertation submitted in requirements for the degree of candidate of technical sciences Supervisor: PhD of physical and mathematical sciences Burtsev Mikhail Sergeevich Dolgoprudny - 2020 Федеральное государственное автономное образовательное учреждение высшего образования ¾Московский физико-технический институт (национальный исследовательский университет)¿ На правах рукописи Ле Тхе Ань ГЛУБОКИЕ НЕЙРОСЕТЕВЫЕ МОДЕЛИ ДЛЯ ЗАДАЧ РАЗМЕТКИ ПОСЛЕДОВАТЕЛЬНОСТИ И РАЗРЕШЕНИЯ КОРЕФЕРЕНЦИИ Специальность 05.13.01 – ¾Системный анализ, управление и обработка информации (информационные и технические системы)¿ Диссертация на соискание учёной степени кандидата технических наук Научный руководитель: кандидат физико-математических наук Бурцев Михаил Сергеевич Долгопрудный - 2020 Contents Abstract 4 Acknowledgments 6 Abbreviations 7 List of Figures 11 List of Tables 13 1 Introduction 14 1.1 Overview of Deep Learning . 14 1.1.1 Artificial Intelligence, Machine Learning, and Deep Learning . 14 1.1.2 Milestones in Deep Learning History . 16 1.1.3 Types of Machine Learning Models . 16 1.2 Brief Overview of Natural Language Processing . 18 1.3 Dissertation Overview . 20 1.3.1 Scientific Actuality of the Research . 20 1.3.2 The Goal and Task of the Dissertation . 20 1.3.3 Scientific Novelty . 21 1.3.4 Theoretical and Practical Value of the Work in the Dissertation . 21 1.3.5 Statements to be Defended . 22 1.3.6 Presentations and Validation of the Research Results . -
Synthetic Video Generation
Synthetic Video Generation Why seeing should not always be believing! Alex Adam Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image Tampering Historically, manipulated Off the shelf software (e.g imagery has deceived Photoshop) exists to do people this now Has become standard in Public have become tabloids/social media somewhat numb to it - it’s no longer as impactful/shocking How does machine learning fit in? Advent of machine learning Video manipulation is now has made image also tractable with enough manipulation even easier data and compute Can make good synthetic Public are largely unaware of videos using a gaming this and the danger it poses! computer in a bedroom Part I: Faceswap ● In 2017, reddit (/u/deepfakes) posted Python code that uses machine learning to swap faces in images/video ● ‘Deepfake’ content flooded reddit, YouTube and adult websites ● Reddit since banned this content (but variants of the code are open source https://github.com/deepfakes/faceswap) ● Autoencoder concepts underlie most ‘Deepfake’ methods Faceswap Algorithm Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 Inference Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 ● Faceswap model is an autoencoder. -
Blue Light: America's First Counter-Terrorism Unit Jack Murphy
Blue Light: America's First Counter-Terrorism Unit Jack Murphy On a dark night in 1977, a dozen Green Berets exited a C-130 aircraft, parachuting into a very different type of war. Aircraft hijackings had become almost commonplace to the point that Johnny Carson would tell jokes about the phenomena on television. But it was no laughing matter for the Department of Defense, who realized after the Israeli raid on Entebbe, that America was woefully unprepared to counter terrorist attacks. This mission would be different. The Special Forces soldiers guided their MC1-1B parachutes towards the ground but their element became separated in the air, some of the Green Berets landing in the trees. The others set down alongside an airfield, landing inside a thick cloud of fog. Their target lay somewhere through the haze, a military C-130 aircraft that had been captured by terrorists. Onboard there were no hostages, but a black box, a classified encryption device that could not be allowed to fall into enemy hands. Airfield seizures were really a Ranger mission, but someone had elected to parachute in an entire Special Forces battalion for the operation. The HALO team was an advanced element, inserted ahead of time to secure the aircraft prior to the main assault force arriving. Despite missing a number of team members at the rally point, the Green Berets knew they were quickly approaching their hit time. They had to take down the aircraft and soon. Armed with suppressed Sten guns, they quietly advanced through the fog. Using the bad weather to their advantage, they were able to slip right between the sentries posted to guard the aircraft. -
Australia Muslim Advocacy Network
1. The Australian Muslim Advocacy Network (AMAN) welcomes the opportunity to input to the UN Special Rapporteur on the Freedom of Religion or Belief as he prepares this report on the Impact of Islamophobia/anti-Muslim hatred and discrimination on the right to freedom of thought, conscience religion or belief. 2. We also welcome the opportunity to participate in your Asia-Pacific Consultation and hear from the experiences of a variety of other Muslims organisations. 3. AMAN is a national body that works through law, policy, research and media, to secure the physical and psychological welfare of Australian Muslims. 4. Our objective to create conditions for the safe exercise of our faith and preservation of faith- based identity, both of which are under persistent pressure from vilification, discrimination and disinformation. 5. We are engaged in policy development across hate crime & vilification laws, online safety, disinformation and democracy. Through using a combination of media, law, research, and direct engagement with decision making parties such as government and digital platforms, we are in a constant process of generating and testing constructive proposals. We also test existing civil and criminal laws to push back against the mainstreaming of hate, and examine whether those laws are fit for purpose. Most recently, we are finalising significant research into how anti-Muslim dehumanising discourse operates on Facebook and Twitter, and the assessment framework that could be used to competently and consistently assess hate actors. A. Definitions What is your working definition of anti-Muslim hatred and/or Islamophobia? What are the advantages and potential pitfalls of such definitions? 6. -
Business Model Blueprints for the Shared Mobility Hub Network
sustainability Article Business Model Blueprints for the Shared Mobility Hub Network Elnert Coenegrachts * , Joris Beckers , Thierry Vanelslander and Ann Verhetsel Department of Transport and Regional Economics (TPR), University of Antwerp, 2000 Antwerp, Belgium; [email protected] (J.B.); [email protected] (T.V.); [email protected] (A.V.) * Correspondence: [email protected] Abstract: Shared (electric) mobility is still facing challenges in terms of reaching its potential as a sustainable mobility solution. Low physical and digital integration with public transport, a lack of charging infrastructure, the regulatory barriers, and the public nuisance are hindering the uptake and organization of shared mobility services. This study examines the case of the shared mobility hub, a location where shared mobility is concentrated, as a solution to overcome these challenges. To find ideas informing how a network of shared mobility hubs can contribute to sustainable urban mobility and to overcome the aforementioned challenges, a business model innovation approach was adopted. Focus groups, consisting of public and private stakeholders, collaboratively designed five business model (BM) blueprints, reaching a consensus about the value creation, delivery, and capture mechanisms of the network. The blueprints, defined as first-/last-mile, clustered, point-of- interest (POI), hybrid, and closed mobility hub networks, provide alternative solutions to integrate sustainable transportation modes into a coherent network, enabling multi- and intermodal travel behaviour, and supporting interoperability, sustainable land use, and ensured access to shared Citation: Coenegrachts, E.; Beckers, (electric) travel modes. However, which kind of network the local key stakeholders need to commit J.; Vanelslander, T.; Verhetsel, A. to depends on the local policy goals and regulatory context. -
Report on Official Visit of the OSCE PA
AD HOC COMMITTEE ON COUNTERING TERRORISM OFFICIAL VISIT TO NORWAY 14-15 January 2020 NOTE-TO-THE-FILE Introduction On the 14th-15th of January 2020, the OSCE PA Ad Hoc Committee on Countering Terrorism (CCT) conducted an official visit to Norway. The 20-member delegation, which consisted of CCT members as well as representatives from the OSCE Transnational Threats Department and the Parliamentary Assembly of the Mediterranean, was led by OSCE PA President George Tsereteli. The visit, initiated by CCT Chair Mr. Abid Q. Raja and organized by the Norwegian Parliament (Stortinget), was also attended by OSCE PA Secretary General Roberto Montella. 1 After being welcomed by the President of the Norwegian Parliament, the delegation paid tribute to the victims of the 22nd July 2011 terror attacks in both Utøya Island and Oslo. During the two- day programme, the delegation also had the opportunity to meet with the Minister of Justice and the Minister of Climate and Environment, the Governing Mayor of Oslo, several members of the Labour Party and the Worker’s Youth League, as well as with representatives of the Norwegian Police Security Service, Norwegian Correctional System, the 22nd July Commission, the 22nd July Parliamentary Committee, the 22nd July Support Group and academia. Notably, many interlocutors had first-hand experience of the 22nd July 2011 tragic events. Ultimately, the visit provided an excellent opportunity to get familiar with the national counter terrorism system and learn more about Norway’s multifaceted response to the 22nd July attacks, which included legislative reform, emergency response preparedness, the prevention of terrorism and radicalization, and the role of public health services.